Clinical prediction models for aneurysmal subarachnoid hemorrhage: a systematic review update

Abstract

BackgroundA systematic review of clinical prediction models for aneurysmal subarachnoid hemorrhage (aSAH) reported in 2011 noted that clinical prediction models for aSAH were developed using poor methods and were not externally validated. This study aimed to update the above review to guide the future development of predictive models in aSAH.MethodsWe systematically searched Embase and MEDLINE databases (January 2010 to February 2022) for articles that reported the development of a clinical prediction model to predict functional outcomes in aSAH. Our reviews are based on the items included in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) checklist, and on data abstracted from each study in accord with the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) 2014 checklist. Bias and applicability were assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST).ResultsWe reviewed data on 30 466 patients contributing to 29 prediction models abstracted from 22 studies identified from an initial search of 7858 studies. Most models were developed using logistic regression (n=20) or machine learning (n=9) with prognostic variables selected through a range of methods. Age (n=13), World Federation of Neurological Surgeons (WFNS) grade (n=11), hypertension (n=6), aneurysm size (n=5), Fisher grade (n=12), Hunt and Hess score (n=5), and Glasgow Coma Scale (n=8) were the variables most frequently included in the reported models. External validation was performed in only four studies. All but one model had a high or unclear risk of bias due to poor performance or lack of validation.ConclusionExternally validated models for the prediction of functional outcome in aSAH patients have now become available. However, most of them still have a high risk of bias.