ArticleInternational journal of environmental research and public health2022
Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- External Validation and Recalibration of a Mortality Prediction Model for Patients with Ischaemic Stroke.Journal of clinical medicine · 2023Article
- The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction.Life (Basel, Switzerland) · 2022Article
- A Focus on the Role of DSC-PWI Dynamic Radiomics Features in Diagnosis and Outcome Prediction of Ischemic Stroke.Journal of clinical medicine · 2022Article
- Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStroke 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.
methodsA 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.
resultsThe 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.
conclusionsIt 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.
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