Prediction Models for Physical, Cognitive, and Mental Health Impairments After Critical Illness: A Systematic Review and Critical Appraisal.
Prediction Models for Physical, Cognitive, and Mental Health Impairments After Critical Illness: A Systematic Review and Critical Appraisal.
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DOI：
10.1097/ccm.0000000000004659
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发表时间：
2020-12
影响因子：
8.8
通讯作者：
Harhay MO
中科院分区：
文献类型：
--
作者：
Haines KJ;Hibbert E;McPeake J;Anderson BJ;Bienvenu OJ;Andrews A;Brummel NE;Ferrante LE;Hopkins RO;Hough CL;Jackson J;Mikkelsen ME;Leggett N;Montgomery-Yates A;Needham DM;Sevin CM;Skidmore B;Still M;van Smeden M;Collins GS;Harhay MO
补充数字内容可在正文中找到。提高预测危重疾病后损害的能力可以指导临床决策，通知试验登记，并促进患者的全面康复。对文献进行了系统的回顾，以调查危重疾病成年幸存者的身体、认知和精神健康损害是否可以预测。对PubMed和Cochrane图书馆(预期系统评价登记ID：CRD42018117255)的系统检索于2018年12月8日进行，最终检索于2019年1月20日更新。四名独立审查员对照研究资格标准评估标题和摘要。如果开发、验证或更新了成人患者危重疾病后损伤的预测模型，则研究符合条件。分歧通过协商一致或由独立裁判者解决。提取了研究特征、结果测量的时间、候选预测因素和使用的分析策略的数据。使用预测模型偏差风险评估工具对偏差风险进行评估。在8549项筛选研究中，有3项研究符合纳入标准。所有这三项研究都集中在建立预测模型，以预测(1)出院后3个月的心理健康综合结果，(2)出院后6个月恢复到ICU前的功能和居住，以及(3)出院2个月后的身体功能。只有一款车型经过了外部验证。所有研究都有很高的偏倚风险，这主要是因为样本大小，以及为已发表的预测模型开发和选择预测因素所使用的统计方法。我们只发现了三项研究，它们开发了ICU后损害的预测模型。未来预测模型的开发有几个改进的机会，包括使用标准化结果和时间范围，以及改进研究设计和统计方法。
Supplemental Digital Content is available in the text. 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.