{
  "abstract": "Introduction Computed coronary physiology (virtual FFR) is now established in clinical practice. Our group recently developed a model for predicting absolute flow and hyperaemic coronary microvascular resistance (hMVR) using pressure wire and angiographic data. However, the original model could not regionalise flow and therefore lacked accuracy for interrogating clinical data. I aimed to address these deficiencies by: 1) mathematically optimising simulated hMVR; 2) validate against invasive measurements; and 3) investigate the importance of hMVR in a landmark clinical trial (ORBITA).Methods I implemented side branch flow using a scaling law relating flow (Q) to side branch diameter (D) (Q ∝ D c). Simulated hMVR was then validated against invasive results obtained with a thermodilution catheter (Rayflow). I performed a global sensitivity analysis (GSA) to identify the model parameters most influential on hMVR results. This informed a systematic review and meta-analysis of the optimal flow-diameter scaling exponent, c, in coronary arteries. I implemented the results into an updated hMVR model. Finally, I used the software to compute hMVR in patients recruited to ORBITA and evaluated the relationship with trial outcomes.Results For validation experiments, hMVR was quantified in 21 mild-moderately stenosed coronary arteries from 19 patients. Correlation was strong (r=0.70, p<0.0001), with a mean bias of -0.06 mmHg.min/mL (95% CI -0.18 to 0.42). The side branch scaling law produced optimal agreement with a flow-diameter scaling exponent of 2.68 (95%CI 2.04 – 3.32). GSA considered 165 million simulations and showed uncertainty in this scaling exponent was strongly influential on hMVR. The systematic review screen 4,772 articles, 18 were suitable for meta-analysis. Included studies contained data from 1,070 unique coronary trees/arteries, taken from 484 participants. The pooled exponent was 2.39 (95% CI 2.24–2.54), which was incorporated into virtual hMVR calculations (Q ∝ D 2.39). I computed hMVR for 131 ORBITA patients (66 PCI and 65 placebo). For patients with low (20th centile) hMVR, PCI increased exercise time by 48 seconds versus placebo (95% CI 6 to 92 seconds, probability of significant difference (Pr)=98.5%). Low hMVR also predicted a benefit of PCI for complete freedom from angina (Pr=98.8%), reduced angina frequency (Pr=97.8%) and improved stress echocardiography score (Pr=99.9%). In patients with high (80th centile) hMVR, PCI offered no benefit versus placebo for any outcome.Conclusions I have optimised virtual hMVR computations by refining a long held scaling law for coronary physiology. The model can now accurately predict absolute flow throughout the branching epicardial tree. I have used it to demonstrate the importance of hMVR in predicting ORBITA trial outcomes. For patients with severe single vessel disease, taking optimal medical therapy, microvascular disease may attenuate the functional and symptomatic benefits of PCI.Introduction Four years ago, an angiography-derived computational fluid dynamics (CFD) model was described which quantifies absolute coronary flow and hMVR. 1 The original model did not consider side-branches, so was unable to regionalise flow throughout the coronary tree. This significance of this was uncertain, but flow should correspond to the same location as distal pressure measurement. This communication first focuses on work implementing inclusion of side branch flow, demonstrating its importance throughout the coronary tree and optimising hMVR predictions.In the second half, I used the CFD model to examine the relationship between hMVR with the placebo-controlled effect of percutaneous coronary intervention (PCI) in stable coronary artery disease. This was a retrospective analysis of ORBITA – the first placebo-controlled trial of PCI as symptomatic treatment for stable angina.2 In the original ORBITA trial, PCI did not significantly improve exercise capacity or anginal symptoms compared with a placebo intervention, an effect which was independent of invasive lesion physiology.3 However, pre-randomisation testing did not assess for coronary microvascular dysfunction (CMD). I used the updated model to compute hMVR and assessed its impact on the effect of PCI on ORBITA endpoints.Methods The CFD technique reconstructs an anatomically representative, single lumen coronary artery and applies pressures measured during fractional flow reserve (FFR) assessment to compute hMVR. 1 A vascular scaling law was used to compute the expected side-branch loss (Q) from diameter (D) of the tapering main vessel:1) The original theoretical law, derived a century ago, considered bifurcations in isolation to derive a flow-diameter exponent (c) of 3.0 4 but this has been disputed by newer derivations5 and my own work on invasive data.6 Flow was regionalised to local taper, utilising a stenosis filter (figure 1). hMVR results were validated against invasive measurements, taken with continuous infusion thermodilution,7patients with FFR<0.95 were considered to satisfy the CFD tool’s requirement for a pressure drop. Agreement was quantified with Bland Altman plots. To identify future areas for model optimisation, a global sensitivity analysis (GSA) was performed including calculating Sobol indices for model input parameters. GSA results were used to inform a systematic review and random-effects meta-analysis of articles quantifying the flow-diameter exponent in mammalian coronary arteries. Risk of bias was assessed with funnel plots, and Egger regression.The updated CFD model was used to compute hMVR in patients recruited to ORBITA.2 Interaction between ORBITA outcomes and hMVR was assessed using regression models within a Bayesian framework. The follow-up value was conditioned on the pre-randomisation value, with non-linearity allowed using a restricted cubic spline with 3 knots (10th, 50th, 90th centiles), and randomisation arm and hMVR. A contrast of a ‘typical’ patient at the 20th against the 80th centile of hMVR is presented, representing the expected placebo-controlled benefit of PCI for patients with low and high hMVR respectively. This is accompanied by 95% Credible Interval (CrI) and probability of significant benefit favouring PCI versus placebo (Pr). The interactional effect between the randomisation arm and hMVR (Printeraction) was also computed.Results For the validation study, hMVR was computed in 21 arteries, taken from 19 patients. Mean FFR was 0.85±0.06 and median hMVR was 0.35 [0.29 – 0.46] mmHg.min/mL. Correlation between computed and measured hMVR was strong (r=0.70, p<0.0001), with a mean bias of -0.06 mmHg.min/mL (95% CI -0.18 to 0.42, figure 1). There was evidence accuracy was dependent on epicardial lesion severity.8 GSA considered 165 million simulation results. For mild-moderate stenoses (virtual FFR 0.75 – 1.00), side branch flow-diameter exponent and reconstruction stenosis severity were the co-dominant factors for flow results. For lower virtual FFR values, stenosis severity became more significant (figure 1).Given the GSA results showed influence of the flow-diameter exponent, but the true value was disputed,4 5 I conducted a systematic review and meta-analysis. From a total of 4524 articles, retrieved from five databases, 18 were suitable for meta-analysis. Studies included data from 1070 unique coronary trees, taken from 372 humans and 112 animals. The pooled flow diameter exponent across both epicardial and transmural arteries was 2.39 (95% CI 2.24 – 2.54, I2 = 99%). Study quality was generally judged as fair or good. There was no evidence of systematic reporting bias (Egger test, P=0.15, figure 2).9 From the 200 patients in ORBITA, 131 (66 from PCI, 65 from placebo) were included. Almost all patients (n=128, 97.7%) had at least one positive ischemia test at randomisation (table 1). Computed median hMVR was 1.38 [0.89 – 2.09] mmHg.min/mL. Mean pre-randomisation exercise time was 495±188 seconds. For a patient with low hMVR (0.77 mmHg.min/mL), PCI increased exercise time by 48 seconds versus placebo (95% CrI 6 to 92, Pr=98.5%). For a patient with high hMVR (2.43 mmHg.min/mL), PCI increased exercise time by 16 seconds (95% CrI -29 to 61, Pr=75.2%). There was modest evidence for this interaction (Printeraction=83.1%). For a patient with low hMVR, PCI also decreased (improved) DSE score by 0.83 units (95% CrI -1.42 to -0.30, Pr=99.9%), increased log odds of complete freedom from angina by 1.24 (95% CrI 0.16 to 2.38, Pr=98.8%) and improved angina frequency score by 7.67 points (95% CrI 0.25 to 15.6, Pr=97.8%) versus placebo. These effects all became non-significant for patients with high hMVR (figure 3). There was little evidence of interaction with hMVR for any other ORBITA outcome variable (table 2).Discussion We have developed and validated a computational technique for computing hMVR which considers side-branch flow. I also derived an optimal law of coronary scaling, which suggests the Huo-Kassab law 5 most accurately describes bifurcation morphology and has additional implications for bifurcation PCI. Our retrospective analysis of the ORBITA trial suggest a relationship between CMD and response to PCI across multiple outcome measures. The benefits of PCI in those with low hMVR included improved exercise time, a 3.5-times greater likelihood of complete freedom from angina, reduced angina frequency and improved DSE scores. These benefits all became non-significant in patients with high hMVR. Therefore, CMD may be a determinant of otherwise successful PCI performed for relief of anginal symptoms.Statement of Contribution DJTValidation study: Coronary reconstruction, side branch optimisation methodology, simulations, statistical analysis, manuscript writing.Sensitivity analysis: Study conception, coronary reconstruction, statistical analysis, manuscript writing.Meta-analysis: Study conception, literature searching, manuscript selection, statistical analysis, manuscript writing.ORBIAT sub-study: Coronary reconstruction, simulations, statistical analysis, manuscript writing.EY: Simulations and coronary reconstruction verification; TN: simulations; HS: Sensitivity analysis simulations, mathematical methods, and statistical analysis; RG: simulations; XX: simulations; KC, AN, RH: development of the original and side branch CFD methodology; AB: simulation and coronary reconstruction technical support; PT, MvV: clinical data collection; MSS: ORBITA trail investigator and statistical analysis; RAL: ORBITA trial investigator; IH: supervision, simulations, mathematical methods, statistical analysis, CFD support; JG: supervision, study conception, coronary reconstruction verification, development of the original CFD methodology; PM: supervision, study conception, development of original CFD methodology, statistical analysis, coronary reconstruction verification, manuscript writing.Abstract E Figure 1Schematic of initial work. Panel A, CFD workflow is shown; Panel B, implementation of side branch flow model and sensitivity analysis results where the Sobol indices computed demonstrated the importance of side branch flow inclusion and the sensitivity of hMVR results to the flow-diameter scaling exponent used. Panel C, invasive validation results in a limited cohort of ANOCA patientsAbstract E Figure 2Summary of meta-analysis results. As the sensitivity analysis showed the influence of flow-diameter exponent, a meta-analysis was undertaken. The take-home result was a pooled flow diameter exponent of 2.39Abstract E Figure 3Relationship of change in pre-randomisation to follow-up outcome variables against pre-randomisation hMVR for ORBITA outcomes. Panel A, exercise time; Panel B, complete freedom from angina; Panel C, angina frequency; Panel D, DSE scoreAbstract E Table 1Patient demographics of ORBITA patients included in our sub-analysis at enrolment and lesion characteristics. Values indicate n (%) or mean±SD Percutaneous coronary intervention (n= 66)Placebo (n= 65)Complete group (n= 131) Age, y64.7 ±9.367.9 ±7.666.3 ±8.6Male44 (66.7%)47 (72.3%)91 (69.5%)Body Mass Index28.5 ±5.029.0 ±4.928.7 ±5.0Current or ex-smoker31 (47.0%)35 (53.8%)66 (50.4%)Comorbidities Hypertension41 (62.1%)50 (76.7%)91 (69.5%)Diabetes Mellitus10 (15.2%)16 (24.6%)26 (19.8%)Hypercholesterolaemia54 (81.8%)44 (67.7%)98 (74.8%)Previous myocardial infarction2 (3.0%)5 (7.7%)7 (5.3%)Previous percutaneous coronary intervention5 (7.6%)11 (16.9%)16 (12.2%)Canadian Cardiovascular Society Angina classI2 (3.0%)1 (1.5%)3 (2.3%)II43 (65.2%)39 (60%)82 (62.6%)III21 (31.8%)25 (38.5%)46 (35.1%)Angina duration, months10.5 ±19.17.8 ±7.09.2 ±14.5Positive functional test at enrolmentDobutamine stress echocardiography13 (19.7%)11 (16.9%)24 (18.3%)Magnetic resonance imaging perfusion0 1 (1.5%)1 (0.8%)Exercise tolerance test17 (25.8%)12 (18.5%)29 (22.1%)Lesion characterisationLeft anterior descending53 (80.3%)50 (76.9%)103 (78.6%)First diagonal branch1 (1.5%)1 (1.5%)2 (1.5%)Left circumflex2 (3.0%)7 (10.8%)9 (6.9%)Obtuse marginal branch2 (3.0%)02 (1.5%)Intermediate branch1 (1.5%)1 (1.5%)2 (1.5%)Right coronary artery 7 (10.6%)6 (9.2%)13 (9.9%)Ostial/proximal44(66.7%)26 (40.0%)70 (53.4%)Mid20 (30.3%)34 (52.3%)54 (41.2%)Distal2 (3.0%)5 (7.7%)7 (5.3%)Serial lesions10 (15.2%)8 (12.3%)18 (13.7%)Area stenosis by quantitative coronary angiography85.3% ±12.5%85.5% ±11.285.4% ±11.9%Diameter stenosis by quantitative coronary angiography65.3% ±16.1%66.1% ±14.9%65.3% ±15.5%FFRMedian [IQR]0.69 ±0.150.71 [0.61 – 0.81]0.69 ±0.160.72 [0.57 – 0.83]0.69 ±0.160.71 [0.59 – 0.81]iFRMedian [IQR]0.86 ±0.120.89 [0.84 – 0.93]0.82 ±0.170.89 [0.81 – 0.92]0.84 ±0.150.89 [0.83 – 0.92]No. of patients with FFR ≤0.8050 (75.8%)46 (70.8%)96 (73.3%)No. of patients with iFR ≤0.8933 (50.0%)33 (50.8%)66 (50.4%)Procedural detailsStent length, mm 23.0 [16.0 – 30.0] --Stent diameter, mm 3.00 [2.75 – 3.50]--FFR post-PCI (n=X)Median [IQR]0.89 ±0.060.88 [0.86 – 0.93]--iFR post-PCIMedian [IQR]0.94 ±0.040.95 [0.92 – 0.96]--No. of patients with post-FFR>0.8061 (92.4%)--Abstract E Table 2Bayesian analysis of primary and secondary ORBITA end points Low microvascular resistanceHigh microvascular resistance Exercise time (seconds)n129Baseline mean495 ±188Correlation with hMVR0.203 (95% CrI 0.184 to 0.218)Incremental effect of PCI over placebo 48(6 to 92)16(-29 to 61)Probability of significant benefit favouring PCI98.5%75.2%Probability of interaction83.1% Dobutamine stress echocardiography scoren107Baseline mean1.55 ±1.75Correlation with hMVR0.015 (95% CrI -0.038 to 0.047)Incremental effect of PCI over placebo -0.83(-1.42 to -0.30)-0.10(-0.63 to 0.42)Probability of significant benefit favouring PCI99.9%65.9%Probability of interaction96.5% SAQ freedom from anginan127Angina present105 (82.7%)Correlation with hMVR0.165 (95% CrI -0.117 to 0.327)Log odds of complete freedom from angina 1.24(0.16 to 2.38)0.10(-1.07 to 1.25)Probability of significant benefit favouring PCI98.8%57.1%Probability of interaction90.0% SAQ angina frequency score n127Baseline mean71.7 ±23.4Correlation with hMVR0.120 (95% CrI 0.097 to 0.148)Incremental effect of PCI over placebo 7.67(0.25 to 15.6)-0.25(-9.24 to 8.80)Probability of significant benefit favouring PCI97.8%47.8%Probability of interaction89.2% SAQ physical limitation scoren114Baseline mean67.7 ±24.7Correlation with hMVR0.257 (95% CrI 0.250 to 0.258)Incremental effect of PCI over placebo 7.88(-0.01 to 15.5)8.96(-0.92 to 17.8)Probability of significant benefit favouring PCI97.9%96.6%Probability of interaction43.6% SAQ treatment satisfaction scoren127Baseline mean86.4 ±15.4Correlation with hMVR0.043 (95% CrI -0.061 to 0.081)Incremental effect of PCI over placebo 0.92(-3.84 to 5.59)-2.46(-8.44 to 2.91)Probability of significant benefit favouring PCI65.3%19.0%Probability of interaction80.2% SAQ quality of life scoren126Baseline mean48.4 ±22.2Correlation with hMVR0.115 (95% CrI 0.062 to 0.147)Incremental effect of PCI over placebo 2.51(-7.50 to 12.6)0.07(-10.9 to 11.4)Probability of significant benefit favouring PCI68.7%50.5%Probability of interaction61.2% SAQ stability scoren126Baseline mean64.5 ±25.8Correlation with hMVR0.196 (95% CrI 0.150 to 0.213)Incremental effect of PCI over placebo 0.36(-10.9 to 11.1)-4.53(-16.5 to 7.23)Probability of significant benefit favouring PCI52.7%22.6%Probability of interaction29.6% EQ-5D-5L quality of life scoren127Baseline mean0.79 ±0.21Correlation with hMVR0.210 (95% CrI 0.184 to 0.243)Incremental effect of PCI over placebo 0.00(-0.05 to 0.06)-0.02(-0.08 to 0.04)Probability of significant benefit favouring PCI57.0%27.8%Probability of interaction69.7% EQ-5D-5L visual analogue scoren129Baseline mean65.6 ±20.8Correlation with hMVR0.139 (95% CrI 0.105 to 0.162)Incremental effect of PCI over placebo 0.79(-6.10 to 7.99)3.05(-4.99 to 10.5)Probability of significant benefit favouring PCI58.8%78.4%Probability of interaction34.7% CCS angina classificationn129Baseline mean and median1.94 ±0.932 [2 – 3]Correlation with hMVR0.259 (95% CrI 0.208 to 0.266)Incremental effect of PCI over placebo 0.10(-0.41 to 0.64)-0.05(-0.57 to 0.48)Probability of significant benefit favouring PCI35.8% 57.0%Probability of interaction63.7%References Morris PD, Gosling R, Zwierzak I, Evans H, Aubiniere-Robb L, Czechowicz K, et al. A novel method for measuring absolute coronary blood flow and microvascular resistance in patients with ischaemic heart disease. Cardiovascular Research 2021;117(6):1567–77.Al-Lamee R, Thompson D, Dehbi H-M, Sen S, Tang K, Davies J, et al. Percutaneous coronary intervention in stable angina (ORBITA): a double-blind, randomised controlled trial. The Lancet 2018;391(10115):31–40.Al-Lamee R, Howard JP, Shun-Shin MJ, Thompson D, Dehbi H-M, Sen S, et al. Fractional flow reserve and instantaneous wave-free ratio as predictors of the placebo-controlled response to percutaneous coronary intervention in stable single-vessel coronary artery disease: physiology-stratified analysis of ORBITA. Circulation 2018;138(17):1780–92.Murray CD. The physiological principle of minimum work: I. the vascular system and the cost of blood volume. Proc Natl Acad Sci U S A. 1926;12(3):207–14.Huo Y, Kassab GS. Intraspecific scaling laws of vascular trees. Journal of The Royal Society Interface 2012;9(66):190–200.Taylor DJ, Feher J, Halliday I, Hose DR, Gosling R, Aubiniere-Robb L, et al. Refining our understanding of the flow through coronary artery branches; revisiting Murray’s law in human epicardial coronary arteries. Frontiers in Physiology 2022;13:871912.van ‘t Veer M, Adjedj J, Wijnbergen I, Tóth GG, Rutten MC, Barbato E, et al. Novel monorail infusion catheter for volumetric coronary blood flow measurement in humans: in vitro validation. EuroIntervention 2016;12(6):701–7.Taylor DJ, Feher J, Czechowicz K, Halliday I, Hose D, Gosling R, et al. Validation of a novel numerical model to predict regionalized blood flow in the coronary arteries. European Heart Journal-Digital Health 2023;4(2):81–9.Taylor DJ, Saxton H, Halliday I, Newman T, Hose D, Kassab GS, et al. A systematic review and meta-analysis of Murray’s Law in the coronary arterial circulation. American Journal of Physiology-Heart and Circulatory Physiology 2024.",
  "authors": [
    {
      "affiliations": [
        "Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK",
        "Insigneo Institute for In Silico medicine, Sheffield, UK",
        "NIHR Sheffield Biomedical Research Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK",
        "*Corresponding author"
      ],
      "name": "Daniel J Taylor"
    },
    {
      "affiliations": [
        "Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK",
        "Department of Cardiology and Cardiothoracic Surgery, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK"
      ],
      "name": "Eron Yones"
    },
    {
      "affiliations": [
        "Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK",
        "Insigneo Institute for In Silico medicine, Sheffield, UK",
        "NIHR Sheffield Biomedical Research Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK",
        "Department of Cardiology and Cardiothoracic Surgery, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK"
      ],
      "name": "Tom Newman"
    },
    {
      "affiliations": [
        "Insigneo Institute for In Silico medicine, Sheffield, UK",
        "School of Computer Science, University of Sheffield, Sheffield, UK"
      ],
      "name": "Harry Saxton"
    },
    {
      "affiliations": [
        "Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK",
        "Insigneo Institute for In Silico medicine, Sheffield, UK",
        "NIHR Sheffield Biomedical Research Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK",
        "Department of Cardiology and Cardiothoracic Surgery, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK"
      ],
      "name": "Rebecca Gosling"
    },
    {
      "affiliations": [
        "Insigneo Institute for In Silico medicine, Sheffield, UK",
        "School of Computer Science, University of Sheffield, Sheffield, UK"
      ],
      "name": "Xu Xu"
    },
    {
      "affiliations": [
        "Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK",
        "Insigneo Institute for In Silico medicine, Sheffield, UK"
      ],
      "name": "Krzysztof Czechowicz"
    },
    {
      "affiliations": [
        "Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK",
        "Insigneo Institute for In Silico medicine, Sheffield, UK"
      ],
      "name": "Andrew Narracott"
    },
    {
      "affiliations": [
        "Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK",
        "Insigneo Institute for In Silico medicine, Sheffield, UK"
      ],
      "name": "Alberto Biancardi"
    },
    {
      "affiliations": [
        "Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK",
        "Insigneo Institute for In Silico medicine, Sheffield, UK"
      ],
      "name": "Rod Hose"
    },
    {
      "affiliations": [
        "Department of Biomechanical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands"
      ],
      "name": "Pim Tonino"
    },
    {
      "affiliations": [
        "Department of Biomechanical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands"
      ],
      "name": "Marcel van’t Veer"
    },
    {
      "affiliations": [
        "Imperial College London, London, UK",
        "Imperial College Healthcare NHS Trust, London, UK"
      ],
      "name": "Matthew Shun-Shin"
    },
    {
      "affiliations": [
        "Imperial College London, London, UK",
        "Imperial College Healthcare NHS Trust, London, UK"
      ],
      "name": "Rasha Al-Lamee"
    },
    {
      "affiliations": [
        "Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK",
        "Insigneo Institute for In Silico medicine, Sheffield, UK"
      ],
      "name": "Ian Halliday"
    },
    {
      "affiliations": [
        "Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK",
        "Insigneo Institute for In Silico medicine, Sheffield, UK",
        "NIHR Sheffield Biomedical Research Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK",
        "Department of Cardiology and Cardiothoracic Surgery, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK"
      ],
      "name": "Julian P Gunn"
    },
    {
      "affiliations": [
        "Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK",
        "Insigneo Institute for In Silico medicine, Sheffield, UK",
        "NIHR Sheffield Biomedical Research Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK",
        "Department of Cardiology and Cardiothoracic Surgery, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK"
      ],
      "name": "Paul D Morris"
    }
  ],
  "title": "E Computed coronary microvascular resistance: optimisation, validation and application in a landmark clinical trial",
  "uid": "ecc03f28-b13f-51d5-a419-d01ce5b21c16"
}