Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges

作者：Alan J. Meehan, Stephanie J. Lewis, Seena Fazel, Paolo Fusar‐Poli, Ewout W. Steyerberg, Daniel Ståhl, Andrea Danese · 发表于：Molecular Psychiatry · 年份：2022 · DOI：10.1038/s41380-022-01528-4 · 被引用次数：228 · 研究领域：Mental Health Research Topics、Machine Learning in Healthcare、Schizophrenia research and treatment

Recent years have seen the rapid proliferation of clinical prediction models aiming to support risk stratification and individualized care within psychiatry. Despite growing interest, attempts to synthesize current evidence in the nascent field of precision psychiatry have remained scarce. This systematic review therefore sought to summarize progress towards clinical implementation of prediction modeling for psychiatric outcomes. We searched MEDLINE, PubMed, Embase, and PsychINFO databases from inception to September 30, 2020, for English-language articles that developed and/or validated multivariable models to predict (at an individual level) onset, course, or treatment response for non-organic psychiatric disorders (PROSPERO: CRD42020216530). Individual prediction models were evaluated based on three key criteria: (i) mitigation of bias and overfitting; (ii) generalizability, and (iii) clinical utility. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used to formally appraise each study's risk of bias. 228 studies detailing 308 prediction models were ultimately eligible for inclusion. 94.5% of developed prediction models were deemed to be at high risk of bias, largely due to inadequate or inappropriate analytic decisions. Insufficient internal validation efforts (within the development sample) were also observed, while only one-fifth of models underwent external validation in an independent sample. Finally, our search identified just one published model whos...