Association of do-not-resuscitate orders and hospital mortality rate among patients with pneumonia
Open accessAbstract
IMPORTANCE: Hospital quality measures that do not account for patient do-not-resuscitate (DNR) status may penalize hospitals admitting a greater proportion of patients with limits on life-sustaining treatments. OBJECTIVE: To evaluate the effect of analytic approaches accounting for DNR status on risk-adjusted hospital mortality rates and performance rankings. DESIGN, SETTING, AND PARTICIPANTS: A retrospective, population-based cohort study was conducted among adults hospitalized with pneumonia in 303 California hospitals between January 1 and December 31, 2011. We used hierarchical logistic regression to determine associations between patient DNR status, hospital-level DNR rates, and mortality measures. Changes in hospital risk-adjusted mortality rates after accounting for patient DNR status and interhospital variation in the association between DNR status and mortality were examined. Data analysis was conducted from January 16 to September 16, 2015. EXPOSURES: Early DNR status (within 24 hours of admission). MAIN OUTCOMES AND MEASURES: In-hospital mortality, determined using hierarchical logistic regression. RESULTS: A total of 90,644 pneumonia cases (5.4% of admissions) were identified among the 303 California hospitals evaluated during 2011; mean (SD) age of the patients was 72.5 (13.7) years, 51.5% were women, and 59.3% were white. Hospital DNR rates varied (median, 15.8%; 25th-75th percentile, 8.9%-22.3%). Without accounting for patient DNR status, higher hospital-level DNR rates were associated with increased patient mortality (adjusted odds ratio [OR] for highest-quartile DNR rate vs lowest quartile, 1.17; 95% CI, 1.04-1.32), corresponding to worse hospital mortality rankings. In contrast, after accounting for patient DNR status and between-hospital variation in the association between DNR status and mortality, hospitals with higher DNR rates had lower mortality (adjusted OR for highest-quartile DNR rate vs lowest quartile, 0.79; 95% CI, 0.70-0.89), with reversal of associations between hospital mortality rankings and DNR rates. Only 14 of 27 hospitals (51.9%) characterized as low-performing outliers without accounting for DNR status remained outliers after DNR adjustment. Hospital DNR rates were not significantly associated with composite quality measures of processes of care for pneumonia (r = 0.11; P = .052); however, DNR rates were positively correlated with patient satisfaction scores (r = 0.35; P < .001). CONCLUSIONS AND RELEVANCE: Failure to account for DNR status may confound the evaluation of hospital quality using mortality outcomes, penalizing hospitals that admit a greater proportion of patients with limits on life-sustaining treatments. Stakeholders should seek to improve methods to standardize and report DNR status in hospital discharge records to allow further assessment of implications of adjusting for DNR in quality measures.
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The findings suggest a higher rate of DNACPR use (31%) than identified in previous studies of other respiratory conditions or sepsis.
“Our findings suggest a higher rate of DNACPR use (31%) than identified in previous studies of other respiratory conditions or sepsis.”
From Do not attempt cardiopulmonary resuscitation (DNACPR) decisions in people admitted with suspected COVID-19: Secondary analysis of the PRIEST observational cohort study (2021) · cites this paper as [9]
The Results section (Hospital Variation in Early DNR Rates; page unavailable) reports a median hospital early DNR rate of 15.8% among patients with pneumonia, providing a lower rate from a respiratory-condition study for comparison with the citing authors’ 31% finding. The paper does not report a comparable DNR-use rate for sepsis, so it does not support that part of the comparison. (LLM, conf=0.78)
Experimental — accuracy not yet measuredStudies of patients admitted with community-acquired pneumonia reported rates of DNACPR use ranging from 13% to 29%.
“Studies of patients admitted with community-acquired pneumonia reported rates of DNACPR use ranging from 13% to 29%,6, 7, 8, 9, 10”
From Do not attempt cardiopulmonary resuscitation (DNACPR) decisions in people admitted with suspected COVID-19: Secondary analysis of the PRIEST observational cohort study (2021) · cites this paper as [9]
The Results section reports a median hospital early-DNR rate of 15.8% among patients hospitalized with pneumonia, which falls within the cited range. It does not report rates spanning 13% to 29%, and the Methods section does not specifically identify the cohort as having community-acquired pneumonia. No page number is available in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPrevious studies have estimated the prevalence of DNACPR decision in patients admitted to hospital with community-acquired pneumonia and sepsis, and have attempted to identify factors associated with DNACPR use, but we currently know very little about how DNACPR decisions have been used in people admitted with suspected COVID-19.
“Previous studies have estimated the prevalence of DNACPR decision in patients admitted to hospital with community-acquired pneumonia6, 7, 8, 9, 10 and sepsis,11, 12, 13, 14 and have attempted to identify factors associated with DNACPR use, but we currently know very little about how DNACPR decisions have been used in people admitted with suspected COVID-19.”
From Do not attempt cardiopulmonary resuscitation (DNACPR) decisions in people admitted with suspected COVID-19: Secondary analysis of the PRIEST observational cohort study (2021) · cites this paper as [9]
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References 40 · Cited by 61
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- [1]Barnato AE, Mohan D, Lane RK, Advance care planning norms may contribute to hospital variation in end-of-life ICU use: a simulation study. Med Decis Making. 2014; 34(4): 473– 484. 24615275 PMC4026761
- [2]Dzeng E, Colaianni A, Roland M, Influence of institutional culture and policies on do-not-resuscitate decision making at the end of life. JAMA Intern Med. 2015; 175(5): 812– 819. 25845020
- [5]Bradford MA, Lindenauer PK, Wiener RS, Walkey AJ. Do-not-resuscitate status and observational comparative effectiveness research in patients with septic shock. Crit Care Med. 2014; 42(9): 2042– 2047. 24810532 PMC4266548
- [6]Baldwin MR, Narain WR, Wunsch H, A prognostic model for 6-month mortality in elderly survivors of critical illness. Chest. 2013; 143(4): 910– 919. 23632902 PMC3616685
- [8]Tabak YP, Johannes RS, Silber JH, Kurtz SG. Should Do-Not-Resuscitate status be included as a mortality risk adjustor? the impact of DNR variations on performance reporting. Med Care. 2005; 43(7): 658– 666. 15970780
- [9]Holloway RG, Quill TE. Mortality as a measure of quality: implications for palliative and end-of-life care. JAMA. 2007; 298(7): 802– 804. 17699014
- [10]Kelly AG, Zahuranec DB, Holloway RG, Morgenstern LB, Burke JF. Variation in do-not-resuscitate orders for patients with ischemic stroke: implications for national hospital comparisons. Stroke. 2014; 45(3): 822– 82. 24523035 PMC3943476
- [11]Escobar GJ, Gardner MN, Greene JD, Draper D, Kipnis P. Risk-adjusting hospital mortality using a comprehensive electronic record in an integrated health care delivery system. Med Care. 2013; 51(5): 446– 453. 23579354
- [12]Localio AR, Berlin JA, Ten Have TR, Kimmel SE. Adjustments for center in multicenter studies: an overview. Ann Intern Med. 2001; 135(2): 112– 123. 11453711
- [13]Finucane MM, Samet JH, Horton NJ. Translational methods in biostatistics: linear mixed effect regression models of alcohol consumption and HIV disease progression overtime. Epidemiol Perspect innov. 2007; 4: 8. 17880699 PMC2147003
- [14]Agresti A, Hartzel J. Strategies for comparing treatments on a binary response with multi-centre data. Stat Med. 2000; 19(8): 1115– 1139. 10790684
- [15]Gould AL. Multi-centre trial analysis revisited. Stat Med. 1998; 17(15–16): 1779– 1797. 9749447
- [16]Bratzler DW, Normand SL, Wang Y, An administrative claims model for profiling hospital 30-day mortality rates for pneumonia patients. PLoS One. 2011; 6(4): e17401. 21532758 PMC3075250
- [19]Goldman LE, Chu PW, Osmond D, Bindman A. Accuracy of do not resuscitate (DNR) in administrative data. Med Care Res Rev. 2013; 70(1): 98– 112. 22955698
- [20]Walkey AJ, Lagu T, Lindenauer PK. Trends in sepsis and infection sources in the United States: a population-based study. Ann Am Thorac Soc. 2015; 12(2): 216– 220. 25569845 PMC4342831
- [21]Rothberg MB, Pekow PS, Priya A, Lindenauer PK. Variation in diagnostic coding of patients with pneumonia and its association with hospital risk-standardized mortality rates: a cross-sectional analysis. Ann intern Med. 2014; 160(6): 380– 388. 24723078 PMC6946057
- [22]Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998; 36(1): 8– 27. 9431328
- [23]Martin GS, Mannino DM, Eaton S, Moss M. The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med. 2003; 348(16): 1546– 1554. 12700374
- [24]Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001; 29(7): 1303– 1310. 11445675
- [25]Drye EE, Normand SL, Wang Y, Comparison of hospital risk-standardized mortality rates calculated by using in-hospital and 30-day models: an observational study with implications for hospital profiling. Ann intern Med. 2012; 156(1, pt 1): 19– 26. 22213491 PMC3319769
- [26]Eilers PHC, Marx BD. Flexible smoothing with B-splines and penalties. Stat Sci. 1996; 11(2): 89– 121.
- [27]Merlo J, Chaix B, Ohlsson H, A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena. J Epidemiol Community Health. 2006; 60(4): 290– 29. 16537344 PMC2566165
- [28]Goldstein H, Browne W, Rasbash J. Multilevel modelling of medical data. Stat Med. 2002; 21(21): 3291– 3315. 12375305
- [29]Seymour CW, Iwashyna TJ, Ehlenbach WJ, Wunsch H, Cooke CR. Hospital-level variation in the use of intensive care. Health Serv Res. 2012; 47(5): 2060– 2080. 22985033 PMC3513618
- [32]Bayer W, Mallinger JB, Krishnan A, Shields CG. Attitudes toward life-sustaining interventions among ambulatory black and white patients. Ethn Dis. 2006; 16(4): 914– 919. 17061746
- [33]Wenger NS, Pearson ML, Desmond KA, Epidemiology of do-not-resuscitate orders: disparity by age, diagnosis, gender, race, and functional impairment. Arch Intern Med. 1995; 155 (19): 2056– 2062. 7575064
- [34]Chang DW, Brass EP. Patient and hospital-level characteristics associated with the use of do-not-resuscitate orders in patients hospitalized for sepsis. J Gen Intern Med. 2014; 29(9): 1256– 1262. 24928264 PMC4139525
- [35]Hart JL, Harhay MO, Gabler NB, Ratcliffe SJ, Quill CM, Halpern SD. Variability among US intensive care units in managing the care of patients admitted with preexisting limits on life-sustaining therapies. JAMA Intern Med. 2015; 175(6): 1019– 1026. 25822402 PMC4451380
- [37]Beach MC, Morrison RS. The effect of do-not-resuscitate orders on physician decision-making. JAm GeriatrSoc. 2002; 50(12): 2057– 2061. 12473020
- [38]La Puma J, Silverstein MD, Stocking CB, Roland D, Siegler M. Life-sustaining treatment: a prospective study of patients with DNR orders in a teaching hospital. Arch Intern Med. 1988; 148(10): 2193– 2198. 3178377
- [40]Manktelow BN, Evans TA, Draper ES. Differences in case-mix can influence the comparison of standardised mortality ratios even with optimal risk adjustment: an analysis of data from paediatric intensive care. BMJ Quai Saf. 2014; 23(9): 782– 788. 24840239
- [41]Marang-van de Mheen PJ, Shojania KG. Simpson’s paradox: how performance measurement can fail even with perfect risk adjustment. BMJ Quai Saf. 2014; 23(9): 701– 705. 25118292
- [42]Feinstein AR, Sosin DM, Wells CK. The Will Rogers phenomenon: stage migration and new diagnostic techniques as a source of misleading statistics for survival in cancer. N Engi J Med. 1985; 312(25): 1604– 1608. 4000199
- [43]Hall WB, Willis LE, Medvedev S, Carson SS. The implications of long-term acute care hospital transfer practices for measures of in-hospital mortality and length of stay. Am J Respir Crit Care Med. 2012; 185(1): 53– 5. 21940788 PMC3361349
- Authors
- Walkey, A. J., Weinberg, J., Wiener, R. S., Cooke, C. R., & Lindenauer, P. K.
- Affiliations
- Boston University (US)
- University of Michigan (US)
- Tufts University (US)
- Corresponding author
- Walkey, Allan J.
- Venue
- JAMA Internal Medicine
- Publisher
- American Medical Association (AMA)
- Published
- 2016-01-01
- Volume
- 176
- Issue
- 1
- Pages
- 97
- ISSN
- 2168-6106 (ISSN-L)
- PMC
- PMC6684128
- OpenAlex
- W2207278592
- Cited by
- 61
- References
- 40
- Citations by year
- 2026: 8 · 2025: 3 · 2024: 2 · 2023: 4 · 2022: 2 · 2021: 9 · 2020: 6 · 2019: 4 · 2018: 8 · 2017: 7 · 2016: 7 · 2015: 1
- FWCI
- 8.22
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- Agency for Healthcare Research and Quality (K08HS020672)
- National Heart, Lung, and Blood Institute (K01HL116768)
- National Cancer Institute (K07CA138772)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Palliative Care and End-of-Life Issues
- Health Sciences › Health Professions › General Health Professions › Primary Care and Health Outcomes
- Health Sciences › Health Professions › Health Information Management › Healthcare Quality and Management
- Keywords
- do-not-resuscitate orders, pneumonia, in-hospital mortality, risk adjustment, hospital rankings, hospital quality indicators, life-sustaining treatment, interhospital variation, California hospitals
- MeSH terms
- Aged, Aged, 80 and over, California, Female, Humans, Male, Middle Aged, Pneumonia, Retrospective Studies, Cohort Studies, Logistic Models, Odds Ratio, Resuscitation Orders, Hospital Mortality, Quality Indicators, Health Care
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