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慢性硬脑膜下血肿钻孔引流术后短期预后不良的预测模型:一项回顾性队列研究。

Prediction model for poor short-term prognosis in patients with chronic subdural hematoma after burr hole drainage: a retrospective cohort study.

机构信息

Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Youzheng Street 23, Nangang District, Harbin, Heilongjiang Province, 150001, P. R. China.

NHC Key Laboratory of Cell Transplantation, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150001, P. R. China.

出版信息

Neurosurg Rev. 2024 Sep 18;47(1):633. doi: 10.1007/s10143-024-02752-y.

Abstract

Chronic subdural hematoma (CSDH) is a common condition in neurosurgery. With an aging population, there is increasing attention on the prognosis of patients following surgical intervention. We developed a postoperative short-term prognostic prediction model using preoperative clinical indicators, aiming to assist in perioperative medical decision-making and management. The dataset was randomly divided into training and validation cohorts. An mRS score greater than 2 one month after discharge was considered indicative of a poor prognosis. In the training cohort, the least absolute shrinkage and selection operator (LASSO) regression analysis was used for multivariate analysis to identify independent risk factors and construct a prediction nomogram for poor prognosis one month after discharge. The performance of the nomogram was assessed using the Receiver Operating Characteristic (ROC) curve and calibration curve. A Decision Curve Analysis (DCA) was also conducted to determine the net benefit threshold of the prediction model. Among the 505 participants, 18.8% (95/505) had a poor prognosis one month after discharge. The baseline characteristics did not significantly differ between the training cohort and the validation cohort. LASSO regression analysis in the training cohort reduced the predictors to four potential factors. Further multivariate logistic analyses in the training cohort identified four independent predictors: age, admission Glasgow Coma Scale (GCS) score, hemiparesis, and hemoglobin count. These predictors were incorporated into the nomogram prediction model. Internal validation using ROC analysis, calibration curves, and other methods demonstrated a strong correlation between the observed and predicted likelihood of poor prognosis one month after discharge. The visualized nomogram prediction model we developed for short-term postoperative prognosis of chronic subdural hematoma after burr hole drainage aids in predicting short-term outcomes and guiding clinical treatment decisions. Further external validation is needed in the future to confirm its effectiveness.

摘要

慢性硬脑膜下血肿(CSDH)是神经外科的一种常见病症。随着人口老龄化,人们越来越关注手术后患者的预后。我们开发了一种基于术前临床指标的术后短期预后预测模型,旨在辅助围手术期的医疗决策和管理。数据集被随机分为训练集和验证集。出院后一个月 mRS 评分大于 2 分被认为是预后不良的标志。在训练集中,使用最小绝对收缩和选择算子(LASSO)回归分析进行多变量分析,以确定独立的危险因素,并构建出院后一个月预后不良的预测列线图。使用受试者工作特征(ROC)曲线和校准曲线评估列线图的性能。还进行了决策曲线分析(DCA)以确定预测模型的净获益阈值。在 505 名参与者中,有 18.8%(95/505)在出院后一个月预后不良。训练集和验证集的基线特征无显著差异。LASSO 回归分析在训练集中将预测指标减少到四个潜在因素。进一步在训练集中进行多变量逻辑分析确定了四个独立的预测因素:年龄、入院格拉斯哥昏迷评分(GCS)、偏瘫和血红蛋白计数。这些预测因素被纳入到列线图预测模型中。使用 ROC 分析、校准曲线和其他方法进行的内部验证表明,出院后一个月预后不良的观察和预测可能性之间存在很强的相关性。我们开发的用于钻孔引流术后慢性硬脑膜下血肿短期术后预后的可视化列线图预测模型有助于预测短期结局并指导临床治疗决策。未来需要进一步的外部验证来确认其有效性。

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