The impact of post-stroke complications on in-hospital mortality depends on stroke severity

Alejandro Bustamante, Dolors Giralt, Teresa García-Berrocoso, Marta Rubiera, José Álvarez-Sabín, Carlos Molina, Joaquín Serena, Joan Montaner

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)

Abstract

© 2016, © European Stroke Organisation 2016. Introduction: Controversies remain on whether post-stroke complications represent an independent predictor of poor outcome or just a reflection of stroke severity. We aimed to identify which post-stroke complications have the highest impact on in-hospital mortality by using machine learning techniques. Secondary aim was identification of patient’s subgroups in which complications have the highest impact. Patients and methods: Registro Nacional de Ictus de la Sociedad Española de Neurología is a stroke registry from 42 centers from the Spanish Neurological Society. Data from ischemic stroke patients were used to build a random forest by combining 500 classification and regression trees, to weight up the impact of baseline characteristics and post-stroke complications on in-hospital mortality. With the selected variables, a logistic regression analysis was performed to test for interactions. Results: 12,227 ischemic stroke patients were included. In-hospital mortality was 5.9% and median hospital stay was 7(4–10) days. Stroke severity [National Institutes of Health Stroke Scale > 10, OR = 5.54(4.55–6.99)], brain edema [OR = 18.93(14.65–24.46)], respiratory infections [OR = 3.67(3.02–4.45)] and age [OR = 2.50(2.07–3.03) for >77 years] had the highest impact on in-hospital mortality in random forest, being independently associated with in-hospital mortality. Complications have higher odds ratios in patients with baseline National Institutes of Health Stroke Scale <10. Discussion: Our study identified brain edema and respiratory infections as independent predictors of in-hospital mortality, rather than just markers of more severe strokes. Moreover, its impact was higher in less severe strokes, despite lower frequency. Conclusion: Brain edema and respiratory infections were the complications with a greater impact on in-hospital mortality, with the highest impact in patients with mild strokes. Further efforts on the prediction of these complications could improve stroke outcome.
Original languageEnglish
Pages (from-to)54-63
JournalEuropean Stroke Journal
Volume2
Issue number1
DOIs
Publication statusPublished - 1 Mar 2017

Keywords

  • classification and regression trees
  • complications
  • machine learning
  • mortality
  • outcome
  • random forest
  • Stroke

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