Analysis identifying minimal governing parameters for clinically accurate in silico fractional flow reserve

BACKGROUND: Personalized hemodynamic models can accurately compute fractional flow reserve (FFR) from coronary angiograms and clinical measurements (FFR [Formula: see text]), but obtaining patient-specific data could be challenging and sometimes not feasible. Understanding which measurements need to be patient-tuned vs. patient-generalized would inform models with minimal inputs that could expedite data collection and simulation pipelines. AIMS: To determine the minimum set of patient-specific inputs to compute FFR using invasive measurement of FFR (FFR [Formula: see text]) as gold standard. MATERIALS AND METHODS: Personalized coronary geometries ([Formula: see text]) were derived from patient coronary angiograms. A computational fluid dynamics framework, FFR [Formula: see text] , was parameterized with patient-specific inputs: coronary geometry, stenosis geometry, mean arterial pressure, cardiac output, heart rate, hematocrit, and distal pressure location. FFR [Formula: see text] was validated against FFR [Formula: see text] and used as the baseline to elucidate the impact of uncertainty on personalized inputs through global uncertainty analysis. FFR [Formula: see text] was created by only incorporating the most sensitive inputs and FFR [Formula: see text] additionally included patient-specific distal location. RESULTS: FFR [Formula: see text] was validated against FFR [Formula: see text] via correlation ([Formula: see text] , [Formula: see text]), agreement (mean difference: [Formula: see text]), and diagnostic performance (sensitivity: 89.5%, specificity: 93.6%, PPV: 89.5%, NPV: 93.6%, AUC: 0.95). FFR [Formula: see text] provided identical diagnostic performance with FFR [Formula: see text]. Compared to FFR [Formula: see text] vs. FFR [Formula: see text] , FFR [Formula: see text] vs. FFR [Formula: see text] had decreased correlation ([Formula: see text] , [Formula: see text]), improved agreement (mean difference: [Formula: see text]), and comparable diagnostic performance (sensitivity: 79.0%, specificity: 90.3%, PPV: 83.3%, NPV: 87.5%, AUC: 0.90). CONCLUSION: Streamlined models could match the diagnostic performance of the baseline with a full gamut of patient-specific measurements. Capturing coronary hemodynamics depended most on accurate geometry reconstruction and cardiac output measurement.