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Naci H, Salcher-Konrad M, Mcguire A, Berger F, Kuehne T, Goubergrits L, Muthurangu V, Wilson B, Kelm M. Impact of predictive medicine on therapeutic decision making: a randomized controlled trial in congenital heart disease. NPJ Digit Med 2019; 2:17. [PMID: 31304365 PMCID: PMC6550204 DOI: 10.1038/s41746-019-0085-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 02/01/2019] [Indexed: 11/09/2022] Open
Abstract
Computational modelling has made significant progress towards clinical application in recent years. In addition to providing detailed diagnostic data, these methods have the potential to simulate patient-specific interventions and to predict their outcome. Our objective was to evaluate to which extent patient-specific modelling influences treatment decisions in coarctation of the aorta (CoA), a common congenital heart disease. We selected three cases with CoA, two of which had borderline indications for intervention according to current clinical guidelines. The third case was not indicated for intervention according to guidelines. For each case, we generated two separate datasets. First dataset included conventional diagnostic parameters (echocardiography and magnetic resonance imaging). In the second, we added modelled parameters (pressure fields). For the two cases with borderline indications for intervention, the second dataset also included pressure fields after virtual stenting simulations. All parameters were computed by modelling methods that were previously validated. In an online-administered, invitation-only survey, we randomized 178 paediatric cardiologists to view either conventional (control) or add-on modelling (experimental) datasets. Primary endpoint was the proportion of participants recommending different therapeutic options: (1) surgery or catheter lab (collectively, "intervention") or (2) no intervention (follow-up with or without medication). Availability of data from computational predictive modelling influenced therapeutic decision making in two of three cases. There was a statistically significant association between group assignment and the recommendation of an intervention for one borderline case and one non-borderline case: 94.3% vs. 72.2% (RR: 1.31, 95% CI: 1.14-1.50, p = 0.00) and 18.8% vs. 5.1% (RR: 3.09, 95% CI: 1.17-8.18, p = 0.01) of participants in the experimental and control groups respectively recommended an intervention. For the remaining case, there was no difference between the experimental and control group and the majority of participants recommended intervention. In sub-group analyses, findings were not affected by the experience level of participating cardiologists. Despite existing clinical guidelines, the therapy recommendations of the participating physicians were heterogeneous. Validated patient-specific computational modelling has the potential to influence treatment decisions. Future studies in broader areas are needed to evaluate whether differences in decisions result in improved outcomes (Trial Registration: NCT02700737).
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Affiliation(s)
- Huseyin Naci
- 1LSE Health, Department of Health Policy, London School of Economics and Political Science, London, UK
| | - Maximilian Salcher-Konrad
- 1LSE Health, Department of Health Policy, London School of Economics and Political Science, London, UK
| | - Alistair Mcguire
- 1LSE Health, Department of Health Policy, London School of Economics and Political Science, London, UK
| | - Felix Berger
- 2German Heart Institute Berlin (DHZB), Berlin, Germany.,3Charité - Universitätsmedizin Berlin, Pediatric Cardiology, Berlin, Germany.,4DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
| | - Titus Kuehne
- 2German Heart Institute Berlin (DHZB), Berlin, Germany.,4DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany.,Institute for Computational and Imaging Science in Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Leonid Goubergrits
- Institute for Computational and Imaging Science in Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Vivek Muthurangu
- 6Great Ormond Street Hospital, University College London, London, UK
| | - Ben Wilson
- 7Department of Sociology, Stockholm University, Stockholm, Sweden.,8Department of Methodology, London School of Economics and Political Science, London, UK
| | - Marcus Kelm
- 2German Heart Institute Berlin (DHZB), Berlin, Germany.,3Charité - Universitätsmedizin Berlin, Pediatric Cardiology, Berlin, Germany.,Institute for Computational and Imaging Science in Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
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