Critical Care in the Military Health System: A 24-h Point Prevalence Study

Critical Care in the Military Health System: A 24-h Point Prevalence Study Abstract Background Healthcare expenditures are a significant economic cost with critical care services constituting one of its largest components. The Military Health System (MHS) is the largest, global healthcare system of its kind. In this project, we sought to describe critical care services and the patients who receive them in the MHS. Methods We surveyed 26 military treatment facilities (MTFs) representing 38 critical care services or intensive care units (ICUs). MTFs with multiple ICUs and critical care services responded to the survey as services (e.g., surgical or medical ICU service), whereas MTFs with only one ICU responded as a unit and gave information about all types of patients (i.e., medical and surgical). Our survey was divided into an administrative portion and a 24-h point prevalence survey of patients and patient care. The administrative portion is reported separately in this journal. The 24-h point prevalence survey collected information about all patients present in, admitted to, or discharged from participating services/units during the same 24-h period in December 2014. The survey was anonymous and protected health information was not collected. Findings Sixteen MTFs (69%) and 27 ICU services/units (71%) returned the point prevalence survey. MTFs with >200 beds (n = 3, 22%) were categorized as “high capacity centers” (HCCs) whereas those with ≤200 beds (n = 13, 78%) were characterized as low capacity centers (LCCs). Two MTFs (one HCC and one LCC) returned only administrative data. The remaining 16 MTFs reported data about 151 patients. In all, 100 (67%) of the patients were at three HCCs during this study period. One HCC accounted for 39% (59 patients) of all patient care during this study. Most patients were cared for in mixed medical/surgical ICUs (34.4%), followed by medical (21.2%), surgical (18.5%), trauma (11.9%), cardiac (7.9%), and burn (6.0%) ICUs. The most common medical indication for admission was cardiac followed by general medical. The most common surgical indications for admission were trauma, other, and cardiothoracic surgery. The average APACHE II score of all patients across both LCCs and HCCs was 11 ± 8.1 (8 ± 7.8 vs. 13 ± 7.7 p = 0.008). The lower acuity of patients in this study is reflected in a high turnover rate, low rate of arterial and central line placements (33%), and low rates of life support (all types, 30%; mechanical ventilation only, 21.2%; noninvasive mechanic ventilation only, 7.9%; and vasoactive medications, 6.6%). Thirty-five (23.2%) patients within the study were affected by a total of 57 complications. The three most common complications experienced were acute kidney injury, bleeding, and sepsis. Discussion This is the first detailed report about MHS critical care services and the patients receiving care. It describes a low acuity ICU patient population, concentrated at larger MTFs. This study highlights the need for the establishment of a system that allows for the continuous collection of high priority information about clinical care in the MHS in order to facilitate implementation of standardized protocols and process improvements. INTRODUCTION In the USA, over 5 million patients are admitted annually to the intensive care unit (ICU), constituting the largest portion of national healthcare expenditures, and for an increasing amount of healthcare dollars ($56.6 to $81.7 billion from 2000 to 2005).1 Costs are likely to continue to rise given that the number of patients over age 65 is projected to double between 2007 and 2030.2 ICU admission and subsequent critical care represent approximately 13% of all hospital costs, 4% of national health expenditures, and 1% of the U.S. gross domestic product.3,4 Each day of intensive care costs on average $3,500 per patient.1 Considering the high volume of critically ill patients with increased complexity, and the significant cost in providing care, there is an added emphasis to maximize patient safety and improve outcomes. A way to meet these goals is through collaboration and teamwork. Over the past 40 yr, healthcare quality organizations such as the Joint Commission and the National Institutes of Health have promoted improving teamwork and collaboration. For example, the New York Hospital Association has collaboratively formed a Critical Care Leaders Network dedicated to improving critical care services in the greater New York metropolitan and surrounding areas.5 Their efforts demonstrate that leadership can change practices across multiple healthcare organizations. Hospitals that are a part of their collaborative effort have been recognized for exceptional clinical care in a variety of areas including emergency care of myocardial infarctions, decreasing central line-associated bloodstream infections, decreasing ventilator-associated pneumonia rates, increasing physician use and understanding of ultrasound and simulation for training, and planning for disaster response. Attitudes and perceptions of the quality of teamwork vary widely between institutions, units, individuals, clinicians and professions. Military treatment facilities (MTFs), which are generally compared with their civilian counterparts, are no exception. Unfortunately, there are no comparable critical care networks and little systematic investigation regarding the nature of critical care services in the Military Health System (MHS). Furthermore, this type of data could have implications on and inform decisions about maintaining readiness of military clinicians. There are a total of 26 MTFs in the MHS with critical care capabilities comprising approximately 300–400 total ICU beds. Past efforts have failed to accurately describe the critical care organization, structure, or patient population at these facilities.6,7 The primary objective of this observational study was to describe current critical care services in the MHS and the costs associated with them. Through better understanding and initial collaboration enabled by this effort, we might begin to enhance cross organizational teamwork and improve critical care practices for the enterprise. METHODS An institutional review board (IRB)-approved, anonymous survey was sent to all 26 MTFs representing 38 critical care services or ICUs. The survey consisted of two parts: an administrative portion and a 24-h prevalence study. This paper reports the findings of the 24-h prevalence survey only; findings from the administrative portion of the survey are reported elsewhere in this Journal. The 24-h prevalence survey underwent a process for face validation by two independent reviewers; questions that were unclear, irrelevant, or duplicate were revised or removed. The prevalence survey collected information about patient demographics, treatments, complications, and outcomes for all patients in ICUs during a 24-h period in December 2014. Respondents personally involved in critical care at each facility completed the administrative survey at any time during the month prior to or the month following the 24-h prevalence study. Protected health information (PHI) was not collected and the surveys were determined to be research not involving human subjects by the IRB. We asked all MHS MTFs with identified critical care beds to participate in this survey. At the beginning of the study, the principal investigator (PI, author JCP) contacted each MTF to identify ICU leadership and obtain their contact information. An e-mail was sent to these points-of-contact (POCs) with a request to participate and included instructions for completing the survey. In order to determine how many centers participated, we asked identified POCs at each center to declare by e-mail if their center participated, but survey data were submitted anonymously. To maintain anonymity of data, POCs created a self-identified code name, known only by the center POCs, to be used in the survey in order to aggregate data for a single facility if that facility reported from multiple critical care services or separate ICUs. In other words, large centers could have each ICU service (i.e., medical, surgical, trauma critical care services) or unit (i.e., medical ICU, surgical ICU, etc.) respond to the survey, but each service/unit identified itself with the site-specific code name. For the purposes of the survey, a service was defined as a dedicated physician team whereas a unit was a physical location that may or may not be dedicated to a specific patient population. A master list of physical sites and their associated code names was not created and POC information was not collected in the survey. Thus, only POCs from a facility could identify which data belonged to that facility. Anonymity allowed sites to submit their data without fear of comparison, judgment, or reprisal. The surveys were completed by POCs or POC designee(s) at each MTF. This POC was usually a medical director or a physician/nurse leader identified by the PI through networking with key leaders in critical care throughout the MHS. POCs worked with leaders of other multidisciplinary groups to identify the most reliable source of information for the survey. POCs personally vouched for the accuracy of the data. A list of POCs and associated facility is provided in the Acknowledgements section. In situations where the requested information was not available, POCs could answer as such. We attempted to collect cost data regarding specific critical care interventions but were unable to do so at each MTF. Consequently, we identified total costs of critical care services for 1 yr at one of the MTFs in the study (the largest) and averaged these costs per bed day. We used these data with the survey data to make a broad estimate of total annual critical care costs for the MHS. We analyzed the data according to high capacity centers (HCCs) and low capacity centers (LCCs). HCCs were defined as having greater than 200 inpatient beds, whereas LCCs had less than or equal to 200 beds. We further described centers in terms of service/unit types, care models, resources, protocol availability, and population data/unit demographics. Differences between HCCs and LCCs were compared where possible using Fisher’s Exact Test with significance set at p < 0.05. RESULTS Sixteen MTFs (62%) and 27 ICU services/units (71%) returned the 24-h point prevalence portion of the survey. Three MTFs (22%) reported a bed capacity of greater than 200 beds and were designated as HCCs; the remaining 13 MTFs were LCCs. Participants submitted information for 151 patients cared for by MHS critical care services during the 24-h period. The three HCCs accounted for 100 (67%) patients; one HCC accounted for 59 patients (39% of all patients reported). Of the 51 patients admitted to LCCs, the majority of patients (n = 38) were admitted to mixed medical/surgical ICUs; fewer were admitted to medical and cardiac ICUs. Of the 100 patients admitted to HCCs, patients were admitted to a larger variety of ICU types (Table I, Supplementary Table S1). The majority of patients (97%) were cared for in the unit they would typically be assigned to; 4 patients (4%) in HCCs and 1 patient (2%) in LCCs were “boarded” in units that would not normally care for patients with the primary diagnosis of the “boarded” patient (Supplementary Table S2). During the time of the survey, three patients in HCCs and one patient in a LCC were eligible for transfer out of the ICU (2.6% of patients), but did not due to unknown reasons (data not collected). Table I. Patients by ICU Type ICU Patient Demographics by ICU  Type of ICU  % Total (n)  % LCC (n)  % HCC (n)  Mixed Medical/Surgical  34.4 (52)  73.1 (38)  26.9 (14)  Medical  21.2 (32)  28.1 (9)  71.9 (23)  Surgical  18.5 (28)  0 (0)  100 (28)  Trauma  11.9 (18)  0 (0)  100 (18)  Cardiac  7.9 (12)  33.3 (4)  66.7 (8)  Burn  6.0 (9)  0 (0)  100 (9)  Total  151  51  100  ICU Patient Demographics by ICU  Type of ICU  % Total (n)  % LCC (n)  % HCC (n)  Mixed Medical/Surgical  34.4 (52)  73.1 (38)  26.9 (14)  Medical  21.2 (32)  28.1 (9)  71.9 (23)  Surgical  18.5 (28)  0 (0)  100 (28)  Trauma  11.9 (18)  0 (0)  100 (18)  Cardiac  7.9 (12)  33.3 (4)  66.7 (8)  Burn  6.0 (9)  0 (0)  100 (9)  Total  151  51  100  Table I. Patients by ICU Type ICU Patient Demographics by ICU  Type of ICU  % Total (n)  % LCC (n)  % HCC (n)  Mixed Medical/Surgical  34.4 (52)  73.1 (38)  26.9 (14)  Medical  21.2 (32)  28.1 (9)  71.9 (23)  Surgical  18.5 (28)  0 (0)  100 (28)  Trauma  11.9 (18)  0 (0)  100 (18)  Cardiac  7.9 (12)  33.3 (4)  66.7 (8)  Burn  6.0 (9)  0 (0)  100 (9)  Total  151  51  100  ICU Patient Demographics by ICU  Type of ICU  % Total (n)  % LCC (n)  % HCC (n)  Mixed Medical/Surgical  34.4 (52)  73.1 (38)  26.9 (14)  Medical  21.2 (32)  28.1 (9)  71.9 (23)  Surgical  18.5 (28)  0 (0)  100 (28)  Trauma  11.9 (18)  0 (0)  100 (18)  Cardiac  7.9 (12)  33.3 (4)  66.7 (8)  Burn  6.0 (9)  0 (0)  100 (9)  Total  151  51  100  We sought to help readers better understand the potential implications of the cost critical care services. We asked the Program Analysis and Evaluation Department for the core site hospital, the largest in this study, to provide an average cost estimate for a single patient day in the ICU for a 1 yr period (FY 2014). For illustrative purposes only, the annual cost data demonstrated an average ICU bed cost of $3,615/day. If we assume similar costs for other facilities in this study, there was approximately $545,865 spent on critical care patients during this 24-h survey. The Defense Health Plan (DHP) Budget estimate for 20148 was $31.6 billion. Consequently, critical care services may constitute approximately 1% of the DHP budget. Of the 51 patients admitted to LCCs, 35 were admitted for medical reasons and 16 were admitted for surgical reasons. Of the 100 patients admitted to HCCs, 72 were admitted for surgical reasons and 28 were admitted for medical reasons. The most common indications for ICU admission according to patient type are listed in Table II. A full list of patient diagnoses on the day of survey according to patient type can be found in Supplementary Table S3. One HCC patient was a “readmission” to the ICU (i.e., was transferred out of the same ICU less than 48 h prior to their admission accounted for in this survey). Table II. Indications for ICU Admission by Patient Type Primary Indication for Admission by Patient Type  Medical Patients (n = 65)  % (n)  Surgical Patients (n = 86)  % (n)  Respiratory distress/failure  15 (10)  Respiratory or cardiovascular monitoring  28 (24)  Sepsis  15 (10)  Neurologic monitoring/management  17 (15)  Not specified  12 (8)  Post-operative/procedural monitoring  13 (11)  Respiratory or cardiovascular monitoring  12 (8)  Hemorrhage  8 (7)  Heart failure  9 (6)  Resuscitation NOS  8 (7)  Hemorrhage  9 (6)  Not specified  6 (5)  Neurologic monitoring/management  8 (5)  Nursing care  6 (5)  Nursing care  8 (5)  Respiratory distress/failure  5 (4)  Post-operative/procedural monitoring  5 (3)  Sepsis  3 (3)  Administrative  2 (1)  Critical care consultation  2 (2)  Critical care consultation  2 (1)  Heart failure  2 (2)  Resuscitation NOS  2 (1)  Shock NOS  1 (1)  Shock NOS  2 (1)  Administrative  0  Primary Indication for Admission by Patient Type  Medical Patients (n = 65)  % (n)  Surgical Patients (n = 86)  % (n)  Respiratory distress/failure  15 (10)  Respiratory or cardiovascular monitoring  28 (24)  Sepsis  15 (10)  Neurologic monitoring/management  17 (15)  Not specified  12 (8)  Post-operative/procedural monitoring  13 (11)  Respiratory or cardiovascular monitoring  12 (8)  Hemorrhage  8 (7)  Heart failure  9 (6)  Resuscitation NOS  8 (7)  Hemorrhage  9 (6)  Not specified  6 (5)  Neurologic monitoring/management  8 (5)  Nursing care  6 (5)  Nursing care  8 (5)  Respiratory distress/failure  5 (4)  Post-operative/procedural monitoring  5 (3)  Sepsis  3 (3)  Administrative  2 (1)  Critical care consultation  2 (2)  Critical care consultation  2 (1)  Heart failure  2 (2)  Resuscitation NOS  2 (1)  Shock NOS  1 (1)  Shock NOS  2 (1)  Administrative  0  NOS, not otherwise specified. Table II. Indications for ICU Admission by Patient Type Primary Indication for Admission by Patient Type  Medical Patients (n = 65)  % (n)  Surgical Patients (n = 86)  % (n)  Respiratory distress/failure  15 (10)  Respiratory or cardiovascular monitoring  28 (24)  Sepsis  15 (10)  Neurologic monitoring/management  17 (15)  Not specified  12 (8)  Post-operative/procedural monitoring  13 (11)  Respiratory or cardiovascular monitoring  12 (8)  Hemorrhage  8 (7)  Heart failure  9 (6)  Resuscitation NOS  8 (7)  Hemorrhage  9 (6)  Not specified  6 (5)  Neurologic monitoring/management  8 (5)  Nursing care  6 (5)  Nursing care  8 (5)  Respiratory distress/failure  5 (4)  Post-operative/procedural monitoring  5 (3)  Sepsis  3 (3)  Administrative  2 (1)  Critical care consultation  2 (2)  Critical care consultation  2 (1)  Heart failure  2 (2)  Resuscitation NOS  2 (1)  Shock NOS  1 (1)  Shock NOS  2 (1)  Administrative  0  Primary Indication for Admission by Patient Type  Medical Patients (n = 65)  % (n)  Surgical Patients (n = 86)  % (n)  Respiratory distress/failure  15 (10)  Respiratory or cardiovascular monitoring  28 (24)  Sepsis  15 (10)  Neurologic monitoring/management  17 (15)  Not specified  12 (8)  Post-operative/procedural monitoring  13 (11)  Respiratory or cardiovascular monitoring  12 (8)  Hemorrhage  8 (7)  Heart failure  9 (6)  Resuscitation NOS  8 (7)  Hemorrhage  9 (6)  Not specified  6 (5)  Neurologic monitoring/management  8 (5)  Nursing care  6 (5)  Nursing care  8 (5)  Respiratory distress/failure  5 (4)  Post-operative/procedural monitoring  5 (3)  Sepsis  3 (3)  Administrative  2 (1)  Critical care consultation  2 (2)  Critical care consultation  2 (1)  Heart failure  2 (2)  Resuscitation NOS  2 (1)  Shock NOS  1 (1)  Shock NOS  2 (1)  Administrative  0  NOS, not otherwise specified. Of the 151 patients reported; 43 were retired military, 38 were military dependents, 33 were civilian, and 17 were active duty military. Notably, 28 (85%) of the civilians were admitted to one HCC. Demographic information is shown in Table III. Table III. Patient Demographics Survey Demographics    Medical (n = 65)  Surgical (n = 86)  Age in years % (n)  <20  3% (2)  1% (1)  21–60  35% (23)  51% (54)  61–80  43% (28)  43% (33)  >80  18% (12)  18% (8)  Duty status % (n)  Active duty  11% (7)  12% (10)  Retired  35% (23)  23% (20)  Dependent  34% (22)  19% (16)  Reserve  2% (1)  0%  Information not available  2% (1)  0%  VA beneficiary  14% (9)  10% (9)  Non-beneficiary (civilian emergency or SECDEF designee)  3% (2)  36% (31)  Service affiliation % (n)  Air Force  26% (17)  12% (10)  Army  29% (19)  26% (22)  Navy  9% (6)  6% (5)  NA  9% (6)  38% (33)  Information not available  26% (17)  19% (16)  Gender % (n)  Male  58% (38)  72% (62)  Female  42% (27)  28% (24)  ICU days at start of survey  Median ICU days (IQR)  2 (0,4)  2 (1,3)  ICU days range  0–44  0–44  Survey Demographics    Medical (n = 65)  Surgical (n = 86)  Age in years % (n)  <20  3% (2)  1% (1)  21–60  35% (23)  51% (54)  61–80  43% (28)  43% (33)  >80  18% (12)  18% (8)  Duty status % (n)  Active duty  11% (7)  12% (10)  Retired  35% (23)  23% (20)  Dependent  34% (22)  19% (16)  Reserve  2% (1)  0%  Information not available  2% (1)  0%  VA beneficiary  14% (9)  10% (9)  Non-beneficiary (civilian emergency or SECDEF designee)  3% (2)  36% (31)  Service affiliation % (n)  Air Force  26% (17)  12% (10)  Army  29% (19)  26% (22)  Navy  9% (6)  6% (5)  NA  9% (6)  38% (33)  Information not available  26% (17)  19% (16)  Gender % (n)  Male  58% (38)  72% (62)  Female  42% (27)  28% (24)  ICU days at start of survey  Median ICU days (IQR)  2 (0,4)  2 (1,3)  ICU days range  0–44  0–44  VA, veterans affairs; SECDEF, Secretary of Defense; IQR, inner quartile range. Table III. Patient Demographics Survey Demographics    Medical (n = 65)  Surgical (n = 86)  Age in years % (n)  <20  3% (2)  1% (1)  21–60  35% (23)  51% (54)  61–80  43% (28)  43% (33)  >80  18% (12)  18% (8)  Duty status % (n)  Active duty  11% (7)  12% (10)  Retired  35% (23)  23% (20)  Dependent  34% (22)  19% (16)  Reserve  2% (1)  0%  Information not available  2% (1)  0%  VA beneficiary  14% (9)  10% (9)  Non-beneficiary (civilian emergency or SECDEF designee)  3% (2)  36% (31)  Service affiliation % (n)  Air Force  26% (17)  12% (10)  Army  29% (19)  26% (22)  Navy  9% (6)  6% (5)  NA  9% (6)  38% (33)  Information not available  26% (17)  19% (16)  Gender % (n)  Male  58% (38)  72% (62)  Female  42% (27)  28% (24)  ICU days at start of survey  Median ICU days (IQR)  2 (0,4)  2 (1,3)  ICU days range  0–44  0–44  Survey Demographics    Medical (n = 65)  Surgical (n = 86)  Age in years % (n)  <20  3% (2)  1% (1)  21–60  35% (23)  51% (54)  61–80  43% (28)  43% (33)  >80  18% (12)  18% (8)  Duty status % (n)  Active duty  11% (7)  12% (10)  Retired  35% (23)  23% (20)  Dependent  34% (22)  19% (16)  Reserve  2% (1)  0%  Information not available  2% (1)  0%  VA beneficiary  14% (9)  10% (9)  Non-beneficiary (civilian emergency or SECDEF designee)  3% (2)  36% (31)  Service affiliation % (n)  Air Force  26% (17)  12% (10)  Army  29% (19)  26% (22)  Navy  9% (6)  6% (5)  NA  9% (6)  38% (33)  Information not available  26% (17)  19% (16)  Gender % (n)  Male  58% (38)  72% (62)  Female  42% (27)  28% (24)  ICU days at start of survey  Median ICU days (IQR)  2 (0,4)  2 (1,3)  ICU days range  0–44  0–44  VA, veterans affairs; SECDEF, Secretary of Defense; IQR, inner quartile range. The average acute physiology and chronic health evaluation II (APACHE II) scores8 of all patients in this survey was 11 ± 8.1. APACHE II scores for patient at LCCs was significantly lower than that for patients at HCCs (8 ± 7.8 vs. 13 ± 7.7, p = 0.008, Fig. 1). Both of these values correspond to an overall low expected mortality (<15%). The APACHE II score for non-beneficiaries (i.e., civilian emergencies) was higher than beneficiaries who were medical patients (15 ± 2.8 vs. 14 ± 8.0) and surgical patients (16 ± 7.5 vs. 13 ± 7.6) (Table IV). Figure 1. View largeDownload slide Apache II scores for LCCs and HCCs. Figure 1. View largeDownload slide Apache II scores for LCCs and HCCs. Table IV. Acute Physiology and Chronic Health Evaluation (APACHE) II Scores by Patient Type, Location, and Duty Status APACHE II Scores    Medical  Surgical    HCC  LCC  HCC  LCC  Duty Status  n  Score (SD)  n  Score (SD)  n  Score (SD)  n  Score (SD)  Active duty  3  11 (9.5)  4  8 (9.3)  9  6 (5.1)  1  2 (na)  Activated reserve  1  7 (na)              Dependent  9  17 (11.0)  13  8 (11.6)  14  11 (7.4)  2  7 (0.7)  Retired  12  12 (5.7)  11  8 (6.9)  16  13 (7.2)  4  10 (4.1)  VA beneficiary      9  7 (4.5)  3  12 (5.7)  6  11 (9.2)  Non-beneficiary  2  15 (2.8)      31  16 (7.5)      Information not available      1  0 (na)          Average  27  14 (8.0)  38  7 (8.2)  73  13 (7.6)  13  9 (6.9)  Average beneficiaries + VA  25  13 (8.3)      42  11 (7.0)      Average beneficiaries      39  8 (9.1)  39  10 (7.2)  7  8 (4.2)  APACHE II Scores    Medical  Surgical    HCC  LCC  HCC  LCC  Duty Status  n  Score (SD)  n  Score (SD)  n  Score (SD)  n  Score (SD)  Active duty  3  11 (9.5)  4  8 (9.3)  9  6 (5.1)  1  2 (na)  Activated reserve  1  7 (na)              Dependent  9  17 (11.0)  13  8 (11.6)  14  11 (7.4)  2  7 (0.7)  Retired  12  12 (5.7)  11  8 (6.9)  16  13 (7.2)  4  10 (4.1)  VA beneficiary      9  7 (4.5)  3  12 (5.7)  6  11 (9.2)  Non-beneficiary  2  15 (2.8)      31  16 (7.5)      Information not available      1  0 (na)          Average  27  14 (8.0)  38  7 (8.2)  73  13 (7.6)  13  9 (6.9)  Average beneficiaries + VA  25  13 (8.3)      42  11 (7.0)      Average beneficiaries      39  8 (9.1)  39  10 (7.2)  7  8 (4.2)  Beneficiaries are all active duty service members including activated reservists, their dependents, and retirees. Non-beneficiaries included civilians, civilian emergencies, and Secretary of Defense Designees View Large Table IV. Acute Physiology and Chronic Health Evaluation (APACHE) II Scores by Patient Type, Location, and Duty Status APACHE II Scores    Medical  Surgical    HCC  LCC  HCC  LCC  Duty Status  n  Score (SD)  n  Score (SD)  n  Score (SD)  n  Score (SD)  Active duty  3  11 (9.5)  4  8 (9.3)  9  6 (5.1)  1  2 (na)  Activated reserve  1  7 (na)              Dependent  9  17 (11.0)  13  8 (11.6)  14  11 (7.4)  2  7 (0.7)  Retired  12  12 (5.7)  11  8 (6.9)  16  13 (7.2)  4  10 (4.1)  VA beneficiary      9  7 (4.5)  3  12 (5.7)  6  11 (9.2)  Non-beneficiary  2  15 (2.8)      31  16 (7.5)      Information not available      1  0 (na)          Average  27  14 (8.0)  38  7 (8.2)  73  13 (7.6)  13  9 (6.9)  Average beneficiaries + VA  25  13 (8.3)      42  11 (7.0)      Average beneficiaries      39  8 (9.1)  39  10 (7.2)  7  8 (4.2)  APACHE II Scores    Medical  Surgical    HCC  LCC  HCC  LCC  Duty Status  n  Score (SD)  n  Score (SD)  n  Score (SD)  n  Score (SD)  Active duty  3  11 (9.5)  4  8 (9.3)  9  6 (5.1)  1  2 (na)  Activated reserve  1  7 (na)              Dependent  9  17 (11.0)  13  8 (11.6)  14  11 (7.4)  2  7 (0.7)  Retired  12  12 (5.7)  11  8 (6.9)  16  13 (7.2)  4  10 (4.1)  VA beneficiary      9  7 (4.5)  3  12 (5.7)  6  11 (9.2)  Non-beneficiary  2  15 (2.8)      31  16 (7.5)      Information not available      1  0 (na)          Average  27  14 (8.0)  38  7 (8.2)  73  13 (7.6)  13  9 (6.9)  Average beneficiaries + VA  25  13 (8.3)      42  11 (7.0)      Average beneficiaries      39  8 (9.1)  39  10 (7.2)  7  8 (4.2)  Beneficiaries are all active duty service members including activated reservists, their dependents, and retirees. Non-beneficiaries included civilians, civilian emergencies, and Secretary of Defense Designees View Large LCC with mixed medical/surgical ICUs had a high patient turnover rate (i.e., the number of patient admitted/transferred into the ICU plus the number of patients discharged/transferred out of the ICU less the number of 48 h readmissions divided by the number of patients in the ICU at the beginning of the 24-h survey) (Supplementary Tables S4 and S5). Patient admission or discharge (i.e., turnover) is a resource intensive activity during which patient risk is higher usually because of handoffs. Overall, LCC mixed ICU turnover rate was 250%, meaning ICUs admitted and discharged 2.5 times the number of patients that started in the ICU on the day of the survey. Comparatively, the only HCC mixed ICU had a 100% turnover rate. On the day of survey, one LCC and two HCC patients died. Data were collected on support therapies utilized during the observation period. Fifteen (29%) patients in LCCs and 34 (34%) patients in HCCs required life support therapies. The most commonly utilized therapies were invasive mechanical ventilation, noninvasive mechanical ventilation, and vasoactive medications (Table V). Evaluation of the different vasoactive medications revealed that 10% of patients in LCCs required vasopressors, compared with 6% of patients in HCCs. The most commonly used vasopressor in both centers was norepinephrine (Supplementary Table S6). Table V. Life support therapies used in low capacity and high capacity centers as well as the number of patients requiring multi-organ life support Life Supporting Therapies    LCC  HCC    n=51  n=100  Single organ support  20% (10)a  27% (27)b  Invasive MV  7.8% (4)a  17% (17)b  Noninvasive MV  5.9% (3)a  7% (7)b  Continuous IV vasopressor  1.9% (1)  1% (1)  Continuous IV vasodilator  1.9% (1)  2% (2)  Continuous IV inotrope  (0)  (0)  Continuous paralysis  (0)  (0)  RRT  3.9% (2)a  2% (2)  Multiple organ support  9.8% (5)  7% (7)  MV + IV vasopressor  (0)  5% (5)  MV + IV vasodilator  (0)  1% (1)  MV + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV vasodilator + IV inotrope  2% (1)  (0)  MV + paralysis  2% (1)  (0)  MV + RRT  (0)  1% (1)  MV + IV vasopressor + RRT  2% (1)  (0)  Total organ support  29% (15)  34% (34)  Total MV (NIPPV + IPPV)  25% (4 + 9)  31% (8 + 23)  Total vasopressor  10% (5)  6% (6)  Total vasodilator  4% (2)  3% (3)  Total inotrope  6% (3)  (0)  Total paralysis  2% (1)  (0)  Total RRT  10% (5)  3% (3)  Life Supporting Therapies    LCC  HCC    n=51  n=100  Single organ support  20% (10)a  27% (27)b  Invasive MV  7.8% (4)a  17% (17)b  Noninvasive MV  5.9% (3)a  7% (7)b  Continuous IV vasopressor  1.9% (1)  1% (1)  Continuous IV vasodilator  1.9% (1)  2% (2)  Continuous IV inotrope  (0)  (0)  Continuous paralysis  (0)  (0)  RRT  3.9% (2)a  2% (2)  Multiple organ support  9.8% (5)  7% (7)  MV + IV vasopressor  (0)  5% (5)  MV + IV vasodilator  (0)  1% (1)  MV + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV vasodilator + IV inotrope  2% (1)  (0)  MV + paralysis  2% (1)  (0)  MV + RRT  (0)  1% (1)  MV + IV vasopressor + RRT  2% (1)  (0)  Total organ support  29% (15)  34% (34)  Total MV (NIPPV + IPPV)  25% (4 + 9)  31% (8 + 23)  Total vasopressor  10% (5)  6% (6)  Total vasodilator  4% (2)  3% (3)  Total inotrope  6% (3)  (0)  Total paralysis  2% (1)  (0)  Total RRT  10% (5)  3% (3)  MV, mechanical ventilation; RRT, renal replacement therapy. aTwo patients in the LCC group received two different types of RRT and one patient received both NIPPV and IPPV. Each of these patients were counted only once in the total number of patients who received single organ support and all were counted separately in the NIPPV, IPPV, and RRT rows. bTwo patients in the HCC group received both NIPPV and IPPV on the day of study. Each of these patients were counted only once in the total number of patients who received single organ support but in both the noninvasive and invasive organ support rows. Table V. Life support therapies used in low capacity and high capacity centers as well as the number of patients requiring multi-organ life support Life Supporting Therapies    LCC  HCC    n=51  n=100  Single organ support  20% (10)a  27% (27)b  Invasive MV  7.8% (4)a  17% (17)b  Noninvasive MV  5.9% (3)a  7% (7)b  Continuous IV vasopressor  1.9% (1)  1% (1)  Continuous IV vasodilator  1.9% (1)  2% (2)  Continuous IV inotrope  (0)  (0)  Continuous paralysis  (0)  (0)  RRT  3.9% (2)a  2% (2)  Multiple organ support  9.8% (5)  7% (7)  MV + IV vasopressor  (0)  5% (5)  MV + IV vasodilator  (0)  1% (1)  MV + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV vasodilator + IV inotrope  2% (1)  (0)  MV + paralysis  2% (1)  (0)  MV + RRT  (0)  1% (1)  MV + IV vasopressor + RRT  2% (1)  (0)  Total organ support  29% (15)  34% (34)  Total MV (NIPPV + IPPV)  25% (4 + 9)  31% (8 + 23)  Total vasopressor  10% (5)  6% (6)  Total vasodilator  4% (2)  3% (3)  Total inotrope  6% (3)  (0)  Total paralysis  2% (1)  (0)  Total RRT  10% (5)  3% (3)  Life Supporting Therapies    LCC  HCC    n=51  n=100  Single organ support  20% (10)a  27% (27)b  Invasive MV  7.8% (4)a  17% (17)b  Noninvasive MV  5.9% (3)a  7% (7)b  Continuous IV vasopressor  1.9% (1)  1% (1)  Continuous IV vasodilator  1.9% (1)  2% (2)  Continuous IV inotrope  (0)  (0)  Continuous paralysis  (0)  (0)  RRT  3.9% (2)a  2% (2)  Multiple organ support  9.8% (5)  7% (7)  MV + IV vasopressor  (0)  5% (5)  MV + IV vasodilator  (0)  1% (1)  MV + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV vasodilator + IV inotrope  2% (1)  (0)  MV + paralysis  2% (1)  (0)  MV + RRT  (0)  1% (1)  MV + IV vasopressor + RRT  2% (1)  (0)  Total organ support  29% (15)  34% (34)  Total MV (NIPPV + IPPV)  25% (4 + 9)  31% (8 + 23)  Total vasopressor  10% (5)  6% (6)  Total vasodilator  4% (2)  3% (3)  Total inotrope  6% (3)  (0)  Total paralysis  2% (1)  (0)  Total RRT  10% (5)  3% (3)  MV, mechanical ventilation; RRT, renal replacement therapy. aTwo patients in the LCC group received two different types of RRT and one patient received both NIPPV and IPPV. Each of these patients were counted only once in the total number of patients who received single organ support and all were counted separately in the NIPPV, IPPV, and RRT rows. bTwo patients in the HCC group received both NIPPV and IPPV on the day of study. Each of these patients were counted only once in the total number of patients who received single organ support but in both the noninvasive and invasive organ support rows. Use of other supportive therapies is described in Supplementary Tables S7 and S8. The most common therapies reported were central venous catheters and arterial lines. Central venous catheters were utilized in 29.4% and 45% of patients in LCCs and HCCs, respectively. Arterial lines were placed in 23.5% and 40% of patients in LCCs and HCCs respectively. Additional data on ventilator days and usage are shown in Supplementary Tables S9 and S10. Nearly 70% of surgical patients in this study received maintenance intravenous (IV) fluids, while only 33–45% of medical patients received maintenance IV fluids. Normal saline was the most common type of maintenance IV fluid across the MHS, even in surgical ICUs, followed by Lactated Ringers (Supplementary Table S11). Two (4%) patients from LCCs and 6 (6%) patients from HCCs received blood transfusions during the study period. The average number of units transfused was 1.4 units and the average hemoglobin for blood transfusion was 7.5 mg/dL. Four (50%) blood transfusions occurred with a hemoglobin concentration greater than 7 mg/dL and 25% of transfusions were given to patients with hemoglobin concentrations greater than 8 mg/dL. Sedation was provided to 35 of the 151 patients within the survey period; 30 (86%) of the 35 patients requiring sedation were at HCCs. Sedation scoring was variable at both HCCs and LCCs: 15 patients had an electronic medical record order targeting a specific sedation level, 25 patients were managed according to a sedation protocol without specific target identified in the order, and 10 patients had a sedation score verbally discussed during rounds with some overlap between the methods. In 8 patients (23%), it was unclear how the sedation score was defined or the goal targeted (Supplementary Tables S16 and S17). Additionally, LCCs did not report any delirium, but twelve patients had no report of a delirium scoring system being used at all. HCCs had 5 episodes of delirium (5%), but had nine patients (9%) for whom no scoring tool was used. Data on patient nutrition while in the ICU are provided in Supplementary Table S12. Enteral feeding was more likely to be used in HCCs (33% vs. 7.8% p = 0.005) and HCCs were twice as likely to achieve more that 60% of goal caloric requirements than LCCs during the study period (29% vs. 14%). For the majority of LCC patients (73%) and a significant percentage of HCC patients (39%), the percent of caloric goal delivered during the study period was unknown or unable to be identified. With respect to standard ICU interventions (Supplementary Table S13), 23 (45.1%) patients in LCCs and 24 (24%) patients in HCCs were not on GI prophylaxis while in the ICU. Deep vein thrombosis (DVT) prophylaxis was reported for only 62% of patients in HCCs and 64% of patients in LCCs. Between 8–16% of patients on the day of the study did not receive DVT prophylaxis. Rehabilitation (i.e., physical therapy given in the ICU) was performed with 80% of patients within the study. Three patients within the study experienced a hypoglycemic event (blood sugar less than 60) during the study time period. Chest radiographs were obtained for 7 (13.7%) patients in LCCs and 32 (32%) patients in HCCs. 28 (88%) of the daily chest radiographs obtained in the HCCs were ordered by one facility. HOB elevation data (Supplementary Table S13) demonstrated that 55% of patients in HCC and 49% of patients in LCC received the recommended 18–24 h of HOB elevation daily. Respondents were unable to identify the patient’s hours of HOB elevation for 31% of patients at HCCs and 27% of patients at LCCs. Complications were noted for 29 (19.2%) patients within the study period for a total of 58 complications; 122 (80.8%) patients did not experience any complications (Table VI). The most commonly reported complications were acute kidney injury, bleeding, sepsis, and ventilator-associated pneumonia. HCCs reported markedly higher incidence of complications with 27 patients (27% of HCC patients) experiencing 55 total complications (median complication per patient of 1 [0,2]) compared with LCCs where 2 patients (4% of LCC patients) experienced 3 total complications. Four new pressure injuries were reported during the study period. Table VI. Complications Identified for Patient in the ICU at the Time of the Survey Complications    HCC  LCC  Total  Complication  n  (%)  n  (%)  n  (%)  None  73  (73)  49  (96)  122  (81)  AKI  13  (13)  2  (4)  15  (10)  Unintended bleeding  7  (7)  0  (0)  7  (5)  Sepsis  6  (6)  0  (0)  6  (4)  VAP  5  (5)  0  (0)  5  (3)  Arrhythmia  4  (4)  0  (0)  4  (3)  New pressure ulcer  4  (4)  0  (0)  4  (3)  Other  2  (2)  0  (0)  3  (2)  Medication error  2  (2)  0  (0)  2  (1)  Stroke  2  (2)  0  (0)  2  (1)  Myocardial infarction  2  (2)  0  (0)  2  (1)  C. Diff  2  (2)  0  (0)  2  (1)  ARDS  1  (1)  0  (0)  1  (1)  Unintentional extubation  1  (1)  0  (0)  1  (1)  Unintentional device removal  1  (1)  0  (0)  1  (1)  DVT/PE  1  (1)  0  (0)  1  (1)  Failed procedure  1  (1)  0  (0)  1  (1)  Re-intubation  1  (1)  0  (0)  1  (1)  PEA arrest  0  (0)  1  (2)  1  (1)  CLABSI  0  (0)  0  (0)  0  (0)  Delayed transfer  0  (0)  0  (0)  0  (0)  Fall  0  (0)  0  (0)  0  (0)  Thrombophlebitis  0  (0)  0  (0)  0  (0)  GI bleed  0  (0)  0  (0)  0  (0)  Total  55  (55)  3  (6)  58  (38)  Complications    HCC  LCC  Total  Complication  n  (%)  n  (%)  n  (%)  None  73  (73)  49  (96)  122  (81)  AKI  13  (13)  2  (4)  15  (10)  Unintended bleeding  7  (7)  0  (0)  7  (5)  Sepsis  6  (6)  0  (0)  6  (4)  VAP  5  (5)  0  (0)  5  (3)  Arrhythmia  4  (4)  0  (0)  4  (3)  New pressure ulcer  4  (4)  0  (0)  4  (3)  Other  2  (2)  0  (0)  3  (2)  Medication error  2  (2)  0  (0)  2  (1)  Stroke  2  (2)  0  (0)  2  (1)  Myocardial infarction  2  (2)  0  (0)  2  (1)  C. Diff  2  (2)  0  (0)  2  (1)  ARDS  1  (1)  0  (0)  1  (1)  Unintentional extubation  1  (1)  0  (0)  1  (1)  Unintentional device removal  1  (1)  0  (0)  1  (1)  DVT/PE  1  (1)  0  (0)  1  (1)  Failed procedure  1  (1)  0  (0)  1  (1)  Re-intubation  1  (1)  0  (0)  1  (1)  PEA arrest  0  (0)  1  (2)  1  (1)  CLABSI  0  (0)  0  (0)  0  (0)  Delayed transfer  0  (0)  0  (0)  0  (0)  Fall  0  (0)  0  (0)  0  (0)  Thrombophlebitis  0  (0)  0  (0)  0  (0)  GI bleed  0  (0)  0  (0)  0  (0)  Total  55  (55)  3  (6)  58  (38)  Complications for these patients could have occurred at any point during their ICU admission. Other includes “hepatic dysfunction” and hypernatremia. AKI, acute kidney injury; VAP, ventilator-associated pneumonia; C. Diff, Clostridium Difficile infection; ARDS, acute respiratory distress syndrome, DVT/PE, deep venous thrombosis/pulmonary embolism; PEA, pulseless electrical activity; CLABSI, central line-associated bloodstream infection; GI, gastrointestinal. Table VI. Complications Identified for Patient in the ICU at the Time of the Survey Complications    HCC  LCC  Total  Complication  n  (%)  n  (%)  n  (%)  None  73  (73)  49  (96)  122  (81)  AKI  13  (13)  2  (4)  15  (10)  Unintended bleeding  7  (7)  0  (0)  7  (5)  Sepsis  6  (6)  0  (0)  6  (4)  VAP  5  (5)  0  (0)  5  (3)  Arrhythmia  4  (4)  0  (0)  4  (3)  New pressure ulcer  4  (4)  0  (0)  4  (3)  Other  2  (2)  0  (0)  3  (2)  Medication error  2  (2)  0  (0)  2  (1)  Stroke  2  (2)  0  (0)  2  (1)  Myocardial infarction  2  (2)  0  (0)  2  (1)  C. Diff  2  (2)  0  (0)  2  (1)  ARDS  1  (1)  0  (0)  1  (1)  Unintentional extubation  1  (1)  0  (0)  1  (1)  Unintentional device removal  1  (1)  0  (0)  1  (1)  DVT/PE  1  (1)  0  (0)  1  (1)  Failed procedure  1  (1)  0  (0)  1  (1)  Re-intubation  1  (1)  0  (0)  1  (1)  PEA arrest  0  (0)  1  (2)  1  (1)  CLABSI  0  (0)  0  (0)  0  (0)  Delayed transfer  0  (0)  0  (0)  0  (0)  Fall  0  (0)  0  (0)  0  (0)  Thrombophlebitis  0  (0)  0  (0)  0  (0)  GI bleed  0  (0)  0  (0)  0  (0)  Total  55  (55)  3  (6)  58  (38)  Complications    HCC  LCC  Total  Complication  n  (%)  n  (%)  n  (%)  None  73  (73)  49  (96)  122  (81)  AKI  13  (13)  2  (4)  15  (10)  Unintended bleeding  7  (7)  0  (0)  7  (5)  Sepsis  6  (6)  0  (0)  6  (4)  VAP  5  (5)  0  (0)  5  (3)  Arrhythmia  4  (4)  0  (0)  4  (3)  New pressure ulcer  4  (4)  0  (0)  4  (3)  Other  2  (2)  0  (0)  3  (2)  Medication error  2  (2)  0  (0)  2  (1)  Stroke  2  (2)  0  (0)  2  (1)  Myocardial infarction  2  (2)  0  (0)  2  (1)  C. Diff  2  (2)  0  (0)  2  (1)  ARDS  1  (1)  0  (0)  1  (1)  Unintentional extubation  1  (1)  0  (0)  1  (1)  Unintentional device removal  1  (1)  0  (0)  1  (1)  DVT/PE  1  (1)  0  (0)  1  (1)  Failed procedure  1  (1)  0  (0)  1  (1)  Re-intubation  1  (1)  0  (0)  1  (1)  PEA arrest  0  (0)  1  (2)  1  (1)  CLABSI  0  (0)  0  (0)  0  (0)  Delayed transfer  0  (0)  0  (0)  0  (0)  Fall  0  (0)  0  (0)  0  (0)  Thrombophlebitis  0  (0)  0  (0)  0  (0)  GI bleed  0  (0)  0  (0)  0  (0)  Total  55  (55)  3  (6)  58  (38)  Complications for these patients could have occurred at any point during their ICU admission. Other includes “hepatic dysfunction” and hypernatremia. AKI, acute kidney injury; VAP, ventilator-associated pneumonia; C. Diff, Clostridium Difficile infection; ARDS, acute respiratory distress syndrome, DVT/PE, deep venous thrombosis/pulmonary embolism; PEA, pulseless electrical activity; CLABSI, central line-associated bloodstream infection; GI, gastrointestinal. Urine output for the 24-h study was reported for 95% of patients. These data demonstrate that approximately 36% of patients with available data (34% of cohort) met the definition for acute kidney injury (AKI) due to low urine output (total 24-h urine output divided by 24 divided by the patient’s ideal body weight). Using the serum creatinine measured during the study period and comparing it to the lowest recorded serum creatinine during the patient’s admission, 17% of patients with available data (15% of cohort) had AKI during the study period. These values are notably different than the participant reported rate of AKI above (Supplementary Table S15). DISCUSSION This is the first prospective survey of ICU patients conducted within the MHS. Overall it highlights significant differences between LCCs and HCCs within the MHS with respect to patient volume and acuity where HCCs care for significantly more patients who are higher acuity according to multiple measurements: APACHE II scores, life support therapies, ventilator days, and invasive devices. Notably, the majority of surgical ICU patients (72%) were in HCCs and only 30% of patients required any form of life support during the study period. The indications for ICU admission in our cohort, together with the low ICU length of stay of patients prior to the survey and the high turnover rate, all suggest that the MHS has a low threshold for ICU admission and that few patients admitted to the ICU require intensive care resources. Importantly, 39% of all data submitted to this survey is from one large MTF. This MTF reported all burn patients and 94% of trauma patients and is the only designated Level 1 trauma center within the military as designated by the Secretary of Defense. This uneven distribution of critical care services across the MHS which may have implications for military healthcare in terms of quality of services delivered and clinician readiness for the deployed military mission. Furthermore, low volume centers compared with high volume centers have demonstrated worse patient outcomes with respect to complex, critically ill patients.8–10 Higher volume and acuity centers may also better prepare our active duty personnel for deployments. The overall illness severity of the patient population cared for in both LCCs and HCCs is much lower than civilian institutions.11 In a study by Checkley et al that surveyed ICUs within 69 centers, the average APACHE II score was 19.3, significantly higher than LCCs (8 ± 8.2) and HCCs (12 ± 7.7) in our study. Interestingly, non-beneficiaries in both medical and surgical populations raised the average APACHE II score. This may be explained by the fact that virtually all non-beneficiary admissions are traumatic and burn injuries that generally result in higher APACHE II scores. Low illness severity may contribute to the high patient turnover noted in this prevalence study and this may contribute to ineffective use of clinical resources because high turnover is associated with an increase in staff workload and adverse outcomes.12 Several findings in this survey suggest that critical care services in the MHS might benefit from system wide monitoring and focused interventions with process improvement efforts. In particular, this survey reported notable variance between sites in key quality indicators related to sedation management, venous thromboembolism prevention, ventilator-associated pneumonia prevention (i.e., head of bed elevation), nutrition therapy, delirium monitoring, intravenous fluid use, glycemic management (particularly avoidance of hypoglycemia), physical rehabilitation, and end tidal carbon dioxide monitoring. In many circumstances, the survey respondent could not find the information needed to accurately report the data. For example, the majority of LCCs and a significant portion of HCCs were unable to identify a patient’s goal caloric requirement. The variance in clinical care across the MHS may contribute to increased risk of medical error and adverse outcomes whereas interventions to decrease variance by standardizing processes improve safety and outcomes.11–19 It is probable that the illness severity and patient volume in MHS ICUs is so low and the turnover rates are so high that the impact of critical care process variance on patient outcome will be difficult to measure. Complication rates across the MHS ICUs are low, likely reflecting the low illness severity and short ICU stays reported. Again, significant differences between HCCs and LCCs are noted with respect to complication rates: 27% of patients in HCCs had at least on complication compared with 4% of patients in LCCs. This may be impacted by differences in patient illness severity at HCCs vs. LCCs. These data suggest that interventions across the MHS to decrease the incidence of kidney injury and infections might improve patient outcomes and decrease costs. It is unclear what impact the use of saline as the predominant intravenous crystalloid in the MHS has on the rate of AKI.20,21 This point prevalence study has some notable limitations. The most obvious is that the study results and conclusions are drawn from data collected over a single 24-h period in December 2014. As such, this may not accurately represent the average demographics of the organization that a longer study period might better identify. Furthermore, because of the pragmatic nature of the study, formal quality controls were not placed on reported data. Survey respondents were clinicians who volunteered time to complete the data entry for each patient in their ICU on the day of study. While this methodology was cost effective and facilitated this first-of-its-kind study in the MHS, more rigorous controls on data entry and data definitions could improve the accuracy of these conclusions. Finally, while avoiding collection of patient PHI expedited the approval this project, lack of patient identifiers prevented our ability to audit or verify reported data. CONCLUSION This survey represents the first 24-h prevalence survey conducted to describe patient care across MHS critical care services. Importantly, these data do not describe why these findings are what they are. For example, the lower acuity level MHS ICUs may reflect the relative inexperience and/or youth of the MHS clinical workforce. In this context, dispositioning of slightly more complex patients to an environment with more resources to monitor patient closely, especially by slightly more experienced nurses (at a minimum, MHS ICU nurses must be at least 1 yr more experience than a comparable nurse in a ward environment because of the additional critical care nursing course training requirement), may be appropriate use of resources in a fixed-cost model where hospital budgets are allocated prior to care being rendered. Overall, these results provide more granular and accurate data for medical decision-making than does review of administrative databases; this information will provide the groundwork for future improvements within MHS critical care. There is a need to establish a system that enables accurate collection of high priority data related to critical care services. Moreover, collaborative quality improvement endeavors across the MHS could use this information to positively improve patient care and potentially reduce costs. Efforts to increase volume of complex, critically ill patients in MHS ICUs, perhaps by increasing non-beneficiary and Veteran care, could improve patient safety and clinician readiness for deployment to operational environments. Supplementary Material Supplementary material is available at Military Medicine online. Acknowledgements Mrs. Nicole Caldwell, U.S. Army Institute of Surgical Research, Fort Sam Houston, TX, for her support with maintaining research and regulatory files. MAJ Craig Ainsworth MD, William Beaumont Army Medical Center, El Paso, TX. MAJ Alain Abellada MD, Blanchfield Army Community Hospital, Fort Campbell, KY. COL Stephen Silvey MD; MAJ Scott Trexler MD, LTC(P) David Bell MD; Houmayoun Ahmadian DO and Col(ret) Jeffrey McNeil MD, Brooke Army Medical Center, JBSA Fort Sam Houston, TX. COL Harold Thomas MD, Darnall Army Medical Center, Fort Hood, TX. Dr. Michael Cole MD, Fort Belvoir Community Hospital, Fort Belvoir, VA. LTC Sean Reilly MD, Landstuhl Regional Medical Center, Landstuhl, Germany. LTC Larry Linville MSN, General Leonard Wood Army Community Hospital, Fort Leonard Wood, MO. Dr. Bruce Lovins MD, Martin Army Community Hospital, Fort Benning, GA . LTC Jessica Bunin MD, MAJ Matthew Aboudara MD, Tripler Army Medical Center, Honolulu, HI. COL Stewart McCarver MD, Walter Reed National Military Medical Center, Bethesda, MD. MAJ Douglas Powell MD, Womack Army Medical Center, Fort Bragg, NC. Col Brian Delmonaco MD, Mike O’Callaghan Federal Medical Center, Nellis AFB, NV. Maj John Untisz DO, Eglin Air Force Base Hospital, Eglin AFB, FL. Maj Tokunbo Matthews MD, Joint Base Elmendorf-Richardson Hospital, Anchorage, AK. Ltc Christopher Dennis DO, David Grant US Air Force Medical Center, Travis AFB, CA. MAJ Dara Regn MD, Wright-Patterson Medical Center, Dayton, OH. Dr. Shawn French MD, Keesler Medical Center, Keesler AFB, MS. LCDR Thuy Lin MD, Naval Medical Center Portsmouth, Portsmouth, VA. LCDR Ryan Maves MD, Naval Medical Center San Diego, San Diego, CA. LCDR Russell Miller MD, Naval Hospital Camp Pendleton, Camp Pendleton, CA. Dr. James Prahl MD, Naval Hospital Guam, Tutuhan, Guam. LTC David E. Bennett, Dwight David Eisenhower Army Medical Center. References 1 Halpern NA, Pastores SM: Critical care medicine in the United States 2000–2005: an analysis of bed numbers, occupancy rates, payer mix, and costs. Crit Care Med  2010; 38( 1): 65– 71. Google Scholar CrossRef Search ADS PubMed  2 Barnato AE, et al.  : Prioritizing the organization and management of intensive care services in the United States: The PrOMIS Conference. Crit Care Med  2007; 34: 1003– 11. Google Scholar CrossRef Search ADS   3 Harvey MA, Penoyer DA, Jastremski C: Building teamwork to improve outcomes. In: Textbook of Critical Care: 6th Edition , pp 1589– 94, Philadelphia, PA, Saunders, 2011. Google Scholar CrossRef Search ADS   4 Chalfin DB, Cohen IL, Lambrinos J: The economics and cost-effectiveness of critical care medicine. Intensive Care Med  1995; 21: 952– 61. Google Scholar CrossRef Search ADS PubMed  5 The United Hospital Fund, “Critical Care Leadership Network”. Available at https://uhfnyc.org/initiatives/quality_improvement/critical-care-leadership-network, accessed October 26, 2017. 6 Checkley WC, et al.  : Structure, process, and annual ICU mortality across 69 centers: United States Critical Illness and Injury Trials Group Critical Illness Outcomes Study. Crit Care Med  2014; 42: 344– 56. Google Scholar CrossRef Search ADS PubMed  7 Kahn JM, Goss CH, Heagerty PJ, Kramer AA, O’Brien CR, Rubenfeld GD: Hospital volume and the outcomes of mechanical ventilation. N Engl J Med  2006; 355( 1): 41– 50. Google Scholar CrossRef Search ADS PubMed  8 Office of the Undersecretary of Defence, Comptroller. Defense Health Program Fiscal Year (FY) 2014 Budget Estimates Appropriation Highlights. 2014:Exhibit PBA-19 (DHP-1). accessed October 30 October 2017. Available at http://comptroller.defense.gov/Portals/45/Documents/defbudget/fy2014/budget_justification/pdf/09_Defense_Health_Program/VOL_I/VOL_I_Sec_1_PBA-19_Introductory_Statement_DHP_PB14.pdf. 9 Chowdhury MM, Dagash H, Pierro A: A systematic review of the impact of volume of surgery and specialization on patient outcome. Br J Surg  2007; 94( 2): 145– 61. Google Scholar CrossRef Search ADS PubMed  10 Halm EA, Lee C, Chassin MR: Is volume related to outcome in health care? A systematic review and methodologic critique of the literature. Ann Intern Med  2002; 137( 6): 511– 20. Google Scholar CrossRef Search ADS PubMed  11 Knaus WA, Draper EA, Wagner DP, Zimmerman JE: APACHE II: a severity of disease classification system. Crit Care Med  1985; 13( 10): 818– 29. Google Scholar CrossRef Search ADS PubMed  12 Park SH, et al.  : An integrative literature review of patient turnover in inpatient hospital settings. West J Nurs Res  2016; 38( 5): 629– 55. Google Scholar CrossRef Search ADS PubMed  13 Pun BT, Ely EW: The importance of diagnosing and managing ICU delirium. Chest  2007; 132( 2): 624– 36. Google Scholar CrossRef Search ADS PubMed  14 Girard TD, Kress JP, Fuchs BD, Thomason JWW, Schweickert WD, Pun BT, et al.  : Efficacy and safety of a paired sedation and ventilator weaning protocol for mechanically ventilated patients in intensive care (Awakening and Breathing Controlled trial): a randomised controlled trial. Lancet  2008; 371( 9607): 126– 34. Google Scholar CrossRef Search ADS PubMed  15 Balas MC, Vasilevskis EE, Olsen KM, Schmid KK, Shostrom V, Cohen MZ, et al.  : Effectiveness and Safety of the awakening and breathing coordination, delirium monitoring/management, and early exercise/mobility bundle. Crit Care Med  2014; 42( 5): 1024– 36. Google Scholar CrossRef Search ADS PubMed  16 Pronovost P, Berenholtz S, Dorman T, Lipsett PA, Simmonds T, Haraden C: Improving communication in the ICU using daily goals. J Crit Care  2003; 18( 2): 71– 5. Google Scholar CrossRef Search ADS PubMed  17 Weiss CH, Moazed F, McEvoy CA, et al.  : Prompting physicians to address a daily checklist and process of care and clinical outcomes: a single-site study. Am J Respir Crit Care Med  2011; 184( 6): 680– 6. Google Scholar CrossRef Search ADS PubMed  18 Lilly CM, Motzkus C, Rincon T, Cody SE, Landry K, Irwin RS: ICU telemedicine program financial outcomes. Chest  2016; 151( 2): 286– 97. Google Scholar CrossRef Search ADS PubMed  19 Lilly CM, Cody S, Zhao H, et al.  : Hospital mortality, length of stay, and preventable complications among critically ill patients before and after tele-ICU reengineering of critical care processes. JAMA  2011; 305( 21): 2175– 83. Google Scholar CrossRef Search ADS PubMed  20 Yunos NM, Bellomo R, Hegarty C, Story D, Ho L, Bailey M: Association between a chloride-liberal vs chloride-restrictive intravenous fluid administration strategy and kidney injury in critically ill adults. JAMA  2012; 308( 15): 1566– 72. Google Scholar CrossRef Search ADS PubMed  21 Young P, Bailey M, Beasley R, et al.  : Effect of a buffered crystalloid solution vs saline on acute kidney injury among patients in the intensive care unit. JAMA  2015; 314( 16): 1701– 10. Google Scholar CrossRef Search ADS PubMed  Author notes The views expressed are those of the authors and do not reflect the official policy or position of the US Army Medical Department, Department of the Army, Department of the Air Force, Department of the Navy, Department of Defense or the U.S. Government. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Military Medicine Oxford University Press

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Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2018.
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Abstract

Abstract Background Healthcare expenditures are a significant economic cost with critical care services constituting one of its largest components. The Military Health System (MHS) is the largest, global healthcare system of its kind. In this project, we sought to describe critical care services and the patients who receive them in the MHS. Methods We surveyed 26 military treatment facilities (MTFs) representing 38 critical care services or intensive care units (ICUs). MTFs with multiple ICUs and critical care services responded to the survey as services (e.g., surgical or medical ICU service), whereas MTFs with only one ICU responded as a unit and gave information about all types of patients (i.e., medical and surgical). Our survey was divided into an administrative portion and a 24-h point prevalence survey of patients and patient care. The administrative portion is reported separately in this journal. The 24-h point prevalence survey collected information about all patients present in, admitted to, or discharged from participating services/units during the same 24-h period in December 2014. The survey was anonymous and protected health information was not collected. Findings Sixteen MTFs (69%) and 27 ICU services/units (71%) returned the point prevalence survey. MTFs with >200 beds (n = 3, 22%) were categorized as “high capacity centers” (HCCs) whereas those with ≤200 beds (n = 13, 78%) were characterized as low capacity centers (LCCs). Two MTFs (one HCC and one LCC) returned only administrative data. The remaining 16 MTFs reported data about 151 patients. In all, 100 (67%) of the patients were at three HCCs during this study period. One HCC accounted for 39% (59 patients) of all patient care during this study. Most patients were cared for in mixed medical/surgical ICUs (34.4%), followed by medical (21.2%), surgical (18.5%), trauma (11.9%), cardiac (7.9%), and burn (6.0%) ICUs. The most common medical indication for admission was cardiac followed by general medical. The most common surgical indications for admission were trauma, other, and cardiothoracic surgery. The average APACHE II score of all patients across both LCCs and HCCs was 11 ± 8.1 (8 ± 7.8 vs. 13 ± 7.7 p = 0.008). The lower acuity of patients in this study is reflected in a high turnover rate, low rate of arterial and central line placements (33%), and low rates of life support (all types, 30%; mechanical ventilation only, 21.2%; noninvasive mechanic ventilation only, 7.9%; and vasoactive medications, 6.6%). Thirty-five (23.2%) patients within the study were affected by a total of 57 complications. The three most common complications experienced were acute kidney injury, bleeding, and sepsis. Discussion This is the first detailed report about MHS critical care services and the patients receiving care. It describes a low acuity ICU patient population, concentrated at larger MTFs. This study highlights the need for the establishment of a system that allows for the continuous collection of high priority information about clinical care in the MHS in order to facilitate implementation of standardized protocols and process improvements. INTRODUCTION In the USA, over 5 million patients are admitted annually to the intensive care unit (ICU), constituting the largest portion of national healthcare expenditures, and for an increasing amount of healthcare dollars ($56.6 to $81.7 billion from 2000 to 2005).1 Costs are likely to continue to rise given that the number of patients over age 65 is projected to double between 2007 and 2030.2 ICU admission and subsequent critical care represent approximately 13% of all hospital costs, 4% of national health expenditures, and 1% of the U.S. gross domestic product.3,4 Each day of intensive care costs on average $3,500 per patient.1 Considering the high volume of critically ill patients with increased complexity, and the significant cost in providing care, there is an added emphasis to maximize patient safety and improve outcomes. A way to meet these goals is through collaboration and teamwork. Over the past 40 yr, healthcare quality organizations such as the Joint Commission and the National Institutes of Health have promoted improving teamwork and collaboration. For example, the New York Hospital Association has collaboratively formed a Critical Care Leaders Network dedicated to improving critical care services in the greater New York metropolitan and surrounding areas.5 Their efforts demonstrate that leadership can change practices across multiple healthcare organizations. Hospitals that are a part of their collaborative effort have been recognized for exceptional clinical care in a variety of areas including emergency care of myocardial infarctions, decreasing central line-associated bloodstream infections, decreasing ventilator-associated pneumonia rates, increasing physician use and understanding of ultrasound and simulation for training, and planning for disaster response. Attitudes and perceptions of the quality of teamwork vary widely between institutions, units, individuals, clinicians and professions. Military treatment facilities (MTFs), which are generally compared with their civilian counterparts, are no exception. Unfortunately, there are no comparable critical care networks and little systematic investigation regarding the nature of critical care services in the Military Health System (MHS). Furthermore, this type of data could have implications on and inform decisions about maintaining readiness of military clinicians. There are a total of 26 MTFs in the MHS with critical care capabilities comprising approximately 300–400 total ICU beds. Past efforts have failed to accurately describe the critical care organization, structure, or patient population at these facilities.6,7 The primary objective of this observational study was to describe current critical care services in the MHS and the costs associated with them. Through better understanding and initial collaboration enabled by this effort, we might begin to enhance cross organizational teamwork and improve critical care practices for the enterprise. METHODS An institutional review board (IRB)-approved, anonymous survey was sent to all 26 MTFs representing 38 critical care services or ICUs. The survey consisted of two parts: an administrative portion and a 24-h prevalence study. This paper reports the findings of the 24-h prevalence survey only; findings from the administrative portion of the survey are reported elsewhere in this Journal. The 24-h prevalence survey underwent a process for face validation by two independent reviewers; questions that were unclear, irrelevant, or duplicate were revised or removed. The prevalence survey collected information about patient demographics, treatments, complications, and outcomes for all patients in ICUs during a 24-h period in December 2014. Respondents personally involved in critical care at each facility completed the administrative survey at any time during the month prior to or the month following the 24-h prevalence study. Protected health information (PHI) was not collected and the surveys were determined to be research not involving human subjects by the IRB. We asked all MHS MTFs with identified critical care beds to participate in this survey. At the beginning of the study, the principal investigator (PI, author JCP) contacted each MTF to identify ICU leadership and obtain their contact information. An e-mail was sent to these points-of-contact (POCs) with a request to participate and included instructions for completing the survey. In order to determine how many centers participated, we asked identified POCs at each center to declare by e-mail if their center participated, but survey data were submitted anonymously. To maintain anonymity of data, POCs created a self-identified code name, known only by the center POCs, to be used in the survey in order to aggregate data for a single facility if that facility reported from multiple critical care services or separate ICUs. In other words, large centers could have each ICU service (i.e., medical, surgical, trauma critical care services) or unit (i.e., medical ICU, surgical ICU, etc.) respond to the survey, but each service/unit identified itself with the site-specific code name. For the purposes of the survey, a service was defined as a dedicated physician team whereas a unit was a physical location that may or may not be dedicated to a specific patient population. A master list of physical sites and their associated code names was not created and POC information was not collected in the survey. Thus, only POCs from a facility could identify which data belonged to that facility. Anonymity allowed sites to submit their data without fear of comparison, judgment, or reprisal. The surveys were completed by POCs or POC designee(s) at each MTF. This POC was usually a medical director or a physician/nurse leader identified by the PI through networking with key leaders in critical care throughout the MHS. POCs worked with leaders of other multidisciplinary groups to identify the most reliable source of information for the survey. POCs personally vouched for the accuracy of the data. A list of POCs and associated facility is provided in the Acknowledgements section. In situations where the requested information was not available, POCs could answer as such. We attempted to collect cost data regarding specific critical care interventions but were unable to do so at each MTF. Consequently, we identified total costs of critical care services for 1 yr at one of the MTFs in the study (the largest) and averaged these costs per bed day. We used these data with the survey data to make a broad estimate of total annual critical care costs for the MHS. We analyzed the data according to high capacity centers (HCCs) and low capacity centers (LCCs). HCCs were defined as having greater than 200 inpatient beds, whereas LCCs had less than or equal to 200 beds. We further described centers in terms of service/unit types, care models, resources, protocol availability, and population data/unit demographics. Differences between HCCs and LCCs were compared where possible using Fisher’s Exact Test with significance set at p < 0.05. RESULTS Sixteen MTFs (62%) and 27 ICU services/units (71%) returned the 24-h point prevalence portion of the survey. Three MTFs (22%) reported a bed capacity of greater than 200 beds and were designated as HCCs; the remaining 13 MTFs were LCCs. Participants submitted information for 151 patients cared for by MHS critical care services during the 24-h period. The three HCCs accounted for 100 (67%) patients; one HCC accounted for 59 patients (39% of all patients reported). Of the 51 patients admitted to LCCs, the majority of patients (n = 38) were admitted to mixed medical/surgical ICUs; fewer were admitted to medical and cardiac ICUs. Of the 100 patients admitted to HCCs, patients were admitted to a larger variety of ICU types (Table I, Supplementary Table S1). The majority of patients (97%) were cared for in the unit they would typically be assigned to; 4 patients (4%) in HCCs and 1 patient (2%) in LCCs were “boarded” in units that would not normally care for patients with the primary diagnosis of the “boarded” patient (Supplementary Table S2). During the time of the survey, three patients in HCCs and one patient in a LCC were eligible for transfer out of the ICU (2.6% of patients), but did not due to unknown reasons (data not collected). Table I. Patients by ICU Type ICU Patient Demographics by ICU  Type of ICU  % Total (n)  % LCC (n)  % HCC (n)  Mixed Medical/Surgical  34.4 (52)  73.1 (38)  26.9 (14)  Medical  21.2 (32)  28.1 (9)  71.9 (23)  Surgical  18.5 (28)  0 (0)  100 (28)  Trauma  11.9 (18)  0 (0)  100 (18)  Cardiac  7.9 (12)  33.3 (4)  66.7 (8)  Burn  6.0 (9)  0 (0)  100 (9)  Total  151  51  100  ICU Patient Demographics by ICU  Type of ICU  % Total (n)  % LCC (n)  % HCC (n)  Mixed Medical/Surgical  34.4 (52)  73.1 (38)  26.9 (14)  Medical  21.2 (32)  28.1 (9)  71.9 (23)  Surgical  18.5 (28)  0 (0)  100 (28)  Trauma  11.9 (18)  0 (0)  100 (18)  Cardiac  7.9 (12)  33.3 (4)  66.7 (8)  Burn  6.0 (9)  0 (0)  100 (9)  Total  151  51  100  Table I. Patients by ICU Type ICU Patient Demographics by ICU  Type of ICU  % Total (n)  % LCC (n)  % HCC (n)  Mixed Medical/Surgical  34.4 (52)  73.1 (38)  26.9 (14)  Medical  21.2 (32)  28.1 (9)  71.9 (23)  Surgical  18.5 (28)  0 (0)  100 (28)  Trauma  11.9 (18)  0 (0)  100 (18)  Cardiac  7.9 (12)  33.3 (4)  66.7 (8)  Burn  6.0 (9)  0 (0)  100 (9)  Total  151  51  100  ICU Patient Demographics by ICU  Type of ICU  % Total (n)  % LCC (n)  % HCC (n)  Mixed Medical/Surgical  34.4 (52)  73.1 (38)  26.9 (14)  Medical  21.2 (32)  28.1 (9)  71.9 (23)  Surgical  18.5 (28)  0 (0)  100 (28)  Trauma  11.9 (18)  0 (0)  100 (18)  Cardiac  7.9 (12)  33.3 (4)  66.7 (8)  Burn  6.0 (9)  0 (0)  100 (9)  Total  151  51  100  We sought to help readers better understand the potential implications of the cost critical care services. We asked the Program Analysis and Evaluation Department for the core site hospital, the largest in this study, to provide an average cost estimate for a single patient day in the ICU for a 1 yr period (FY 2014). For illustrative purposes only, the annual cost data demonstrated an average ICU bed cost of $3,615/day. If we assume similar costs for other facilities in this study, there was approximately $545,865 spent on critical care patients during this 24-h survey. The Defense Health Plan (DHP) Budget estimate for 20148 was $31.6 billion. Consequently, critical care services may constitute approximately 1% of the DHP budget. Of the 51 patients admitted to LCCs, 35 were admitted for medical reasons and 16 were admitted for surgical reasons. Of the 100 patients admitted to HCCs, 72 were admitted for surgical reasons and 28 were admitted for medical reasons. The most common indications for ICU admission according to patient type are listed in Table II. A full list of patient diagnoses on the day of survey according to patient type can be found in Supplementary Table S3. One HCC patient was a “readmission” to the ICU (i.e., was transferred out of the same ICU less than 48 h prior to their admission accounted for in this survey). Table II. Indications for ICU Admission by Patient Type Primary Indication for Admission by Patient Type  Medical Patients (n = 65)  % (n)  Surgical Patients (n = 86)  % (n)  Respiratory distress/failure  15 (10)  Respiratory or cardiovascular monitoring  28 (24)  Sepsis  15 (10)  Neurologic monitoring/management  17 (15)  Not specified  12 (8)  Post-operative/procedural monitoring  13 (11)  Respiratory or cardiovascular monitoring  12 (8)  Hemorrhage  8 (7)  Heart failure  9 (6)  Resuscitation NOS  8 (7)  Hemorrhage  9 (6)  Not specified  6 (5)  Neurologic monitoring/management  8 (5)  Nursing care  6 (5)  Nursing care  8 (5)  Respiratory distress/failure  5 (4)  Post-operative/procedural monitoring  5 (3)  Sepsis  3 (3)  Administrative  2 (1)  Critical care consultation  2 (2)  Critical care consultation  2 (1)  Heart failure  2 (2)  Resuscitation NOS  2 (1)  Shock NOS  1 (1)  Shock NOS  2 (1)  Administrative  0  Primary Indication for Admission by Patient Type  Medical Patients (n = 65)  % (n)  Surgical Patients (n = 86)  % (n)  Respiratory distress/failure  15 (10)  Respiratory or cardiovascular monitoring  28 (24)  Sepsis  15 (10)  Neurologic monitoring/management  17 (15)  Not specified  12 (8)  Post-operative/procedural monitoring  13 (11)  Respiratory or cardiovascular monitoring  12 (8)  Hemorrhage  8 (7)  Heart failure  9 (6)  Resuscitation NOS  8 (7)  Hemorrhage  9 (6)  Not specified  6 (5)  Neurologic monitoring/management  8 (5)  Nursing care  6 (5)  Nursing care  8 (5)  Respiratory distress/failure  5 (4)  Post-operative/procedural monitoring  5 (3)  Sepsis  3 (3)  Administrative  2 (1)  Critical care consultation  2 (2)  Critical care consultation  2 (1)  Heart failure  2 (2)  Resuscitation NOS  2 (1)  Shock NOS  1 (1)  Shock NOS  2 (1)  Administrative  0  NOS, not otherwise specified. Table II. Indications for ICU Admission by Patient Type Primary Indication for Admission by Patient Type  Medical Patients (n = 65)  % (n)  Surgical Patients (n = 86)  % (n)  Respiratory distress/failure  15 (10)  Respiratory or cardiovascular monitoring  28 (24)  Sepsis  15 (10)  Neurologic monitoring/management  17 (15)  Not specified  12 (8)  Post-operative/procedural monitoring  13 (11)  Respiratory or cardiovascular monitoring  12 (8)  Hemorrhage  8 (7)  Heart failure  9 (6)  Resuscitation NOS  8 (7)  Hemorrhage  9 (6)  Not specified  6 (5)  Neurologic monitoring/management  8 (5)  Nursing care  6 (5)  Nursing care  8 (5)  Respiratory distress/failure  5 (4)  Post-operative/procedural monitoring  5 (3)  Sepsis  3 (3)  Administrative  2 (1)  Critical care consultation  2 (2)  Critical care consultation  2 (1)  Heart failure  2 (2)  Resuscitation NOS  2 (1)  Shock NOS  1 (1)  Shock NOS  2 (1)  Administrative  0  Primary Indication for Admission by Patient Type  Medical Patients (n = 65)  % (n)  Surgical Patients (n = 86)  % (n)  Respiratory distress/failure  15 (10)  Respiratory or cardiovascular monitoring  28 (24)  Sepsis  15 (10)  Neurologic monitoring/management  17 (15)  Not specified  12 (8)  Post-operative/procedural monitoring  13 (11)  Respiratory or cardiovascular monitoring  12 (8)  Hemorrhage  8 (7)  Heart failure  9 (6)  Resuscitation NOS  8 (7)  Hemorrhage  9 (6)  Not specified  6 (5)  Neurologic monitoring/management  8 (5)  Nursing care  6 (5)  Nursing care  8 (5)  Respiratory distress/failure  5 (4)  Post-operative/procedural monitoring  5 (3)  Sepsis  3 (3)  Administrative  2 (1)  Critical care consultation  2 (2)  Critical care consultation  2 (1)  Heart failure  2 (2)  Resuscitation NOS  2 (1)  Shock NOS  1 (1)  Shock NOS  2 (1)  Administrative  0  NOS, not otherwise specified. Of the 151 patients reported; 43 were retired military, 38 were military dependents, 33 were civilian, and 17 were active duty military. Notably, 28 (85%) of the civilians were admitted to one HCC. Demographic information is shown in Table III. Table III. Patient Demographics Survey Demographics    Medical (n = 65)  Surgical (n = 86)  Age in years % (n)  <20  3% (2)  1% (1)  21–60  35% (23)  51% (54)  61–80  43% (28)  43% (33)  >80  18% (12)  18% (8)  Duty status % (n)  Active duty  11% (7)  12% (10)  Retired  35% (23)  23% (20)  Dependent  34% (22)  19% (16)  Reserve  2% (1)  0%  Information not available  2% (1)  0%  VA beneficiary  14% (9)  10% (9)  Non-beneficiary (civilian emergency or SECDEF designee)  3% (2)  36% (31)  Service affiliation % (n)  Air Force  26% (17)  12% (10)  Army  29% (19)  26% (22)  Navy  9% (6)  6% (5)  NA  9% (6)  38% (33)  Information not available  26% (17)  19% (16)  Gender % (n)  Male  58% (38)  72% (62)  Female  42% (27)  28% (24)  ICU days at start of survey  Median ICU days (IQR)  2 (0,4)  2 (1,3)  ICU days range  0–44  0–44  Survey Demographics    Medical (n = 65)  Surgical (n = 86)  Age in years % (n)  <20  3% (2)  1% (1)  21–60  35% (23)  51% (54)  61–80  43% (28)  43% (33)  >80  18% (12)  18% (8)  Duty status % (n)  Active duty  11% (7)  12% (10)  Retired  35% (23)  23% (20)  Dependent  34% (22)  19% (16)  Reserve  2% (1)  0%  Information not available  2% (1)  0%  VA beneficiary  14% (9)  10% (9)  Non-beneficiary (civilian emergency or SECDEF designee)  3% (2)  36% (31)  Service affiliation % (n)  Air Force  26% (17)  12% (10)  Army  29% (19)  26% (22)  Navy  9% (6)  6% (5)  NA  9% (6)  38% (33)  Information not available  26% (17)  19% (16)  Gender % (n)  Male  58% (38)  72% (62)  Female  42% (27)  28% (24)  ICU days at start of survey  Median ICU days (IQR)  2 (0,4)  2 (1,3)  ICU days range  0–44  0–44  VA, veterans affairs; SECDEF, Secretary of Defense; IQR, inner quartile range. Table III. Patient Demographics Survey Demographics    Medical (n = 65)  Surgical (n = 86)  Age in years % (n)  <20  3% (2)  1% (1)  21–60  35% (23)  51% (54)  61–80  43% (28)  43% (33)  >80  18% (12)  18% (8)  Duty status % (n)  Active duty  11% (7)  12% (10)  Retired  35% (23)  23% (20)  Dependent  34% (22)  19% (16)  Reserve  2% (1)  0%  Information not available  2% (1)  0%  VA beneficiary  14% (9)  10% (9)  Non-beneficiary (civilian emergency or SECDEF designee)  3% (2)  36% (31)  Service affiliation % (n)  Air Force  26% (17)  12% (10)  Army  29% (19)  26% (22)  Navy  9% (6)  6% (5)  NA  9% (6)  38% (33)  Information not available  26% (17)  19% (16)  Gender % (n)  Male  58% (38)  72% (62)  Female  42% (27)  28% (24)  ICU days at start of survey  Median ICU days (IQR)  2 (0,4)  2 (1,3)  ICU days range  0–44  0–44  Survey Demographics    Medical (n = 65)  Surgical (n = 86)  Age in years % (n)  <20  3% (2)  1% (1)  21–60  35% (23)  51% (54)  61–80  43% (28)  43% (33)  >80  18% (12)  18% (8)  Duty status % (n)  Active duty  11% (7)  12% (10)  Retired  35% (23)  23% (20)  Dependent  34% (22)  19% (16)  Reserve  2% (1)  0%  Information not available  2% (1)  0%  VA beneficiary  14% (9)  10% (9)  Non-beneficiary (civilian emergency or SECDEF designee)  3% (2)  36% (31)  Service affiliation % (n)  Air Force  26% (17)  12% (10)  Army  29% (19)  26% (22)  Navy  9% (6)  6% (5)  NA  9% (6)  38% (33)  Information not available  26% (17)  19% (16)  Gender % (n)  Male  58% (38)  72% (62)  Female  42% (27)  28% (24)  ICU days at start of survey  Median ICU days (IQR)  2 (0,4)  2 (1,3)  ICU days range  0–44  0–44  VA, veterans affairs; SECDEF, Secretary of Defense; IQR, inner quartile range. The average acute physiology and chronic health evaluation II (APACHE II) scores8 of all patients in this survey was 11 ± 8.1. APACHE II scores for patient at LCCs was significantly lower than that for patients at HCCs (8 ± 7.8 vs. 13 ± 7.7, p = 0.008, Fig. 1). Both of these values correspond to an overall low expected mortality (<15%). The APACHE II score for non-beneficiaries (i.e., civilian emergencies) was higher than beneficiaries who were medical patients (15 ± 2.8 vs. 14 ± 8.0) and surgical patients (16 ± 7.5 vs. 13 ± 7.6) (Table IV). Figure 1. View largeDownload slide Apache II scores for LCCs and HCCs. Figure 1. View largeDownload slide Apache II scores for LCCs and HCCs. Table IV. Acute Physiology and Chronic Health Evaluation (APACHE) II Scores by Patient Type, Location, and Duty Status APACHE II Scores    Medical  Surgical    HCC  LCC  HCC  LCC  Duty Status  n  Score (SD)  n  Score (SD)  n  Score (SD)  n  Score (SD)  Active duty  3  11 (9.5)  4  8 (9.3)  9  6 (5.1)  1  2 (na)  Activated reserve  1  7 (na)              Dependent  9  17 (11.0)  13  8 (11.6)  14  11 (7.4)  2  7 (0.7)  Retired  12  12 (5.7)  11  8 (6.9)  16  13 (7.2)  4  10 (4.1)  VA beneficiary      9  7 (4.5)  3  12 (5.7)  6  11 (9.2)  Non-beneficiary  2  15 (2.8)      31  16 (7.5)      Information not available      1  0 (na)          Average  27  14 (8.0)  38  7 (8.2)  73  13 (7.6)  13  9 (6.9)  Average beneficiaries + VA  25  13 (8.3)      42  11 (7.0)      Average beneficiaries      39  8 (9.1)  39  10 (7.2)  7  8 (4.2)  APACHE II Scores    Medical  Surgical    HCC  LCC  HCC  LCC  Duty Status  n  Score (SD)  n  Score (SD)  n  Score (SD)  n  Score (SD)  Active duty  3  11 (9.5)  4  8 (9.3)  9  6 (5.1)  1  2 (na)  Activated reserve  1  7 (na)              Dependent  9  17 (11.0)  13  8 (11.6)  14  11 (7.4)  2  7 (0.7)  Retired  12  12 (5.7)  11  8 (6.9)  16  13 (7.2)  4  10 (4.1)  VA beneficiary      9  7 (4.5)  3  12 (5.7)  6  11 (9.2)  Non-beneficiary  2  15 (2.8)      31  16 (7.5)      Information not available      1  0 (na)          Average  27  14 (8.0)  38  7 (8.2)  73  13 (7.6)  13  9 (6.9)  Average beneficiaries + VA  25  13 (8.3)      42  11 (7.0)      Average beneficiaries      39  8 (9.1)  39  10 (7.2)  7  8 (4.2)  Beneficiaries are all active duty service members including activated reservists, their dependents, and retirees. Non-beneficiaries included civilians, civilian emergencies, and Secretary of Defense Designees View Large Table IV. Acute Physiology and Chronic Health Evaluation (APACHE) II Scores by Patient Type, Location, and Duty Status APACHE II Scores    Medical  Surgical    HCC  LCC  HCC  LCC  Duty Status  n  Score (SD)  n  Score (SD)  n  Score (SD)  n  Score (SD)  Active duty  3  11 (9.5)  4  8 (9.3)  9  6 (5.1)  1  2 (na)  Activated reserve  1  7 (na)              Dependent  9  17 (11.0)  13  8 (11.6)  14  11 (7.4)  2  7 (0.7)  Retired  12  12 (5.7)  11  8 (6.9)  16  13 (7.2)  4  10 (4.1)  VA beneficiary      9  7 (4.5)  3  12 (5.7)  6  11 (9.2)  Non-beneficiary  2  15 (2.8)      31  16 (7.5)      Information not available      1  0 (na)          Average  27  14 (8.0)  38  7 (8.2)  73  13 (7.6)  13  9 (6.9)  Average beneficiaries + VA  25  13 (8.3)      42  11 (7.0)      Average beneficiaries      39  8 (9.1)  39  10 (7.2)  7  8 (4.2)  APACHE II Scores    Medical  Surgical    HCC  LCC  HCC  LCC  Duty Status  n  Score (SD)  n  Score (SD)  n  Score (SD)  n  Score (SD)  Active duty  3  11 (9.5)  4  8 (9.3)  9  6 (5.1)  1  2 (na)  Activated reserve  1  7 (na)              Dependent  9  17 (11.0)  13  8 (11.6)  14  11 (7.4)  2  7 (0.7)  Retired  12  12 (5.7)  11  8 (6.9)  16  13 (7.2)  4  10 (4.1)  VA beneficiary      9  7 (4.5)  3  12 (5.7)  6  11 (9.2)  Non-beneficiary  2  15 (2.8)      31  16 (7.5)      Information not available      1  0 (na)          Average  27  14 (8.0)  38  7 (8.2)  73  13 (7.6)  13  9 (6.9)  Average beneficiaries + VA  25  13 (8.3)      42  11 (7.0)      Average beneficiaries      39  8 (9.1)  39  10 (7.2)  7  8 (4.2)  Beneficiaries are all active duty service members including activated reservists, their dependents, and retirees. Non-beneficiaries included civilians, civilian emergencies, and Secretary of Defense Designees View Large LCC with mixed medical/surgical ICUs had a high patient turnover rate (i.e., the number of patient admitted/transferred into the ICU plus the number of patients discharged/transferred out of the ICU less the number of 48 h readmissions divided by the number of patients in the ICU at the beginning of the 24-h survey) (Supplementary Tables S4 and S5). Patient admission or discharge (i.e., turnover) is a resource intensive activity during which patient risk is higher usually because of handoffs. Overall, LCC mixed ICU turnover rate was 250%, meaning ICUs admitted and discharged 2.5 times the number of patients that started in the ICU on the day of the survey. Comparatively, the only HCC mixed ICU had a 100% turnover rate. On the day of survey, one LCC and two HCC patients died. Data were collected on support therapies utilized during the observation period. Fifteen (29%) patients in LCCs and 34 (34%) patients in HCCs required life support therapies. The most commonly utilized therapies were invasive mechanical ventilation, noninvasive mechanical ventilation, and vasoactive medications (Table V). Evaluation of the different vasoactive medications revealed that 10% of patients in LCCs required vasopressors, compared with 6% of patients in HCCs. The most commonly used vasopressor in both centers was norepinephrine (Supplementary Table S6). Table V. Life support therapies used in low capacity and high capacity centers as well as the number of patients requiring multi-organ life support Life Supporting Therapies    LCC  HCC    n=51  n=100  Single organ support  20% (10)a  27% (27)b  Invasive MV  7.8% (4)a  17% (17)b  Noninvasive MV  5.9% (3)a  7% (7)b  Continuous IV vasopressor  1.9% (1)  1% (1)  Continuous IV vasodilator  1.9% (1)  2% (2)  Continuous IV inotrope  (0)  (0)  Continuous paralysis  (0)  (0)  RRT  3.9% (2)a  2% (2)  Multiple organ support  9.8% (5)  7% (7)  MV + IV vasopressor  (0)  5% (5)  MV + IV vasodilator  (0)  1% (1)  MV + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV vasodilator + IV inotrope  2% (1)  (0)  MV + paralysis  2% (1)  (0)  MV + RRT  (0)  1% (1)  MV + IV vasopressor + RRT  2% (1)  (0)  Total organ support  29% (15)  34% (34)  Total MV (NIPPV + IPPV)  25% (4 + 9)  31% (8 + 23)  Total vasopressor  10% (5)  6% (6)  Total vasodilator  4% (2)  3% (3)  Total inotrope  6% (3)  (0)  Total paralysis  2% (1)  (0)  Total RRT  10% (5)  3% (3)  Life Supporting Therapies    LCC  HCC    n=51  n=100  Single organ support  20% (10)a  27% (27)b  Invasive MV  7.8% (4)a  17% (17)b  Noninvasive MV  5.9% (3)a  7% (7)b  Continuous IV vasopressor  1.9% (1)  1% (1)  Continuous IV vasodilator  1.9% (1)  2% (2)  Continuous IV inotrope  (0)  (0)  Continuous paralysis  (0)  (0)  RRT  3.9% (2)a  2% (2)  Multiple organ support  9.8% (5)  7% (7)  MV + IV vasopressor  (0)  5% (5)  MV + IV vasodilator  (0)  1% (1)  MV + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV vasodilator + IV inotrope  2% (1)  (0)  MV + paralysis  2% (1)  (0)  MV + RRT  (0)  1% (1)  MV + IV vasopressor + RRT  2% (1)  (0)  Total organ support  29% (15)  34% (34)  Total MV (NIPPV + IPPV)  25% (4 + 9)  31% (8 + 23)  Total vasopressor  10% (5)  6% (6)  Total vasodilator  4% (2)  3% (3)  Total inotrope  6% (3)  (0)  Total paralysis  2% (1)  (0)  Total RRT  10% (5)  3% (3)  MV, mechanical ventilation; RRT, renal replacement therapy. aTwo patients in the LCC group received two different types of RRT and one patient received both NIPPV and IPPV. Each of these patients were counted only once in the total number of patients who received single organ support and all were counted separately in the NIPPV, IPPV, and RRT rows. bTwo patients in the HCC group received both NIPPV and IPPV on the day of study. Each of these patients were counted only once in the total number of patients who received single organ support but in both the noninvasive and invasive organ support rows. Table V. Life support therapies used in low capacity and high capacity centers as well as the number of patients requiring multi-organ life support Life Supporting Therapies    LCC  HCC    n=51  n=100  Single organ support  20% (10)a  27% (27)b  Invasive MV  7.8% (4)a  17% (17)b  Noninvasive MV  5.9% (3)a  7% (7)b  Continuous IV vasopressor  1.9% (1)  1% (1)  Continuous IV vasodilator  1.9% (1)  2% (2)  Continuous IV inotrope  (0)  (0)  Continuous paralysis  (0)  (0)  RRT  3.9% (2)a  2% (2)  Multiple organ support  9.8% (5)  7% (7)  MV + IV vasopressor  (0)  5% (5)  MV + IV vasodilator  (0)  1% (1)  MV + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV vasodilator + IV inotrope  2% (1)  (0)  MV + paralysis  2% (1)  (0)  MV + RRT  (0)  1% (1)  MV + IV vasopressor + RRT  2% (1)  (0)  Total organ support  29% (15)  34% (34)  Total MV (NIPPV + IPPV)  25% (4 + 9)  31% (8 + 23)  Total vasopressor  10% (5)  6% (6)  Total vasodilator  4% (2)  3% (3)  Total inotrope  6% (3)  (0)  Total paralysis  2% (1)  (0)  Total RRT  10% (5)  3% (3)  Life Supporting Therapies    LCC  HCC    n=51  n=100  Single organ support  20% (10)a  27% (27)b  Invasive MV  7.8% (4)a  17% (17)b  Noninvasive MV  5.9% (3)a  7% (7)b  Continuous IV vasopressor  1.9% (1)  1% (1)  Continuous IV vasodilator  1.9% (1)  2% (2)  Continuous IV inotrope  (0)  (0)  Continuous paralysis  (0)  (0)  RRT  3.9% (2)a  2% (2)  Multiple organ support  9.8% (5)  7% (7)  MV + IV vasopressor  (0)  5% (5)  MV + IV vasodilator  (0)  1% (1)  MV + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV inotrope  2% (1)  (0)  MV + IV vasopressor + IV vasodilator + IV inotrope  2% (1)  (0)  MV + paralysis  2% (1)  (0)  MV + RRT  (0)  1% (1)  MV + IV vasopressor + RRT  2% (1)  (0)  Total organ support  29% (15)  34% (34)  Total MV (NIPPV + IPPV)  25% (4 + 9)  31% (8 + 23)  Total vasopressor  10% (5)  6% (6)  Total vasodilator  4% (2)  3% (3)  Total inotrope  6% (3)  (0)  Total paralysis  2% (1)  (0)  Total RRT  10% (5)  3% (3)  MV, mechanical ventilation; RRT, renal replacement therapy. aTwo patients in the LCC group received two different types of RRT and one patient received both NIPPV and IPPV. Each of these patients were counted only once in the total number of patients who received single organ support and all were counted separately in the NIPPV, IPPV, and RRT rows. bTwo patients in the HCC group received both NIPPV and IPPV on the day of study. Each of these patients were counted only once in the total number of patients who received single organ support but in both the noninvasive and invasive organ support rows. Use of other supportive therapies is described in Supplementary Tables S7 and S8. The most common therapies reported were central venous catheters and arterial lines. Central venous catheters were utilized in 29.4% and 45% of patients in LCCs and HCCs, respectively. Arterial lines were placed in 23.5% and 40% of patients in LCCs and HCCs respectively. Additional data on ventilator days and usage are shown in Supplementary Tables S9 and S10. Nearly 70% of surgical patients in this study received maintenance intravenous (IV) fluids, while only 33–45% of medical patients received maintenance IV fluids. Normal saline was the most common type of maintenance IV fluid across the MHS, even in surgical ICUs, followed by Lactated Ringers (Supplementary Table S11). Two (4%) patients from LCCs and 6 (6%) patients from HCCs received blood transfusions during the study period. The average number of units transfused was 1.4 units and the average hemoglobin for blood transfusion was 7.5 mg/dL. Four (50%) blood transfusions occurred with a hemoglobin concentration greater than 7 mg/dL and 25% of transfusions were given to patients with hemoglobin concentrations greater than 8 mg/dL. Sedation was provided to 35 of the 151 patients within the survey period; 30 (86%) of the 35 patients requiring sedation were at HCCs. Sedation scoring was variable at both HCCs and LCCs: 15 patients had an electronic medical record order targeting a specific sedation level, 25 patients were managed according to a sedation protocol without specific target identified in the order, and 10 patients had a sedation score verbally discussed during rounds with some overlap between the methods. In 8 patients (23%), it was unclear how the sedation score was defined or the goal targeted (Supplementary Tables S16 and S17). Additionally, LCCs did not report any delirium, but twelve patients had no report of a delirium scoring system being used at all. HCCs had 5 episodes of delirium (5%), but had nine patients (9%) for whom no scoring tool was used. Data on patient nutrition while in the ICU are provided in Supplementary Table S12. Enteral feeding was more likely to be used in HCCs (33% vs. 7.8% p = 0.005) and HCCs were twice as likely to achieve more that 60% of goal caloric requirements than LCCs during the study period (29% vs. 14%). For the majority of LCC patients (73%) and a significant percentage of HCC patients (39%), the percent of caloric goal delivered during the study period was unknown or unable to be identified. With respect to standard ICU interventions (Supplementary Table S13), 23 (45.1%) patients in LCCs and 24 (24%) patients in HCCs were not on GI prophylaxis while in the ICU. Deep vein thrombosis (DVT) prophylaxis was reported for only 62% of patients in HCCs and 64% of patients in LCCs. Between 8–16% of patients on the day of the study did not receive DVT prophylaxis. Rehabilitation (i.e., physical therapy given in the ICU) was performed with 80% of patients within the study. Three patients within the study experienced a hypoglycemic event (blood sugar less than 60) during the study time period. Chest radiographs were obtained for 7 (13.7%) patients in LCCs and 32 (32%) patients in HCCs. 28 (88%) of the daily chest radiographs obtained in the HCCs were ordered by one facility. HOB elevation data (Supplementary Table S13) demonstrated that 55% of patients in HCC and 49% of patients in LCC received the recommended 18–24 h of HOB elevation daily. Respondents were unable to identify the patient’s hours of HOB elevation for 31% of patients at HCCs and 27% of patients at LCCs. Complications were noted for 29 (19.2%) patients within the study period for a total of 58 complications; 122 (80.8%) patients did not experience any complications (Table VI). The most commonly reported complications were acute kidney injury, bleeding, sepsis, and ventilator-associated pneumonia. HCCs reported markedly higher incidence of complications with 27 patients (27% of HCC patients) experiencing 55 total complications (median complication per patient of 1 [0,2]) compared with LCCs where 2 patients (4% of LCC patients) experienced 3 total complications. Four new pressure injuries were reported during the study period. Table VI. Complications Identified for Patient in the ICU at the Time of the Survey Complications    HCC  LCC  Total  Complication  n  (%)  n  (%)  n  (%)  None  73  (73)  49  (96)  122  (81)  AKI  13  (13)  2  (4)  15  (10)  Unintended bleeding  7  (7)  0  (0)  7  (5)  Sepsis  6  (6)  0  (0)  6  (4)  VAP  5  (5)  0  (0)  5  (3)  Arrhythmia  4  (4)  0  (0)  4  (3)  New pressure ulcer  4  (4)  0  (0)  4  (3)  Other  2  (2)  0  (0)  3  (2)  Medication error  2  (2)  0  (0)  2  (1)  Stroke  2  (2)  0  (0)  2  (1)  Myocardial infarction  2  (2)  0  (0)  2  (1)  C. Diff  2  (2)  0  (0)  2  (1)  ARDS  1  (1)  0  (0)  1  (1)  Unintentional extubation  1  (1)  0  (0)  1  (1)  Unintentional device removal  1  (1)  0  (0)  1  (1)  DVT/PE  1  (1)  0  (0)  1  (1)  Failed procedure  1  (1)  0  (0)  1  (1)  Re-intubation  1  (1)  0  (0)  1  (1)  PEA arrest  0  (0)  1  (2)  1  (1)  CLABSI  0  (0)  0  (0)  0  (0)  Delayed transfer  0  (0)  0  (0)  0  (0)  Fall  0  (0)  0  (0)  0  (0)  Thrombophlebitis  0  (0)  0  (0)  0  (0)  GI bleed  0  (0)  0  (0)  0  (0)  Total  55  (55)  3  (6)  58  (38)  Complications    HCC  LCC  Total  Complication  n  (%)  n  (%)  n  (%)  None  73  (73)  49  (96)  122  (81)  AKI  13  (13)  2  (4)  15  (10)  Unintended bleeding  7  (7)  0  (0)  7  (5)  Sepsis  6  (6)  0  (0)  6  (4)  VAP  5  (5)  0  (0)  5  (3)  Arrhythmia  4  (4)  0  (0)  4  (3)  New pressure ulcer  4  (4)  0  (0)  4  (3)  Other  2  (2)  0  (0)  3  (2)  Medication error  2  (2)  0  (0)  2  (1)  Stroke  2  (2)  0  (0)  2  (1)  Myocardial infarction  2  (2)  0  (0)  2  (1)  C. Diff  2  (2)  0  (0)  2  (1)  ARDS  1  (1)  0  (0)  1  (1)  Unintentional extubation  1  (1)  0  (0)  1  (1)  Unintentional device removal  1  (1)  0  (0)  1  (1)  DVT/PE  1  (1)  0  (0)  1  (1)  Failed procedure  1  (1)  0  (0)  1  (1)  Re-intubation  1  (1)  0  (0)  1  (1)  PEA arrest  0  (0)  1  (2)  1  (1)  CLABSI  0  (0)  0  (0)  0  (0)  Delayed transfer  0  (0)  0  (0)  0  (0)  Fall  0  (0)  0  (0)  0  (0)  Thrombophlebitis  0  (0)  0  (0)  0  (0)  GI bleed  0  (0)  0  (0)  0  (0)  Total  55  (55)  3  (6)  58  (38)  Complications for these patients could have occurred at any point during their ICU admission. Other includes “hepatic dysfunction” and hypernatremia. AKI, acute kidney injury; VAP, ventilator-associated pneumonia; C. Diff, Clostridium Difficile infection; ARDS, acute respiratory distress syndrome, DVT/PE, deep venous thrombosis/pulmonary embolism; PEA, pulseless electrical activity; CLABSI, central line-associated bloodstream infection; GI, gastrointestinal. Table VI. Complications Identified for Patient in the ICU at the Time of the Survey Complications    HCC  LCC  Total  Complication  n  (%)  n  (%)  n  (%)  None  73  (73)  49  (96)  122  (81)  AKI  13  (13)  2  (4)  15  (10)  Unintended bleeding  7  (7)  0  (0)  7  (5)  Sepsis  6  (6)  0  (0)  6  (4)  VAP  5  (5)  0  (0)  5  (3)  Arrhythmia  4  (4)  0  (0)  4  (3)  New pressure ulcer  4  (4)  0  (0)  4  (3)  Other  2  (2)  0  (0)  3  (2)  Medication error  2  (2)  0  (0)  2  (1)  Stroke  2  (2)  0  (0)  2  (1)  Myocardial infarction  2  (2)  0  (0)  2  (1)  C. Diff  2  (2)  0  (0)  2  (1)  ARDS  1  (1)  0  (0)  1  (1)  Unintentional extubation  1  (1)  0  (0)  1  (1)  Unintentional device removal  1  (1)  0  (0)  1  (1)  DVT/PE  1  (1)  0  (0)  1  (1)  Failed procedure  1  (1)  0  (0)  1  (1)  Re-intubation  1  (1)  0  (0)  1  (1)  PEA arrest  0  (0)  1  (2)  1  (1)  CLABSI  0  (0)  0  (0)  0  (0)  Delayed transfer  0  (0)  0  (0)  0  (0)  Fall  0  (0)  0  (0)  0  (0)  Thrombophlebitis  0  (0)  0  (0)  0  (0)  GI bleed  0  (0)  0  (0)  0  (0)  Total  55  (55)  3  (6)  58  (38)  Complications    HCC  LCC  Total  Complication  n  (%)  n  (%)  n  (%)  None  73  (73)  49  (96)  122  (81)  AKI  13  (13)  2  (4)  15  (10)  Unintended bleeding  7  (7)  0  (0)  7  (5)  Sepsis  6  (6)  0  (0)  6  (4)  VAP  5  (5)  0  (0)  5  (3)  Arrhythmia  4  (4)  0  (0)  4  (3)  New pressure ulcer  4  (4)  0  (0)  4  (3)  Other  2  (2)  0  (0)  3  (2)  Medication error  2  (2)  0  (0)  2  (1)  Stroke  2  (2)  0  (0)  2  (1)  Myocardial infarction  2  (2)  0  (0)  2  (1)  C. Diff  2  (2)  0  (0)  2  (1)  ARDS  1  (1)  0  (0)  1  (1)  Unintentional extubation  1  (1)  0  (0)  1  (1)  Unintentional device removal  1  (1)  0  (0)  1  (1)  DVT/PE  1  (1)  0  (0)  1  (1)  Failed procedure  1  (1)  0  (0)  1  (1)  Re-intubation  1  (1)  0  (0)  1  (1)  PEA arrest  0  (0)  1  (2)  1  (1)  CLABSI  0  (0)  0  (0)  0  (0)  Delayed transfer  0  (0)  0  (0)  0  (0)  Fall  0  (0)  0  (0)  0  (0)  Thrombophlebitis  0  (0)  0  (0)  0  (0)  GI bleed  0  (0)  0  (0)  0  (0)  Total  55  (55)  3  (6)  58  (38)  Complications for these patients could have occurred at any point during their ICU admission. Other includes “hepatic dysfunction” and hypernatremia. AKI, acute kidney injury; VAP, ventilator-associated pneumonia; C. Diff, Clostridium Difficile infection; ARDS, acute respiratory distress syndrome, DVT/PE, deep venous thrombosis/pulmonary embolism; PEA, pulseless electrical activity; CLABSI, central line-associated bloodstream infection; GI, gastrointestinal. Urine output for the 24-h study was reported for 95% of patients. These data demonstrate that approximately 36% of patients with available data (34% of cohort) met the definition for acute kidney injury (AKI) due to low urine output (total 24-h urine output divided by 24 divided by the patient’s ideal body weight). Using the serum creatinine measured during the study period and comparing it to the lowest recorded serum creatinine during the patient’s admission, 17% of patients with available data (15% of cohort) had AKI during the study period. These values are notably different than the participant reported rate of AKI above (Supplementary Table S15). DISCUSSION This is the first prospective survey of ICU patients conducted within the MHS. Overall it highlights significant differences between LCCs and HCCs within the MHS with respect to patient volume and acuity where HCCs care for significantly more patients who are higher acuity according to multiple measurements: APACHE II scores, life support therapies, ventilator days, and invasive devices. Notably, the majority of surgical ICU patients (72%) were in HCCs and only 30% of patients required any form of life support during the study period. The indications for ICU admission in our cohort, together with the low ICU length of stay of patients prior to the survey and the high turnover rate, all suggest that the MHS has a low threshold for ICU admission and that few patients admitted to the ICU require intensive care resources. Importantly, 39% of all data submitted to this survey is from one large MTF. This MTF reported all burn patients and 94% of trauma patients and is the only designated Level 1 trauma center within the military as designated by the Secretary of Defense. This uneven distribution of critical care services across the MHS which may have implications for military healthcare in terms of quality of services delivered and clinician readiness for the deployed military mission. Furthermore, low volume centers compared with high volume centers have demonstrated worse patient outcomes with respect to complex, critically ill patients.8–10 Higher volume and acuity centers may also better prepare our active duty personnel for deployments. The overall illness severity of the patient population cared for in both LCCs and HCCs is much lower than civilian institutions.11 In a study by Checkley et al that surveyed ICUs within 69 centers, the average APACHE II score was 19.3, significantly higher than LCCs (8 ± 8.2) and HCCs (12 ± 7.7) in our study. Interestingly, non-beneficiaries in both medical and surgical populations raised the average APACHE II score. This may be explained by the fact that virtually all non-beneficiary admissions are traumatic and burn injuries that generally result in higher APACHE II scores. Low illness severity may contribute to the high patient turnover noted in this prevalence study and this may contribute to ineffective use of clinical resources because high turnover is associated with an increase in staff workload and adverse outcomes.12 Several findings in this survey suggest that critical care services in the MHS might benefit from system wide monitoring and focused interventions with process improvement efforts. In particular, this survey reported notable variance between sites in key quality indicators related to sedation management, venous thromboembolism prevention, ventilator-associated pneumonia prevention (i.e., head of bed elevation), nutrition therapy, delirium monitoring, intravenous fluid use, glycemic management (particularly avoidance of hypoglycemia), physical rehabilitation, and end tidal carbon dioxide monitoring. In many circumstances, the survey respondent could not find the information needed to accurately report the data. For example, the majority of LCCs and a significant portion of HCCs were unable to identify a patient’s goal caloric requirement. The variance in clinical care across the MHS may contribute to increased risk of medical error and adverse outcomes whereas interventions to decrease variance by standardizing processes improve safety and outcomes.11–19 It is probable that the illness severity and patient volume in MHS ICUs is so low and the turnover rates are so high that the impact of critical care process variance on patient outcome will be difficult to measure. Complication rates across the MHS ICUs are low, likely reflecting the low illness severity and short ICU stays reported. Again, significant differences between HCCs and LCCs are noted with respect to complication rates: 27% of patients in HCCs had at least on complication compared with 4% of patients in LCCs. This may be impacted by differences in patient illness severity at HCCs vs. LCCs. These data suggest that interventions across the MHS to decrease the incidence of kidney injury and infections might improve patient outcomes and decrease costs. It is unclear what impact the use of saline as the predominant intravenous crystalloid in the MHS has on the rate of AKI.20,21 This point prevalence study has some notable limitations. The most obvious is that the study results and conclusions are drawn from data collected over a single 24-h period in December 2014. As such, this may not accurately represent the average demographics of the organization that a longer study period might better identify. Furthermore, because of the pragmatic nature of the study, formal quality controls were not placed on reported data. Survey respondents were clinicians who volunteered time to complete the data entry for each patient in their ICU on the day of study. While this methodology was cost effective and facilitated this first-of-its-kind study in the MHS, more rigorous controls on data entry and data definitions could improve the accuracy of these conclusions. Finally, while avoiding collection of patient PHI expedited the approval this project, lack of patient identifiers prevented our ability to audit or verify reported data. CONCLUSION This survey represents the first 24-h prevalence survey conducted to describe patient care across MHS critical care services. Importantly, these data do not describe why these findings are what they are. For example, the lower acuity level MHS ICUs may reflect the relative inexperience and/or youth of the MHS clinical workforce. In this context, dispositioning of slightly more complex patients to an environment with more resources to monitor patient closely, especially by slightly more experienced nurses (at a minimum, MHS ICU nurses must be at least 1 yr more experience than a comparable nurse in a ward environment because of the additional critical care nursing course training requirement), may be appropriate use of resources in a fixed-cost model where hospital budgets are allocated prior to care being rendered. Overall, these results provide more granular and accurate data for medical decision-making than does review of administrative databases; this information will provide the groundwork for future improvements within MHS critical care. There is a need to establish a system that enables accurate collection of high priority data related to critical care services. Moreover, collaborative quality improvement endeavors across the MHS could use this information to positively improve patient care and potentially reduce costs. Efforts to increase volume of complex, critically ill patients in MHS ICUs, perhaps by increasing non-beneficiary and Veteran care, could improve patient safety and clinician readiness for deployment to operational environments. Supplementary Material Supplementary material is available at Military Medicine online. Acknowledgements Mrs. Nicole Caldwell, U.S. Army Institute of Surgical Research, Fort Sam Houston, TX, for her support with maintaining research and regulatory files. MAJ Craig Ainsworth MD, William Beaumont Army Medical Center, El Paso, TX. MAJ Alain Abellada MD, Blanchfield Army Community Hospital, Fort Campbell, KY. COL Stephen Silvey MD; MAJ Scott Trexler MD, LTC(P) David Bell MD; Houmayoun Ahmadian DO and Col(ret) Jeffrey McNeil MD, Brooke Army Medical Center, JBSA Fort Sam Houston, TX. COL Harold Thomas MD, Darnall Army Medical Center, Fort Hood, TX. Dr. Michael Cole MD, Fort Belvoir Community Hospital, Fort Belvoir, VA. LTC Sean Reilly MD, Landstuhl Regional Medical Center, Landstuhl, Germany. LTC Larry Linville MSN, General Leonard Wood Army Community Hospital, Fort Leonard Wood, MO. Dr. Bruce Lovins MD, Martin Army Community Hospital, Fort Benning, GA . LTC Jessica Bunin MD, MAJ Matthew Aboudara MD, Tripler Army Medical Center, Honolulu, HI. COL Stewart McCarver MD, Walter Reed National Military Medical Center, Bethesda, MD. MAJ Douglas Powell MD, Womack Army Medical Center, Fort Bragg, NC. Col Brian Delmonaco MD, Mike O’Callaghan Federal Medical Center, Nellis AFB, NV. Maj John Untisz DO, Eglin Air Force Base Hospital, Eglin AFB, FL. Maj Tokunbo Matthews MD, Joint Base Elmendorf-Richardson Hospital, Anchorage, AK. Ltc Christopher Dennis DO, David Grant US Air Force Medical Center, Travis AFB, CA. MAJ Dara Regn MD, Wright-Patterson Medical Center, Dayton, OH. Dr. Shawn French MD, Keesler Medical Center, Keesler AFB, MS. LCDR Thuy Lin MD, Naval Medical Center Portsmouth, Portsmouth, VA. LCDR Ryan Maves MD, Naval Medical Center San Diego, San Diego, CA. LCDR Russell Miller MD, Naval Hospital Camp Pendleton, Camp Pendleton, CA. Dr. James Prahl MD, Naval Hospital Guam, Tutuhan, Guam. LTC David E. Bennett, Dwight David Eisenhower Army Medical Center. References 1 Halpern NA, Pastores SM: Critical care medicine in the United States 2000–2005: an analysis of bed numbers, occupancy rates, payer mix, and costs. Crit Care Med  2010; 38( 1): 65– 71. Google Scholar CrossRef Search ADS PubMed  2 Barnato AE, et al.  : Prioritizing the organization and management of intensive care services in the United States: The PrOMIS Conference. Crit Care Med  2007; 34: 1003– 11. Google Scholar CrossRef Search ADS   3 Harvey MA, Penoyer DA, Jastremski C: Building teamwork to improve outcomes. In: Textbook of Critical Care: 6th Edition , pp 1589– 94, Philadelphia, PA, Saunders, 2011. Google Scholar CrossRef Search ADS   4 Chalfin DB, Cohen IL, Lambrinos J: The economics and cost-effectiveness of critical care medicine. Intensive Care Med  1995; 21: 952– 61. Google Scholar CrossRef Search ADS PubMed  5 The United Hospital Fund, “Critical Care Leadership Network”. Available at https://uhfnyc.org/initiatives/quality_improvement/critical-care-leadership-network, accessed October 26, 2017. 6 Checkley WC, et al.  : Structure, process, and annual ICU mortality across 69 centers: United States Critical Illness and Injury Trials Group Critical Illness Outcomes Study. Crit Care Med  2014; 42: 344– 56. Google Scholar CrossRef Search ADS PubMed  7 Kahn JM, Goss CH, Heagerty PJ, Kramer AA, O’Brien CR, Rubenfeld GD: Hospital volume and the outcomes of mechanical ventilation. N Engl J Med  2006; 355( 1): 41– 50. Google Scholar CrossRef Search ADS PubMed  8 Office of the Undersecretary of Defence, Comptroller. Defense Health Program Fiscal Year (FY) 2014 Budget Estimates Appropriation Highlights. 2014:Exhibit PBA-19 (DHP-1). accessed October 30 October 2017. Available at http://comptroller.defense.gov/Portals/45/Documents/defbudget/fy2014/budget_justification/pdf/09_Defense_Health_Program/VOL_I/VOL_I_Sec_1_PBA-19_Introductory_Statement_DHP_PB14.pdf. 9 Chowdhury MM, Dagash H, Pierro A: A systematic review of the impact of volume of surgery and specialization on patient outcome. Br J Surg  2007; 94( 2): 145– 61. Google Scholar CrossRef Search ADS PubMed  10 Halm EA, Lee C, Chassin MR: Is volume related to outcome in health care? A systematic review and methodologic critique of the literature. Ann Intern Med  2002; 137( 6): 511– 20. Google Scholar CrossRef Search ADS PubMed  11 Knaus WA, Draper EA, Wagner DP, Zimmerman JE: APACHE II: a severity of disease classification system. 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Google Scholar CrossRef Search ADS PubMed  Author notes The views expressed are those of the authors and do not reflect the official policy or position of the US Army Medical Department, Department of the Army, Department of the Air Force, Department of the Navy, Department of Defense or the U.S. Government. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US.

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Military MedicineOxford University Press

Published: Apr 11, 2018

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