Hospital Variation in Utilization of Life‐Sustaining Treatments among Patients with Do Not Resuscitate Orders

Walkey, Allan J.; Weinberg, Janice; Wiener, Renda Soylemez; Cooke, Colin R.; Lindenauer, Peter K.

doi:10.1111/1475-6773.12651

Cited by 18 publications

(12 citation statements)

References 40 publications

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“…Our findings expand upon previous reports in that early DNR use varies widely between hospitals, and is not sufficiently explained by patient comorbidity case mix. 10,12,19,31 Previous work by our study group has demonstrated similar findings for patients with Heart Failure and Pneumonia. Our findings in this study reinforce that DNR status is a strong confounder of hospital mortality metrics.…”

Section: Discussionsupporting

confidence: 79%

Do-Not-Resuscitate Status and Risk-Standardized Mortality and Readmission Rates Following Acute Myocardial Infarction

Bruckel

Nallamothu

Ling

et al. 2019

Circ: Cardiovascular Quality and Outcomes

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Background-Important administrative-based measures of hospital quality, including those used by Centers for Medicare/Medicaid Services (CMS), may not adequately account for patient illness and social factors that vary between hospitals and can strongly affect outcomes. Do-notresuscitate (DNR) order on admission (within the first 24 hours) is one such factor that may reflect higher pre-admission illness burden as well as patients' desire for less-intense therapeutic interventions, and has been shown to vary widely between hospitals. We sought to evaluate how accounting for early DNR affected hospital quality measures for acute myocardial infarction (AMI). Methods-We identified all patients admitted with AMI using the California State Inpatient Database, which captures early DNR use within 24-hours of admission. We generated hospital risk-standardized mortality and readmissions using random-effects logistic regression, before and after including early DNR status, to examine changes in overall model fit and hospital outlier designations.

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Section: Discussionsupporting

confidence: 79%

Do-Not-Resuscitate Status and Risk-Standardized Mortality and Readmission Rates Following Acute Myocardial Infarction

Bruckel

Nallamothu

Ling

et al. 2019

Circ: Cardiovascular Quality and Outcomes

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“…(1)(2)(3)(4) Mechanistically, patient preferences for treatment limitations can be conceptualized as both predictors of mortality risk and effectors of mortality risk. Older patients and patients with preexisting serious illness who are more likely to die with an acute illness are more likely to have treatment limitations.…”

mentioning

confidence: 99%

Accounting for Patient Preferences Regarding Life-Sustaining Treatment in Evaluations of Medical Effectiveness and Quality

Walkey

Barnato

Wiener

et al. 2017

Am J Respir Crit Care Med

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The importance of understanding patient preferences for life-sustaining treatment is well described for individual clinical decisions; however, its role in evaluations of healthcare outcomes and quality has received little attention. Decisions to limit life-sustaining therapies are strongly associated with high risks for death in ways that are unaccounted for by routine measures of illness severity. However, this essential information is generally unavailable to researchers, with the potential for spurious inferences. This may lead to "confounding by unmeasured patient preferences" (a type of confounding by indication) and has implications for assessments of treatment effectiveness and healthcare quality, especially in acute and critical care settings in which risk for death and adverse events are high. Through a collection of case studies, we explore the effect of unmeasured patient resuscitation preferences on issues critical for researchers and research consumers to understand. We then propose strategies to more consistently elicit, record, and harmonize documentation of patient preferences that can be used to attenuate confounding by unmeasured patient preferences and provide novel opportunities to improve the patient centeredness of medical care for serious illness.

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“…We were able to leverage new ICD-9 codes identifying a representative sample of patients with DNR orders across the United States. Prior studies from the state of California-which routinely collects information on patient DNR status at hospital admission-have shown similarly high levels of hospital variation in DNR orders (9) and inverse associations between hospital DNR rates and use of life-support interventions (but not measures of hospital EOL treatment intensity) among patients with DNR orders (10).…”

Section: Discussionmentioning

confidence: 94%

“…Multiple randomized controlled trials have demonstrated that PCT-based algorithms can substantially reduce antibiotic exposure without increasing mortality or infection-related complications (5)(6)(7)(8)(9). These have led to clinical practice guidelines suggesting use of PCT to guide antibiotic duration for patients with sepsis or suspected infection (1,10,11).…”

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confidence: 99%

Hospital Variation in Do-Not-Resuscitate Orders and End-of-Life Healthcare Use in the United States

et al. 2017

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Use of health care resources by individuals at the end of life (EOL) varies widely across the United States (1). Whereas individual patients who participate in advance care planning tend to have lower rates of in-hospital death and higher rates of hospice use (2), scant information in national databases regarding patient advance directives has led to knowledge gaps regarding the associations between hospital use of advance directives and EOL healthcare use. We leveraged new International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9) (3) identifiers of patient "do-not-resuscitate" (DNR) status to characterize national variation in acute care hospital DNR orders-a common method of documenting directives for treatment limitation-across the United States, and correlated hospital DNR rates with measures of EOL healthcare use. Methods We used the Hospital Cost and Utilization Project, National Inpatient Sample (NIS), a representative sample of hospitalized patients in the United States during 2011 and 2012 (4). During 2011, the NIS was a 20% stratified probability sample of nonfederal acute-care hospitals, with hospital identifiers allowing linkage to the Dartmouth Atlas of Healthcare measures of hospital EOL healthcare use (http://www.dartmouthatlas.org/tools/downloads.aspx?tab=40). During 2012, the NIS eliminated hospital identifiers, but used a 20% sample of patients from nonfederal acute-care hospitals across the United States, allowing improved characterization of variation across U.S. hospitals. We used ICD-9-Clinical Modification code V49.86 (introduced October 1, 2010) to identify patient DNR status, and conducted survey-weighted analyses to identify population estimates of hospital DNR rates. Analyses included cases 65 years old or older at each U.S. hospital, excluding patients admitted to hospitals with 0 DNR orders (5%). We calculated risk-standardized hospital DNR rates for 2011 and 2012 from multivariable hierarchical logistic regression models adjusted for patient demographics, 235 Clinical Classification Codes characterizing principal reason for hospitalization, comorbidities (5), acute organ failures (6), and hospital characteristics. We summarized hospital variation in DNR orders using the median odds ratio (7), a measure of the median odds of DNR status for similar patients selected from among all possible pairs of hospitals. We abstracted a priori 12 measures of hospital EOL healthcare utilization from the Dartmouth Atlas that correspond to proposed measures of quality care at EOL (8), and used linear regression to evaluate LETTERS

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Hospital Variation in Utilization of Life‐Sustaining Treatments among Patients with Do Not Resuscitate Orders

Abstract: Hospitals vary widely in the scope of invasive or organ-supporting treatments provided to patients with DNR orders.

Cited by 18 publications

References 40 publications

Do-Not-Resuscitate Status and Risk-Standardized Mortality and Readmission Rates Following Acute Myocardial Infarction

Do-Not-Resuscitate Status and Risk-Standardized Mortality and Readmission Rates Following Acute Myocardial Infarction

Accounting for Patient Preferences Regarding Life-Sustaining Treatment in Evaluations of Medical Effectiveness and Quality

Hospital Variation in Do-Not-Resuscitate Orders and End-of-Life Healthcare Use in the United States

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