Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
Department of Neurology, Chongzhou People's Hospital, Sichuan, People's Republic of China.
Clin Interv Aging. 2024 Aug 9;19:1423-1436. doi: 10.2147/CIA.S471885. eCollection 2024.
Ischemic stroke is a leading cause of mortality and disability globally, necessitating accurate prediction of intra-hospital mortality (IHM) for improved patient care. This study aimed to develop a practical nomogram for personalized IHM risk prediction in ischemic stroke patients.
A retrospective study of 422 ischemic stroke patients (April 2020 - December 2021) from Chongqing Medical University's First Affiliated Hospital was conducted, with patients divided into training (n=295) and validation (n=127) groups. Data on demographics, comorbidities, stroke risk factors, and lab results were collected. Stroke severity was assessed using NIHSS, and stroke types were classified by TOAST criteria. Least absolute shrinkage and selection operator (LASSO) regression was employed for predictor selection and nomogram construction, with evaluation through ROC curves, calibration curves, and decision curve analysis.
LASSO regression and multivariate logistic regression identified four independent IHM predictors: age, admission NIHSS score, chronic obstructive pulmonary disease (COPD) diagnosis, and white blood cell count (WBC). A highly accurate nomogram based on these variables exhibited excellent predictive performance, with AUCs of 0.958 (training) and 0.962 (validation), sensitivities of 93.2% and 95.7%, and specificities of 93.1% and 90.9%, respectively. Calibration curves and decision curve analysis validated its clinical applicability.
Age, admission NIHSS score, COPD history, and WBC were identified as independent IHM predictors in ischemic stroke patients. The developed nomogram demonstrated high predictive accuracy and practical utility for mortality risk estimation. External validation and prospective studies are warranted for further confirmation of its clinical efficacy.
缺血性脑卒中是全球范围内导致死亡和残疾的主要原因，因此需要准确预测院内死亡率（IHM），以改善患者的治疗效果。本研究旨在开发一种实用的列线图，用于预测缺血性脑卒中患者的个体化 IHM 风险。
本研究回顾性分析了 2020 年 4 月至 2021 年 12 月期间重庆医科大学附属第一医院的 422 例缺血性脑卒中患者的数据，将患者分为训练集（n=295）和验证集（n=127）。收集患者的人口统计学、合并症、卒中危险因素和实验室结果等数据。采用 NIHSS 评估卒中严重程度，根据 TOAST 标准对卒中类型进行分类。采用最小绝对收缩和选择算子（LASSO）回归进行预测因子选择和列线图构建，并通过 ROC 曲线、校准曲线和决策曲线分析进行评估。
LASSO 回归和多因素逻辑回归分析确定了 4 个独立的 IHM 预测因子：年龄、入院 NIHSS 评分、慢性阻塞性肺疾病（COPD）诊断和白细胞计数（WBC）。基于这些变量构建的高度准确的列线图具有优异的预测性能，其 AUC 在训练集和验证集中分别为 0.958 和 0.962，敏感度分别为 93.2%和 95.7%，特异性分别为 93.1%和 90.9%。校准曲线和决策曲线分析验证了其临床适用性。
年龄、入院 NIHSS 评分、COPD 病史和 WBC 是缺血性脑卒中患者发生 IHM 的独立预测因子。本研究开发的列线图具有较高的预测准确性和实用价值，可用于评估患者的死亡风险。需要进一步的外部验证和前瞻性研究来确认其临床疗效。
