Pre-treatment re-bleeding following aneurysmal subarachnoid hemorrhage: a systematic review of prediction models

Author:

Dissanayake Arosha S1,Ho Kwok M2,Phillips Timothy J1,Honeybul Stephen1,Hankey Graeme J3

Affiliation:

1. Sir Charles Gairdner Hospital

2. Royal Perth Hospital

3. The University of Western Australia

Abstract

Abstract Pre-treatment rebleeding following aneurysmal subarachnoid hemorrhage (aSAH) increases the risk of a poor outcome. Treatment as early as practicable is recommend to mitigate this risk though the benefit of emergency treatment is debated, emphasising the need for individualised risk prediction. Predictive models have been recently described incorporating established risk factors. Following prospective registration on the International prospective register of systematic reviews (PROSPERO) CRD 42023421235; Ovid Medline (Pubmed), Embase and Googlescholar were searched for English language studies describing clinical prediction models between May 2002 and May 2023 for pre-treatment rebleed prediction following aSAH in adults ³18 years. Of 763 unique records, 17 full texts were scrutinised with 5 publications describing 4 models reviewed. The Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were used. Reported performance varied from 0.790 to 0.939. The Intracranial Aneurysm Risk Score demonstrated a high risk of bias and low clinical applicability. The risk score of Oppong et al. was not validated or calibrated and the Clinical + Morphological model of Liu et al. did not have a sufficient event-to-predictor ratio. The ARISE extended model was formulated using patient data from multiple centres and time periods increasing the risk of non-standardised predictor assessment. Furthermore, patient recruitment overlapped the period of the endovascular paradigm shift in aneurysm treatment, limiting the applicability of this model to patients treated according to modern conditions. Thus, no published predictive model could be recommended for clinical use.

Publisher

Research Square Platform LLC

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