ORIGINAL RESEARCH article
Front. Surg.
Sec. Neurosurgery
Volume 12 - 2025 | doi: 10.3389/fsurg.2025.1589876
This article is part of the Research TopicDoing More with Less: Neurosurgery Strategies and Tricks of the Trade in the Technological EraView all 11 articles
Development and Validation of a Predictive Model for Postoperative Functional Recovery in Patients with Spontaneous Intracerebral Hemorrhage
Provisionally accepted
- Taizhou Central Hospital, Taizhou, China
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Background This study aimed to develop and validate a prognostic nomogram for predicting 3-month functional recovery in patients undergoing surgery for spontaneous intracerebral hemorrhage (ICH). Methods A retrospective cohort of 289 patients diagnosed with spontaneous intracerebral hemorrhage (ICH) underwent surgical management at the Intensive Care Unit of Taizhou Central Hospital between January 2021 and December 2024 was enrolled. Patients were randomly allocated into a training set (n=203, 70%) and validation set (n=86, 30%). A prognostic nomogram integrating imaging characteristics and clinical parameters was developed to predict 90-day functional recovery (modified Rankin Scale ≤2). Feature selection employed the Boruta algorithm, followed by multivariable logistic regression. Model discrimination was quantified by area under the ROC curve (AUC), while calibration curve was performed to evaluate model performance. Clinical utility was evaluated through decision curve analysis (DCA). Results The multivariable model retained six significant predictors: midline shift (OR:2.09, 95%CI: 1.56-2.79), hematoma volume (OR:1.10, 95%CI: 1.05-1.15), age (OR:1.03, 95%CI: 1.01-1.05), mean arterial pressure (OR:0.93, 95%CI: 0.89-0.98), body mass index (OR:0.78, 95%CI: 0.66-0.92), and Glasgow Coma Scale (GCS) score (OR:0.92, 95%CI: 0.79-1.06). Discriminative performance was robust, with area under the receiver operating characteristic curve (AUC) of 0.90 (95% CI: 0.85–0.96) in the training set and 0.83 (95% CI: 0.73–0.93) in the validation set. Calibration plots demonstrated excellent agreement between predicted and observed probabilities. DCA confirmed the clinical value of the model and its good impact on actual decision-making. Conclusion This study developed and validated a pragmatic prognostic nomogram for spontaneous ICH patients undergoing surgical intervention, integrating six clinically actionable predictors: midline shift, hematoma volume, age, MAP, BMI, and GCS. The model demonstrated robust discriminative capacity, calibration and clinical applicability, which provides evidence-based support for the formulation of individualized rehabilitation programs and the optimization of medical resources.
Keywords: Spontaneous intracerebral hemorrhage, predictive model, Boruta, Logistic regression, nomogram
Received: 23 Apr 2025; Accepted: 01 Oct 2025.
Copyright: © 2025 Jiang, Zhang, Weng, Lv and Dong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Liang Dong, dongliang@tzc.edu.cn
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