PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■缺血性中风是全球死亡率和致残的主要原因，需要准确预测院内死亡率（IHM）以改善患者护理。本研究旨在开发一种实用的列线图，用于缺血性卒中患者个性化IHM风险预测。
■对重庆医科大学附属第一医院422例缺血性脑卒中患者（2020年4月至2021年12月）进行了回顾性研究，患者分为训练组(n=295)和验证组(n=127)。人口统计数据，合并症,卒中危险因素,并收集了实验室结果。使用NIHSS评估卒中严重程度，卒中类型按TOAST标准进行分类。最小绝对收缩和选择算子(LASSO)回归用于预测因子选择和列线图构建，通过ROC曲线进行评估，校正曲线,和决策曲线分析。
■LASSO回归和多变量逻辑回归确定了四个独立的IHM预测因子：年龄，入学NIHSS成绩,慢性阻塞性肺疾病(COPD)诊断,和白细胞计数（WBC）。基于这些变量的高度精确的列线图表现出出色的预测性能，AUC为0.958(训练)和0.962(验证)，灵敏度为93.2%和95.7%，以及93.1%和90.9%的特异性，分别。校准曲线和决策曲线分析验证了其临床适用性。
■年龄，入学NIHSS成绩,COPD病史,和WBC被确定为缺血性卒中患者的独立IHM预测因子。所开发的列线图显示出高预测准确性和用于死亡率风险估计的实用性。需要外部验证和前瞻性研究以进一步确认其临床疗效。
■对重庆医科大学附属第一医院422例缺血性脑卒中患者（2020年4月至2021年12月）进行了回顾性研究，患者分为训练组(n=295)和验证组(n=127)。人口统计数据，合并症,卒中危险因素,并收集了实验室结果。使用NIHSS评估卒中严重程度，卒中类型按TOAST标准进行分类。最小绝对收缩和选择算子(LASSO)回归用于预测因子选择和列线图构建，通过ROC曲线进行评估，校正曲线,和决策曲线分析。
■LASSO回归和多变量逻辑回归确定了四个独立的IHM预测因子：年龄，入学NIHSS成绩,慢性阻塞性肺疾病(COPD)诊断,和白细胞计数（WBC）。基于这些变量的高度精确的列线图表现出出色的预测性能，AUC为0.958(训练)和0.962(验证)，灵敏度为93.2%和95.7%，以及93.1%和90.9%的特异性，分别。校准曲线和决策曲线分析验证了其临床适用性。
■年龄，入学NIHSS成绩,COPD病史,和WBC被确定为缺血性卒中患者的独立IHM预测因子。所开发的列线图显示出高预测准确性和用于死亡率风险估计的实用性。需要外部验证和前瞻性研究以进一步确认其临床疗效。
