PDF(Pubmed)
Abstract：
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■我们的目标是开发一种列线图，该线图整合了从计算机断层扫描（CT）扫描获得的临床和放射学数据，能够预测动脉瘤性蛛网膜下腔出血（aSAH）患者的慢性脑积水。
■共纳入2020年1月至2022年12月江苏大学附属人民医院神经外科收治的318例蛛网膜下腔出血(SAH)患者。我们从医院的病历系统收集临床特征。确定与慢性脑积水相关的危险因素，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，伴随着通过十倍交叉验证进行的惩罚参数调整。然后将所有特征纳入多变量逻辑回归分析。基于这些发现,我们制作了临床-放射学列线图.为了评估其歧视表现，我们进行了受试者工作特征（ROC）曲线分析，并计算了曲线下面积（AUC）。此外,我们采用了校准曲线，并利用Brier分数作为一致性的指标。此外,进行决策曲线分析（DCA）以通过估计训练和测试组在各种阈值概率下的净收益来确定我们的模型的临床效用。
■该研究包括181名患者，确定的慢性脑积水患病率为17.7%。单因素logistic回归分析确定了11个潜在的危险因素，而LASSO回归确定了7个与慢性脑积水相关的显著危险因素。多因素logistic回归分析显示aSAH后慢性脑积水的三个独立预测因素：脑室周围白质改变，腰椎外引流,和修改的费舍尔等级。包含这些因素的列线图可以准确预测训练和测试队列中慢性脑积水的风险。每个队列的AUC值分别计算为0.810和0.811，表明列线图模型具有良好的判别能力。校准曲线以及Hosmer-Lemeshow测试在两个队列中的预测概率和观察结果之间显示出极好的一致性。此外，Brier得分（训练为0.127，测试组为0.09）进一步验证了我们的列线图模型的预测性能。DCA证实，在预测慢性脑积水时，此列线图在各种风险阈值上提供了优越的净收益。决策曲线表明，当个体的阈值概率范围为5%至62%时，该模型更有效地预测aSAH后慢性脑积水的发生。
■开发了临床-放射学列线图，以结合CT扫描的临床特征和放射学特征，旨在提高预测aSAH患者慢性脑积水的准确性。这个创新的列线图显示了通过提供aSAH患者中慢性脑积水的精确预测来帮助临床医生创建个性化和最佳治疗计划的有希望的潜力。
■共纳入2020年1月至2022年12月江苏大学附属人民医院神经外科收治的318例蛛网膜下腔出血(SAH)患者。我们从医院的病历系统收集临床特征。确定与慢性脑积水相关的危险因素，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，伴随着通过十倍交叉验证进行的惩罚参数调整。然后将所有特征纳入多变量逻辑回归分析。基于这些发现,我们制作了临床-放射学列线图.为了评估其歧视表现，我们进行了受试者工作特征（ROC）曲线分析，并计算了曲线下面积（AUC）。此外,我们采用了校准曲线，并利用Brier分数作为一致性的指标。此外,进行决策曲线分析（DCA）以通过估计训练和测试组在各种阈值概率下的净收益来确定我们的模型的临床效用。
■该研究包括181名患者，确定的慢性脑积水患病率为17.7%。单因素logistic回归分析确定了11个潜在的危险因素，而LASSO回归确定了7个与慢性脑积水相关的显著危险因素。多因素logistic回归分析显示aSAH后慢性脑积水的三个独立预测因素：脑室周围白质改变，腰椎外引流,和修改的费舍尔等级。包含这些因素的列线图可以准确预测训练和测试队列中慢性脑积水的风险。每个队列的AUC值分别计算为0.810和0.811，表明列线图模型具有良好的判别能力。校准曲线以及Hosmer-Lemeshow测试在两个队列中的预测概率和观察结果之间显示出极好的一致性。此外，Brier得分（训练为0.127，测试组为0.09）进一步验证了我们的列线图模型的预测性能。DCA证实，在预测慢性脑积水时，此列线图在各种风险阈值上提供了优越的净收益。决策曲线表明，当个体的阈值概率范围为5%至62%时，该模型更有效地预测aSAH后慢性脑积水的发生。
■开发了临床-放射学列线图，以结合CT扫描的临床特征和放射学特征，旨在提高预测aSAH患者慢性脑积水的准确性。这个创新的列线图显示了通过提供aSAH患者中慢性脑积水的精确预测来帮助临床医生创建个性化和最佳治疗计划的有希望的潜力。
