Predicting aortic stenosis progression using a video-based deep learning model of aortic stenosis built for single-view two-dimensional echocardiography
Oikonomou E, Holste G, Mcnamara R, Velazquez E, Nadkarni G, Ouyang D, Krumholz H, Wang Z, Khera R. Predicting aortic stenosis progression using a video-based deep learning model of aortic stenosis built for single-view two-dimensional echocardiography. European Heart Journal 2023, 44: ehad655.040. DOI: 10.1093/eurheartj/ehad655.040.Peer-Reviewed Original ResearchLeft ventricular ejection fractionSevere aortic stenosisAortic stenosisAS progressionAV VmaxTransthoracic echocardiographyYale New Haven Health SystemBaseline left ventricular ejection fractionAortic stenosis progressionModerate aortic stenosisRetrospective cohort studyVentricular ejection fractionTwo-dimensional echocardiographyMean rateModerate ASAS severityCohort studyEjection fractionPatient sexStenosis progressionTTE studiesEligible participantsSerial monitoringSpecialized centersTimely diagnosisReclassification of moderate aortic stenosis based on data-driven phenotyping of hemodynamic progression
Cho I, Kim W, Kim S, Ko K, Seong Y, Kim D, Seo J, Shim C, Ha J, Mori M, Gupta A, You S, Hong G, Krumholz H. Reclassification of moderate aortic stenosis based on data-driven phenotyping of hemodynamic progression. Scientific Reports 2023, 13: 6694. PMID: 37095171, PMCID: PMC10125992, DOI: 10.1038/s41598-023-33683-1.Peer-Reviewed Original ResearchConceptsRapid progression groupModerate aortic stenosisAortic valve replacementSlow progression groupAortic stenosisProgression groupHemodynamic progressionRapid progressionMore rapid progressionLatent class trajectory modelingTransthoracic echocardiography studyBetween-group differencesData-driven phenotypingPressure gradient measurementAVR ratesModerate ASCause mortalityValve replacementEchocardiography studyAtrial fibrillationTTE studiesEchocardiographic dataRisk factorsPredictive valuePatients