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Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment

Author

Listed:
  • María Carmen Lea-Pereira

    (Internal Medicine Department, Hospital de Poniente, El Ejido, 04700 Almería, Spain)

  • Laura Amaya-Pascasio

    (Department of Neurology and Stroke Unit, Hospital Universitario Torrecárdenas, 04009 Almería, Spain)

  • Patricia Martínez-Sánchez

    (Department of Neurology and Stroke Unit, Hospital Universitario Torrecárdenas, 04009 Almería, Spain)

  • María del Mar Rodríguez Salvador

    (Nurse in Almería Primary Care District, 04009 Almería, Spain)

  • José Galván-Espinosa

    (Alejandro Otero Research Foundation (FIBAO), Hospital Universitario Torrecárdenas, 04009 Almería, Spain)

  • Luis Téllez-Ramírez

    (Biomedical Research Unit, Hospital Universitario Torrecárdenas, 04009 Almería, Spain)

  • Fernando Reche-Lorite

    (Department of Mathematics, University of Almería, 04120 Almería, Spain)

  • María-José Sánchez

    (Escuela Andaluza de Salud Pública, 18011 Granada, Spain
    Instituto de Investigación Biomédica Ibs. Granada, 18012 Granada, Spain
    Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain
    Department of Preventive Medicine and Public Health, University of Granada, 18071 Granada, Spain)

  • Juan Manuel García-Torrecillas

    (Biomedical Research Unit, Hospital Universitario Torrecárdenas, 04009 Almería, Spain
    Instituto de Investigación Biomédica Ibs. Granada, 18012 Granada, Spain
    Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain
    Department of Emergency Medicine, Hospital Universitario Torrecárdenas, 04009 Almería, Spain)

Abstract

Background: Stroke is the second cause of mortality worldwide and the first in women. The aim of this study is to develop a predictive model to estimate the risk of mortality in the admission of patients who have not received reperfusion treatment. Methods: A retrospective cohort study was conducted of a clinical–administrative database, reflecting all cases of non-reperfused ischaemic stroke admitted to Spanish hospitals during the period 2008–2012. A predictive model based on logistic regression was developed on a training cohort and later validated by the “hold-out” method. Complementary machine learning techniques were also explored. Results: The resulting model had the following nine variables, all readily obtainable during initial care. Age (OR 1.069), female sex (OR 1.202), readmission (OR 2.008), hypertension (OR 0.726), diabetes (OR 1.105), atrial fibrillation (OR 1.537), dyslipidaemia (0.638), heart failure (OR 1.518) and neurological symptoms suggestive of posterior fossa involvement (OR 2.639). The predictability was moderate (AUC 0.742, 95% CI: 0.737–0.747), with good visual calibration; Pearson’s chi-square test revealed non-significant calibration. An easily consulted risk score was prepared. Conclusions: It is possible to create a predictive model of mortality for patients with ischaemic stroke from which important advances can be made towards optimising the quality and efficiency of care. The model results are available within a few minutes of admission and would provide a valuable complementary resource for the neurologist.

Suggested Citation

  • María Carmen Lea-Pereira & Laura Amaya-Pascasio & Patricia Martínez-Sánchez & María del Mar Rodríguez Salvador & José Galván-Espinosa & Luis Téllez-Ramírez & Fernando Reche-Lorite & María-José Sánchez, 2022. "Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment," IJERPH, MDPI, vol. 19(6), pages 1-16, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3182-:d:766565
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    References listed on IDEAS

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    1. F.A.G. Windmeijer, 1990. "The asymptotic distribution of the sum of weighted squared residuals in binary choice models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 44(2), pages 69-78, June.
    2. Songhee Cheon & Jungyoon Kim & Jihye Lim, 2019. "The Use of Deep Learning to Predict Stroke Patient Mortality," IJERPH, MDPI, vol. 16(11), pages 1-12, May.
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