One-year outcomes in individual domains of the cerebral performance category extended

Flickinger, Katharyn L; Jaramillo, Stephany; Repine, Melissa; Koller, Allison C; Holm, Margo B.; Skidmore, Elizabeth R.; Callaway, Clif; Rittenberger, Jon C.

doi:10.1016/j.resplu.2021.100184

Cited by 5 publications

(11 citation statements)

References 23 publications

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“…In our system of care, recovery to a CPC of 3 is generally viewed as sufficient to allow discharge to a postacute care setting (e.g., inpatient rehabilitation). Many of these patients are long-term survivors and demonstrated improvement to 1 or 2 at 6 and 12 months, 16 a finding that has been replicated by others. 17…”

Section: Discussionmentioning

confidence: 53%

“…CPC is an imperfect outcome scale and overlooks many clinically important domains of postarrest recovery, and assessment at hospital discharge does not account for continued recovery over time. 16,27 To address the latter limitation, because long-term outcome assessments were unavailable, we considered CPC 3 patients to have favorable recoveries because many will have a CPC of 1 or 2 after 6 months. 16 This means our findings do not directly map to studies that have considered CPC 3 to be unfavorable.…”

Section: Discussionmentioning

confidence: 99%

“…16,27 To address the latter limitation, because long-term outcome assessments were unavailable, we considered CPC 3 patients to have favorable recoveries because many will have a CPC of 1 or 2 after 6 months. 16 This means our findings do not directly map to studies that have considered CPC 3 to be unfavorable. Finally, although Bayesian methods have advantages compared with the frequentist approach, they are computationally intensive to fit (for example, requiring several days to estimate per model).…”

Section: Discussionmentioning

confidence: 99%

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Bayesian Outcome Prediction After Resuscitation From Cardiac Arrest

et al. 2022

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Background and ObjectivesPostarrest prognostication research does not typically account for the sequential nature of real-life data acquisition and interpretation and reports nonintuitive estimates of uncertainty. Bayesian approaches offer advantages well suited to prognostication. We used Bayesian regression to explore the usefulness of sequential prognostic indicators in the context of prior knowledge and compared this with a guideline-concordant algorithm.MethodsWe included patients hospitalized at a single center after cardiac arrest. We extracted prospective data and assumed these data accrued over time as in routine practice. We considered predictors demographic and arrest characteristics, initial and daily neurologic examination, laboratory results, therapeutic interventions, brain imaging, and EEG. We fit Bayesian hierarchical generalized linear multivariate models predicting discharge Cerebral Performance Category (CPC) 4 or 5 (poor outcomes) vs 1–3 including sequential clinical and prognostic data. We explored outcome posterior probability distributions (PPDs) for individual patients and overall. As a comparator, we applied the 2021 European Resuscitation Council and European Society of Intensive Care Medicine (ERC/ESICM) guidelines.ResultsWe included 2,692 patients of whom 864 (35%) were discharged with a CPC 1–3. Patients' outcome PPDs became narrow and shifted toward 0 or 1 as sequentially acquired information was added to models. These changes were largest after arrest characteristics and initial neurologic examination were included. Using information typically available at or before intensive care unit admission, sensitivity predicting poor outcome was 51% with a 0.6% false-positive rate. In our most comprehensive model, sensitivity for poor outcome prediction was 76% with 0.6% false-positive rate (FPR). The ERC/ESICM algorithm applied to 547 of 2,692 patients and yielded 36% sensitivity with 0% FPR.DiscussionBayesian models offer advantages well suited to prognostication research. On balance, our findings support the view that in expert hands, accurate neurologic prognostication is possible in many cases before 72 hours postarrest. Although we caution against early withdrawal of life-sustaining therapies, rapid outcome prediction can inform clinical decision making and future clinical trials.

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Section: Discussionmentioning

confidence: 53%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Bayesian Outcome Prediction After Resuscitation From Cardiac Arrest

et al. 2022

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“…The mRS is a 6-point validated scale to assess functional status with "0" indicating no disability and "5" indicating severe disability, bedridden, and requiring nursing care and attention. 6 Our primary outcome was change in depressive symptoms measured with the Patient Health Questionnaire (PHQ-9) score before and after completion of CR. The PHQ-9 is a 9-question, self-administered questionnaire that is representative of the Diagnostic and Statistical Manual of Mental Disorders criteria for major depressive episodes.…”

Section: Methodsmentioning

confidence: 99%

“…[1][2][3][4][5][6] Recovery after CA continues for at least 6 to 12 mo. 2,3,6 The extent that postacute care and rehabilitation can improve recovery is not clear, and CA survivors do not consistently receive rehabilitation services. 1 Cardiac rehabilitation (CR) is one type of postacute intervention available to CA survivors, and therapists can personalize CR for the unique physical, cognitive, and psychological symptoms of each patient.…”

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confidence: 99%