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

Personalized hemodynamic models can accurately compute fractional flow reserve (FFR) from coronary angiograms and clinical measurements (FFR $$ ), 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.

To determine the minimum set of patient-specific inputs to compute FFR using invasive measurement of FFR (FFR $$ ) as gold standard.

Personalized coronary geometries ( $$ ) were derived from patient coronary angiograms. A computational fluid dynamics framework, FFR $$ , was parameterized with patient-specific inputs: coronary geometry, stenosis geometry, mean arterial pressure, cardiac output, heart rate, hematocrit, and distal pressure location. FFR $$ was validated against FFR $$ and used as the baseline to elucidate the impact of uncertainty on personalized inputs through global uncertainty analysis. FFR $$ was created by only incorporating the most sensitive inputs and FFR $$ additionally included patient-specific distal location.

FFR $$ was validated against FFR $$ via correlation ( $$ , $$ ), agreement (mean difference: $$ ), and diagnostic performance (sensitivity: 89.5%, specificity: 93.6%, PPV: 89.5%, NPV: 93.6%, AUC: 0.95). FFR $$ provided identical diagnostic performance with FFR $$ . Compared to FFR $$ vs. FFR $$ , FFR $$ vs. FFR $$ had decreased correlation ( $$ , $$ ), improved agreement (mean difference: $$ ), and comparable diagnostic performance (sensitivity: 79.0%, specificity: 90.3%, PPV: 83.3%, NPV: 87.5%, AUC: 0.90).

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.