动脉瘤性蛛网膜下腔出血后慢性脑积水患者的预测模型:一项回顾性队列研究

Dai Rao, Li Yang, Xu Enxi, Siyuan Lu, Qian Yu, Li Zheng, Zhou Zhou, Yerong Chen, Chen Bo, Shan Xiuhong, Sun Eryi
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引用次数: 0

摘要

江苏大学附属人民医院神经外科在2020年1月至2022年12月期间共收治了318例蛛网膜下腔出血(SAH)患者。我们从医院的病历系统中收集了患者的临床特征。为了确定与慢性脑积水相关的风险因素,我们对这些临床特征和放射学特征进行了单变量和LASSO回归模型,并通过十倍交叉验证对惩罚参数进行了调整。然后将所有特征纳入多变量逻辑回归分析。基于这些发现,我们开发了临床放射学提名图。为了评估其分辨性能,我们进行了接收者操作特征曲线(ROC)分析,并计算了曲线下面积(AUC)。此外,我们还采用了校准曲线,并将 Brier 评分作为一致性指标。此外,我们还进行了决策曲线分析 (DCA),通过估算训练组和测试组在不同阈值概率下的净收益,确定我们模型的临床实用性。单变量逻辑回归分析确定了 11 个潜在风险因素,而 LASSO 回归则确定了 7 个与慢性脑积水相关的重要风险因素。多变量逻辑回归分析发现了三个独立的预测因素:脑室周围白质改变、腰椎外引流和改良费舍尔分级。包含这些因素的提名图可以准确预测训练组和测试组的慢性脑积水风险。每个队列的 AUC 值分别为 0.810 和 0.811,表明提名图模型具有良好的判别能力。校准曲线和 Hosmer-Lemeshow 检验表明,在两个队列中,预测概率与观察结果之间的一致性极佳。此外,Brier 评分(训练组为 0.127,测试组为 0.09)进一步验证了我们的提名图模型的预测性能。DCA 证实,在预测慢性脑积水时,该提名图在各种风险阈值下都能提供卓越的净效益。决策曲线显示,当个体的阈值概率在 5% 到 62% 之间时,该模型在预测 aSAH 后慢性脑积水的发生方面更有效。这一创新的提名图显示出了巨大的潜力,它能准确预测aSAH患者的慢性脑积水情况,从而帮助临床医生制定个性化的最佳治疗方案。
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A predictive model in patients with chronic hydrocephalus following aneurysmal subarachnoid hemorrhage: a retrospective cohort study
Our aim was to develop a nomogram that integrates clinical and radiological data obtained from computed tomography (CT) scans, enabling the prediction of chronic hydrocephalus in patients with aneurysmal subarachnoid hemorrhage (aSAH).A total of 318 patients diagnosed with subarachnoid hemorrhage (SAH) and admitted to the Department of Neurosurgery at the Affiliated People’s Hospital of Jiangsu University between January 2020 and December 2022 were enrolled in our study. We collected clinical characteristics from the hospital’s medical record system. To identify risk factors associated with chronic hydrocephalus, we conducted both univariate and LASSO regression models on these clinical characteristics and radiological features, accompanied with penalty parameter adjustments conducted through tenfold cross-validation. All features were then incorporated into multivariate logistic regression analyses. Based on these findings, we developed a clinical-radiological nomogram. To evaluate its discrimination performance, we conducted Receiver Operating Characteristic (ROC) curve analysis and calculated the Area Under the Curve (AUC). Additionally, we employed calibration curves, and utilized Brier scores as an indicator of concordance. Additionally, Decision Curve Analysis (DCA) was performed to determine the clinical utility of our models by estimating net benefits at various threshold probabilities for both training and testing groups.The study included 181 patients, with a determined chronic hydrocephalus prevalence of 17.7%. Univariate logistic regression analysis identified 11 potential risk factors, while LASSO regression identified 7 significant risk factors associated with chronic hydrocephalus. Multivariate logistic regression analysis revealed three independent predictors for chronic hydrocephalus following aSAH: Periventricular white matter changes, External lumbar drainage, and Modified Fisher Grade. A nomogram incorporating these factors accurately predicted the risk of chronic hydrocephalus in both the training and testing cohorts. The AUC values were calculated as 0.810 and 0.811 for each cohort respectively, indicating good discriminative ability of the nomogram model. Calibration curves along with Hosmer-Lemeshow tests demonstrated excellent agreement between predicted probabilities and observed outcomes in both cohorts. Furthermore, Brier scores (0.127 for the training and 0.09 for testing groups) further validated the predictive performance of our nomogram model. The DCA confirmed that this nomogram provides superior net benefit across various risk thresholds when predicting chronic hydrocephalus. The decision curve demonstrated that when an individual’s threshold probability ranged from 5 to 62%, this model is more effective in predicting the occurrence of chronic hydrocephalus after aSAH.A clinical-radiological nomogram was developed to combine clinical characteristics and radiological features from CT scans, aiming to enhance the accuracy of predicting chronic hydrocephalus in patients with aSAH. This innovative nomogram shows promising potential in assisting clinicians to create personalized and optimal treatment plans by providing precise predictions of chronic hydrocephalus among aSAH patients.
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