Prediction Models for Physical, Cognitive, and Mental Health Impairments After Critical Illness: A Systematic Review and Critical Appraisal.

Abstract

Improved ability to predict impairments after critical illness could guide clinical decision-making, inform trial enrollment, and facilitate comprehensive patient recovery. A systematic review of the literature was conducted to investigate whether physical, cognitive, and mental health impairments could be predicted in adult survivors of critical illness. A systematic search of PubMed and the Cochrane Library (Prospective Register of Systematic Reviews ID: CRD42018117255) was undertaken on December 8, 2018, and the final searches updated on January 20, 2019. Four independent reviewers assessed titles and abstracts against study eligibility criteria. Studies were eligible if a prediction model was developed, validated, or updated for impairments after critical illness in adult patients. Discrepancies were resolved by consensus or an independent adjudicator. Data on study characteristics, timing of outcome measurement, candidate predictors, and analytic strategies used were extracted. Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool. Of 8,549 screened studies, three studies met inclusion. All three studies focused on the development of a prediction model to predict (1) a mental health composite outcome at 3 months post discharge, (2) return-to-pre-ICU functioning and residence at 6 months post discharge, and (3) physical function 2 months post discharge. Only one model had been externally validated. All studies had a high risk of bias, primarily due to the sample size, and statistical methods used to develop and select the predictors for the prediction published model. We only found three studies that developed a prediction model of any post-ICU impairment. There are several opportunities for improvement for future prediction model development, including the use of standardized outcomes and time horizons, and improved study design and statistical methodology.