C-3 | A Machine Learning Algorithm to Detect Right Heart Failure in Cardiogenic Shock when Using Impella
نویسندگان
چکیده
Right heart failure (RHF) is common amongst patients in cardiogenic shock (CS) and best diagnosed with use of a pulmonary artery catheter (PAC). Unfortunately PACs remains low (<20%) even cases CS. Early identification treatment RVF may improve outcomes for such patients. We propose machine learning algorithm to detect presenting who are treated an Impella CP. leveraged clinical data collected the National Cardiogenic Shock Initiative (NCSI) develop train RHF Indicator model: RHFNet, which dense neural network model that can infer likelihood from CP (Figure). The was developed using 129 NCSI (N=129 development subjects: 49 (32%) RHF, 80 (68%) No RHF) on first 24 hours support. For validation, 84 independent were identified Abiomed IQ database had concurrent CVP PAPI measurements. These assigned cohort based their values create validation set. RHFNet trained 66% random sample subjects (N=86 training subjects) tested remaining 34% (N=43 test establish viability solution. achieved mean AUC score 0.80 subjects. On (N=84 subjects), similar 0.78. be only signals left-sided MCS device (Impella CP).
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ژورنال
عنوان ژورنال: Journal of the Society for Cardiovascular Angiography & Interventions
سال: 2023
ISSN: ['2772-9303']
DOI: https://doi.org/10.1016/j.jscai.2023.100788