Effects of the National Institutes of Health Stroke Scale and Modified Rankin Scale on Predictive Models of 30-Day Nonelective Readmission and Mortality After Ischemic Stroke: Cohort Study.

IF 3.1 3区医学 Q2 MEDICAL INFORMATICS

JMIR Medical Informatics Pub Date : 2025-05-09 DOI:10.2196/69102

Mai N Nguyen-Huynh, Janet Alexander, Zheng Zhu, Melissa Meighan, Gabriel Escobar

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引用次数: 0

Abstract

Background: Patients with stroke have high rates of all-cause readmission and case fatality. Limited information is available on how to predict these outcomes.

Objective: We aimed to assess whether adding the initial National Institutes of Health Stroke Scale (NIHSS) score or modified Rankin scale (mRS) score at discharge improved predictive models of 30-day nonelective readmission or 30-day mortality poststroke.

Methods: Using a cohort of patients with ischemic stroke in a large multiethnic integrated health care system from June 15, 2018, to April 29, 2020, we tested 2 predictive models for a composite outcome (30-day nonelective readmission or death). The models were based on administrative data (Length of Stay, Acuity, Charlson Comorbidities, Emergency Department Use score; LACE) as well as a comprehensive model (Transition Support Level; TSL). The models, initial NIHSS score, and mRS scores at discharge, were tested independently and in combination with age and sex. We assessed model performance using the area under the receiver operator characteristic (c-statistic), Nagelkerke pseudo-R2, and Brier score.

Results: The study cohort included 4843 patients with 5014 stroke hospitalizations. Average age was 71.9 (SD 14) years, 50.6% (2537/5014) were female, and 52.1% (2614/5014) were White. Median initial NIHSS score was 4 (IQR 2-8). There were 538 (10.7%) nonelective readmissions and 150 (3.9%) deaths within 30 days. The logistic models revealed that the best performing models were TSL (c-statistic=0.69) and TSL plus mRS score at discharge (c-statistic=0.69).

Conclusions: We found that neither the initial NIHSS score nor the mRS score at discharge significantly enhanced the predictive ability of the LACE or TSL models. Future efforts at prediction of short-term stroke outcomes will need to incorporate new data elements.

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美国国立卫生研究院卒中量表和改良Rankin量表对缺血性卒中后30天非选择性再入院和死亡率预测模型的影响：队列研究

背景：卒中患者有很高的全因再入院率和病死率。关于如何预测这些结果的信息有限。目的：我们旨在评估在出院时加入最初的美国国立卫生研究院卒中量表（NIHSS）评分或修改的Rankin量表（mRS）评分是否能改善卒中后30天非选择性再入院或30天死亡率的预测模型。方法：使用2018年6月15日至2020年4月29日在大型多民族综合医疗保健系统中的缺血性卒中患者队列，我们测试了两种复合结局（30天非选择性再入院或死亡）的预测模型。模型基于行政数据(住院时间、视力、查理森合并症、急诊科使用评分；LACE)以及一个综合模型(过渡支持水平；台盟)。模型、初始NIHSS评分和出院时的mRS评分分别独立测试，并结合年龄和性别进行测试。我们使用接收算子特征下的面积（c-statistic）、Nagelkerke伪r2和Brier评分来评估模型的性能。结果：研究队列包括4843例5014例卒中住院患者。平均年龄71.9 （SD 14）岁，女性占50.6%(2537/5014)，白人占52.1%（2614/5014）。初始NIHSS评分中位数为4 （IQR 2-8）。有538例（10.7%）非选择性再入院，150例（3.9%）在30天内死亡。logistic模型显示TSL （c-statistic=0.69）和TSL + mRS出院评分（c-statistic=0.69）是表现最好的模型。结论：我们发现初始NIHSS评分和出院时mRS评分均未显著提高LACE或TSL模型的预测能力。未来在预测短期中风结果方面的努力将需要纳入新的数据元素。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

JMIR Medical Informatics Medicine-Health Informatics

CiteScore

7.90

自引率

3.10%

发文量

173

审稿时长

12 weeks

期刊介绍： JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.