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

Archie Parekh; Samarth Satish; Louise Dulhanty; Carlo Berzuini; Hiren Patel

doi:10.1136/jnis-2023-021107

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

J Neurointerv Surg. 2023 Dec 21:jnis-2023-021107. doi: 10.1136/jnis-2023-021107. Online ahead of print.

Authors

Archie Parekh¹, Samarth Satish¹, Louise Dulhanty², Carlo Berzuini³, Hiren Patel⁴

Affiliations

¹ The University of Manchester, Manchester, UK.
² Salford Royal Hospital Manchester Centre for Clinical Neurosciences, Salford, UK.
³ Centre for Biostatistics, The University of Manchester, Manchester, UK.
⁴ Greater Manchester Neurosciences Centre, Salford Royal NHS Foundation Trust, Salford, UK Hiren.Patel@srft.nhs.uk.

PMID: 38129109
DOI: 10.1136/jnis-2023-021107

Abstract

Background: A 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.

Methods: We 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).

Results: We 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.

Conclusion: Externally 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.

Keywords: aneurysm; hemorrhage; subarachnoid.