With a multitude of echocardiographic (echo) parameters at a clinician’s disposal and clinical efficiency paramount, determining the most reliable and relevant pediatric echo parameters remains challenging. Using machine learning (ML), clinical relevance, and inter/intra-rater reliability, we aimed to identify a core set of echo parameters from the PCS2 cohort of childhood cancer survivors and healthy controls to guide pediatric research and clinical care. A standard set of 94 echocardiographic parameters were chosen and screened for missing variation, linear combinations, and high correlations. A hierarchical cluster analysis using Ward’s method was performed on the remaining variables to produce a clustering dendrogram. Thereafter, inter- and intra-rater reliability analyses were conducted using intraclass correlation coefficients (ICC) and Bland-Altman (B-A) plots. Using highly reliable (\u3e0.65 ICC) and available (\u3e80% scored) parameters, five pediatric cardiologists ranked each parameter within cluster for clinical relevance. Of the 61 echo parameters selected for the dendrogram, only 54 were scored due to feasibility of sonographer acquisition. ≥73% of all scored parameters had good (0.60-0.74) or excellent (≥0.75) ICC in the inter- and intra-rater analyses. Mean within cluster ranks were assigned per parameter to identify a core set of 10, and minimal set of 5 parameters: ejection fraction (EF), mitral valve E/E’, tissue doppler interventricular septum valve S-velocity, average global longitudinal strain, and LV end diastolic diameter. Using clustering analysis, clinical relevance rankings, and reliability we have identified 10 core and 5 minimal echo indices to guide further pediatric echocardiographic research and clinical care
