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Stroke Measures Analysis of pRognostic Testing-Mortality nomogram predicts long-term mortality after ischemic stroke.
Kim, Tae Jung; Lee, Ji Sung; Oh, Mi Sun; Kim, Ji-Woo; Park, Soo-Hyun; Yu, Kyung-Ho; Lee, Byung-Chul; Yoon, Byung-Woo; Ko, Sang-Bae.
Afiliación
  • Kim TJ; Department of Neurology, Seoul National University Hospital, Seoul, Korea.
  • Lee JS; Department of Critical Care Medicine, Seoul National University Hospital, Seoul, Korea.
  • Oh MS; Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Kim JW; Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, Korea.
  • Park SH; Health Insurance Review and Assessment Service, Wonju, Korea.
  • Yu KH; Department of Neurology, Soonchunhyang University Seoul Hospital, Seoul, Korea.
  • Lee BC; Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, Korea.
  • Yoon BW; Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, Korea.
  • Ko SB; Department of Neurology, Uijeongbu Eulji Medical Center, Uijeongbu, Korea.
Int J Stroke ; : 17474930241278808, 2024 Sep 15.
Article en En | MEDLINE | ID: mdl-39150095
ABSTRACT

BACKGROUND:

Predicting long-term mortality is essential for understanding prognosis and guiding treatment decisions in patients with ischemic stroke. Therefore, this study aimed to develop and validate the method for predicting 1- and 5-year mortality after ischemic stroke.

METHODS:

We used data from the linked dataset comprising the administrative claims database of the Health Insurance Review and Assessment Service and the Clinical Research Center for Stroke registry data for patients with acute stroke within 7 days of onset. The outcome was all-cause mortality following ischemic stroke. Clinical variables linked to long-term mortality following ischemic stroke were determined. A nomogram was constructed based on the Cox's regression analysis. The performance of the risk prediction model was evaluated using the Harrell's C-index.

RESULTS:

This study included 42,207 ischemic stroke patients, with a mean age of 66.6 years and 59.2% being male. The patients were randomly divided into training (n = 29,916) and validation (n = 12,291) groups. Variables correlated with long-term mortality in patients with ischemic stroke, including age, sex, body mass index, stroke severity, stroke mechanisms, onset-to-door time, pre-stroke dependency, history of stroke, diabetes mellitus, hypertension, coronary artery disease, chronic kidney disease, cancer, smoking, fasting glucose level, previous statin therapy, thrombolytic therapy, such as intravenous thrombolysis and endovascular recanalization therapy, medications, and discharge modified Rankin Scale were identified as predictors. We developed a predictive system named Stroke Measures Analysis of pRognostic Testing-Mortality (SMART-M) by constructing a nomogram using the identified features. The C-statistics of the nomogram in the developing and validation groups were 0.806 (95% confidence interval (CI), 0.802-0.812) and 0.803 (95% CI, 0.795-0.811), respectively.

CONCLUSION:

The SMART-M method demonstrated good performance in predicting long-term mortality in ischemic stroke patients. This method may help physicians and family members understand the long-term outcomes and guide the appropriate decision-making process.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Int J Stroke Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Int J Stroke Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos