Machine learning validation of a simple prediction model for the correlation between advanced age and clinical...

Adverse effects of advanced age and poor initial neurological status on outcomes of patients with aneurysmal subarachnoid hemorrhage (SAH) have been documented. While a predictive model of the non-linear correlation between advanced age and clinical outcome has been reported, no previous model has been validated. Therefore, we created a prediction model of the non-linear correlation between advanced age and clinical outcome by machine learning and validated it using a separate cohort. Data of aneurysmal SAH patients treated by surgical clipping or endovascular coiling between 2003 and 2019 were obtained from the Japanese Stroke Databank (derivation cohort, n = 9,657) and "Predict for Outcome Study of Aneurysmal Subarachnoid Hemorrhage" (validation cohort, n = 5,085). Generalized additive models (GAMs) for poor outcome (modified Rankin Scale score ≥ 3 at discharge) were fitted with age transformation using spline curves for each World Federation of Neurological Societies grade. The discrimination property and calibration plot of unadjusted and adjusted models were assessed using the validation cohort. The derivation and validation cohorts included 3,610 and 3,251 patients, respectively. Regarding discrimination, areas under the receiver operating characteristic curve for the derivation and validation cohorts were 0.835 and 0.827, respectively, in the unadjusted model and 0.844 and 0.836, respectively, in the adjusted model. An unbiased correlation was confirmed between predicted and observed probabilities of poor outcomes. GAM could help visualize the correlation between age and clinical outcomes. Our prediction model can quantitatively aid in treatment decision-making and can be effective for most diseases and treatment settings. Trial Registration: UMIN Clinical Trials Registry (Date 2/22/2022/ ID, UMIN000046282 number, R000052809 URL, https//www.umin.ac.jp/ctr/index.htm) and the Japan Registry of Clinical Trials (Date 3/28/2022 /No. jRCT1060210092 URL, https//jrct.niph.go.jp/).