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Regional and Institutional Variation in the Initiation of Early Do-Not-Resuscitate Orders

Regional and Institutional Variation in the Initiation of Early Do-Not-Resuscitate Orders Abstract Background Do-not-resuscitate (DNR) orders are an important step in decision making about aggressiveness of care for patients in hospitals. The use of DNR orders is known to vary with patient characteristics, but few studies have investigated the role of hospital factors or of regional variation. We examined these influences on the use of early DNR orders (written <24 hours after admission). Methods We conducted a retrospective cross-sectional study of patients 50 years and older admitted to acute-care hospitals in California in 2000 from the most prevalent medical and surgical diagnosis related groups. We performed multivariate logistic regression predicting use of DNR by hospital characteristics while accounting for patient characteristics, and estimated indirectly standardized rates of DNR use by county. Results In the selected diagnosis related groups, 819 686 persons were admitted to 386 hospitals. Early DNR orders varied from 2% (patients aged 50-59 years) to 17% (patients aged ≥80 years). In multivariate analyses, the odds of having early DNR orders written were significantly lower in for-profit (vs private nonprofit) hospitals, higher in the smallest (vs the largest) hospitals, and lower in academic (vs nonacademic) hospitals. Standardized rates of DNR order use varied 10-fold across counties. The highest rates were among patients from rural areas. However, variation in use did not correspond well to county population, hospital bed availability, or population density. Conclusions Hospital characteristics appear to be associated with the use of DNR orders, even after accounting for differences in patient characteristics. This association reflects institutional culture, technological bent, and physician practice patterns. If these factors do not match patient preferences, then improvements in care are needed. Do-not-resuscitate (DNR) orders are an important step in decision making about aggressiveness of care for patients in hospitals. These orders are essential to guiding the care provided to hospitalized patients, because medicine has the capability to maintain survival in health states that many patients and health care providers find undesirable.1,2 Treatments like resuscitation may be inappropriate or may afford short-term benefits without achieving valued longer-term goals. Because life-sustaining treatments are increasingly available in hospitals, not surprisingly, DNR orders are also increasingly common.3,4 Early DNR orders (written <48 hours after admission) tend to reflect patient preferences and prognosis and are independently related to hospital survival.5,6 If DNR orders reflect patients’ preferences for care and guide care that is consistent with these preferences, these orders can be considered indicators of the quality of health care.5,7 The use of DNR orders is known to vary with patient characteristics. Women, nonwhite patients, younger individuals, patients admitted from noninstitutional settings, and healthier patients (at admission) are less likely to receive DNR orders after hospital admission.8-12 However, few studies have investigated the role of hospital factors or geographic variation on the use of DNR orders. Data for older Medicare beneficiaries from the 1980s showed patients were less likely to have DNR orders written if they received care at rural hospitals or hospitals with more Medicaid-covered patients.8 Patients from 30 hospitals in Cleveland, Ohio, showed substantial variation between hospitals, mostly explained by case mix.11 A sample of patients treated for a single condition at California hospitals showed no differences by hospital characteristics.10 Geographic variation in other aspects of care have been shown within Medicare and in other populations.13 Other data show a 4- to 8-fold geographic variation in end-of-life inpatient utilization of health care resources (eg, costs, days hospitalized, days in the intensive care unit).14 Physician attitudes,15 utilization incentives, and local culture may contribute to such variation. We endeavored to study whether institutional factors influence the use of DNR orders. We analyzed and evaluated the role of hospital characteristics (ie, size, profit status, control, and academic status) on the use of a DNR order written within the first 24 hours after acute hospital admission among patients 50 years and older admitted to California hospitals in 2000. To better understand regional variation in DNR use, we also examined the geographic distribution of DNR orders by county. Methods Data We analyzed hospitalization discharge abstracts obtained from the California Office of Statewide Health Planning and Development Patient Discharge Database for the year 2000, nonconfidential Version B. Discharge abstracts are required for all patients receiving inpatient care within all California nonfederal hospitals. Records include patient race, ethnicity, age, sex, insurance type, residence ZIP code, primary and secondary diagnosis and procedure codes, quarter year of admission, level of care (ie, acute, rehabilitative, skilled nursing, or psychiatric), source of admission, scheduled admission, hospital identification code, and hospital ZIP code. Since 1999, hospitals have been required to report whether DNR orders were written within the first 24 hours of each admission. Income data by ZIP code were obtained from the 1990 US census data (analogous 2000 ZIP code level data were not yet available). Population estimates were from the 2000 US census. Information regarding California hospitals was obtained from the California Office of Statewide Health Planning and Development annual Hospital Financial and Utilization reports for 2000 and from the Council of Teaching Hospitals. Characteristics included hospital size (number of beds), ownership/control, location (rural or urban), and academic status (membership in the Council of Teaching Hospitals). Each hospital record has an identification code allowing linkage between hospital reports and Patient Discharge Database records. Variables The primary outcome variable was receipt of a DNR order within the first 24 hours after hospital admission (early DNR). Independent variables included age (in 5-year intervals), sex, race/ethnicity (non-Latino white, non-Latino black, Latino, Asian/Pacific Islander, and other), type of insurance (Medicare, Medi-Cal, private, other, or indigent), admission source (home, nursing home, or other), and diagnosis-related group (DRG). Patient illness was measured by comorbidity index and illness severity score. Comorbid medical conditions were identified from the Patient Discharge Database, and a Charlson Comorbidity Index was calculated for each hospitalization.16,17 Illness severity scores derived from multivariate logistic regression models of hospital death were categorized by quartile of the estimated probability of hospital death.18 Proxy measure of income was estimated from household income by residence ZIP code. To account for patient referral, we calculated the distance between the patient’s residence and the hospital using ZIP code centroids. For comparability between institutions, percentile distances traveled by patients treated within the same hospital were calculated. Distances were categorized as 75th percentile, above the 75th to the 95th percentile, and above the 95th percentile. For patients without valid ZIP codes (<3%), hot-deck imputation (random sampling of observed characteristics) was used to impute income, rural residence location, and distance traveled.19 Hospital-level predictors included hospital size (number of beds), control/profit status (private nonprofit, district, county, or for-profit), and academic hospital status. Statistical analysis Because DNR orders are uncommon among younger patients (<1% in those aged <50 years), analyses were restricted to hospitalized patients 50 years or older. To further reduce heterogeneity, records were restricted to the 20 most common medical and 20 most common surgical/procedural DRGs, representing 51% of the 1.6 million records for patients 50 years and older. In unadjusted analyses, we evaluated DNR use in the context of each independent predictor, stratified by patient age. For each bivariate comparison, we estimated significance of effect using the Pearson χ2 test. We predicted use of DNR by means of multivariate logistic regression to estimate the impact of hospital characteristics (size, control/profit status, location, and academic status) after accounting for patient-case mix (age, race, sex, expected payer, insurance plan type, residence type [rural or urban], admission source, estimated income, relative distance to hospital, Charlson Comorbidity Index, illness severity score, and DRG). We used robust regression to improve standard error estimates with a clustering correction to account for within-hospital correlation. Analyses were stratified by DRG type (procedural vs medical). Analyses were performed using Intercooled Stata 7.0 (Stata Corp, College Station, Tex). To examine regional differences in DNR use, we calculated indirectly standardized DNR rates by county of patient residence. First, we calculated unadjusted DNR rates for each county for all DRGs, medical DRGs, and procedural DRGs. Second, we calculated standardized rates according to the equation Ii = S × (Oi/Pi), where Ii is the indirectly standardized DNR rate in the ith county, S is the average rate for DNR orders in California, Oi is the observed number of DNR orders in the ith county, and Pi is the predicted number of DNR orders in the ith county.20 The Ii values were calculated for all DRGs and stratified to medical and procedural DRGs. We mapped county rates using ArcMap 8.3 (Environmental Systems Research Institute, Redlands, Calif). Results Demographics In California in 2000, a total of 3 816 900 discharges were reported in the Patient Discharge Database. After exclusions, 1 507 450 patients 50 years or older were admitted for acute care at 386 California hospitals, and 819 686 admissions occurred under the 40 most common medical and surgical/procedural DRGs (Table 1). Patients admitted to the hospital under these DRGs were primarily white (71% overall; 77% among the oldest patients), with sizeable numbers of racial/ethnic minorities, especially in the younger groups (36% of subjects in the youngest group were nonwhite). Overall, most admissions were for women (54%), with women predominating among the oldest patients (62% of those aged ≥80 years). Median Charlson Comorbidity Index increased by patient age. Among patients surviving to discharge, nearly half of the oldest patients, but fewer than 10% of the youngest patients, were discharged to skilled nursing care or to home care. Older patients were more likely than younger patients to be admitted to smaller nonacademic medical centers (Table 2). Use of dnr orders Unadjusted Overall, DNR orders written within the first 24 hours of hospital admission varied from 2% of admissions in the youngest group to 17% in the oldest group (Table 3). Across all age strata, DNR orders were more likely to be written within the first 24 hours for white patients, those admitted from or discharged to assisted-living locations, those with greater comorbid illnesses (by Charlson Comorbidity Index), those with greater likelihood of dying (by illness severity score), and those dying within the hospital. Patients coming from a greater relative distance were less likely to have DNR orders written. Patients receiving care at larger, for-profit, and academic medical centers were less likely to have DNR orders written. Multivariate Results In overall multivariate logistic regression analyses, many hospital and patient characteristics remained significant predictors of use of early DNR orders (Table 4). After accounting for patient characteristics (described in this section), hospital characteristics continued to be significant predictors of use of early DNR orders. Compared with those treated in private, nonprofit hospitals, patients treated in for-profit hospitals had lower odds of having early DNR orders written (odds ratio [OR], 0.69; 95% confidence interval [CI], 0.53-0.89). District and county hospitals were not significantly different from private nonprofit hospitals. Patients treated at the smallest hospitals had greater odds of having early DNR orders compared with those treated at the largest hospitals (OR, 1.60; 95% CI, 1.14-2.23). Do-not-resuscitate orders for patients treated at intermediate-size hospitals were not significantly different from those for patients at the largest hospitals. Patients treated at academic medical centers had significantly lower odds of having DNR orders compared with those treated at nonacademic medical centers (OR, 0.62; 95% CI, 0.41-0.94). Younger patients, men, and black and Asian patients had significantly greater odds of having DNR orders. Patients with managed care insurance were more likely than those with traditional fee-for-service insurance to have DNR orders. Greater Charlson Comorbidity Index, greater illness severity, and admission from a nursing home were associated with greater odds of having DNR orders. Patients who lived farther from the treating hospital were less likely to have DNR orders. Income and rural residence were not significantly related to use of DNR orders. Regression analyses were repeated, stratified by medical or surgical DRG (Table 4). In analyses restricted to surgical conditions, no difference was seen by hospital control or profit status. In addition, younger patients, black patients, and those living furthest from the hospital had lower odds of having DNR orders, but other patient characteristics were not significant independent predictors of having DNR orders. Results of analyses restricted to medical conditions were similar to those of the overall analyses. Regression analyses further stratified by illness severity score did not substantially change results and are not reported herein. Use of dnr by geographic distribution The use of DNR orders in California differs on a regional basis with a 10-fold difference across counties. The large urban regions of the southern part of the state (metropolitan Los Angeles) (Figure 1, region A) have distinctly lower rates of DNR use compared with the urban counties of the San Francisco Bay area, San Luis Obispo, Santa Barbara, and San Diego (Figure 1, regions B-E, respectively). The highest rates of DNR orders among hospitalized individuals are for patients from rural areas. This is most apparent when the map of standardized DNR rates is compared with the map showing county population (Figure 2A). Geographic variation in early DNR use does not correspond to county population (Figure 2A), to availability of hospital beds in the county (normalized to population) (Figure 2B), or to population density (Figure 2C). Comment Hospital characteristics and regional variation are important predictors of early DNR orders in hospitalized patients. Patients treated at smaller hospitals have greater odds of having early DNR orders, whereas patients treated in academic medical centers or for-profit hospitals have lower odds of receiving early DNR orders. Moreover, a 10-fold variation in such orders exists by region in California. Because it is unlikely that preferences vary to this degree, DNR variation likely reflects local practice patterns. Lower odds of having early DNR orders at larger and academic hospitals suggest a greater focus on aggressive care. In turn, this result would be expected to be related to patients’ preferences, which could not be measured in this study. Previous work21 has suggested a 2-fold variation in resuscitation preference across larger institutions after adjustment for patient characteristics. This variation might be expected to be even greater across more varied types of hospitals. The lower use of DNR orders among patients cared for in academic hospitals is troubling because such institutions teach both the science and art of medicine. However, these findings are not unexpected, because the environment of the academic hospitals focuses on intervention and cutting-edge medical care. Consideration of patients’ goals of care may be relegated to lower importance in the context of aggressive care initiated during the admission period. Tertiary-care medical centers tend to be high-intensity referral centers to which patients come seeking care that includes heroic, life-saving interventions. The adjustment of DRG and stratification of analyses to medical or surgical conditions do not qualitatively change these observations. Other factors may contribute to the lower prevalence of early DNR orders at academic hospitals, such as having physician trainees. Resident physicians are often the first health care providers to see newly hospitalized patients, but they lack continuity relationships with these patients, which potentially impedes initiating conversations about goals of care. In addition, their experience with initiating end-of-life care discussions may be limited. Finally, they may defer to the continuity of care provided by the attending physician, especially when questions of nontreatment are involved, rather than working contrary to a milieu in which intervention is the rule. However, training venues and institutions that provide the most aggressive care are precisely where one would want to emphasize the discussion of preferences and goals. These data suggest a need for renewed efforts to integrate preferences into care on admission for patients at teaching hospitals. Patients treated at the smallest community hospitals were most likely to have early DNR orders. This is understandable, as physicians at small community hospitals are more likely to have mature physician-patient relationships, giving greater opportunity to initiate end-of-life decision making before admission or early in the hospitalization. Again, patient preferences may underlie this difference, as patients expecting less heroic care may go to smaller hospitals. Exploration of the role of patient preferences and physician-patient continuity in the use of early DNR orders awaits data that include these measures. The relationship between use of DNR orders and profit status raises other questions. For-profit hospitals have been hypothesized to align their behavior with financial incentives, which may not optimize patient care or outcomes.22,23 Care in for-profit hospitals has been associated with overall higher costs and greater intensity of care without improved patient outcome.24 Do-not-resuscitate orders represent a limit on the intensity of resuscitative care or may lead to earlier discharge, saving hospitals money.25,26 If lower-intensity care leads to lower reimbursement for the hospital or the treating physician, then for-profit hospitals may have less perceived incentive to institute DNR orders in as timely a fashion as nonprofit hospitals. Alternatively, DNR orders might be related to attempts to conserve resources, although this is more likely to be true of DNR orders written later in the course of hospitalization,5 which were not measured in this study. Patient factors associated with DNR orders are consistent with findings from earlier studies. Patient factors associated with having early DNR orders include white race, female sex, greater age, admission from a nursing home, and greater illness or comorbidity. Living farther from a hospital was inversely related to having a DNR order, suggesting that these individuals may be receiving referrals for a higher level of care. Why managed care insurance is related to DNR orders and whether this is related to conservation of resources or patient preferences need exploration. Regional differences appear to exist in the use of DNR orders. Even after standardizing results, the largest urban county, Los Angeles, had a much lower rate of DNR orders than surrounding urban counties or the large urban area in northern California. This variation is remarkably similar to that seen for other measures of end-of-life care, ie, the variation in intensive care unit use at the end of life in California.14 Regional differences in DNR orders are tantalizing because these orders are the result of the agency of physicians and the choices of their patients. Regional variation reflects a diverse set of circumstances that include physician practice patterns, local physician leadership in promoting the use of DNR orders and other end-of-life care, and the understanding and sophistication of the patient population.27,28 This variation, the dependence on local culture to determine use, may underlay a broader lack of agreement or standards on when and how to use DNR orders. Higher rates of early DNR orders among individuals from sparsely populated areas may reflect a greater acceptance of the prognosis and nonaggressive treatment plans among these individuals. Alternatively, individuals from rural areas are more likely to have primary admissions rather than tertiary referrals. A better understanding of factors related to the high level of variation in DNR use across California counties could contribute to efforts to better align prognosis with aggressive care. This was a retrospective study using administrative data. Do-not-resuscitate orders may be heterogeneous interventions. The significance of such orders and policies regarding their use may vary by institution. Do-not-resuscitate orders written after 24 hours were not available. The appropriateness of DNR orders cannot be ascertained by these data because patient preferences are not measured. Generally agreed-on levels of appropriateness for DNR orders are not known. Long-term outcomes of patients—indicators of unmeasured severity of illness—were not available in these data. Although the comprehensive nature of the database is a substantial strength of this study, the findings cannot necessarily be generalized to other states or to other DRG groups. Conclusions The initiation of end-of-life discussions and the implementation of DNR orders are important toward ensuring that patients receive care appropriate to their prognosis and preferences. Hospital characteristics appear to be associated with the use of DNR orders, even after accounting for differences in patient characteristics. This association reflects institutional culture, technological bent, and physician practice patterns. Variation in the use of early DNR orders may also reflect, in part, financial incentives. A better understanding of the impact of DNR orders on costs and utilization of health care resources may clarify financial incentives. If incentives do not match patient preferences, then improvements in care are needed. Correspondence: David S. Zingmond, MD, PhD, Division of General Internal Medicine and Health Services Research, The David Geffen School of Medicine at UCLA, 911 Broxton Plaza, Los Angeles, CA 90095-1736 (dzingmond@mednet.ucla.edu). Financial Disclosure: None. Accepted for Publication: January 18, 2005. References 1. Patrick DLStarks HECain KCUhlmann RFPearlman RA Measuring preferences for health states worse than death: functional status among survivors of in-hospital cardiopulmonary resuscitation. Med Decis Making 1994;149- 18PubMedGoogle ScholarCrossref 2. FitzGerald JDWenger NSCaliff RM et al. SUPPORT Investigators, Functional status among survivors of in-hospital cardiopulmonary resuscitation: Study to Understand Progress and Preferences for Outcomes and Risks of Treatment. Arch Intern Med 1997;15772- 76PubMedGoogle ScholarCrossref 3. Wenger NSPearson MLDesmond KAKahn KL Changes over time in the use of do not resuscitate orders and the outcomes of patients receiving them. Med Care 1997;35311- 319PubMedGoogle ScholarCrossref 4. Baker DWEinstadter DHusak SCebul RD Changes in the use of do-not-resuscitate orders after implementation of the Patient Self-Determination Act. J Gen Intern Med 2003;18343- 349PubMedGoogle ScholarCrossref 5. Wenger NSPearson MLDesmond KABrook RHKahn KL Outcomes of patients with do-not-resuscitate orders: toward an understanding of what do-not-resuscitate orders mean and how they affect patients. Arch Intern Med 1995;1552063- 2068PubMedGoogle ScholarCrossref 6. Shepardson LBYoungner SJSperoff TRosenthal GE Increased risk of death in patients with do-not-resuscitate orders. Med Care 1999;37727- 737PubMedGoogle ScholarCrossref 7. Wenger NSRosenfeld K Quality indicators for end-of-life care in vulnerable elders. Ann Intern Med 2001;135677- 685PubMedGoogle ScholarCrossref 8. Wenger NSPearson MLDesmond KA et al. Epidemiology of do-not-resuscitate orders: disparity by age, diagnosis, gender, race, and functional impairment. Arch Intern Med 1995;1552056- 2062PubMedGoogle ScholarCrossref 9. Shepardson LBGordon HSIbrahim SAHarper DLRosenthal GE Racial variation in the use of do-not-resuscitate orders. J Gen Intern Med 1999;1415- 20PubMedGoogle ScholarCrossref 10. Garcia JARomano PSChan BKKass PHRobbins JA Sociodemographic factors and the assignment of do-not-resuscitate orders in patients with acute myocardial infarctions. Med Care 2000;38670- 678PubMedGoogle ScholarCrossref 11. Shepardson LBYoungner SJSperoff TO’Brien RGSmyth KARosenthal GE Variation in the use of do-not-resuscitate orders in patients with stroke. Arch Intern Med 1997;1571841- 1847PubMedGoogle ScholarCrossref 12. Hakim RBTeno JMHarrell FE Jr et al. SUPPORT Investigators, Factors associated with do-not-resuscitate orders: patients’ preferences, prognoses, and physicians’ judgments: Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatment. Ann Intern Med 1996;125284- 293PubMedGoogle ScholarCrossref 13. Wennberg JCooper M The Dartmouth Atlas of Health Care. Chicago, Ill American Hospital Publishing Inc1996; 14. Wennberg JCooper M Selected measures of inpatient care at the end of life–2000, by hospital referral region. The Dartmouth Atlas of Health Care Available at: http://www.dartmouthatlas.org/tables/2000_eol_variables_hrr.xls. Accessed November 17, 2004Google Scholar 15. O’Donnell HPhillips RSWenger NTeno JDavis RBHamel MB Preferences for cardiopulmonary resuscitation among patients 80 years or older: the views of patients and their physicians. J Am Med Dir Assoc 2003;4139- 144PubMedGoogle ScholarCrossref 16. Charlson MEPompei PAles KLMacKenzie CR A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40373- 383PubMedGoogle ScholarCrossref 17. Cleves MASanchez NDraheim M Evaluation of two competing methods for calculating Charlson’s comorbidity index when analyzing short-term mortality using administrative data. J Clin Epidemiol 1997;50903- 908PubMedGoogle ScholarCrossref 18. D’Agostino RB Jr Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 1998;172265- 2281PubMedGoogle ScholarCrossref 19. Little RJARubin DB Statistical Analysis With Missing Data. New York, NY Wiley-Interscience1987; 20. Iezzoni LIed Risk Adjustment for Measuring Healthcare Outcomes. 2nd ed. Chicago, Ill Health Administration Press1997; 21. Phillips RSWenger NSTeno J et al. SUPPORT Investigators, Choices of seriously ill patients about cardiopulmonary resuscitation: correlates and outcomes: Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments. Am J Med 1996;100128- 137PubMedGoogle ScholarCrossref 22. Gray BHMcNerney WJ For-profit enterprise in health care: the Institute of Medicine Study. N Engl J Med 1986;3141523- 1528PubMedGoogle Scholar 23. Kuttner R Columbia/HCA and the resurgence of the for-profit hospital business. N Engl J Med 1996;335362- 367PubMedGoogle ScholarCrossref 24. Watt JMDerzon RARenn SCSchramm CJHahn JSPillari GD The comparative economic performance of investor-owned chain and not-for-profit hospitals. N Engl J Med 1986;31489- 96PubMedGoogle ScholarCrossref 25. Bock KRTeres DRapoport J Economic implications of the timing of do-not-resuscitate orders for ICU patients. New Horiz 1997;551- 55PubMedGoogle Scholar 26. Baker DWEinstadter DHusak SSCebul RD Trends in postdischarge mortality and readmissions: has length of stay declined too far? Arch Intern Med 2004;164538- 544PubMedGoogle ScholarCrossref 27. Skinner JWennberg JE Exceptionalism or extravagance? what’s different about health care in South Florida. Health Aff (Millwood) 2003;Suppl Web ExclusivesW3-372- 375PubMedGoogle Scholar 28. Wennberg JEFisher ESStukel TASkinner JSSharp SMBronner KK Use of hospitals, physician visits, and hospice care during last six months of life among cohorts loyal to highly respected hospitals in the United States. BMJ 2004;328607PubMedGoogle ScholarCrossref http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Internal Medicine American Medical Association

Regional and Institutional Variation in the Initiation of Early Do-Not-Resuscitate Orders

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American Medical Association
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Copyright © 2005 American Medical Association. All Rights Reserved.
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0003-9926
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1538-3679
DOI
10.1001/archinte.165.15.1705
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Abstract

Abstract Background Do-not-resuscitate (DNR) orders are an important step in decision making about aggressiveness of care for patients in hospitals. The use of DNR orders is known to vary with patient characteristics, but few studies have investigated the role of hospital factors or of regional variation. We examined these influences on the use of early DNR orders (written <24 hours after admission). Methods We conducted a retrospective cross-sectional study of patients 50 years and older admitted to acute-care hospitals in California in 2000 from the most prevalent medical and surgical diagnosis related groups. We performed multivariate logistic regression predicting use of DNR by hospital characteristics while accounting for patient characteristics, and estimated indirectly standardized rates of DNR use by county. Results In the selected diagnosis related groups, 819 686 persons were admitted to 386 hospitals. Early DNR orders varied from 2% (patients aged 50-59 years) to 17% (patients aged ≥80 years). In multivariate analyses, the odds of having early DNR orders written were significantly lower in for-profit (vs private nonprofit) hospitals, higher in the smallest (vs the largest) hospitals, and lower in academic (vs nonacademic) hospitals. Standardized rates of DNR order use varied 10-fold across counties. The highest rates were among patients from rural areas. However, variation in use did not correspond well to county population, hospital bed availability, or population density. Conclusions Hospital characteristics appear to be associated with the use of DNR orders, even after accounting for differences in patient characteristics. This association reflects institutional culture, technological bent, and physician practice patterns. If these factors do not match patient preferences, then improvements in care are needed. Do-not-resuscitate (DNR) orders are an important step in decision making about aggressiveness of care for patients in hospitals. These orders are essential to guiding the care provided to hospitalized patients, because medicine has the capability to maintain survival in health states that many patients and health care providers find undesirable.1,2 Treatments like resuscitation may be inappropriate or may afford short-term benefits without achieving valued longer-term goals. Because life-sustaining treatments are increasingly available in hospitals, not surprisingly, DNR orders are also increasingly common.3,4 Early DNR orders (written <48 hours after admission) tend to reflect patient preferences and prognosis and are independently related to hospital survival.5,6 If DNR orders reflect patients’ preferences for care and guide care that is consistent with these preferences, these orders can be considered indicators of the quality of health care.5,7 The use of DNR orders is known to vary with patient characteristics. Women, nonwhite patients, younger individuals, patients admitted from noninstitutional settings, and healthier patients (at admission) are less likely to receive DNR orders after hospital admission.8-12 However, few studies have investigated the role of hospital factors or geographic variation on the use of DNR orders. Data for older Medicare beneficiaries from the 1980s showed patients were less likely to have DNR orders written if they received care at rural hospitals or hospitals with more Medicaid-covered patients.8 Patients from 30 hospitals in Cleveland, Ohio, showed substantial variation between hospitals, mostly explained by case mix.11 A sample of patients treated for a single condition at California hospitals showed no differences by hospital characteristics.10 Geographic variation in other aspects of care have been shown within Medicare and in other populations.13 Other data show a 4- to 8-fold geographic variation in end-of-life inpatient utilization of health care resources (eg, costs, days hospitalized, days in the intensive care unit).14 Physician attitudes,15 utilization incentives, and local culture may contribute to such variation. We endeavored to study whether institutional factors influence the use of DNR orders. We analyzed and evaluated the role of hospital characteristics (ie, size, profit status, control, and academic status) on the use of a DNR order written within the first 24 hours after acute hospital admission among patients 50 years and older admitted to California hospitals in 2000. To better understand regional variation in DNR use, we also examined the geographic distribution of DNR orders by county. Methods Data We analyzed hospitalization discharge abstracts obtained from the California Office of Statewide Health Planning and Development Patient Discharge Database for the year 2000, nonconfidential Version B. Discharge abstracts are required for all patients receiving inpatient care within all California nonfederal hospitals. Records include patient race, ethnicity, age, sex, insurance type, residence ZIP code, primary and secondary diagnosis and procedure codes, quarter year of admission, level of care (ie, acute, rehabilitative, skilled nursing, or psychiatric), source of admission, scheduled admission, hospital identification code, and hospital ZIP code. Since 1999, hospitals have been required to report whether DNR orders were written within the first 24 hours of each admission. Income data by ZIP code were obtained from the 1990 US census data (analogous 2000 ZIP code level data were not yet available). Population estimates were from the 2000 US census. Information regarding California hospitals was obtained from the California Office of Statewide Health Planning and Development annual Hospital Financial and Utilization reports for 2000 and from the Council of Teaching Hospitals. Characteristics included hospital size (number of beds), ownership/control, location (rural or urban), and academic status (membership in the Council of Teaching Hospitals). Each hospital record has an identification code allowing linkage between hospital reports and Patient Discharge Database records. Variables The primary outcome variable was receipt of a DNR order within the first 24 hours after hospital admission (early DNR). Independent variables included age (in 5-year intervals), sex, race/ethnicity (non-Latino white, non-Latino black, Latino, Asian/Pacific Islander, and other), type of insurance (Medicare, Medi-Cal, private, other, or indigent), admission source (home, nursing home, or other), and diagnosis-related group (DRG). Patient illness was measured by comorbidity index and illness severity score. Comorbid medical conditions were identified from the Patient Discharge Database, and a Charlson Comorbidity Index was calculated for each hospitalization.16,17 Illness severity scores derived from multivariate logistic regression models of hospital death were categorized by quartile of the estimated probability of hospital death.18 Proxy measure of income was estimated from household income by residence ZIP code. To account for patient referral, we calculated the distance between the patient’s residence and the hospital using ZIP code centroids. For comparability between institutions, percentile distances traveled by patients treated within the same hospital were calculated. Distances were categorized as 75th percentile, above the 75th to the 95th percentile, and above the 95th percentile. For patients without valid ZIP codes (<3%), hot-deck imputation (random sampling of observed characteristics) was used to impute income, rural residence location, and distance traveled.19 Hospital-level predictors included hospital size (number of beds), control/profit status (private nonprofit, district, county, or for-profit), and academic hospital status. Statistical analysis Because DNR orders are uncommon among younger patients (<1% in those aged <50 years), analyses were restricted to hospitalized patients 50 years or older. To further reduce heterogeneity, records were restricted to the 20 most common medical and 20 most common surgical/procedural DRGs, representing 51% of the 1.6 million records for patients 50 years and older. In unadjusted analyses, we evaluated DNR use in the context of each independent predictor, stratified by patient age. For each bivariate comparison, we estimated significance of effect using the Pearson χ2 test. We predicted use of DNR by means of multivariate logistic regression to estimate the impact of hospital characteristics (size, control/profit status, location, and academic status) after accounting for patient-case mix (age, race, sex, expected payer, insurance plan type, residence type [rural or urban], admission source, estimated income, relative distance to hospital, Charlson Comorbidity Index, illness severity score, and DRG). We used robust regression to improve standard error estimates with a clustering correction to account for within-hospital correlation. Analyses were stratified by DRG type (procedural vs medical). Analyses were performed using Intercooled Stata 7.0 (Stata Corp, College Station, Tex). To examine regional differences in DNR use, we calculated indirectly standardized DNR rates by county of patient residence. First, we calculated unadjusted DNR rates for each county for all DRGs, medical DRGs, and procedural DRGs. Second, we calculated standardized rates according to the equation Ii = S × (Oi/Pi), where Ii is the indirectly standardized DNR rate in the ith county, S is the average rate for DNR orders in California, Oi is the observed number of DNR orders in the ith county, and Pi is the predicted number of DNR orders in the ith county.20 The Ii values were calculated for all DRGs and stratified to medical and procedural DRGs. We mapped county rates using ArcMap 8.3 (Environmental Systems Research Institute, Redlands, Calif). Results Demographics In California in 2000, a total of 3 816 900 discharges were reported in the Patient Discharge Database. After exclusions, 1 507 450 patients 50 years or older were admitted for acute care at 386 California hospitals, and 819 686 admissions occurred under the 40 most common medical and surgical/procedural DRGs (Table 1). Patients admitted to the hospital under these DRGs were primarily white (71% overall; 77% among the oldest patients), with sizeable numbers of racial/ethnic minorities, especially in the younger groups (36% of subjects in the youngest group were nonwhite). Overall, most admissions were for women (54%), with women predominating among the oldest patients (62% of those aged ≥80 years). Median Charlson Comorbidity Index increased by patient age. Among patients surviving to discharge, nearly half of the oldest patients, but fewer than 10% of the youngest patients, were discharged to skilled nursing care or to home care. Older patients were more likely than younger patients to be admitted to smaller nonacademic medical centers (Table 2). Use of dnr orders Unadjusted Overall, DNR orders written within the first 24 hours of hospital admission varied from 2% of admissions in the youngest group to 17% in the oldest group (Table 3). Across all age strata, DNR orders were more likely to be written within the first 24 hours for white patients, those admitted from or discharged to assisted-living locations, those with greater comorbid illnesses (by Charlson Comorbidity Index), those with greater likelihood of dying (by illness severity score), and those dying within the hospital. Patients coming from a greater relative distance were less likely to have DNR orders written. Patients receiving care at larger, for-profit, and academic medical centers were less likely to have DNR orders written. Multivariate Results In overall multivariate logistic regression analyses, many hospital and patient characteristics remained significant predictors of use of early DNR orders (Table 4). After accounting for patient characteristics (described in this section), hospital characteristics continued to be significant predictors of use of early DNR orders. Compared with those treated in private, nonprofit hospitals, patients treated in for-profit hospitals had lower odds of having early DNR orders written (odds ratio [OR], 0.69; 95% confidence interval [CI], 0.53-0.89). District and county hospitals were not significantly different from private nonprofit hospitals. Patients treated at the smallest hospitals had greater odds of having early DNR orders compared with those treated at the largest hospitals (OR, 1.60; 95% CI, 1.14-2.23). Do-not-resuscitate orders for patients treated at intermediate-size hospitals were not significantly different from those for patients at the largest hospitals. Patients treated at academic medical centers had significantly lower odds of having DNR orders compared with those treated at nonacademic medical centers (OR, 0.62; 95% CI, 0.41-0.94). Younger patients, men, and black and Asian patients had significantly greater odds of having DNR orders. Patients with managed care insurance were more likely than those with traditional fee-for-service insurance to have DNR orders. Greater Charlson Comorbidity Index, greater illness severity, and admission from a nursing home were associated with greater odds of having DNR orders. Patients who lived farther from the treating hospital were less likely to have DNR orders. Income and rural residence were not significantly related to use of DNR orders. Regression analyses were repeated, stratified by medical or surgical DRG (Table 4). In analyses restricted to surgical conditions, no difference was seen by hospital control or profit status. In addition, younger patients, black patients, and those living furthest from the hospital had lower odds of having DNR orders, but other patient characteristics were not significant independent predictors of having DNR orders. Results of analyses restricted to medical conditions were similar to those of the overall analyses. Regression analyses further stratified by illness severity score did not substantially change results and are not reported herein. Use of dnr by geographic distribution The use of DNR orders in California differs on a regional basis with a 10-fold difference across counties. The large urban regions of the southern part of the state (metropolitan Los Angeles) (Figure 1, region A) have distinctly lower rates of DNR use compared with the urban counties of the San Francisco Bay area, San Luis Obispo, Santa Barbara, and San Diego (Figure 1, regions B-E, respectively). The highest rates of DNR orders among hospitalized individuals are for patients from rural areas. This is most apparent when the map of standardized DNR rates is compared with the map showing county population (Figure 2A). Geographic variation in early DNR use does not correspond to county population (Figure 2A), to availability of hospital beds in the county (normalized to population) (Figure 2B), or to population density (Figure 2C). Comment Hospital characteristics and regional variation are important predictors of early DNR orders in hospitalized patients. Patients treated at smaller hospitals have greater odds of having early DNR orders, whereas patients treated in academic medical centers or for-profit hospitals have lower odds of receiving early DNR orders. Moreover, a 10-fold variation in such orders exists by region in California. Because it is unlikely that preferences vary to this degree, DNR variation likely reflects local practice patterns. Lower odds of having early DNR orders at larger and academic hospitals suggest a greater focus on aggressive care. In turn, this result would be expected to be related to patients’ preferences, which could not be measured in this study. Previous work21 has suggested a 2-fold variation in resuscitation preference across larger institutions after adjustment for patient characteristics. This variation might be expected to be even greater across more varied types of hospitals. The lower use of DNR orders among patients cared for in academic hospitals is troubling because such institutions teach both the science and art of medicine. However, these findings are not unexpected, because the environment of the academic hospitals focuses on intervention and cutting-edge medical care. Consideration of patients’ goals of care may be relegated to lower importance in the context of aggressive care initiated during the admission period. Tertiary-care medical centers tend to be high-intensity referral centers to which patients come seeking care that includes heroic, life-saving interventions. The adjustment of DRG and stratification of analyses to medical or surgical conditions do not qualitatively change these observations. Other factors may contribute to the lower prevalence of early DNR orders at academic hospitals, such as having physician trainees. Resident physicians are often the first health care providers to see newly hospitalized patients, but they lack continuity relationships with these patients, which potentially impedes initiating conversations about goals of care. In addition, their experience with initiating end-of-life care discussions may be limited. Finally, they may defer to the continuity of care provided by the attending physician, especially when questions of nontreatment are involved, rather than working contrary to a milieu in which intervention is the rule. However, training venues and institutions that provide the most aggressive care are precisely where one would want to emphasize the discussion of preferences and goals. These data suggest a need for renewed efforts to integrate preferences into care on admission for patients at teaching hospitals. Patients treated at the smallest community hospitals were most likely to have early DNR orders. This is understandable, as physicians at small community hospitals are more likely to have mature physician-patient relationships, giving greater opportunity to initiate end-of-life decision making before admission or early in the hospitalization. Again, patient preferences may underlie this difference, as patients expecting less heroic care may go to smaller hospitals. Exploration of the role of patient preferences and physician-patient continuity in the use of early DNR orders awaits data that include these measures. The relationship between use of DNR orders and profit status raises other questions. For-profit hospitals have been hypothesized to align their behavior with financial incentives, which may not optimize patient care or outcomes.22,23 Care in for-profit hospitals has been associated with overall higher costs and greater intensity of care without improved patient outcome.24 Do-not-resuscitate orders represent a limit on the intensity of resuscitative care or may lead to earlier discharge, saving hospitals money.25,26 If lower-intensity care leads to lower reimbursement for the hospital or the treating physician, then for-profit hospitals may have less perceived incentive to institute DNR orders in as timely a fashion as nonprofit hospitals. Alternatively, DNR orders might be related to attempts to conserve resources, although this is more likely to be true of DNR orders written later in the course of hospitalization,5 which were not measured in this study. Patient factors associated with DNR orders are consistent with findings from earlier studies. Patient factors associated with having early DNR orders include white race, female sex, greater age, admission from a nursing home, and greater illness or comorbidity. Living farther from a hospital was inversely related to having a DNR order, suggesting that these individuals may be receiving referrals for a higher level of care. Why managed care insurance is related to DNR orders and whether this is related to conservation of resources or patient preferences need exploration. Regional differences appear to exist in the use of DNR orders. Even after standardizing results, the largest urban county, Los Angeles, had a much lower rate of DNR orders than surrounding urban counties or the large urban area in northern California. This variation is remarkably similar to that seen for other measures of end-of-life care, ie, the variation in intensive care unit use at the end of life in California.14 Regional differences in DNR orders are tantalizing because these orders are the result of the agency of physicians and the choices of their patients. Regional variation reflects a diverse set of circumstances that include physician practice patterns, local physician leadership in promoting the use of DNR orders and other end-of-life care, and the understanding and sophistication of the patient population.27,28 This variation, the dependence on local culture to determine use, may underlay a broader lack of agreement or standards on when and how to use DNR orders. Higher rates of early DNR orders among individuals from sparsely populated areas may reflect a greater acceptance of the prognosis and nonaggressive treatment plans among these individuals. Alternatively, individuals from rural areas are more likely to have primary admissions rather than tertiary referrals. A better understanding of factors related to the high level of variation in DNR use across California counties could contribute to efforts to better align prognosis with aggressive care. This was a retrospective study using administrative data. Do-not-resuscitate orders may be heterogeneous interventions. The significance of such orders and policies regarding their use may vary by institution. Do-not-resuscitate orders written after 24 hours were not available. The appropriateness of DNR orders cannot be ascertained by these data because patient preferences are not measured. Generally agreed-on levels of appropriateness for DNR orders are not known. Long-term outcomes of patients—indicators of unmeasured severity of illness—were not available in these data. Although the comprehensive nature of the database is a substantial strength of this study, the findings cannot necessarily be generalized to other states or to other DRG groups. Conclusions The initiation of end-of-life discussions and the implementation of DNR orders are important toward ensuring that patients receive care appropriate to their prognosis and preferences. Hospital characteristics appear to be associated with the use of DNR orders, even after accounting for differences in patient characteristics. This association reflects institutional culture, technological bent, and physician practice patterns. Variation in the use of early DNR orders may also reflect, in part, financial incentives. A better understanding of the impact of DNR orders on costs and utilization of health care resources may clarify financial incentives. If incentives do not match patient preferences, then improvements in care are needed. Correspondence: David S. Zingmond, MD, PhD, Division of General Internal Medicine and Health Services Research, The David Geffen School of Medicine at UCLA, 911 Broxton Plaza, Los Angeles, CA 90095-1736 (dzingmond@mednet.ucla.edu). Financial Disclosure: None. Accepted for Publication: January 18, 2005. References 1. Patrick DLStarks HECain KCUhlmann RFPearlman RA Measuring preferences for health states worse than death: functional status among survivors of in-hospital cardiopulmonary resuscitation. Med Decis Making 1994;149- 18PubMedGoogle ScholarCrossref 2. FitzGerald JDWenger NSCaliff RM et al. SUPPORT Investigators, Functional status among survivors of in-hospital cardiopulmonary resuscitation: Study to Understand Progress and Preferences for Outcomes and Risks of Treatment. Arch Intern Med 1997;15772- 76PubMedGoogle ScholarCrossref 3. Wenger NSPearson MLDesmond KAKahn KL Changes over time in the use of do not resuscitate orders and the outcomes of patients receiving them. Med Care 1997;35311- 319PubMedGoogle ScholarCrossref 4. Baker DWEinstadter DHusak SCebul RD Changes in the use of do-not-resuscitate orders after implementation of the Patient Self-Determination Act. J Gen Intern Med 2003;18343- 349PubMedGoogle ScholarCrossref 5. Wenger NSPearson MLDesmond KABrook RHKahn KL Outcomes of patients with do-not-resuscitate orders: toward an understanding of what do-not-resuscitate orders mean and how they affect patients. Arch Intern Med 1995;1552063- 2068PubMedGoogle ScholarCrossref 6. Shepardson LBYoungner SJSperoff TRosenthal GE Increased risk of death in patients with do-not-resuscitate orders. Med Care 1999;37727- 737PubMedGoogle ScholarCrossref 7. Wenger NSRosenfeld K Quality indicators for end-of-life care in vulnerable elders. Ann Intern Med 2001;135677- 685PubMedGoogle ScholarCrossref 8. Wenger NSPearson MLDesmond KA et al. Epidemiology of do-not-resuscitate orders: disparity by age, diagnosis, gender, race, and functional impairment. Arch Intern Med 1995;1552056- 2062PubMedGoogle ScholarCrossref 9. Shepardson LBGordon HSIbrahim SAHarper DLRosenthal GE Racial variation in the use of do-not-resuscitate orders. J Gen Intern Med 1999;1415- 20PubMedGoogle ScholarCrossref 10. Garcia JARomano PSChan BKKass PHRobbins JA Sociodemographic factors and the assignment of do-not-resuscitate orders in patients with acute myocardial infarctions. Med Care 2000;38670- 678PubMedGoogle ScholarCrossref 11. Shepardson LBYoungner SJSperoff TO’Brien RGSmyth KARosenthal GE Variation in the use of do-not-resuscitate orders in patients with stroke. Arch Intern Med 1997;1571841- 1847PubMedGoogle ScholarCrossref 12. Hakim RBTeno JMHarrell FE Jr et al. SUPPORT Investigators, Factors associated with do-not-resuscitate orders: patients’ preferences, prognoses, and physicians’ judgments: Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatment. Ann Intern Med 1996;125284- 293PubMedGoogle ScholarCrossref 13. Wennberg JCooper M The Dartmouth Atlas of Health Care. Chicago, Ill American Hospital Publishing Inc1996; 14. Wennberg JCooper M Selected measures of inpatient care at the end of life–2000, by hospital referral region. The Dartmouth Atlas of Health Care Available at: http://www.dartmouthatlas.org/tables/2000_eol_variables_hrr.xls. Accessed November 17, 2004Google Scholar 15. O’Donnell HPhillips RSWenger NTeno JDavis RBHamel MB Preferences for cardiopulmonary resuscitation among patients 80 years or older: the views of patients and their physicians. J Am Med Dir Assoc 2003;4139- 144PubMedGoogle ScholarCrossref 16. Charlson MEPompei PAles KLMacKenzie CR A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40373- 383PubMedGoogle ScholarCrossref 17. Cleves MASanchez NDraheim M Evaluation of two competing methods for calculating Charlson’s comorbidity index when analyzing short-term mortality using administrative data. J Clin Epidemiol 1997;50903- 908PubMedGoogle ScholarCrossref 18. D’Agostino RB Jr Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 1998;172265- 2281PubMedGoogle ScholarCrossref 19. Little RJARubin DB Statistical Analysis With Missing Data. New York, NY Wiley-Interscience1987; 20. Iezzoni LIed Risk Adjustment for Measuring Healthcare Outcomes. 2nd ed. Chicago, Ill Health Administration Press1997; 21. Phillips RSWenger NSTeno J et al. SUPPORT Investigators, Choices of seriously ill patients about cardiopulmonary resuscitation: correlates and outcomes: Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments. Am J Med 1996;100128- 137PubMedGoogle ScholarCrossref 22. Gray BHMcNerney WJ For-profit enterprise in health care: the Institute of Medicine Study. N Engl J Med 1986;3141523- 1528PubMedGoogle Scholar 23. Kuttner R Columbia/HCA and the resurgence of the for-profit hospital business. N Engl J Med 1996;335362- 367PubMedGoogle ScholarCrossref 24. Watt JMDerzon RARenn SCSchramm CJHahn JSPillari GD The comparative economic performance of investor-owned chain and not-for-profit hospitals. N Engl J Med 1986;31489- 96PubMedGoogle ScholarCrossref 25. Bock KRTeres DRapoport J Economic implications of the timing of do-not-resuscitate orders for ICU patients. New Horiz 1997;551- 55PubMedGoogle Scholar 26. Baker DWEinstadter DHusak SSCebul RD Trends in postdischarge mortality and readmissions: has length of stay declined too far? Arch Intern Med 2004;164538- 544PubMedGoogle ScholarCrossref 27. Skinner JWennberg JE Exceptionalism or extravagance? what’s different about health care in South Florida. Health Aff (Millwood) 2003;Suppl Web ExclusivesW3-372- 375PubMedGoogle Scholar 28. Wennberg JEFisher ESStukel TASkinner JSSharp SMBronner KK Use of hospitals, physician visits, and hospice care during last six months of life among cohorts loyal to highly respected hospitals in the United States. BMJ 2004;328607PubMedGoogle ScholarCrossref

Journal

Archives of Internal Medicine – American Medical Association

Published: Aug 8, 2005

Keywords: do-not-resuscitate orders,beds,diagnosis-related groups,acute care

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