Clinical trial NCT02700737: Evaluation of Image-Based Modelling on Clinical Decisions in Coarctation of the Aorta
Official title: Evaluation of Image-Based Modelling on Clinical Decisions in Coarctation of the Aorta
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Congenital Heart Disease
Aortic Coarctation
Cardiovascular Disease
Keywords: cardiovascular modeling
aortic coarctation
virtual stenting
Lead sponsor: London School of Economics and Political Science
Sponsor class: OTHER
Collaborators: name: German Heart Institute
name: Bambino Gesù Hospital and Research Institute
name: University College, London
First posted: 2016-03-07
Enrollment: 206
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief summary: To answer the research question: "Would image-based modelling result in different clinical decisions as compared to clinical practice guidelines?", we will conduct a randomized controlled experiment in which we will compare the hypothetical decisions made by interventional cardiologists who are presented with imaging parameters currently recommended by clinical practice guidelines vs. hypothetical decisions made by interventional cardiologists receiving an expanded list of parameters, including simulation modelling.

Detailed description: In collaboration with our three clinical partners, we will first generate two separate imaging datasets for a maximum of three patients recruited to participate in CARDIOPROOF. The first dataset will include the imaging parameters currently recommended by clinical practice guidelines (referred to as "limited dataset"). The second dataset will include an expanded list of parameters, inclusive of information that is available from traditional imaging parameters (as recommended by the guidelines) and simulation modeling (referred to as "image-based modelling dataset").

We will generate both limited and image-based modelling datasets from fully de-identified patients already enrolled in CARDIOPROOF (NCT02591940) who have consented to publication of data in anonymized form.

Using a computerized random-sample function, we will randomly allocate interventional cardiologists into two separate groups and present them with one set of imaging data. The first group will receive a "limited" dataset including only information that is available from traditional diagnostics (as recommended by the clinical practice guidelines) for a pre-specified number of patients (maximum of 3). The second group will receive the full, detailed dataset inclusive of information that is available from traditional diagnostics (as recommended by the guidelines) and simulation modelling for the same set of patients.

We will then ask the interventional cardiologists in the two groups to make (hypothetical) clinical decisions using the dataset of imaging parameters presented to them. The clinical decisions will be hypothetical because patients will have been treated according to clinical practice guidelines and this experiment will retrospectively involve interventional cardiologists who are not directly involved in the care of the patients participating in CARDIOPROOF.

The analysis will focus on each hypothetical scenario and compare the proportions of cardiologists making different types of intervention decisions in the two randomly allocated groups.

Eligibility criteria: Inclusion Criteria:

* Practicing interventional cardiologists
* Has treated patients with coarctation of the aorta during the past 6 months

Exclusion Criteria:

* Participation in CARDIOPROOF trial

Primary outcomes:
measure: Decision to intervene
timeFrame: Immediate
Secondary outcomes:
Other outcomes:
Locations:
city: London
country: United Kingdom
facility: London School of Economics and Political Science
Study officials:
affiliation: London School of Economics and Political Science
name: Huseyin Naci, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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 Mar 19;2:17. doi: 10.1038/s41746-019-0085-1. eCollection 2019.