Geng Zhi, Guo Tao, Cao Ziwei, He Xiaolu, Chen Jing, Yue Hong, Wu Aimei, Wei Lichao
Department of Neurology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China.
Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui, China.
Front Neurol. 2023 Sep 26;14:1260104. doi: 10.3389/fneur.2023.1260104. eCollection 2023.
BACKGROUND: Spontaneous intracerebral hemorrhage (SICH) is associated with high mortality and disability. Accurately predicting adverse prognostic risks of SICH is helpful in developing risk stratification and precision medicine strategies for this phenomenon. METHODS: We analyzed 413 patients with SICH admitted to Hefei Second People's Hospital as a training cohort, considering 74 patients from the First Affiliated Hospital of Anhui Medical University for external validation. Univariate and multivariate logistic regression analyses were used to select risk factors for 90-day functional outcomes, and a nomogram was developed to predict their incidence in patients. Discrimination, fitting performance, and clinical utility of the resulting nomogram were evaluated through receiver operating characteristic (ROC) curves, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), calibration plots, and decision curves analysis (DCA), respectively. RESULTS: Of the 413 patients, 180 had a poor prognosis. Univariate analysis showed significant variance of age, systolic pressure, intraventricular hemorrhage (IVH), Glasgow Coma Scale (GCS) scores, National Institute of Health Stroke Scale (NIHSS) scores, and hematoma volume between the groups ( < 0.05). Logistic multivariate regression analysis showed that age, IVH, NIHSS, and hematoma volume were associated with unfavorable outcomes. Based on the results, a nomogram model was developed with an area under the ROC curve of 0.91 (95% CI; 0.88-0.94) and 0.89 (95% CI; 0.80-0.95) in the training and validation sets, respectively. In the validation set, the accuracy, sensitivity, specificity, PPV, and NPV of the model were 0.851, 0.923, 0.812, 0.727, and 0.951, respectively. The calibration plot demonstrates the goodness of fit between the nomogram predictions and actual observations. Finally, DCA indicated significant clinical adaptability. CONCLUSION: We developed and validated a short-term prognostic nomogram model for patients with SICH including NIHSS scores, age, hematoma volume, and IVH. This model has valuable potential in predicting the prognosis of patients with SICH.
背景：自发性脑出血（SICH）与高死亡率和残疾率相关。准确预测SICH的不良预后风险有助于制定针对这一现象的风险分层和精准医疗策略。 方法：我们分析了413例入住合肥市第二人民医院的SICH患者作为训练队列，并纳入安徽医科大学第一附属医院的74例患者进行外部验证。采用单因素和多因素逻辑回归分析选择90天功能结局的危险因素，并绘制列线图以预测患者中这些因素的发生率。分别通过受试者工作特征（ROC）曲线、准确性、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）、校准图和决策曲线分析（DCA）评估所得列线图的区分度、拟合性能和临床实用性。 结果：413例患者中，180例预后不良。单因素分析显示，两组间年龄、收缩压、脑室内出血（IVH）、格拉斯哥昏迷量表（GCS）评分、美国国立卫生研究院卒中量表（NIHSS）评分和血肿体积存在显著差异（<0.05）。多因素逻辑回归分析显示，年龄、IVH、NIHSS和血肿体积与不良结局相关。基于这些结果，构建了一个列线图模型，训练集和验证集的ROC曲线下面积分别为0.91（95%CI；0.88 - 0.94）和0.89（95%CI；0.80 - 0.95）。在验证集中，该模型的准确性、敏感性、特异性、PPV和NPV分别为0.851、0.923、0.812、0.727和0.951。校准图显示了列线图预测与实际观察之间的良好拟合度。最后，DCA表明该模型具有显著的临床适用性。 结论：我们开发并验证了一个用于SICH患者的短期预后列线图模型，该模型包括NIHSS评分、年龄、血肿体积和IVH。该模型在预测SICH患者的预后方面具有重要潜力。
Ther Clin Risk Manag. 2023-11-29
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