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BackgroundPersonalized hemodynamic models can accurately compute fractional flow reserve (FFR) from coronary angiograms and clinical measurements (FFR ${}_{\mathrm{baseline}}$), 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. AimsTo determine the minimum set of patient-specific inputs to compute FFR using invasive measurement of FFR (FFR ${}_{\mathrm{invasive}}$) as gold standard. Materials and MethodsPersonalized coronary geometries ( $N=50$) were derived from patient coronary angiograms. A computational fluid dynamics framework, FFR ${}_{\mathrm{baseline}}$, was parameterized with patient-specific inputs: coronary geometry, stenosis geometry, mean arterial pressure, cardiac output, heart rate, hematocrit, and distal pressure location. FFR ${}_{\mathrm{baseline}}$was validated against FFR ${}_{\mathrm{invasive}}$and used as the baseline to elucidate the impact of uncertainty on personalized inputs through global uncertainty analysis. FFR ${}_{\mathrm{streamlined}}$was created by only incorporating the most sensitive inputs and FFR ${}_{\text{semi-streamlined}}$additionally included patient-specific distal location. ResultsFFR ${}_{\mathrm{baseline}}$was validated against FFR ${}_{\mathrm{invasive}}$via correlation ( $r=0.714$, $p<0.001$), agreement (mean difference: $0.01\pm 0.09$), and diagnostic performance (sensitivity: 89.5%, specificity: 93.6%, PPV: 89.5%, NPV: 93.6%, AUC: 0.95). FFR ${}_{\text{semi-streamlined}}$provided identical diagnostic performance with FFR ${}_{\mathrm{baseline}}$. Compared to FFR ${}_{\mathrm{baseline}}$vs. FFR ${}_{\mathrm{invasive}}$, FFR ${}_{\mathrm{streamlined}}$vs. FFR ${}_{\mathrm{invasive}}$had decreased correlation ( $r=0.64$, $p<0.001$), improved agreement (mean difference: $0.01\pm 0.08$), and comparable diagnostic performance (sensitivity: 79.0%, specificity: 90.3%, PPV: 83.3%, NPV: 87.5%, AUC: 0.90). ConclusionStreamlined 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.