Evangelos K Oikonomou1, David Van Dijk2,3, Helen Parise2, Marc A Suchard4,5, James de Lemos6, Charalambos Antoniades7, Eric J Velazquez2, Edward J Miller2, Rohan Khera2,8. 1. Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA. 2. Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA. 3. Department of Computer Science, Yale University, 51 Prospect St, New Haven, CT 06520-8285, USA. 4. Department of Biostatistics, Fielding School of Public Health, University of California, 650 Charles E. Young Drive S, Los Angeles, CA 90095, USA. 5. Departments of Computational Medicine and Human Genetics, David Geffen School of Medicine at UCLA, University of California, 695 Charles E. Young Drive S, Los Angeles, CA 90095, USA. 6. Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-8830, USA. 7. Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Headley Way, OX3 9DU, Oxford, UK. 8. Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, MS 1 Church Street, Suite 200, New Haven, CT 06510, USA.
Abstract
AIMS: Coronary artery disease is frequently diagnosed following evaluation of stable chest pain with anatomical or functional testing. A more granular understanding of patient phenotypes that benefit from either strategy may enable personalized testing. METHODS AND RESULTS: Using participant-level data from 9572 patients undergoing anatomical (n = 4734) vs. functional (n = 4838) testing in the PROMISE (PROspective Multicenter Imaging Study for Evaluation of Chest Pain) trial, we created a topological representation of the study population based on 57 pre-randomization variables. Within each patient's 5% topological neighbourhood, Cox regression models provided individual patient-centred hazard ratios for major adverse cardiovascular events and revealed marked heterogeneity across the phenomap [median 1.11 (10th to 90th percentile: 0.52-2.61]), suggestive of distinct phenotypic neighbourhoods favouring anatomical or functional testing. Based on this risk phenomap, we employed an extreme gradient boosting algorithm in 80% of the PROMISE population to predict the personalized benefit of anatomical vs. functional testing using 12 model-derived, routinely collected variables and created a decision support tool named ASSIST (Anatomical vs. Stress teSting decIsion Support Tool). In both the remaining 20% of PROMISE and an external validation set consisting of patients from SCOT-HEART (Scottish COmputed Tomography of the HEART Trial) undergoing anatomical-first vs. functional-first assessment, the testing strategy recommended by ASSIST was associated with a significantly lower incidence of each study's primary endpoint (P = 0.0024 and P = 0.0321 for interaction, respectively), as well as a harmonized endpoint of all-cause mortality or non-fatal myocardial infarction (P = 0.0309 and P < 0.0001 for interaction, respectively). CONCLUSION: We propose a novel phenomapping-derived decision support tool to standardize the selection of anatomical vs. functional testing in the evaluation of stable chest pain, validated in two large and geographically diverse clinical trial populations. Published on behalf of the European Society of Cardiology. All rights reserved.
AIMS: Coronary artery disease is frequently diagnosed following evaluation of stable chest pain with anatomical or functional testing. A more granular understanding of patient phenotypes that benefit from either strategy may enable personalized testing. METHODS AND RESULTS: Using participant-level data from 9572 patients undergoing anatomical (n = 4734) vs. functional (n = 4838) testing in the PROMISE (PROspective Multicenter Imaging Study for Evaluation of Chest Pain) trial, we created a topological representation of the study population based on 57 pre-randomization variables. Within each patient's 5% topological neighbourhood, Cox regression models provided individual patient-centred hazard ratios for major adverse cardiovascular events and revealed marked heterogeneity across the phenomap [median 1.11 (10th to 90th percentile: 0.52-2.61]), suggestive of distinct phenotypic neighbourhoods favouring anatomical or functional testing. Based on this risk phenomap, we employed an extreme gradient boosting algorithm in 80% of the PROMISE population to predict the personalized benefit of anatomical vs. functional testing using 12 model-derived, routinely collected variables and created a decision support tool named ASSIST (Anatomical vs. Stress teSting decIsion Support Tool). In both the remaining 20% of PROMISE and an external validation set consisting of patients from SCOT-HEART (Scottish COmputed Tomography of the HEART Trial) undergoing anatomical-first vs. functional-first assessment, the testing strategy recommended by ASSIST was associated with a significantly lower incidence of each study's primary endpoint (P = 0.0024 and P = 0.0321 for interaction, respectively), as well as a harmonized endpoint of all-cause mortality or non-fatal myocardial infarction (P = 0.0309 and P < 0.0001 for interaction, respectively). CONCLUSION: We propose a novel phenomapping-derived decision support tool to standardize the selection of anatomical vs. functional testing in the evaluation of stable chest pain, validated in two large and geographically diverse clinical trial populations. Published on behalf of the European Society of Cardiology. All rights reserved.
Authors: Juhani Knuuti; Haitham Ballo; Luis Eduardo Juarez-Orozco; Antti Saraste; Philippe Kolh; Anne Wilhelmina Saskia Rutjes; Peter Jüni; Stephan Windecker; Jeroen J Bax; William Wijns Journal: Eur Heart J Date: 2018-09-14 Impact factor: 29.983
Authors: Scott M Lundberg; Gabriel Erion; Hugh Chen; Alex DeGrave; Jordan M Prutkin; Bala Nair; Ronit Katz; Jonathan Himmelfarb; Nisha Bansal; Su-In Lee Journal: Nat Mach Intell Date: 2020-01-17
Authors: Neha J Pagidipati; Kshipra Hemal; Adrian Coles; Daniel B Mark; Rowena J Dolor; Patricia A Pellikka; Udo Hoffmann; Sheldon E Litwin; James Udelson; Melissa A Daubert; Svati H Shah; Beth Martinez; Kerry L Lee; Pamela S Douglas Journal: J Am Coll Cardiol Date: 2016-04-04 Impact factor: 24.094
Authors: Michael Poon; John R Lesser; Cathleen Biga; Ron Blankstein; Christopher M Kramer; James K Min; Pamela S Noack; Christina Farrow; Udo Hoffman; Jaime Murillo; Koen Nieman; Leslee J Shaw Journal: J Am Coll Cardiol Date: 2020-09-15 Impact factor: 24.094
Authors: David E Newby; Philip D Adamson; Colin Berry; Nicholas A Boon; Marc R Dweck; Marcus Flather; John Forbes; Amanda Hunter; Stephanie Lewis; Scott MacLean; Nicholas L Mills; John Norrie; Giles Roditi; Anoop S V Shah; Adam D Timmis; Edwin J R van Beek; Michelle C Williams Journal: N Engl J Med Date: 2018-08-25 Impact factor: 91.245
Authors: Andrew J Foy; Sanket S Dhruva; Brandon Peterson; John M Mandrola; Daniel J Morgan; Rita F Redberg Journal: JAMA Intern Med Date: 2017-11-01 Impact factor: 21.873