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Impact of predictive medicine on therapeutic decision making: a randomized controlled trial in congenital heart disease.

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).

SUBMITTER: Naci H

PROVIDER: S-EPMC6550204 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Impact of predictive medicine on therapeutic decision making: a randomized controlled trial in congenital heart disease.

Naci Huseyin H Salcher-Konrad Maximilian M Mcguire Alistair A Berger Felix F Kuehne Titus T Goubergrits Leonid L Muthurangu Vivek V Wilson Ben B Kelm Marcus M

NPJ digital medicine 20190319

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 indicat ...[more]

PMID: 31304365

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Project description:BackgroundShared decision making (SDM) is a widely recommended yet unproven strategy for increasing colorectal cancer (CRC) screening uptake. Previous trials of decision aids to increase SDM and CRC screening uptake have yielded mixed results.PurposeTo assess the impact of decision aid-assisted SDM on CRC screening uptake.DesignRCT.Setting/participantsThe study was conducted at an urban, academic safety-net hospital and community health center between 2005 and 2010. Participants were asymptomatic, average-risk patients aged 50-75 years due for CRC screening.InterventionStudy participants (n=825) were randomized to one of two intervention arms (decision aid plus personalized risk assessment or decision aid alone) or control arm. The interventions took place just prior to a routine office visit with their primary care providers.Main outcome measuresThe primary outcome was completion of a CRC screening test within 12 months of the study visit. Logistic regression was used to identify predictors of test completion and mediators of the intervention effect. Analysis was completed in 2011.ResultsPatients in the decision-aid group were more likely to complete a screening test than control patients (43.1% vs 34.8%, p=0.046) within 12 months of the study visit; conversely, test uptake for the decision aid and decision aid plus personalized risk assessment arms was similar (43.1% vs 37.1%, p=0.15). Assignment to the decision-aid arm (AOR=1.48, 95% CI=1.04, 2.10), black race (AOR=1.52, 95% CI=1.12, 2.06) and a preference for a patient-dominant decision-making approach (AOR=1.55, 95% CI=1.02, 2.35) were independent determinants of test completion. Activation of the screening discussion and enhanced screening intentions mediated the intervention effect.ConclusionsDecision aid-assisted SDM has a modest impact on CRC screening uptake. A decision aid plus personalized risk assessment tool is no more effective than a decision aid alone.Trial registrationThis study is registered at www.clinicaltrials.govNCT00251862.

Project description:BackgroundClinicians face challenges in promoting colorectal cancer screening due to multiple competing demands. A decision aid that clarifies patient preferences and improves decision quality can aid shared decision making and be effective at increasing colorectal cancer screening rates. However, exactly how such an intervention improves shared decision making is unclear. This study, funded by the National Cancer Institute, seeks to provide detailed understanding of how an interactive decision aid that elicits patient's risks and preferences impacts patient-clinician communication and shared decision making, and ultimately colorectal cancer screening adherence.Methods/designThis is a two-armed single-blinded randomized controlled trial with the target of 300 patients per arm. The setting is eleven community and three academic primary care practices in Metro Detroit. Patients are men and women aged between 50 and 75 years who are not up to date on colorectal cancer screening. ColoDATES Web (intervention arm), a decision aid that incorporates interactive personal risk assessment and preference clarification tools, is compared to a non-interactive website that matches ColoDATES Web in content but does not contain interactive tools (control arm). Primary outcomes are patient uptake of colorectal cancer screening; patient decision quality (knowledge, preference clarification, intent); clinician's degree of shared decision making; and patient-clinician concordance in the screening test chosen. Secondary outcome incorporates a Structural Equation Modeling approach to understand the mechanism of the causal pathway and test the validity of the proposed conceptual model based on Theory of Planned Behavior. Clinicians and those performing the analysis are blinded to arms.DiscussionThe central hypothesis is that ColoDATES Web will improve colorectal cancer screening adherence through improvement in patient behavioral factors, shared decision making between the patient and the clinician, and concordance between the patient's and clinician's preferred colorectal cancer screening test. The results of this study will be among the first to examine the effect of a real-time preference assessment exercise on colorectal cancer screening and mediators, and, in doing so, will shed light on the patient-clinician communication and shared decision making 'black box' that currently exists between the delivery of decision aids to patients and subsequent patient behavior.Trial registrationClinicalTrials.gov ID NCT01514786.

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Impact of predictive medicine on therapeutic decision making: a randomized controlled trial in congenital heart disease.

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Impact of predictive medicine on therapeutic decision making: a randomized controlled trial in congenital heart disease.

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