Journal
JOURNAL OF CLINICAL NEUROSCIENCE
Volume 119, Issue -, Pages 102-111Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jocn.2023.10.020
Keywords
Prediction; Re -bleed; Subarachnoid hemorrhage; Ultra -early; Systematic review
Categories
Ask authors/readers for more resources
This study systematically reviews models that aim to provide patient-specific predictions of pre-treatment rebleeding risk in patients with aneurysmal subarachnoid hemorrhage. The reported discriminative performance of the models varied, and no model showed consistently low bias risk and clinical applicability in all domains. Only one model was formulated using a patient cohort that underwent contemporary, evidence-based aneurysm treatment practices, but this model lacked calibration or clinical utility.
Background: Pre-treatment rebleeding following aneurysmal subarachnoid hemorrhage (aSAH) increases the risk of death and a poor neurological outcome. Current guidelines recommend aneurysm treatment as early as feasible after presentation, preferably within 24 h of onset to mitigate this risk, a practice termed ultra-early treatment. However, ongoing debate regarding whether ultra-early treatment is independently associated with reduced re-bleeding risk, together with the recognition that re-bleeding occurs even in centres practicing ultra -early treatment due to the presence of other risk-factors has resulted in a renewed need for patient-specific re -bleed risk prediction. Here, we systematically review models which seek to provide patient specific predictions of pre-treatment rebleeding risk. Methods: Following registration on the International prospective register of systematic reviews (PROSPERO) CRD 42023421235; Ovid Medline (Pubmed), Embase and Googlescholar were searched for English language studies between 1st May 2002 and 1st June 2023 describing pre-treatment rebleed prediction models following aSAH in adults >= 18 years. Of 763 unique records, 17 full texts were scrutinised with 5 publications describing 4 models reviewed. We used the semi-automated template of Fernandez-Felix et al. incorporating the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and the Pre-diction model Risk Of Bias ASsessment Tool (PROBAST) for data extraction, risk of bias and clinical applicability assessment. To further standardize risk of bias and clinical applicability assessment, we also used the published explanatory notes for the PROBAST tool and compared the aneurysm treatment practices each prediction model's formulation cohort experienced to a prespecified benchmark representative of contemporary aneurysm treatment practices as outlined in recent evidence-based guidelines and published practice pattern reports from four developed countries. Results: Reported model discriminative performance varied between 0.77 and 0.939, however, no single model demonstrated a consistently low risk of bias and low concern for clinical applicability in all domains. Only the score of Darkwah Oppong et al. was formulated using a patient cohort in which the majority of patients were managed in accordance with contemporary, evidence-based aneurysm treatment practices defined by ultra-early and predominantly endovascular treatment. However, this model did not undergo calibration or clinical utility
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available