The relationship between ICU hypotension and in-hospital mortality and morbidity in septic patients

The relationship between ICU hypotension and in-hospital mortality and morbidity in septic patients Purpose: Current guidelines recommend maintaining a mean arterial pressure (MAP) ≥ 65 mmHg in septic patients. However, the relationship between hypotension and major complications in septic patients remains unclear. We, therefore, evaluated associations of MAPs below various thresholds and in-hospital mortality, acute kidney injury (AKI), and myocardial injury. Methods: We conducted a retrospective analysis using electronic health records from 110 US hospitals. We evalu- ated septic adults with intensive care unit (ICU) stays ≥ 24 h from 2010 to 2016. Patients were excluded with inad- equate blood pressure recordings, poorly documented potential confounding factors, or renal or myocardial histories documented within 6 months of ICU admission. Hypotension exposure was defined by time-weighted average mean arterial pressure ( TWA-MAP) and cumulative time below 55, 65, 75, and 85 mmHg thresholds. Multivariable logistic regressions determined the associations between hypotension exposure and in-hospital mortality, AKI, and myocar- dial injury. Results: In total, 8,782 patients met study criteria. For every one unit increase in TWA-MAP < 65 mmHg, the odds of in-hospital mortality increased 11.4% (95% CI 7.8%, 15.1%, p < 0.001); the odds of AKI increased 7.0% (4.7, 9.5%, p < 0.001); and the odds of myocardial injury increased 4.5% (0.4, 8.7%, p = 0.03). For mortality and AKI, odds progres- sively increased as thresholds decreased from 85 to 55 mmHg. Conclusions: Risks for mortality, AKI, and myocardial injury were apparent at 85 mmHg, and for mortality and AKI risk progressively worsened at lower thresholds. Maintaining MAP well above 65 mmHg may be prudent in septic ICU patients. Keywords: Sepsis, Hypotension, Blood pressure monitoring, Mortality, Acute kidney injury, Myocardial injury estimated $14.6 billion was spent in the US on hospitali- Introduction zations for septicemia [1]. The syndrome is caused by a Sepsis affects approximately a million people each year in dysregulated inflammatory response to bacterial infec - the United States, and many more globally; it is the lead- tions [2, 3]. Among the major risks is end-organ damage ing cause of death in intensive care patients. In 2008, an consequent to hypoperfusion and cellular/metabolic dys- function [2, 4, 5], especially renal and myocardial injury. Since hypotension worsens tissue perfusion, it seems *Correspondence: maheshk@ccf.org Department of General Anesthesiology, Anesthesiology Institute, likely that some organ injury can be prevented by main- Cleveland Clinic, 9500 Euclid Avenue, E-31, Cleveland, OH 44195, USA taining a suitable arterial pressure. Full author information is available at the end of the article 858 Preventing hypotension is therefore a crucial compo- Take‑home message: nent of sepsis management [5, 6]. The Society of Critical Care Medicine’s Surviving Sepsis Guidelines [3] suggest In septic adults exposed to hypotension in the ICU, risks for in- initially maintaining mean arterial pressure (MAP) > 65 hospital mortality, acute kidney injury (AKI) and myocardial injury mmHg (higher for older patients and those with cardio- were apparent by a mean arterial pressure of 85 mmHg, and for mortality and AKI risk progressively worsened at lower thresholds. vascular morbidities), followed by monitoring via mul- Maintaining mean pressure well above 65 mmHg may be prudent tiple hemodynamic parameters to an endpoint of tissue in septic ICU patients. perfusion [3]. Systolic blood pressure of 100  mmHg or less is a component of the quick Sequential Organ Fail- ure Assessment score (qSOFA) which helps identify adult Exact ICU admission times are not recorded in Cerner patients with suspected infections who are more likely to Health Facts. Consequently, we defined admission time have poor outcomes typical of sepsis [7]. Despite these to be when the first laboratory test or medication order guidelines, relationships between various durations and was noted in an ICU care setting. Discharge times were depths of hypotension and serious complications remain available for a proportion of patients, but when missing, unclear. The evidence for clearly defining blood pressure we used the order location to estimate discharge time. targets in septic patients is currently contentious and Hypotension exposure extended from ICU admis- weak [3]. We, therefore, evaluated associations between sion through the first of: ICU discharge, development of hypotension and in-hospital mortality, acute kidney, and an outcome, or 7  days. We selected MAP as our global myocardial injury in septic patients [8]. measure of blood pressure to be consistent with exist- ing literature [6]. Hypotension exposure was character- Methods ized by: (1) Time-weighted average of MAP (TWA-MAP) We analyzed in-patient data from the Cerner Health below MAP thresholds of 55, 65, 75, or 85  mmHg. Facts electronic health records database (Kansas City, TWA-MAP was calculated as the area below the MAP MO, USA), which includes clinical and administrative threshold curve divided by the total time exposure was data from 720 US hospitals and health systems. Our anal- monitored; (2) cumulative time measured in minutes ysis of de-identified data was determined to be exempt during which MAP was below absolute thresholds of 55, from local institutional review board (IRB) review in 65, 75, or 85 mmHg. advance by Western IRB (Puyallup, WA, USA). We used absolute thresholds because a previous study In-patients admitted and discharged between Janu- showed that absolute and relative thresholds were compa- ary 1, 2010 and November 30, 2016 were analyzed. The rably predictive of myocardial injury and AKI [9]. To cal- study included adults ≥ 18 years old with a sepsis diagno- culate relative thresholds, baseline MAP is required which sis (primary or secondary, any priority) via International could not be reliably determined in this analysis. We Classification of Disease (ICD) 9 and 10 codes as shown used recorded MAP values when available, or estimated in Online Resource 1 and an ICU stay ≥ 24 h. We consid- MAP from systolic and diastolic pressures using the for- ered only the initial episode when patients had more than mula: [(2 × diastolic) + systolic]/3. MAP readings were one qualifying hospitalization containing an ICU admis- deemed invalid and excluded if diastolic blood pressure sion for sepsis within the database. (DBP) ≤ 5  mmHg, DBP ≥ 225  mmHg, or systolic blood Patients were excluded when they lacked at least a pressure (SBP) ≤ DBP + 5  mmHg [9]. An average of 357 6-month database history before the septic hospital admis- MAP readings were available per patient per ICU day. sion; had more than a single ICU stay during the index admission; lacked a baseline serum creatinine measure- Outcomes ment within 6  months before ICU admission and at least The primary outcome was in-hospital mortality; second - one measurement during the ICU stay; had a history of ary outcomes were acute kidney injury (AKI) and myo- acute kidney or myocardial injury within 6 months before cardial injury. Mortality was defined by a discharge status ICU admission (based on ICD-9 or ICD-10 codes, Online of “deceased” for the hospital visit. Secondary outcomes Resource 2); received dialysis within 6 months before ICU were determined from 24  h after ICU admission until admission through the first 24  h within the ICU (time the first of: ICU discharge, 7 days, death, or diagnosis of from which outcomes were analyzed); or had fewer than AKI or myocardial injury (Online Resource 3). Outcomes five valid blood pressure readings during each ICU day in were largely limited to the ICU to maintain proximity to which exposure was analyzed. We also excluded patients the hypotension exposure. whose records contained more than two 5-h gaps between AKI was defined as stage 1 or higher based upon serum MAP recordings or were missing age, sex, diagnosis codes, creatinine (SCr) readings according to the Kidney Dis- or medication records for the index hospitalization. ease Improving Global Outcomes 2012 guidelines (using 859 criteria for SCr increase over baseline [defined as the sample size would need to be 1766 to detect a difference lowest reading within 6 months prior, and closest to ICU as great as this or larger with 90% power and alpha = 0.05 admission] and with respect to SCr values within 48  h) [14–17]. These power calculations further assumed a low [10]. Urine output was not used because there were insuf- correlation of 0.2 between the hypotension exposure and ficient data in the registry. Myocardial injury was defined other predictors in the model. Consequently, we con- by at least one elevated troponin value > 0.03  ng/mL of cluded that the sample size would be more than adequate “Troponin I”, “Troponin T”, or “Troponin” before onset of to detect clinically significant associations with hypoten - AKI. Myocardial injury was not evaluated past the date sion and the primary outcome of mortality. All statistical upon which AKI was identified because renal dysfunc - analyses were performed using Stata/MP 15.1 for Win- tion might have falsely elevated troponin concentrations. dows (StataCorp, College Station, TX, USA). Statistical analyses Results Baseline patient characteristics were summarized via We identified 8782 patients from 110 hospitals after counts and percentages for binary or categorical variables applying all inclusion and exclusion criteria (Fig.  1). and with means and standard deviations, or via medians The mean (SD) age of the patients was 63 (18) years. Of and interquartile ranges for continuous variables. For these, 79% were self-identified as Caucasian and 48% univariate inferences, Chi square test or t test were used, were male. The mean (SD) APACHE III score was 61 (20) as appropriate. Multivariable logistic regression quan- [18]. The unadjusted in-hospital mortality rate was 14.6% tified the relationship between hypotension exposure (n = 1283). Fifteen percent (n = 1315) experienced AKI (TWA-MAP and cumulative time measured in minutes and 0.7% (n = 63) experienced myocardial injury during spent below MAP thresholds) and the primary and two the study period (AKI and myocardial injury rates appear secondary outcomes. low because patients who developed AKI or myocardial Specifically, we created individual logistic regression injury during the initial 24 ICU hours were excluded, models, each with one hypotension exposure and one of Fig.  1). Table  1 and Online Resource 4 list all the covari- the outcomes. We assessed the need for restricted cubic ates included in the regression models for the outcomes splines by plotting deciles and ventiles (20 equal-sized of in-hospital mortality and AKI, respectively. For myo- groups) of the hypotension exposure variable versus cardial injury, the regression models adjusted for hypo- the mean proportion of the outcome for each exposure tension along with age, sex, APACHE III score, and the and outcome, and looked for any substantial non-linear Elixhauser comorbidities of congestive heart failure, dia- trend. No substantial non-linear trend was evident so the betes with complications, and renal failure. exposures were modeled as linear predictors. The odds ratios with 95% confidence intervals for Table  1 lists all the covariates included in the models the regression models with TWA-MAP are graphed to reduce potential confounding. This includes the Acute in Fig.  2. The primary hypotension exposure of TWA- Physiology and Chronic Health Evaluation (APACHE) III MAP < 65  mmHg was positively correlated with in-hos- score used to adjust for patient acuity and the Elixhauser pital mortality. The analysis indicates that for every one comorbidities used to adjust for chronic comorbidities. mmHg increase in TWA-MAP < 65  mmHg, the odds For the uncommon outcome of myocardial injury, an of in-hospital mortality increase by 11.4%; 95% CI (7.8, algorithm that used bootstrapping and stepwise regres- 15.1%); p < 0.001 (Fig. 2 and Online Resource 5). Sensitiv- sion was used to determine a parsed model containing ity analyses show the odds ratios decreased as the MAP a limited set of potential confounders with hypotension threshold increased from 55 to 85 mmHg. The predicted exposure and the outcome [11]. To adjust for a potential marginal probabilities of in-hospital mortality across lack of independence among observations within hospi- TWA-MAP < 65 mmHg are shown Fig. 3a. tals, we derived robust (Huber–White) standard errors Cumulative time below a MAP threshold of 65 mmHg clustered at the hospital level for all regression models revealed that every 2  h (120  min) increased the odds of [12, 13]. We plotted the results of the logistic regression in-hospital mortality by 3.6%; 95% CI (2.5, 4.8%); p < 0.001 models as marginal probabilities of the outcome across (Online Resource 6). The predicted marginal prob - the observed range of the hypotension exposure variable abilities of in-hospital mortality for cumulative time of to facilitate interpretation of the results. MAP < 65  mmHg showed similar trends to probabilities We conservatively estimated that if the probability for TWA-MAP (Fig.  3). Predicted marginal probabilities of in-hospital mortality was 13% with the hypotension are shown in Online Resource 7. exposure of interest at its mean and the probability of in- The relationship between TWA-MAP and AKI was hospital mortality was 16% when hypotension exposure similar to in-hospital mortality (Fig.  4a). For every one was one standard deviation above the mean, then the mmHg increase in TWA-MAP < 65  mmHg, the odds 860 Table 1 Comprehensive list of potentially confounding variables for MAP threshold groups < 55 mmHg, < 65 mmHg, < 75 mmHg and < 85 mmHg Variable group Variable type Min. MAP < 55 mmHg (n = 3308) Min. MAP < 65 mmHg (n = 6310) Min. MAP < 75 mmHg (n = 8039) Min. MAP < 85 mmHg (n = 8609) Mortality n (%)** Mortality n (%) Mortality n (%) Mortality n (%) Yes No p value Yes No p value Yes No p value Yes No p value Gender* Male 329 (44%) 1079 (42%) 0.522 51 (47%) 2343 (45%) 0.302 592 (48%) 3183 (47%) 0.473 617 (48%) 3481 (48%) 0.521 Race African American 77 (10%) 234 (10%) 0.243 120 (11%) 502 (10%) 0.240 137 (11%) 705 (10%) 0.380 142 (11%) 801 (11%) 0.592 Caucasian 585 (78%) 206 (81%) 858 (78%) 4164 (80%) 964 (78%) 5400 (79%) 994 (78%) 5777 (79%) Other 74 (10%) 211 (9%) 97 (9%) 455 (9%) 107 (9%) 586 (8%) 110 (9%) 635 (9%) Unknown 19 (3%) 48 (2%) 26 (2%) 88 (2%) 28 (2%) 112 (2%) 28 (2%) 122 (2%) Age (years)* Mean 70 65 < 0.001 69 63 < 0.001 69 62 < 0.001 69 63 < 0.001 Admission type Elective 40 (5%) 119 (5%) 0.779 62 (6%) 213 (4%) 0.065 70 (6%) 274 (4%) 0.025 73 (6%) 291 (4%) 0.009 Emergency 671 (89%) 2276 (89%) 975 (89%) 4711 (90%) 1092 (88%) 6173 (91%) 1125 (88%) 6672 (91%) Trauma Center 5 (0.7%) 12 (0.5%) 3 (0.3%) 10 (0.2%) 4 (0.3%) 11 (0.2%) 4 (0.3%) 11 (0.2%) Urgent 2 (0.3%) 4 (0.2%) 53 (5%) 257 (5%) 62 (5%) 321 (5%) 64 (5%) 337 (5%) Unknown 37 (5%) 142 (6%) 8 (0.7%) 18 (0.4%) 8 (0.7%) 24 (0.4%) 8 (0.6%) 24 (0.3%) Discharge year 2010 3 (0.4%) 23 (0.9%) 0.009 6 (0.5%) 44 (0.8%) 0.009 9 (0.7%) 53 (0.8%) 0.001 9 (0.7%) 55 (0.8%) < 0.001 2011 31 (4.1%) 79 (3%) 41 (4%) 172 (3%) 49 (4%) 220 (3%) 50 (4%) 232 (3%) 2012 82 (10.9%) 185 (7%) 115 (11%) 383 (7%) 130 (11%) 510 (8%) 134 (11%) 539 (7%) 2013 141 (19%) 434 (17%) 196 (18%) 848 (16%) 215 (17%) 1064 (16%) 223 (18%) 1125 (15%) 2014 183 (24%) 679 (27%) 270 (25%) 1320 (25%) 305 (25%) 1685 (25%) 313 (25%) 1818 (25%) 2015 165 (22%) 584 (23%) 236 (21%) 1202 (23%) 265 (21%) 1592 (23%) 275 (22%) 1733 (24%) 2016 150 (20%) 569 (22%) 237 (22%) 1240 (24%) 263 (21%) 1679 (25%) 270 (21%) 1833 (25%) Census region Midwest 93 (12%) 365 (14%) < 0.001 141 (13%) 772 (15%) < 0.001 161 (13%) 1054 (16%) < 0.001 167 (13%) 1137 (16%) < 0.001 Northeast 308 (41%) 793 (31%) 430 (39%) 1501 (29%) 477 (39%) 1922 (28%) 484 (38%) 2062 (28%) South 213 (28%) 905 (36%) 336 (31%) 2003 (39%) 388 (31%) 2654 (39%) 404 (38%) 2873 (39%) West 141 (19%) 490 (19%) 194 (18%) 933 (18%) 210 (17%) 1173 (17%) 219 (17%) 1263 (17%) Hospital bed size < 100 44 (6%) 208 (8%) < 0.001 63 (6%) 443 (9%) < 0.001 70 (6%) 603 (9%) < 0.001 74 (6%) 665 (9%) < 0.001 100–199 91 (12%) 403 (16%) 141 (13%) 871 (17%) 159 (13%) 1149 (17%) 162 (13%) 1232 (17%) 200–299 115 (15%) 496 (19%) 174 (16%) 994 (19%) 192 (16%) 1332 (20%) 197 (16%) 1457 (20%) 300–499 191 (25%) 558 (22%) 296 (27%) 1220 (23%) 344 (28%) 1612 (24%) 356 (28%) 1746 (24%) 500+ 314 (42%) 888 (35%) 427 (39%) 1681 (32%) 471 (38%) 2107 (31%) 485 (38%) 2235 (31%) ICU type General ICU 472 (63%) 1722 (68%) 0.001 675 (61%) 3465 (67%) 0.001 765 (62%) 4549 (67%) 0.002 791 (62%) 4920 (67%) < 0.001 Medical ICU 95 (13%) 278 (11%) 156 (14%) 668 (13%) 172 (14%) 880 (13%) 178 (14%) 947 (13%) Surgical ICU 46 (6%) 181 (7%) 69 (6%) 309 (6%) 75 (6%) 382 (6%) 75 (6%) 407 (6%) Cardiac ICU 70 (9%) 138 (5%) 99 (9%) 301 (6%) 103 (8%) 392 (6%) 109 (9%) 418 (6%) 861 Table 1 continued Variable group Variable type Min. MAP < 55 mmHg (n = 3308) Min. MAP < 65 mmHg (n = 6310) Min. MAP < 75 mmHg (n = 8039) Min. MAP < 85 mmHg (n = 8609) Mortality n (%)** Mortality n (%) Mortality n (%) Mortality n (%) Yes No p value Yes No p value Yes No p value Yes No p value Coronary care unit 72 (10%) 234 (9%) 102 (9%) 466 (9%) 121 (10%) 600 (9%) 121 (10%) 643 (9%) Drugs received Diuretics received 171 (23%) 341 (13%) < 0.001 264 (24%) 653 (13%) < 0.001 291 (23%) 838 (12%) < 0.001 303 (24%) 897 (12%) < 0.001 ACE inhibitors 68 (9%) 232 (9%) 0.946 115 (11%) 453 (9%) 0.066 130 (11%) 594 (9%) 0.044 137 (11%) 648 (9%) 0.028 received Beta blockers 157 (21%) 378 (15%) < 0.001 252 (23%) 773 (15%) < 0.001 282 (23%) 1020 (15%) < 0.001 298 (23%) 1105 (15%) < 0.001 received Calcium channel 82 (11%) 156 (6%) < 0.001 136 (12%) 332 (6%) < 0.001 156 (13%) 465 (9%) < 0.0001 165 (13%) 519 (7%) < 0.001 blockers received *Modified APACHE III Mean 76 65 < 0.001 74 62 < 0.001 73 60 < 0.001 72 59 < 0.001 score Serum lactate No reading available 335 (44%) 125 (49%) < 0.001 505 (46%) 2745 (53%) < 0.001 592 (48%) 3729 (55%) < 0.001 614 (48%) 4077 (56%) < 0.001 Normal < 2 mmol/L 183 (24%) 813 (32%) 279 25%) 1555 (30%) 304 (25%) 1934 (28%) 309 (24%) 2045 (28%) Mild 2 to < 5 mmol/L 172 (23%) 406 (16%) 230 (21%) 780 (15%) 250 (20%) 983 (15%) 257 (20%) 1050 (14%) Moderate 5 40 (5%) 57 (2%) 58 (5%) 95 (2%) 61 (5%) 119 (2%) 63 (5%) 123 (2%) to < 8 mmol/L Severe ≥ 8 mmol/L 25 (3%) 20 (0.8%) 29 (3%) 34 (0.7%) 29 (2%) 38 (0.6%) 31 (2%) 40 (0.6%) Elixhauser index Mean 18 13 < 0.001 18 13 < 0.001 18 13 < 0.001 18 12 < 0.001 Payer Commercial 102 (14%) 403 (16%) < 0.001 145 (13%) 883 (17%) < 0.001 165 (13%) 1176 (17%) < 0.001 168 (13%) 1293 (18%) < 0.001 Medicaid 53 (7%) 334 (13%) 93 (8%) 696 (13%) 114 (9%) 917 (14%) 116 (9%) 989 (14%) Medicare 494 (65%) 145 (57%) 700 (64%) 2839 (55%) 780 (63%) 3668 (54%) 807 (63%) 3907 (53%) Other 48 (6%) 141 (6%) 70 (6%) 317 (6%) 74 (6%) 415 (6%) 75 (6%) 461 (6%) Unknown 58 (7%) 225 (9%) 93 (9%) 474 (9%) 103 (8%) 627 (9%) 108 (9%) 685 (9%) Teaching status Yes 516 (68%) 1620 (64%) 0.014 723 (65.7%) 3161 (61%) 0.002 803 (65%) 4057 (60%) < 0.001 824 (65%) 4342 (59%) 0.000 Urban/rural status Urban 553 (73%) 2029 (80%) < 0.001 270 (25%) 991 (19%) < 0.001 930 (75%) 5513 (81%) < 0.001 963 (76%) 5965 (81%) < 0.001 Hospital acute status Acute 755 (100%) 2553 (100%) 1101 (100%) 5209 (100%) 1235 (100%) 6803 (100%) 1273 (100%) 7335 (100%) Elixhauser comorbidi- Congestive heart 316 (42%) 784 (31%) < 0.001 438 (40%) 1477 (28%) < 0.001 477 (39%) 1855 (27%) < 0.001 494 (39%) 1953 (27%) < 0.001 ties failure Valvular disease 163 (22%) 444 (17%) 0.009 234 (21%) 832 (16%) < 0.001 259 (21%) 1051 (16%) < 0.001 264 (21%) 1114 (15%) < 0.001 Pulmonary circulation 139 (18%) 306 (12%) < 0.001 190 (17%) 596 (11%) < 0.001 211 (17%) 740 (11%) < 0.001 218 (17%) 777 (11%) < 0.001 disease Peripheral vascular 174 (23%) 490 (19%) 0.020 253 (23%) 950 (18%) 0.000 276 (22%) 1218 (18%) < 0.001 285 (22%) 1302 (18%) < 0.001 disease Paralysis 68 (9%) 301 (11%) 0.033 101 (9%) 588 (11%) 0.041 117 (10%) 726 (11%) 0.203 125 (10%) 766 (10%) 0.495 Other neurological 250 (33%) 875 (34%) 0.554 368 (33%) 1702 (33%) 0.630 413 (33%) 2202 (32%) 0.470 422 (33%) 2352 (32%) 0.456 disorders 862 Table 1 continued Variable group Variable type Min. MAP < 55 mmHg (n = 3308) Min. MAP < 65 mmHg (n = 6310) Min. MAP < 75 mmHg (n = 8039) Min. MAP < 85 mmHg (n = 8609) Mortality n (%)** Mortality n (%) Mortality n (%) Mortality n (%) Yes No p value Yes No p value Yes No p value Yes No p value Chronic pulmonary 333 (44%) 1062 (42%) 0.220 510 (46%) 2159 (42%) 0.003 565 (46%) 2825 (42%) 0.006 589 (46%) 3019 (41%) 0.00 disease Diabetes w/o chronic 164 (22%) 604 (24%) 0.268 242 (22%) 1223 (24%) 0.285 273 (22%) 1581 (23%) 0.376 286 (23%) 1721 (24%) 0.430 complications Diabetes w/chronic 117 (16%) 341 (13%) 0.135 161 (15%) 695 (13%) 0.260 184 (15%) 950 (14%) 0.392 189 (15%) 1051 (14%) 0.635 complications Hypothyroidism 178 (24%) 602 (24%) 0.998 251 (23%) 1182 (23%) 0.939 281 (23%) 1469 (22%) 0.371 286 (23%) 1556 (21%) 0.321 Renal failure 210 (28%) 535 (21%) < 0.001 304 (28%) 1007 (19%) < 0.001 336 (27%) 1342 (20%) < 0.001 344 (27%) 1440 (20%) < 0.001 Liver disease 138 (18%) 279 (11%) < 0.001 210 (19%) 587 (11%) < 0.001 233 (19%) 790 (12%) < 0.001 241 (19%) 852 (12%) < 0.001 Peptic ulcer disease 9 (1%) 28 (1%) 0.827 15 (1%) 53 (1%) 0.314 16 (1%) 64 (0.9%) 0.249 16 (1%) 69 (0.9%) 0.294 excl. bleeding Acquired immune 6 (0.8%) 27 (1%) 0.523 10 (0.9%) 43 (0.8%) 0.785 11 (0.9%) 58 (0.9%) 0.896 12 (0.9%) 63 (0.9%) 0.769 deficiency syn- drome Lymphoma 29 (4%) 72 (3%) 0.152 52 (5%) 160 (3%) 0.006 59 (5%) 188 (3%) < 0.001 61 (5%) 200 (3%) < 0.001 Metastatic cancer 112 (15%) 174 (7%) < 0.001 154 (14%) 365 (7%) < 0.001 184 (15%) 458 (7%) < 0.001 192 (15%) 487 (7%) < 0.001 Solid tumor without 73 (10%) 234 (9%) 0.676 110 (10%) 461 (9%) 0.231 119 (10%) 574 (8%) 0.170 122 (10%) 608 (8%) 0.128 metastasis Rheumatoid arthritis/ 46 (6%) 188 (7%) 0.231 71 (7%) 367 (7%) 0.479 83 (7%) 453 (7%) 0.942 84 (7%) 494 (7%) 0.852 collagen vascular diseases Coagulopathy 285 (38%) 650 (26%) < 0.001 410 (37%) 1244 (24%) < 0.001 462 (37%) 1570 (23%) < 0.001 474 (37%) 1654 (23%) < 0.001 Obesity 137 (18%) 590 (23%) 0.004 201 (18%) 1186 (23%) 0.001 229 (19%) 1618 (24%) < 0.001 237 (19%) 1767 (24%) < 0.001 Weight loss 276 (37%) 730 (29%) < 0.001 405 (37%) 1360 (26%) <0.001 447 (36%) 1674 (25%) < 0.001 458 (36%) 1752 (24%) < 0.001 Fluid and electrolyte 638 (85%) 1986 (78%) <0.001 922 (84%) 4007 (77%) < 0.001 1032 (84%) 5195 (76%) <0.001 1063 (83%) 5575 (76%) < 0.001 disorders Chronic blood loss 41 (5%) 122 (5%) 0.467 69 (6%) 227 (4%) 0.007 76 (6%) 285 (4%) 0.002 76 (6%) 303 (4%) 0.003 anemia Deficiency anemias 448 (59%) 1399 (55%) 0.027 644 (59%) 2759 (53%) 0.001 723 (59%) 3516 (52%) < 0.001 747 (59%) 3744 (51%) < 0.001 Alcohol abuse 106 (14%) 294 (12%) 0.062 156 (14%) 579 (11%) 0.004 175 (15%) 785 (12%) 0.009 179 (14%) 869 (12%) 0.026 Drug abuse 65 (9%) 272 (11%) 0.103 90 (8%) 583 (11%) 0.003 100 (8%) 772 (11%) 0.001 105 (8%) 842 (12%) 0.001 Psychoses 88 (12%) 353 (14%) 0.123 13 (12%) 750 (14%) 0.044 150 (12%) 990 (15%) 0.025 156 (12%) 1066 (15%) 0.031 Depression 197 (26%) 722 (28%) 0.238 294 (27%) 1464 (28%) 0.346 325 (26%) 1876 (28%) 0.353 339 (27%) 2022 (28%) 0.480 Hypertension 556 (74%) 1782 (70%) 0.042 808 (73%) 3606 (69%) 0.006 914 (74%) 4741 (70%) 0.003 944 (74%) 5140 (71%) 0.004 Because of rounding, categories will not always add to 100% APACHE III score includes physiology, chronic health investigation, and age variables [18] *Variables included in the adjustment of regression models for myocardial injury **Percentages for the variables types in this table are calculated by using a denominator which is the total number of patients within that particular variable group 863 Fig. 1 Patient attrition diagram. AKI acute kidney injury Fig. 2 Association of hypotension exposure with in-hospital mortality, AKI and myocardial injury. Adjusted odds ratios and 95% confidence intervals for a 1 mmHg increase in TWA-MAP, below different thresholds are shown for the primary outcome of in-hospital mortality and secondary outcomes of acute kidney injury and myocardial injury 864 Fig. 3 Predicted mortality outcome for time-weighted average ( TWA)-MAP below 65 mmHg and cumulative hours of MAP below 65 mmHg. Predicted probability of mortality from the TWA-MAP < 65 mmHg threshold and cumulative hours of MAP < 65 mmHg are represented in panels a and b, respectively Fig. 4 Predicted marginal probability for AKI and myocardial injury for TWA-MAP below 65 mmHg threshold. AKI and myocardial injury predicted probability from the TWA-MAP below 65 mmHg threshold are shown in panels a and b, respectively. Both exposures showed a linear relationship with the secondary outcomes of AKI and myocardial injury. AKI acute kidney injury of developing AKI increase by 7.0%; 95% CI (4.7, 9.5%); MAP threshold in the longest duration category, as these p < 0.001 (Fig.  2). Patients who spent between 6 and 8  h patient records had more and longer gaps between MAP in the ICU with MAP < 65 mmHg had odds of developing readings. AKI 37% higher (95% CI 3, 82%; p = 0.031) compared to For every one mmHg increase in TWA-MAP < 65 mmHg, patients with no time below MAP of 65  mmHg (Online the odds of developing myocardial injury increased by 3.7%; Resource 6). Although TWA-MAP below 55, 75, and 95% CI (0.3, 7.3%), p = 0.03 (Fig.  2, Online Resource 5). 85  mmHg showed a positive correlation (p < 0.001) with We derived marginal probabilities of developing myocar- developing AKI, we did not see similar trends for cumu- dial injury for TWA-MAP (Fig.  4b) and cumulative hours lative time below thresholds of 75 and 85 mmHg (Online of MAP below 65  mmHg (Online Resource 7). Unlike the Resource 6). Here, patients with the longest times below relationship for in-hospital mortality and AKI, there was no the thresholds tended to have fewer AKI events than significant worsening of myocardial injury at lower MAP the increasingly small number of patients with no time thresholds (Fig.  4b and Online Resource 8). A sensitivity below these thresholds. This apparent contradiction is, analysis that repeated the regression modeling restricted to in part, due to over-representation of the time below survivors found similar associations between hypotension 865 at the 65  mmHg threshold and the outcomes of AKI and norepinephrine does not significantly affect systemic myocardial injury (data not shown). oxygen metabolism, skin microcirculatory blood flow, urine output, renal function, or splanchnic perfusion— Discussion although cardiac index increased [19, 20]. However, a Given the complexity of defining hypotension expo - prospective study of thirteen patients with septic shock sure, we analyzed both time-weighted average (TWA) found that increasing MAP to above 65 mmHg with nor- and the cumulative time under specific thresholds. epinephrine increased cardiac output, improved micro- TWA-MAP is a comprehensive measure of hypoten- vascular function, and was associated with decreased sion exposure because it measures both the degree and blood lactate concentrations. The investigators noticed the duration below a threshold. Continuous time below that microvascular responses varied considerably among a MAP threshold is intuitive but neglects severity of patients, suggesting that individualization of blood pres- hypotension and is subject to two types of variation: the sure targets may be warranted [21]. frequency of MAP readings, and the total time for MAP Only limited evidence from randomized trials pro- exposure calculation. Spurious extreme values can occur vides guidance on optimal thresholds. Asfar et  al. [22] and distort the results due to (1)  gaps between readings randomized 776 septic shock patients and reported (carried forward) that can over-represent times below a that 28- and 90-day mortality did not differ significantly given MAP threshold, especially as there were more and between those who were treated to reach a target MAP longer gaps in patients who spent long periods below of 80–85 mmHg and those who were treated to reach a various thresholds; and (2) patients with longer ICU stays target of 65–70 mmHg [19]. However, even in the lower may have been especially prone to hypotension. To mini- MAP target group the blood pressure was maintained at mize both sources of error, we excluded patients with 70–75 mmHg and authors noticed a lower than expected fewer than five blood pressures recorded per each 24-h death rate, which supports our findings using a much period, and those who had more than two gaps exceeding larger cohort and suggests a threshold above 65  mmHg 5 h between readings. may be more appropriate. This study also highlights the We observed strong associations between the TWA- complexity of performing randomized trials in this popu- MAP below various thresholds and in-hospital mortality lation and the value of our analysis. and kidney injury in septic patients. Substantial mor- We observed that 14.6% of patients with sepsis died tality risk was evident even among the higher thresh- during hospitalization over the period from 2010 to olds, and the risk progressively increased as the MAP 2016. Presumably patients in our cohort were sicker than thresholds decreased from 85 to 55  mmHg. A similar all sepsis patients given a required ICU stay of at least relationship between in-hospital mortality and cumula- 1 day. However, two recent European studies found sep- tive time < 65  mmHg was also observed. It is important sis mortality to range from 8 to 26%. [18, 19] This is in to note that the TWA-MAP and cumulative time meas- contrast to higher in-hospital mortality of 26% reported ures below a given threshold are nested and not mutu- from analysis of a German patient population from 2007 ally exclusive (e.g., patients with TWA-MAP < 55 mmHg to 2013 [23]. Furthermore, Freund and colleagues [24], are included in the analysis of TWA-MAP < 65  mmHg). based on 2016 European hospital data, observed 8% in- We, therefore, cannot definitively determine an optimal hospital mortality in patients with suspected sepsis. threshold with this study design alone. These results suggest that outcomes from sepsis may be As with in-hospital mortality, we observed that odds of improving over time. developing AKI was associated with hypotension char- In general, AKI in critically ill patients affects approxi - acterized by TWA-MAP, with the odds of developing mately 40% of the patients at some time during their AKI being greatest for MAP readings < 55  mmHg and stay and one third who develop renal injury die within lowest for MAP < 85 mmHg. However, similar trends 90 days [16]. In a study consistent with ours, hypotensive were not observed in the cumulative minutes of MAP episodes of MAP < 73  mmHg were associated with pro- below threshold groups. We theorize that is because we gression of AKI in critically ill patients with severe sepsis excluded AKI and myocardial injury before and within [17]. 24 h of ICU admission and restricted this outcome to the Myocardial injury, measured by troponin eleva- first 7  days of exposure. Also, myocardial injury may be tion, may be as high as 15–25% in ICU patients, but is undercounted when routine troponin monitoring is not often unrecognized because routine troponin monitor- performed. ing remains uncommon [16, 25]. When troponins are Overall, our results are consistent with previous lit- routinely monitored in septic ICU patients, only 7% erature with notable exceptions. Prior research found of biomarker elevations happened within 24  h of ICU that increasing the MAP from 65 to 85  mmHg with admission [26]. While our definition of myocardial injury 866 artificially lowered the observed rate by excluding cases on the timing of laboratory and medication orders, given within 24  h of ICU admission and after AKI develop- their frequency in critically ill patients, this limitation ment), we still observed significant association between seems unlikely to bias our results substantially. TWA-MAP < 65  mmHg and myocardial injury. How- In summary, the Surviving Sepsis Guidelines suggest ever, it is possible that a raised troponin value is present keeping mean arterial pressure initially above 65 mmHg, in the absence of myocardial injury [27], although raised followed by individualized treatment to optimize tissue troponin values have been tied with myocardial injury perfusion. In our analysis, risks for mortality, AKI and within the septic population [28–31]. Our results of myo- myocardial injury were apparent by 85  mmHg, and for cardial injury analysis should be interpreted with caution mortality and AKI risk progressively worsened at lower due to lack of universal troponin screening and diverse thresholds. Until randomized trials show that the rela- troponin tests employed among various U.S. Hospitals. tionship between hypotension and serious complications We report associations between hypotension in ICU is not causal, it would probably be prudent to keep mean patients and both myocardial and kidney injury. How- arterial pressure well above 65  mmHg in septic ICU ever, we report associations which are surely at least to patients. some degree confounded by unobserved baseline patient Electronic supplementary material characteristics. Randomized trials will be required to The online version of this article (https ://doi.org/10.1007/s0013 4-018-5218-5) confirm causal relationships that may benefit from contains supplementary material, which is available to authorized users. intervention. Another study limitation is our inability to distinguish between untreated hypotension and hypoten- Author details sion that persisted despite treatment—and thus presum- Department of Outcomes Research, Center for Perioperative Intelligence, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH, USA. OptiStatim, ably indicated worse sepsis. To address this, we adjusted LLC, Longmeadow, MA, USA. Department of Health Economics and Out- for medication use and other potential confounders. 4 comes Research, Boston Strategic Partners, Inc., Boston, MA, USA. Edwards Nevertheless, unmeasured confounding remains likely. Lifesciences, Irvine, CA, USA. Department of Outcomes Research, Center for Critical Care, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH, USA. For example, septic patients are always given antibiot- Department of Outcomes Research, Anesthesiology Institute, Cleveland ics, some of which are nephrotoxic. However, we did not 7 Clinic, Cleveland, OH, USA. Department of General Anesthesiology, Anes- attempt to link specific antibiotics to AKI. Hypotension thesiology Institute, Cleveland Clinic, 9500 Euclid Avenue, E-31, Cleveland, OH 44195, USA. identification and duration is dependent upon the fre - quency of recorded blood pressure readings. While some Acknowledgements MAP data (up to two 5-h gaps per record) were missing, The authors wish to thank Dr. Seungyoung Hwang for his assistance with data analysis. This research was supported by Edwards Lifesciences, Irvine, CA. an average 357 MAP readings were available per ICU day which indicates that the exposure was generally well Compliance with ethical standards characterized. Conflicts of interest We were also unable to distinguish hypotension that is Drs. Maheshwari and Sessler work as consultants for Edwards Lifesciences. Dr. a marker of severe sepsis from hypotension that directly Khanna consults for La Jolla pharmaceuticals. Drs. Khangulov, Munson and contributed to organ dysfunction. The distinction is Badani work as consultants for Boston Strategic Partners, Inc. who received funds from Edwards Lifesciences to perform the research. Dr. Nathanson is important because interventions to reduce hypotension an employee of OptiStatim, LLC, which received consulting fees from Boston will only improve the fraction of organ dysfunction that Strategic Partners, Inc. is causally related to blood pressure. Additionally, some Open Access treatments for hypotension can themselves provoke This article is distributed under the terms of the Creative Commons Attribu- organ injury. For example, increased rates of atrial fibril - tion-NonCommercial 4.0 International License (http://creativecommons.org/ lation are noted with higher vasopressor use [22]. None- licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the theless, our results suggest that harm may begin to accrue original author(s) and the source, provide a link to the Creative Commons well above the currently recommended initial threshold license, and indicate if changes were made. of 65  mmHg, and higher for older patients and those with cardiovascular comorbidities [3]. However, the Received: 6 March 2018 Accepted: 7 May 2018 definitive way to answer how the duration of hypotension Published online: 5 June 2018 impacts mortality and other outcomes in critically ill sep- sis patients is via a prospective, randomized controlled trial that follows a standard protocol for vasopressor and References intravenous fluid use. This study did not examine out - 1. 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Medicine (Baltimore) 97:e0263 Intensive Care Med 40:1018–1020 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Intensive Care Medicine Springer Journals

The relationship between ICU hypotension and in-hospital mortality and morbidity in septic patients

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Medicine & Public Health; Intensive / Critical Care Medicine; Anesthesiology; Emergency Medicine; Pneumology/Respiratory System; Pain Medicine; Pediatrics
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Abstract

Purpose: Current guidelines recommend maintaining a mean arterial pressure (MAP) ≥ 65 mmHg in septic patients. However, the relationship between hypotension and major complications in septic patients remains unclear. We, therefore, evaluated associations of MAPs below various thresholds and in-hospital mortality, acute kidney injury (AKI), and myocardial injury. Methods: We conducted a retrospective analysis using electronic health records from 110 US hospitals. We evalu- ated septic adults with intensive care unit (ICU) stays ≥ 24 h from 2010 to 2016. Patients were excluded with inad- equate blood pressure recordings, poorly documented potential confounding factors, or renal or myocardial histories documented within 6 months of ICU admission. Hypotension exposure was defined by time-weighted average mean arterial pressure ( TWA-MAP) and cumulative time below 55, 65, 75, and 85 mmHg thresholds. Multivariable logistic regressions determined the associations between hypotension exposure and in-hospital mortality, AKI, and myocar- dial injury. Results: In total, 8,782 patients met study criteria. For every one unit increase in TWA-MAP < 65 mmHg, the odds of in-hospital mortality increased 11.4% (95% CI 7.8%, 15.1%, p < 0.001); the odds of AKI increased 7.0% (4.7, 9.5%, p < 0.001); and the odds of myocardial injury increased 4.5% (0.4, 8.7%, p = 0.03). For mortality and AKI, odds progres- sively increased as thresholds decreased from 85 to 55 mmHg. Conclusions: Risks for mortality, AKI, and myocardial injury were apparent at 85 mmHg, and for mortality and AKI risk progressively worsened at lower thresholds. Maintaining MAP well above 65 mmHg may be prudent in septic ICU patients. Keywords: Sepsis, Hypotension, Blood pressure monitoring, Mortality, Acute kidney injury, Myocardial injury estimated $14.6 billion was spent in the US on hospitali- Introduction zations for septicemia [1]. The syndrome is caused by a Sepsis affects approximately a million people each year in dysregulated inflammatory response to bacterial infec - the United States, and many more globally; it is the lead- tions [2, 3]. Among the major risks is end-organ damage ing cause of death in intensive care patients. In 2008, an consequent to hypoperfusion and cellular/metabolic dys- function [2, 4, 5], especially renal and myocardial injury. Since hypotension worsens tissue perfusion, it seems *Correspondence: maheshk@ccf.org Department of General Anesthesiology, Anesthesiology Institute, likely that some organ injury can be prevented by main- Cleveland Clinic, 9500 Euclid Avenue, E-31, Cleveland, OH 44195, USA taining a suitable arterial pressure. Full author information is available at the end of the article 858 Preventing hypotension is therefore a crucial compo- Take‑home message: nent of sepsis management [5, 6]. The Society of Critical Care Medicine’s Surviving Sepsis Guidelines [3] suggest In septic adults exposed to hypotension in the ICU, risks for in- initially maintaining mean arterial pressure (MAP) > 65 hospital mortality, acute kidney injury (AKI) and myocardial injury mmHg (higher for older patients and those with cardio- were apparent by a mean arterial pressure of 85 mmHg, and for mortality and AKI risk progressively worsened at lower thresholds. vascular morbidities), followed by monitoring via mul- Maintaining mean pressure well above 65 mmHg may be prudent tiple hemodynamic parameters to an endpoint of tissue in septic ICU patients. perfusion [3]. Systolic blood pressure of 100  mmHg or less is a component of the quick Sequential Organ Fail- ure Assessment score (qSOFA) which helps identify adult Exact ICU admission times are not recorded in Cerner patients with suspected infections who are more likely to Health Facts. Consequently, we defined admission time have poor outcomes typical of sepsis [7]. Despite these to be when the first laboratory test or medication order guidelines, relationships between various durations and was noted in an ICU care setting. Discharge times were depths of hypotension and serious complications remain available for a proportion of patients, but when missing, unclear. The evidence for clearly defining blood pressure we used the order location to estimate discharge time. targets in septic patients is currently contentious and Hypotension exposure extended from ICU admis- weak [3]. We, therefore, evaluated associations between sion through the first of: ICU discharge, development of hypotension and in-hospital mortality, acute kidney, and an outcome, or 7  days. We selected MAP as our global myocardial injury in septic patients [8]. measure of blood pressure to be consistent with exist- ing literature [6]. Hypotension exposure was character- Methods ized by: (1) Time-weighted average of MAP (TWA-MAP) We analyzed in-patient data from the Cerner Health below MAP thresholds of 55, 65, 75, or 85  mmHg. Facts electronic health records database (Kansas City, TWA-MAP was calculated as the area below the MAP MO, USA), which includes clinical and administrative threshold curve divided by the total time exposure was data from 720 US hospitals and health systems. Our anal- monitored; (2) cumulative time measured in minutes ysis of de-identified data was determined to be exempt during which MAP was below absolute thresholds of 55, from local institutional review board (IRB) review in 65, 75, or 85 mmHg. advance by Western IRB (Puyallup, WA, USA). We used absolute thresholds because a previous study In-patients admitted and discharged between Janu- showed that absolute and relative thresholds were compa- ary 1, 2010 and November 30, 2016 were analyzed. The rably predictive of myocardial injury and AKI [9]. To cal- study included adults ≥ 18 years old with a sepsis diagno- culate relative thresholds, baseline MAP is required which sis (primary or secondary, any priority) via International could not be reliably determined in this analysis. We Classification of Disease (ICD) 9 and 10 codes as shown used recorded MAP values when available, or estimated in Online Resource 1 and an ICU stay ≥ 24 h. We consid- MAP from systolic and diastolic pressures using the for- ered only the initial episode when patients had more than mula: [(2 × diastolic) + systolic]/3. MAP readings were one qualifying hospitalization containing an ICU admis- deemed invalid and excluded if diastolic blood pressure sion for sepsis within the database. (DBP) ≤ 5  mmHg, DBP ≥ 225  mmHg, or systolic blood Patients were excluded when they lacked at least a pressure (SBP) ≤ DBP + 5  mmHg [9]. An average of 357 6-month database history before the septic hospital admis- MAP readings were available per patient per ICU day. sion; had more than a single ICU stay during the index admission; lacked a baseline serum creatinine measure- Outcomes ment within 6  months before ICU admission and at least The primary outcome was in-hospital mortality; second - one measurement during the ICU stay; had a history of ary outcomes were acute kidney injury (AKI) and myo- acute kidney or myocardial injury within 6 months before cardial injury. Mortality was defined by a discharge status ICU admission (based on ICD-9 or ICD-10 codes, Online of “deceased” for the hospital visit. Secondary outcomes Resource 2); received dialysis within 6 months before ICU were determined from 24  h after ICU admission until admission through the first 24  h within the ICU (time the first of: ICU discharge, 7 days, death, or diagnosis of from which outcomes were analyzed); or had fewer than AKI or myocardial injury (Online Resource 3). Outcomes five valid blood pressure readings during each ICU day in were largely limited to the ICU to maintain proximity to which exposure was analyzed. We also excluded patients the hypotension exposure. whose records contained more than two 5-h gaps between AKI was defined as stage 1 or higher based upon serum MAP recordings or were missing age, sex, diagnosis codes, creatinine (SCr) readings according to the Kidney Dis- or medication records for the index hospitalization. ease Improving Global Outcomes 2012 guidelines (using 859 criteria for SCr increase over baseline [defined as the sample size would need to be 1766 to detect a difference lowest reading within 6 months prior, and closest to ICU as great as this or larger with 90% power and alpha = 0.05 admission] and with respect to SCr values within 48  h) [14–17]. These power calculations further assumed a low [10]. Urine output was not used because there were insuf- correlation of 0.2 between the hypotension exposure and ficient data in the registry. Myocardial injury was defined other predictors in the model. Consequently, we con- by at least one elevated troponin value > 0.03  ng/mL of cluded that the sample size would be more than adequate “Troponin I”, “Troponin T”, or “Troponin” before onset of to detect clinically significant associations with hypoten - AKI. Myocardial injury was not evaluated past the date sion and the primary outcome of mortality. All statistical upon which AKI was identified because renal dysfunc - analyses were performed using Stata/MP 15.1 for Win- tion might have falsely elevated troponin concentrations. dows (StataCorp, College Station, TX, USA). Statistical analyses Results Baseline patient characteristics were summarized via We identified 8782 patients from 110 hospitals after counts and percentages for binary or categorical variables applying all inclusion and exclusion criteria (Fig.  1). and with means and standard deviations, or via medians The mean (SD) age of the patients was 63 (18) years. Of and interquartile ranges for continuous variables. For these, 79% were self-identified as Caucasian and 48% univariate inferences, Chi square test or t test were used, were male. The mean (SD) APACHE III score was 61 (20) as appropriate. Multivariable logistic regression quan- [18]. The unadjusted in-hospital mortality rate was 14.6% tified the relationship between hypotension exposure (n = 1283). Fifteen percent (n = 1315) experienced AKI (TWA-MAP and cumulative time measured in minutes and 0.7% (n = 63) experienced myocardial injury during spent below MAP thresholds) and the primary and two the study period (AKI and myocardial injury rates appear secondary outcomes. low because patients who developed AKI or myocardial Specifically, we created individual logistic regression injury during the initial 24 ICU hours were excluded, models, each with one hypotension exposure and one of Fig.  1). Table  1 and Online Resource 4 list all the covari- the outcomes. We assessed the need for restricted cubic ates included in the regression models for the outcomes splines by plotting deciles and ventiles (20 equal-sized of in-hospital mortality and AKI, respectively. For myo- groups) of the hypotension exposure variable versus cardial injury, the regression models adjusted for hypo- the mean proportion of the outcome for each exposure tension along with age, sex, APACHE III score, and the and outcome, and looked for any substantial non-linear Elixhauser comorbidities of congestive heart failure, dia- trend. No substantial non-linear trend was evident so the betes with complications, and renal failure. exposures were modeled as linear predictors. The odds ratios with 95% confidence intervals for Table  1 lists all the covariates included in the models the regression models with TWA-MAP are graphed to reduce potential confounding. This includes the Acute in Fig.  2. The primary hypotension exposure of TWA- Physiology and Chronic Health Evaluation (APACHE) III MAP < 65  mmHg was positively correlated with in-hos- score used to adjust for patient acuity and the Elixhauser pital mortality. The analysis indicates that for every one comorbidities used to adjust for chronic comorbidities. mmHg increase in TWA-MAP < 65  mmHg, the odds For the uncommon outcome of myocardial injury, an of in-hospital mortality increase by 11.4%; 95% CI (7.8, algorithm that used bootstrapping and stepwise regres- 15.1%); p < 0.001 (Fig. 2 and Online Resource 5). Sensitiv- sion was used to determine a parsed model containing ity analyses show the odds ratios decreased as the MAP a limited set of potential confounders with hypotension threshold increased from 55 to 85 mmHg. The predicted exposure and the outcome [11]. To adjust for a potential marginal probabilities of in-hospital mortality across lack of independence among observations within hospi- TWA-MAP < 65 mmHg are shown Fig. 3a. tals, we derived robust (Huber–White) standard errors Cumulative time below a MAP threshold of 65 mmHg clustered at the hospital level for all regression models revealed that every 2  h (120  min) increased the odds of [12, 13]. We plotted the results of the logistic regression in-hospital mortality by 3.6%; 95% CI (2.5, 4.8%); p < 0.001 models as marginal probabilities of the outcome across (Online Resource 6). The predicted marginal prob - the observed range of the hypotension exposure variable abilities of in-hospital mortality for cumulative time of to facilitate interpretation of the results. MAP < 65  mmHg showed similar trends to probabilities We conservatively estimated that if the probability for TWA-MAP (Fig.  3). Predicted marginal probabilities of in-hospital mortality was 13% with the hypotension are shown in Online Resource 7. exposure of interest at its mean and the probability of in- The relationship between TWA-MAP and AKI was hospital mortality was 16% when hypotension exposure similar to in-hospital mortality (Fig.  4a). For every one was one standard deviation above the mean, then the mmHg increase in TWA-MAP < 65  mmHg, the odds 860 Table 1 Comprehensive list of potentially confounding variables for MAP threshold groups < 55 mmHg, < 65 mmHg, < 75 mmHg and < 85 mmHg Variable group Variable type Min. MAP < 55 mmHg (n = 3308) Min. MAP < 65 mmHg (n = 6310) Min. MAP < 75 mmHg (n = 8039) Min. MAP < 85 mmHg (n = 8609) Mortality n (%)** Mortality n (%) Mortality n (%) Mortality n (%) Yes No p value Yes No p value Yes No p value Yes No p value Gender* Male 329 (44%) 1079 (42%) 0.522 51 (47%) 2343 (45%) 0.302 592 (48%) 3183 (47%) 0.473 617 (48%) 3481 (48%) 0.521 Race African American 77 (10%) 234 (10%) 0.243 120 (11%) 502 (10%) 0.240 137 (11%) 705 (10%) 0.380 142 (11%) 801 (11%) 0.592 Caucasian 585 (78%) 206 (81%) 858 (78%) 4164 (80%) 964 (78%) 5400 (79%) 994 (78%) 5777 (79%) Other 74 (10%) 211 (9%) 97 (9%) 455 (9%) 107 (9%) 586 (8%) 110 (9%) 635 (9%) Unknown 19 (3%) 48 (2%) 26 (2%) 88 (2%) 28 (2%) 112 (2%) 28 (2%) 122 (2%) Age (years)* Mean 70 65 < 0.001 69 63 < 0.001 69 62 < 0.001 69 63 < 0.001 Admission type Elective 40 (5%) 119 (5%) 0.779 62 (6%) 213 (4%) 0.065 70 (6%) 274 (4%) 0.025 73 (6%) 291 (4%) 0.009 Emergency 671 (89%) 2276 (89%) 975 (89%) 4711 (90%) 1092 (88%) 6173 (91%) 1125 (88%) 6672 (91%) Trauma Center 5 (0.7%) 12 (0.5%) 3 (0.3%) 10 (0.2%) 4 (0.3%) 11 (0.2%) 4 (0.3%) 11 (0.2%) Urgent 2 (0.3%) 4 (0.2%) 53 (5%) 257 (5%) 62 (5%) 321 (5%) 64 (5%) 337 (5%) Unknown 37 (5%) 142 (6%) 8 (0.7%) 18 (0.4%) 8 (0.7%) 24 (0.4%) 8 (0.6%) 24 (0.3%) Discharge year 2010 3 (0.4%) 23 (0.9%) 0.009 6 (0.5%) 44 (0.8%) 0.009 9 (0.7%) 53 (0.8%) 0.001 9 (0.7%) 55 (0.8%) < 0.001 2011 31 (4.1%) 79 (3%) 41 (4%) 172 (3%) 49 (4%) 220 (3%) 50 (4%) 232 (3%) 2012 82 (10.9%) 185 (7%) 115 (11%) 383 (7%) 130 (11%) 510 (8%) 134 (11%) 539 (7%) 2013 141 (19%) 434 (17%) 196 (18%) 848 (16%) 215 (17%) 1064 (16%) 223 (18%) 1125 (15%) 2014 183 (24%) 679 (27%) 270 (25%) 1320 (25%) 305 (25%) 1685 (25%) 313 (25%) 1818 (25%) 2015 165 (22%) 584 (23%) 236 (21%) 1202 (23%) 265 (21%) 1592 (23%) 275 (22%) 1733 (24%) 2016 150 (20%) 569 (22%) 237 (22%) 1240 (24%) 263 (21%) 1679 (25%) 270 (21%) 1833 (25%) Census region Midwest 93 (12%) 365 (14%) < 0.001 141 (13%) 772 (15%) < 0.001 161 (13%) 1054 (16%) < 0.001 167 (13%) 1137 (16%) < 0.001 Northeast 308 (41%) 793 (31%) 430 (39%) 1501 (29%) 477 (39%) 1922 (28%) 484 (38%) 2062 (28%) South 213 (28%) 905 (36%) 336 (31%) 2003 (39%) 388 (31%) 2654 (39%) 404 (38%) 2873 (39%) West 141 (19%) 490 (19%) 194 (18%) 933 (18%) 210 (17%) 1173 (17%) 219 (17%) 1263 (17%) Hospital bed size < 100 44 (6%) 208 (8%) < 0.001 63 (6%) 443 (9%) < 0.001 70 (6%) 603 (9%) < 0.001 74 (6%) 665 (9%) < 0.001 100–199 91 (12%) 403 (16%) 141 (13%) 871 (17%) 159 (13%) 1149 (17%) 162 (13%) 1232 (17%) 200–299 115 (15%) 496 (19%) 174 (16%) 994 (19%) 192 (16%) 1332 (20%) 197 (16%) 1457 (20%) 300–499 191 (25%) 558 (22%) 296 (27%) 1220 (23%) 344 (28%) 1612 (24%) 356 (28%) 1746 (24%) 500+ 314 (42%) 888 (35%) 427 (39%) 1681 (32%) 471 (38%) 2107 (31%) 485 (38%) 2235 (31%) ICU type General ICU 472 (63%) 1722 (68%) 0.001 675 (61%) 3465 (67%) 0.001 765 (62%) 4549 (67%) 0.002 791 (62%) 4920 (67%) < 0.001 Medical ICU 95 (13%) 278 (11%) 156 (14%) 668 (13%) 172 (14%) 880 (13%) 178 (14%) 947 (13%) Surgical ICU 46 (6%) 181 (7%) 69 (6%) 309 (6%) 75 (6%) 382 (6%) 75 (6%) 407 (6%) Cardiac ICU 70 (9%) 138 (5%) 99 (9%) 301 (6%) 103 (8%) 392 (6%) 109 (9%) 418 (6%) 861 Table 1 continued Variable group Variable type Min. MAP < 55 mmHg (n = 3308) Min. MAP < 65 mmHg (n = 6310) Min. MAP < 75 mmHg (n = 8039) Min. MAP < 85 mmHg (n = 8609) Mortality n (%)** Mortality n (%) Mortality n (%) Mortality n (%) Yes No p value Yes No p value Yes No p value Yes No p value Coronary care unit 72 (10%) 234 (9%) 102 (9%) 466 (9%) 121 (10%) 600 (9%) 121 (10%) 643 (9%) Drugs received Diuretics received 171 (23%) 341 (13%) < 0.001 264 (24%) 653 (13%) < 0.001 291 (23%) 838 (12%) < 0.001 303 (24%) 897 (12%) < 0.001 ACE inhibitors 68 (9%) 232 (9%) 0.946 115 (11%) 453 (9%) 0.066 130 (11%) 594 (9%) 0.044 137 (11%) 648 (9%) 0.028 received Beta blockers 157 (21%) 378 (15%) < 0.001 252 (23%) 773 (15%) < 0.001 282 (23%) 1020 (15%) < 0.001 298 (23%) 1105 (15%) < 0.001 received Calcium channel 82 (11%) 156 (6%) < 0.001 136 (12%) 332 (6%) < 0.001 156 (13%) 465 (9%) < 0.0001 165 (13%) 519 (7%) < 0.001 blockers received *Modified APACHE III Mean 76 65 < 0.001 74 62 < 0.001 73 60 < 0.001 72 59 < 0.001 score Serum lactate No reading available 335 (44%) 125 (49%) < 0.001 505 (46%) 2745 (53%) < 0.001 592 (48%) 3729 (55%) < 0.001 614 (48%) 4077 (56%) < 0.001 Normal < 2 mmol/L 183 (24%) 813 (32%) 279 25%) 1555 (30%) 304 (25%) 1934 (28%) 309 (24%) 2045 (28%) Mild 2 to < 5 mmol/L 172 (23%) 406 (16%) 230 (21%) 780 (15%) 250 (20%) 983 (15%) 257 (20%) 1050 (14%) Moderate 5 40 (5%) 57 (2%) 58 (5%) 95 (2%) 61 (5%) 119 (2%) 63 (5%) 123 (2%) to < 8 mmol/L Severe ≥ 8 mmol/L 25 (3%) 20 (0.8%) 29 (3%) 34 (0.7%) 29 (2%) 38 (0.6%) 31 (2%) 40 (0.6%) Elixhauser index Mean 18 13 < 0.001 18 13 < 0.001 18 13 < 0.001 18 12 < 0.001 Payer Commercial 102 (14%) 403 (16%) < 0.001 145 (13%) 883 (17%) < 0.001 165 (13%) 1176 (17%) < 0.001 168 (13%) 1293 (18%) < 0.001 Medicaid 53 (7%) 334 (13%) 93 (8%) 696 (13%) 114 (9%) 917 (14%) 116 (9%) 989 (14%) Medicare 494 (65%) 145 (57%) 700 (64%) 2839 (55%) 780 (63%) 3668 (54%) 807 (63%) 3907 (53%) Other 48 (6%) 141 (6%) 70 (6%) 317 (6%) 74 (6%) 415 (6%) 75 (6%) 461 (6%) Unknown 58 (7%) 225 (9%) 93 (9%) 474 (9%) 103 (8%) 627 (9%) 108 (9%) 685 (9%) Teaching status Yes 516 (68%) 1620 (64%) 0.014 723 (65.7%) 3161 (61%) 0.002 803 (65%) 4057 (60%) < 0.001 824 (65%) 4342 (59%) 0.000 Urban/rural status Urban 553 (73%) 2029 (80%) < 0.001 270 (25%) 991 (19%) < 0.001 930 (75%) 5513 (81%) < 0.001 963 (76%) 5965 (81%) < 0.001 Hospital acute status Acute 755 (100%) 2553 (100%) 1101 (100%) 5209 (100%) 1235 (100%) 6803 (100%) 1273 (100%) 7335 (100%) Elixhauser comorbidi- Congestive heart 316 (42%) 784 (31%) < 0.001 438 (40%) 1477 (28%) < 0.001 477 (39%) 1855 (27%) < 0.001 494 (39%) 1953 (27%) < 0.001 ties failure Valvular disease 163 (22%) 444 (17%) 0.009 234 (21%) 832 (16%) < 0.001 259 (21%) 1051 (16%) < 0.001 264 (21%) 1114 (15%) < 0.001 Pulmonary circulation 139 (18%) 306 (12%) < 0.001 190 (17%) 596 (11%) < 0.001 211 (17%) 740 (11%) < 0.001 218 (17%) 777 (11%) < 0.001 disease Peripheral vascular 174 (23%) 490 (19%) 0.020 253 (23%) 950 (18%) 0.000 276 (22%) 1218 (18%) < 0.001 285 (22%) 1302 (18%) < 0.001 disease Paralysis 68 (9%) 301 (11%) 0.033 101 (9%) 588 (11%) 0.041 117 (10%) 726 (11%) 0.203 125 (10%) 766 (10%) 0.495 Other neurological 250 (33%) 875 (34%) 0.554 368 (33%) 1702 (33%) 0.630 413 (33%) 2202 (32%) 0.470 422 (33%) 2352 (32%) 0.456 disorders 862 Table 1 continued Variable group Variable type Min. MAP < 55 mmHg (n = 3308) Min. MAP < 65 mmHg (n = 6310) Min. MAP < 75 mmHg (n = 8039) Min. MAP < 85 mmHg (n = 8609) Mortality n (%)** Mortality n (%) Mortality n (%) Mortality n (%) Yes No p value Yes No p value Yes No p value Yes No p value Chronic pulmonary 333 (44%) 1062 (42%) 0.220 510 (46%) 2159 (42%) 0.003 565 (46%) 2825 (42%) 0.006 589 (46%) 3019 (41%) 0.00 disease Diabetes w/o chronic 164 (22%) 604 (24%) 0.268 242 (22%) 1223 (24%) 0.285 273 (22%) 1581 (23%) 0.376 286 (23%) 1721 (24%) 0.430 complications Diabetes w/chronic 117 (16%) 341 (13%) 0.135 161 (15%) 695 (13%) 0.260 184 (15%) 950 (14%) 0.392 189 (15%) 1051 (14%) 0.635 complications Hypothyroidism 178 (24%) 602 (24%) 0.998 251 (23%) 1182 (23%) 0.939 281 (23%) 1469 (22%) 0.371 286 (23%) 1556 (21%) 0.321 Renal failure 210 (28%) 535 (21%) < 0.001 304 (28%) 1007 (19%) < 0.001 336 (27%) 1342 (20%) < 0.001 344 (27%) 1440 (20%) < 0.001 Liver disease 138 (18%) 279 (11%) < 0.001 210 (19%) 587 (11%) < 0.001 233 (19%) 790 (12%) < 0.001 241 (19%) 852 (12%) < 0.001 Peptic ulcer disease 9 (1%) 28 (1%) 0.827 15 (1%) 53 (1%) 0.314 16 (1%) 64 (0.9%) 0.249 16 (1%) 69 (0.9%) 0.294 excl. bleeding Acquired immune 6 (0.8%) 27 (1%) 0.523 10 (0.9%) 43 (0.8%) 0.785 11 (0.9%) 58 (0.9%) 0.896 12 (0.9%) 63 (0.9%) 0.769 deficiency syn- drome Lymphoma 29 (4%) 72 (3%) 0.152 52 (5%) 160 (3%) 0.006 59 (5%) 188 (3%) < 0.001 61 (5%) 200 (3%) < 0.001 Metastatic cancer 112 (15%) 174 (7%) < 0.001 154 (14%) 365 (7%) < 0.001 184 (15%) 458 (7%) < 0.001 192 (15%) 487 (7%) < 0.001 Solid tumor without 73 (10%) 234 (9%) 0.676 110 (10%) 461 (9%) 0.231 119 (10%) 574 (8%) 0.170 122 (10%) 608 (8%) 0.128 metastasis Rheumatoid arthritis/ 46 (6%) 188 (7%) 0.231 71 (7%) 367 (7%) 0.479 83 (7%) 453 (7%) 0.942 84 (7%) 494 (7%) 0.852 collagen vascular diseases Coagulopathy 285 (38%) 650 (26%) < 0.001 410 (37%) 1244 (24%) < 0.001 462 (37%) 1570 (23%) < 0.001 474 (37%) 1654 (23%) < 0.001 Obesity 137 (18%) 590 (23%) 0.004 201 (18%) 1186 (23%) 0.001 229 (19%) 1618 (24%) < 0.001 237 (19%) 1767 (24%) < 0.001 Weight loss 276 (37%) 730 (29%) < 0.001 405 (37%) 1360 (26%) <0.001 447 (36%) 1674 (25%) < 0.001 458 (36%) 1752 (24%) < 0.001 Fluid and electrolyte 638 (85%) 1986 (78%) <0.001 922 (84%) 4007 (77%) < 0.001 1032 (84%) 5195 (76%) <0.001 1063 (83%) 5575 (76%) < 0.001 disorders Chronic blood loss 41 (5%) 122 (5%) 0.467 69 (6%) 227 (4%) 0.007 76 (6%) 285 (4%) 0.002 76 (6%) 303 (4%) 0.003 anemia Deficiency anemias 448 (59%) 1399 (55%) 0.027 644 (59%) 2759 (53%) 0.001 723 (59%) 3516 (52%) < 0.001 747 (59%) 3744 (51%) < 0.001 Alcohol abuse 106 (14%) 294 (12%) 0.062 156 (14%) 579 (11%) 0.004 175 (15%) 785 (12%) 0.009 179 (14%) 869 (12%) 0.026 Drug abuse 65 (9%) 272 (11%) 0.103 90 (8%) 583 (11%) 0.003 100 (8%) 772 (11%) 0.001 105 (8%) 842 (12%) 0.001 Psychoses 88 (12%) 353 (14%) 0.123 13 (12%) 750 (14%) 0.044 150 (12%) 990 (15%) 0.025 156 (12%) 1066 (15%) 0.031 Depression 197 (26%) 722 (28%) 0.238 294 (27%) 1464 (28%) 0.346 325 (26%) 1876 (28%) 0.353 339 (27%) 2022 (28%) 0.480 Hypertension 556 (74%) 1782 (70%) 0.042 808 (73%) 3606 (69%) 0.006 914 (74%) 4741 (70%) 0.003 944 (74%) 5140 (71%) 0.004 Because of rounding, categories will not always add to 100% APACHE III score includes physiology, chronic health investigation, and age variables [18] *Variables included in the adjustment of regression models for myocardial injury **Percentages for the variables types in this table are calculated by using a denominator which is the total number of patients within that particular variable group 863 Fig. 1 Patient attrition diagram. AKI acute kidney injury Fig. 2 Association of hypotension exposure with in-hospital mortality, AKI and myocardial injury. Adjusted odds ratios and 95% confidence intervals for a 1 mmHg increase in TWA-MAP, below different thresholds are shown for the primary outcome of in-hospital mortality and secondary outcomes of acute kidney injury and myocardial injury 864 Fig. 3 Predicted mortality outcome for time-weighted average ( TWA)-MAP below 65 mmHg and cumulative hours of MAP below 65 mmHg. Predicted probability of mortality from the TWA-MAP < 65 mmHg threshold and cumulative hours of MAP < 65 mmHg are represented in panels a and b, respectively Fig. 4 Predicted marginal probability for AKI and myocardial injury for TWA-MAP below 65 mmHg threshold. AKI and myocardial injury predicted probability from the TWA-MAP below 65 mmHg threshold are shown in panels a and b, respectively. Both exposures showed a linear relationship with the secondary outcomes of AKI and myocardial injury. AKI acute kidney injury of developing AKI increase by 7.0%; 95% CI (4.7, 9.5%); MAP threshold in the longest duration category, as these p < 0.001 (Fig.  2). Patients who spent between 6 and 8  h patient records had more and longer gaps between MAP in the ICU with MAP < 65 mmHg had odds of developing readings. AKI 37% higher (95% CI 3, 82%; p = 0.031) compared to For every one mmHg increase in TWA-MAP < 65 mmHg, patients with no time below MAP of 65  mmHg (Online the odds of developing myocardial injury increased by 3.7%; Resource 6). Although TWA-MAP below 55, 75, and 95% CI (0.3, 7.3%), p = 0.03 (Fig.  2, Online Resource 5). 85  mmHg showed a positive correlation (p < 0.001) with We derived marginal probabilities of developing myocar- developing AKI, we did not see similar trends for cumu- dial injury for TWA-MAP (Fig.  4b) and cumulative hours lative time below thresholds of 75 and 85 mmHg (Online of MAP below 65  mmHg (Online Resource 7). Unlike the Resource 6). Here, patients with the longest times below relationship for in-hospital mortality and AKI, there was no the thresholds tended to have fewer AKI events than significant worsening of myocardial injury at lower MAP the increasingly small number of patients with no time thresholds (Fig.  4b and Online Resource 8). A sensitivity below these thresholds. This apparent contradiction is, analysis that repeated the regression modeling restricted to in part, due to over-representation of the time below survivors found similar associations between hypotension 865 at the 65  mmHg threshold and the outcomes of AKI and norepinephrine does not significantly affect systemic myocardial injury (data not shown). oxygen metabolism, skin microcirculatory blood flow, urine output, renal function, or splanchnic perfusion— Discussion although cardiac index increased [19, 20]. However, a Given the complexity of defining hypotension expo - prospective study of thirteen patients with septic shock sure, we analyzed both time-weighted average (TWA) found that increasing MAP to above 65 mmHg with nor- and the cumulative time under specific thresholds. epinephrine increased cardiac output, improved micro- TWA-MAP is a comprehensive measure of hypoten- vascular function, and was associated with decreased sion exposure because it measures both the degree and blood lactate concentrations. The investigators noticed the duration below a threshold. Continuous time below that microvascular responses varied considerably among a MAP threshold is intuitive but neglects severity of patients, suggesting that individualization of blood pres- hypotension and is subject to two types of variation: the sure targets may be warranted [21]. frequency of MAP readings, and the total time for MAP Only limited evidence from randomized trials pro- exposure calculation. Spurious extreme values can occur vides guidance on optimal thresholds. Asfar et  al. [22] and distort the results due to (1)  gaps between readings randomized 776 septic shock patients and reported (carried forward) that can over-represent times below a that 28- and 90-day mortality did not differ significantly given MAP threshold, especially as there were more and between those who were treated to reach a target MAP longer gaps in patients who spent long periods below of 80–85 mmHg and those who were treated to reach a various thresholds; and (2) patients with longer ICU stays target of 65–70 mmHg [19]. However, even in the lower may have been especially prone to hypotension. To mini- MAP target group the blood pressure was maintained at mize both sources of error, we excluded patients with 70–75 mmHg and authors noticed a lower than expected fewer than five blood pressures recorded per each 24-h death rate, which supports our findings using a much period, and those who had more than two gaps exceeding larger cohort and suggests a threshold above 65  mmHg 5 h between readings. may be more appropriate. This study also highlights the We observed strong associations between the TWA- complexity of performing randomized trials in this popu- MAP below various thresholds and in-hospital mortality lation and the value of our analysis. and kidney injury in septic patients. Substantial mor- We observed that 14.6% of patients with sepsis died tality risk was evident even among the higher thresh- during hospitalization over the period from 2010 to olds, and the risk progressively increased as the MAP 2016. Presumably patients in our cohort were sicker than thresholds decreased from 85 to 55  mmHg. A similar all sepsis patients given a required ICU stay of at least relationship between in-hospital mortality and cumula- 1 day. However, two recent European studies found sep- tive time < 65  mmHg was also observed. It is important sis mortality to range from 8 to 26%. [18, 19] This is in to note that the TWA-MAP and cumulative time meas- contrast to higher in-hospital mortality of 26% reported ures below a given threshold are nested and not mutu- from analysis of a German patient population from 2007 ally exclusive (e.g., patients with TWA-MAP < 55 mmHg to 2013 [23]. Furthermore, Freund and colleagues [24], are included in the analysis of TWA-MAP < 65  mmHg). based on 2016 European hospital data, observed 8% in- We, therefore, cannot definitively determine an optimal hospital mortality in patients with suspected sepsis. threshold with this study design alone. These results suggest that outcomes from sepsis may be As with in-hospital mortality, we observed that odds of improving over time. developing AKI was associated with hypotension char- In general, AKI in critically ill patients affects approxi - acterized by TWA-MAP, with the odds of developing mately 40% of the patients at some time during their AKI being greatest for MAP readings < 55  mmHg and stay and one third who develop renal injury die within lowest for MAP < 85 mmHg. However, similar trends 90 days [16]. In a study consistent with ours, hypotensive were not observed in the cumulative minutes of MAP episodes of MAP < 73  mmHg were associated with pro- below threshold groups. We theorize that is because we gression of AKI in critically ill patients with severe sepsis excluded AKI and myocardial injury before and within [17]. 24 h of ICU admission and restricted this outcome to the Myocardial injury, measured by troponin eleva- first 7  days of exposure. Also, myocardial injury may be tion, may be as high as 15–25% in ICU patients, but is undercounted when routine troponin monitoring is not often unrecognized because routine troponin monitor- performed. ing remains uncommon [16, 25]. When troponins are Overall, our results are consistent with previous lit- routinely monitored in septic ICU patients, only 7% erature with notable exceptions. Prior research found of biomarker elevations happened within 24  h of ICU that increasing the MAP from 65 to 85  mmHg with admission [26]. While our definition of myocardial injury 866 artificially lowered the observed rate by excluding cases on the timing of laboratory and medication orders, given within 24  h of ICU admission and after AKI develop- their frequency in critically ill patients, this limitation ment), we still observed significant association between seems unlikely to bias our results substantially. TWA-MAP < 65  mmHg and myocardial injury. How- In summary, the Surviving Sepsis Guidelines suggest ever, it is possible that a raised troponin value is present keeping mean arterial pressure initially above 65 mmHg, in the absence of myocardial injury [27], although raised followed by individualized treatment to optimize tissue troponin values have been tied with myocardial injury perfusion. In our analysis, risks for mortality, AKI and within the septic population [28–31]. Our results of myo- myocardial injury were apparent by 85  mmHg, and for cardial injury analysis should be interpreted with caution mortality and AKI risk progressively worsened at lower due to lack of universal troponin screening and diverse thresholds. Until randomized trials show that the rela- troponin tests employed among various U.S. Hospitals. tionship between hypotension and serious complications We report associations between hypotension in ICU is not causal, it would probably be prudent to keep mean patients and both myocardial and kidney injury. How- arterial pressure well above 65  mmHg in septic ICU ever, we report associations which are surely at least to patients. some degree confounded by unobserved baseline patient Electronic supplementary material characteristics. Randomized trials will be required to The online version of this article (https ://doi.org/10.1007/s0013 4-018-5218-5) confirm causal relationships that may benefit from contains supplementary material, which is available to authorized users. intervention. Another study limitation is our inability to distinguish between untreated hypotension and hypoten- Author details sion that persisted despite treatment—and thus presum- Department of Outcomes Research, Center for Perioperative Intelligence, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH, USA. OptiStatim, ably indicated worse sepsis. To address this, we adjusted LLC, Longmeadow, MA, USA. Department of Health Economics and Out- for medication use and other potential confounders. 4 comes Research, Boston Strategic Partners, Inc., Boston, MA, USA. Edwards Nevertheless, unmeasured confounding remains likely. Lifesciences, Irvine, CA, USA. Department of Outcomes Research, Center for Critical Care, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH, USA. For example, septic patients are always given antibiot- Department of Outcomes Research, Anesthesiology Institute, Cleveland ics, some of which are nephrotoxic. However, we did not 7 Clinic, Cleveland, OH, USA. Department of General Anesthesiology, Anes- attempt to link specific antibiotics to AKI. Hypotension thesiology Institute, Cleveland Clinic, 9500 Euclid Avenue, E-31, Cleveland, OH 44195, USA. identification and duration is dependent upon the fre - quency of recorded blood pressure readings. While some Acknowledgements MAP data (up to two 5-h gaps per record) were missing, The authors wish to thank Dr. Seungyoung Hwang for his assistance with data analysis. This research was supported by Edwards Lifesciences, Irvine, CA. an average 357 MAP readings were available per ICU day which indicates that the exposure was generally well Compliance with ethical standards characterized. Conflicts of interest We were also unable to distinguish hypotension that is Drs. Maheshwari and Sessler work as consultants for Edwards Lifesciences. Dr. a marker of severe sepsis from hypotension that directly Khanna consults for La Jolla pharmaceuticals. Drs. Khangulov, Munson and contributed to organ dysfunction. The distinction is Badani work as consultants for Boston Strategic Partners, Inc. who received funds from Edwards Lifesciences to perform the research. Dr. Nathanson is important because interventions to reduce hypotension an employee of OptiStatim, LLC, which received consulting fees from Boston will only improve the fraction of organ dysfunction that Strategic Partners, Inc. is causally related to blood pressure. Additionally, some Open Access treatments for hypotension can themselves provoke This article is distributed under the terms of the Creative Commons Attribu- organ injury. For example, increased rates of atrial fibril - tion-NonCommercial 4.0 International License (http://creativecommons.org/ lation are noted with higher vasopressor use [22]. None- licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the theless, our results suggest that harm may begin to accrue original author(s) and the source, provide a link to the Creative Commons well above the currently recommended initial threshold license, and indicate if changes were made. of 65  mmHg, and higher for older patients and those with cardiovascular comorbidities [3]. However, the Received: 6 March 2018 Accepted: 7 May 2018 definitive way to answer how the duration of hypotension Published online: 5 June 2018 impacts mortality and other outcomes in critically ill sep- sis patients is via a prospective, randomized controlled trial that follows a standard protocol for vasopressor and References intravenous fluid use. This study did not examine out - 1. 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Medicine (Baltimore) 97:e0263 Intensive Care Med 40:1018–1020

Journal

Intensive Care MedicineSpringer Journals

Published: Jun 5, 2018

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