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Risk Factors for Perinatal Arterial Ischemic Stroke: A Machine Learning Approach
Ratika Srivastava1, Lauran Cole1, Kimberly Amador1
1From the Division of Pediatric Neurology (R.S., L.C.), Department of Pediatrics, University of Alberta; Alberta Children's Hospital Research Institute and Department of Clinical Neurosciences (K.A.); Department of Clinical Neurosciences (N.D.F.); Department of Pediatrics and Clinical Neurosciences (M.D.), University of Calgary, Alberta; Departments of Pediatrics and Neurology/Neurosurgery (M.I.S., M.O.), McGill University, Montreal, Quebec, Canada; Newcastle upon Tyne Hospitals (A.P.B.), NHS Foundation Trust, Newcastle upon Tyne, United Kingdom; Department of Neurology (M.J.R.), Boston Children's Hospital and Department of Neurology, Harvard Medical School, Boston, MA; Department of Neonatology (E.S.), Soroka University Medical Center and Faculty of Health sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel; Department of Neonatology (L.S.V.), University Medical Center Utrecht, The Netherlands; Departments of Pediatrics and Community Health Sciences (D.D.), Owerko Centre at the Alberta Children's Hospital Research Institute, Hotchkiss Brain Institute, Cummings School of Medicine; Faculty of Nursing and Cumming School of Medicine (N.L.), Departments of Pediatrics, Psychiatry and Community Health Sciences; Alberta Children's Hospital Research Institute and Department of Clinical Neurosciences (P.M.); Departments of Clinical Neurosciences (M.D.H.), Community Health Sciences, Medicine and Radiology, Hotchkiss Brain Institute and Department of Pediatrics (A.K.), Cumming School of Medicine, University of Calgary, Alberta, Canada.
Machine learning identified key clinical factors for perinatal arterial ischemic stroke (PAIS), a leading cause of cerebral palsy. This data-driven approach accurately predicts PAIS risk in neonates, outperforming traditional models.
Area of Science:
- Neurology
- Pediatrics
- Data Science
Background:
- Perinatal arterial ischemic stroke (PAIS) is a significant cause of hemiparetic cerebral palsy.
- Previous studies on PAIS predictors were limited by sample size and complex factor interactions.
Purpose of the Study:
- To apply machine learning to large datasets for unbiased identification of PAIS clinical predictors.
- To compare a data-driven machine learning model with traditional literature-driven prediction models for PAIS.
Main Methods:
- Utilized common data elements from three PAIS registries and a healthy control cohort.
- Employed a random forest machine learning pipeline on data from 2,571 neonates (527 cases, 2,044 controls).
- Included maternal/pregnancy, intrapartum, and neonatal factors in the analysis.
Main Results:
- The machine learning model achieved 86.5% balanced accuracy in predicting PAIS.
- Identified key predictors including maternal age, substance exposure, intrapartum fever, and Apgar scores.
- The machine learning model (AUC 0.93) significantly outperformed the literature-driven model (AUC 0.73).
Conclusions:
- Machine learning offers an unbiased method for identifying PAIS clinical predictors.
- The findings support the multifactorial nature of PAIS pathophysiology.
- Neonates at risk for PAIS can be identified using this approach.
