A decision tree model to help treatment decision-making for severe spontaneous intracerebral hemorrhage

Abstract

Background: Surgical treatment demonstrated a reduction in mortality among patients suffering from severe spontaneous intracerebral hemorrhage (SSICH). However, which SSICH patients could benefit from surgical treatment was unclear. This study aimed to establish and validate a decision tree (DT) model to help determine which SSICH patients could benefit from surgical treatment. Materials and methods: SSICH patients from a prospective, multicenter cohort study were analyzed retrospectively. The primary outcome was the incidence of neurological poor outcome (modified Rankin scale as 4-6) on the 180th day post-hemorrhage. Then, surgically-treated SSICH patients were set as the derivation cohort (from a referring hospital) and validation cohort (from multiple hospitals). A DT model to evaluate the risk of 180-day poor outcome was developed within the derivation cohort and validated within the validation cohort. The performance of clinicians in identifying patients with poor outcome before and after the help of the DT model was compared using the area under curve (AUC). Results: 1260 SSICH patients were included in this study (middle age as 56, and 984 male patients). Surgically-treated patients had a lower incidence of 180-day poor outcome compared to conservatively-treated patients (147/794 vs. 128/466, P<0.001). Based on 794 surgically-treated patients, multivariate logistic analysis revealed the ischemic cerebro-cardiovascular disease history, renal dysfunction, dual antiplatelet therapy, hematoma volume, and Glasgow coma score at admission as poor outcome factors. The DT model, incorporating these above factors, was highly predictive of 180-day poor outcome within the derivation cohort (AUC, 0.94) and validation cohort (AUC, 0.92). Within 794 surgically-treated patients, the DT improved junior clinicians’ performance to identify patients at risk for poor outcomes (AUC from 0.81 to 0.89, P<0.001). Conclusions: This study provided a DT model for predicting the poor outcome of SSICH patients post-surgically, which may serve as a useful tool assisting clinicians in treatment decision-making for SSICH.