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

• Published in: International journal of environmental research and public health Vol. 19; no. 6; p. 3182
• Main Authors: Lea-Pereira, María Carmen, Amaya-Pascasio, Laura, Martínez-Sánchez, Patricia, Rodríguez Salvador, María Del Mar, Galván-Espinosa, José, Téllez-Ramírez, Luis, Reche-Lorite, Fernando, Sánchez, María-José, García-Torrecillas, Juan Manuel
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08-03-2022; MDPI
• Subjects: Brain Ischemia - complications; Calibration; Cardiovascular disease; Chi-square test; Conservative Treatment; Diabetes mellitus; Emergency medical care; Female; Fibrillation; Health services; Heart diseases; Heart failure; Hospitalization; Humans; Hypertension; Ischemia; Ischemic Stroke; Length of stay; Mortality; Neural networks; Patients; Prediction models; Reperfusion; Retrospective Studies; Risk; Risk Assessment - methods; Risk Factors; Sepsis; Statistical analysis; Statistical tests; Stroke; Stroke - etiology; Variables; United States > US; Spain; mortality; predictive model; stroke; risk score; vascular neurology
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph19063182

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.
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.
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.
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.