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1. Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular disease: a pilot study

   https://doi.org/10.1093/ehjdh/ztaa008  

   Farjo, Peter D; Yanamala, Naveena; Kagiyama, Nobuyuki; Patel, Heenaben B; Casaclang-Verzosa, Grace; Nezarat, Negin; Budoff, Matthew J; Sengupta, Partho P (November 2020, European Heart Journal - Digital Health)

   null (Ed.)

   Abstract Aims Coronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline recommended CAC score categories from clinical features and surface electrocardiograms. Methods and results In this substudy of a prospective, multicentre trial, a total of 534 subjects referred for CAC scores and electrocardiographic data were split into 80% training and 20% testing sets. Two binary outcome ML logistic regression models were developed for prediction of CAC scores equal to 0 and ≥400. Both CAC = 0 and CAC ≥400 models yielded values for the area under the curve, sensitivity, specificity, and accuracy of 84%, 92%, 70%, and 75%, and 87%, 91%, 75%, and 81%, respectively. We further tested the CAC ≥400 model to risk stratify a cohort of 87 subjects referred for invasive coronary angiography. Using an intermediate or higher pretest probability (≥15%) to predict CAC ≥400, the model predicted the presence of significant coronary artery stenosis (P = 0.025), the need for revascularization (P < 0.001), notably bypass surgery (P = 0.021), and major adverse cardiovascular events (P = 0.023) during a median follow-up period of 2 years. Conclusion ML techniques can extract information from electrocardiographic data and clinical variables to predict CAC score categories and similarly risk-stratify patients with suspected coronary artery disease. 

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

   https://doi.org/10.3389/fmedt.2022.1034801  

   Tanade, Cyrus; Chen, S James; Leopold, Jane A; Randles, Amanda (December 2022, Frontiers in Medical Technology)

   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. 

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3. Virtual Transcatheter Interventions for Peripheral Pulmonary Artery Stenosis in Williams and Alagille Syndromes

   https://doi.org/10.1161/JAHA.121.023532  

   Lan, Ingrid S.; Yang, Weiguang; Feinstein, Jeffrey A.; Kreutzer, Jacqueline; Collins, R. Thomas; Ma, Michael; Adamson, Gregory T.; Marsden, Alison L. (March 2022, Journal of the American Heart Association)

   Background Despite favorable outcomes of surgical pulmonary artery (PA) reconstruction, isolated proximal stenting of the central PAs is common clinical practice for patients with peripheral PA stenosis in association with Williams and Alagille syndromes. Given the technical challenges of PA reconstruction and the morbidities associated with transcatheter interventions, the hemodynamic consequences of all treatment strategies must be rigorously assessed. Our study aims to model, assess, and predict hemodynamic outcomes of transcatheter interventions in these patients. Methods and Results Isolated proximal and “extensive” interventions (stenting and/or balloon angioplasty of proximal and lobar vessels) were performed in silico on 6 patient‐specific PA models. Autoregulatory adaptation of the cardiac output and downstream arterial resistance was modeled in response to intervention‐induced hemodynamic perturbations. Postintervention computational fluid dynamics predictions were validated in 2 stented patients and quantitatively assessed in 4 surgical patients. Our computational methods accurately predicted postinterventional PA pressures, the primary indicators of success for treatment of peripheral PA stenosis. Proximal and extensive treatment achieved median reductions of 14% and 40% in main PA systolic pressure, 27% and 56% in pulmonary vascular resistance, and 10% and 45% in right ventricular stroke work, respectively. Conclusions In patients with Williams and Alagille syndromes, extensive transcatheter intervention is required to sufficiently reduce PA pressures and right ventricular stroke work. Transcatheter therapy was shown to be ineffective for long‐segment stenosis and pales hemodynamically in comparison with published outcomes of surgical reconstruction. Regardless of the chosen strategy, a virtual treatment planning platform could identify lesions most critical for optimizing right ventricular afterload. 

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4. Coronary Artery Disease Phenotype Detection in an Academic Hospital System Setting

   https://doi.org/10.1055/s-0040-1721012  

   Joseph, Amy; Mullett, Charles; Lilly, Christa; Armistead, Matthew; Cox, Harold J.; Denney, Michael; Varma, Misha; Rich, David; Adjeroh, Donald A.; Doretto, Gianfranco; et al (January 2021, Applied Clinical Informatics)

   null (Ed.)

   Abstract Background The United States, and especially West Virginia, have a tremendous burden of coronary artery disease (CAD). Undiagnosed familial hypercholesterolemia (FH) is an important factor for CAD in the U.S. Identification of a CAD phenotype is an initial step to find families with FH. Objective We hypothesized that a CAD phenotype detection algorithm that uses discrete data elements from electronic health records (EHRs) can be validated from EHR information housed in a data repository. Methods We developed an algorithm to detect a CAD phenotype which searched through discrete data elements, such as diagnosis, problem lists, medical history, billing, and procedure (International Classification of Diseases [ICD]-9/10 and Current Procedural Terminology [CPT]) codes. The algorithm was applied to two cohorts of 500 patients, each with varying characteristics. The second (younger) cohort consisted of parents from a school child screening program. We then determined which patients had CAD by systematic, blinded review of EHRs. Following this, we revised the algorithm by refining the acceptable diagnoses and procedures. We ran the second algorithm on the same cohorts and determined the accuracy of the modification. Results CAD phenotype Algorithm I was 89.6% accurate, 94.6% sensitive, and 85.6% specific for group 1. After revising the algorithm (denoted CAD Algorithm II) and applying it to the same groups 1 and 2, sensitivity 98.2%, specificity 87.8%, and accuracy 92.4; accuracy 93% for group 2. Group 1 F1 score was 92.4%. Specific ICD-10 and CPT codes such as “coronary angiography through a vein graft” were more useful than generic terms. Conclusion We have created an algorithm, CAD Algorithm II, that detects CAD on a large scale with high accuracy and sensitivity (recall). It has proven useful among varied patient populations. Use of this algorithm can extend to monitor a registry of patients in an EHR and/or to identify a group such as those with likely FH. 

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5. Inlet and Outlet Boundary Conditions and Uncertainty Quantification in Volumetric Lattice Boltzmann Method for Image-Based Computational Hemodynamics

   https://doi.org/10.3390/fluids7010030  

   Yu, Huidan; Khan, Monsurul; Wu, Hao; Zhang, Chunze; Du, Xiaoping; Chen, Rou; Fang, Xin; Long, Jianyun; Sawchuk, Alan P. (January 2022, Fluids)

   Inlet and outlet boundary conditions (BCs) play an important role in newly emerged image-based computational hemodynamics for blood flows in human arteries anatomically extracted from medical images. We developed physiological inlet and outlet BCs based on patients’ medical data and integrated them into the volumetric lattice Boltzmann method. The inlet BC is a pulsatile paraboloidal velocity profile, which fits the real arterial shape, constructed from the Doppler velocity waveform. The BC of each outlet is a pulsatile pressure calculated from the three-element Windkessel model, in which three physiological parameters are tuned by the corresponding Doppler velocity waveform. Both velocity and pressure BCs are introduced into the lattice Boltzmann equations through Guo’s non-equilibrium extrapolation scheme. Meanwhile, we performed uncertainty quantification for the impact of uncertainties on the computation results. An application study was conducted for six human aortorenal arterial systems. The computed pressure waveforms have good agreement with the medical measurement data. A systematic uncertainty quantification analysis demonstrates the reliability of the computed pressure with associated uncertainties in the Windkessel model. With the developed physiological BCs, the image-based computation hemodynamics is expected to provide a computation potential for the noninvasive evaluation of hemodynamic abnormalities in diseased human vessels. 

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