Increased Risk of Death in Patients With Do-Not-Resuscitate Orders

Shepardson, Laura B.; Youngner, Stuart J.; Speroff, Theodore; Rosenthal, Gary E.

doi:10.1097/00005650-199908000-00003

Cited by 116 publications

(90 citation statements)

References 27 publications

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“…Early DNR orders are common after ICH 9,10 and have been associated with a doubling in the chances of death after ICH even despite adjusting for traditional predictors of mortality. 10 While the association between DNR orders and death is strong and consistently seen in several studies, [9][10][11] the interpretation of this association is complex. DNR orders may simply represent a proxy for disease severity or comorbidities not accounted for in statistical models representative of a reduction in the overall level of aggressiveness of treatment provided to ICH patients.…”

mentioning

confidence: 96%

Do-not-resuscitate orders and predictive models after intracerebral hemorrhage

et al. 2010

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Objective: To quantify the accuracy of commonly used intracerebral hemorrhage (ICH) predictive models in ICH patients with and without early do-not-resuscitate orders (DNR).Methods: Spontaneous ICH cases (n ϭ 487) from the Brain Attack Surveillance in Corpus Christi study (2000 -2003) Conclusions: ICH prognostic model performance is substantially impacted when stratifying by early DNR status, possibly giving a false sense of model accuracy when DNR status is not considered. Clinicians should be cautious when applying these predictive models to individual patients. Many predictive models have been proposed to stratify risk of death following intracerebral hemorrhage (ICH) based on factors known early in the hospital course. 1-5 While many of these models have been shown to perform well on average, 1,6-8 using them to predict the outcome for an individual patient may be problematic.None of the commonly used predictive models have accounted for the impact of do-notresuscitate (DNR) orders on mortality after ICH. Early DNR orders are common after ICH 9,10 and have been associated with a doubling in the chances of death after ICH even despite adjusting for traditional predictors of mortality.10 While the association between DNR orders and death is strong and consistently seen in several studies, 9-11 the interpretation of this association is complex. DNR orders may simply represent a proxy for disease severity or comorbidities not accounted for in statistical models 12 ; alternatively, there is evidence to suggest that DNR orders after ICH may be

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mentioning

confidence: 96%

Do-not-resuscitate orders and predictive models after intracerebral hemorrhage

et al. 2010

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“…Early deaths are common in stroke, and most occur as a result of brain death or in the setting of withholding or withdrawing life-sustaining therapies when prognosis for recovery is believed to be poor. [20][21][22][23][24][25] Survivors of severe stroke often have a gradual improvement in function but may experience significant disability with loss of independence, change in role functioning, and secondary stroke symptoms, all of which may benefit from skilled palliative care. 7,26 The palliative care of patients and families should be individualized and tailored to the phase of illness, the patient's life stage and values, the benefits and burdens of treatment, comorbidities, and cultural attitudes.…”

Section: Strokementioning

confidence: 99%

“…21,37,38,40,[109][110][111][112][113] There is concern that this association can lead to a false prognostic pessimism that may lead to premature withdrawal of life-sustaining measures and thereby to a self-fulfilling prophecy. In such studies, however, it is difficult to determine causality.…”

Section: Self-fulfilling Prophecymentioning

confidence: 99%

Palliative and End-of-Life Care in Stroke

Holloway¹,

Arnold²,

Creutzfeldt³

et al. 2014

Stroke

243

120

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Background and Purpose-The purpose of this statement is to delineate basic expectations regarding primary palliative care competencies and skills to be considered, learned, and practiced by providers and healthcare services across hospitals and community settings when caring for patients and families with stroke. Methods-Members of the writing group were appointed by the American Heart Association Stroke Council's Scientific Statement Oversight Committee and the American Heart Association's Manuscript Oversight Committee. Members were chosen to reflect the diversity and expertise of professional roles in delivering optimal palliative care. Writing group members were assigned topics relevant to their areas of expertise, reviewed the appropriate literature, and drafted manuscript content and recommendations in accordance with the American Heart Association's framework for defining classes and level of evidence and recommendations. Results-The palliative care needs of patients with serious or life-threatening stroke and their families are enormous: complex decision making, aligning treatment with goals, and symptom control. Primary palliative care should be available to all patients with serious or life-threatening stroke and their families throughout the entire course of illness. To optimally deliver primary palliative care, stroke systems of care and provider teams should (1) promote and practice patient-and family-centered care; (2) effectively estimate prognosis; (3) develop appropriate goals of care; (4) be familiar with the evidence for common stroke decisions with end-of-life implications; (5) assess and effectively manage emerging stroke symptoms; (6) possess experience with palliative treatments at the end of life; (7) assist with care coordination, including referral to a palliative care specialist or hospice if necessary; (8) provide the patient and family the opportunity for personal growth and make bereavement resources available if death is anticipated; and (9) actively participate in continuous quality improvement and research. Conclusions-Addressing the palliative care needs of patients and families throughout the course of illness can complement

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“…The more widely applied PSM method [19][20][21][22][23][24][25] consists of estimating a logistic model to derive a propensity score measuring the likelihood a given member is in the treatment group compared to the comparison group based on a common set of explanatory variables. A principal assumption of PSM pertains to independence of irrelevant alternatives (IIA).…”

Section: Matching Techniquesmentioning

confidence: 99%

Exploring Robust Methods for Evaluating Treatment and Comparison Groups in Chronic Care Management Programs

Wells

Hamar

Bradley

et al. 2013

Population Health Management

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Evaluation of chronic care management (CCM) programs is necessary to determine the behavioral, clinical, and financial value of the programs. Financial outcomes of members who are exposed to interventions (treatment group) typically are compared to those not exposed (comparison group) in a quasi-experimental study design. However, because member assignment is not randomized, outcomes reported from these designs may be biased or inefficient if study groups are not comparable or balanced prior to analysis. Two matching techniques used to achieve balanced groups are Propensity Score Matching (PSM) and Coarsened Exact Matching (CEM). Unlike PSM, CEM has been shown to yield estimates of causal (program) effects that are lowest in variance and bias for any given sample size. The objective of this case study was to provide a comprehensive comparison of these 2 matching methods within an evaluation of a CCM program administered to a large health plan during a 2-year time period. Descriptive and statistical methods were used to assess the level of balance between comparison and treatment members pre matching. Compared with PSM, CEM retained more members, achieved better balance between matched members, and resulted in a statistically insignificant Wald test statistic for group aggregation. In terms of program performance, the results showed an overall higher medical cost savings among treatment members matched using CEM compared with those matched using PSM

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Increased Risk of Death in Patients With Do-Not-Resuscitate Orders

Cited by 116 publications

References 27 publications

Do-not-resuscitate orders and predictive models after intracerebral hemorrhage

Do-not-resuscitate orders and predictive models after intracerebral hemorrhage

Palliative and End-of-Life Care in Stroke

Exploring Robust Methods for Evaluating Treatment and Comparison Groups in Chronic Care Management Programs

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