2024
Development and multinational validation of an algorithmic strategy for high Lp(a) screening
Aminorroaya A, Dhingra L, Oikonomou E, Saadatagah S, Thangaraj P, Vasisht Shankar S, Spatz E, Khera R. Development and multinational validation of an algorithmic strategy for high Lp(a) screening. Nature Cardiovascular Research 2024, 3: 558-566. PMID: 39195936, DOI: 10.1038/s44161-024-00469-1.Peer-Reviewed Original ResearchElectronic health recordsAssociated with premature atherosclerotic cardiovascular diseaseElevated Lp(aHealth recordsUK BiobankPremature atherosclerotic cardiovascular diseaseMachine learning modelsAtherosclerotic cardiovascular diseaseCohort studyReal-world settingsTargeted screeningCardiovascular diseaseLearning modelsNovel targeted therapeuticsAlgorithmic strategiesCohortProbability thresholdScreeningClinical featuresValidation cohortElevated lipoproteinRisk inspectionARICLp(a
2021
Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction
Khera R, Haimovich J, Hurley NC, McNamara R, Spertus JA, Desai N, Rumsfeld JS, Masoudi FA, Huang C, Normand SL, Mortazavi BJ, Krumholz HM. Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction. JAMA Cardiology 2021, 6: 633-641. PMID: 33688915, PMCID: PMC7948114, DOI: 10.1001/jamacardio.2021.0122.Peer-Reviewed Original ResearchConceptsMachine learning modelsMeta-classifier modelLearning modelNeural networkGradient descent boostingAcute myocardial infarctionContemporary machineGradient descentXGBoost modelXGBoostHospital mortalityCohort studyLogistic regressionMyocardial infarctionNetworkChest Pain-MI RegistryPrecise classificationIndependent validation dataInitial laboratory valuesNovel methodLarge national registryHigh-risk individualsData analysisValidation dataResolution of risk