Timothy Conover1, Anthony M Hlavacek2, Francesco Migliavacca3, Ethan Kung1, Adam Dorfman4, Richard S Figliola1, Tain-Yen Hsia5. 1. Department of Mechanical Engineering, Clemson University, Clemson, SC. 2. Division of Pediatric Cardiology, Medical University of South Carolina, Charleston, SC. 3. Laboratory of Biological Structure Mechanics, Politecnico di Milano, Milan, Italy. 4. Division of Pediatric Cardiology, C.S. Mott Children's Hospital, Ann Arbor, Mich. 5. Cardiac Unit, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom. Electronic address: hsiat@gosh.nhs.uk.
Abstract
OBJECTIVE: Modeling of single-ventricle circulations has yielded important insights into their unique flow dynamics and physiology. Here we translated a state-of-the-art mathematical model into a patient-specific clinical decision support interactive Web-based simulation tool and show validation for all 3 stages of single-ventricular palliation. METHODS: Via the adoption a validated lumped parameter method, complete cardiovascular-pulmonary circulatory models of all 3 stages of single-ventricle physiology were created within a simulation tool. The closed-loop univentricular heart model includes scaling for growth and respiratory effects, and typical patient-specific parameters are entered through an intuitive user interface. The effects of medical or surgical interventions can be simulated and compared. To validate the simulator, patient parameters were collected from catheterization reports. Four simulator outputs were compared against catheterization findings: pulmonary to systemic flow ratio (Qp:Qs), systemic arterial saturation (SaO2), mean pulmonary arterial pressure (mPAp), and systemic-venous oxygen difference (SaO2-SvO2). RESULTS: Data from 60 reports were used. Compared with the clinical values, the simulator results were not significantly different in mean Qp:Qs, SaO2, or mPAp (P > .09). There was a statistical but clinically insignificant difference in average SaO-SvO2 (average difference 1%, P < .01). Linear regression analyses revealed a good prediction for each variable (Qp:Qs, R2 = 0.79; SaO2, R2 = 0.64; mPAp, R2 = 0.69; SaO2-SvO2, R2 = 0.93). CONCLUSIONS: This simulator responds quickly and predicts patient-specific hemodynamics with good clinical accuracy. By predicting postoperative and postintervention hemodynamics in all 3 stages of single-ventricle physiology, the simulator could assist in clinical decision-making, training, and consultation. Continuing model refinement and validation will further its application to the bedside.
OBJECTIVE: Modeling of single-ventricle circulations has yielded important insights into their unique flow dynamics and physiology. Here we translated a state-of-the-art mathematical model into a patient-specific clinical decision support interactive Web-based simulation tool and show validation for all 3 stages of single-ventricular palliation. METHODS: Via the adoption a validated lumped parameter method, complete cardiovascular-pulmonary circulatory models of all 3 stages of single-ventricle physiology were created within a simulation tool. The closed-loop univentricular heart model includes scaling for growth and respiratory effects, and typical patient-specific parameters are entered through an intuitive user interface. The effects of medical or surgical interventions can be simulated and compared. To validate the simulator, patient parameters were collected from catheterization reports. Four simulator outputs were compared against catheterization findings: pulmonary to systemic flow ratio (Qp:Qs), systemic arterial saturation (SaO2), mean pulmonary arterial pressure (mPAp), and systemic-venous oxygen difference (SaO2-SvO2). RESULTS: Data from 60 reports were used. Compared with the clinical values, the simulator results were not significantly different in mean Qp:Qs, SaO2, or mPAp (P > .09). There was a statistical but clinically insignificant difference in average SaO-SvO2 (average difference 1%, P < .01). Linear regression analyses revealed a good prediction for each variable (Qp:Qs, R2 = 0.79; SaO2, R2 = 0.64; mPAp, R2 = 0.69; SaO2-SvO2, R2 = 0.93). CONCLUSIONS: This simulator responds quickly and predicts patient-specific hemodynamics with good clinical accuracy. By predicting postoperative and postintervention hemodynamics in all 3 stages of single-ventricle physiology, the simulator could assist in clinical decision-making, training, and consultation. Continuing model refinement and validation will further its application to the bedside.
Keywords:
Glenn or superior cavopulmonary circulation; clinical decision support system; computer modeling and simulation; fontan circulation; single ventricle physiology
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