Clinical trial NCT05853536: In Silico Trials of Surgical Interventions
Official title: In Silico Trials of Surgical Interventions - Using Routinely Collected Data to Model Trial Feasibility and Design Efficiency In Vivo Randomised Controlled Trials
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Cardiovascular Diseases
Lead sponsor: University of Leicester
Sponsor class: OTHER
Collaborators: name: King's College London
name: University of Glasgow
name: Cornell University
name: Queen Mary University of London
name: Newcastle University
name: University of Bristol
First posted: 2023-05-10
Enrollment: 13977257
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All Adult patients (18 years and above) with cardiovascular diseases (ICD10 I00-I99) treated in NHS hospitals in England.

Brief summary: The project aims to establish a database of cardiovascular patients using HES and linked mortality data. This database will be used to model trials in silico with the aim of informing the design of future cardiovascular trials in the United Kingdom.

Detailed description: Routinely collected health data such as National Health Service Hospital Episode Statistics (HES) contains a wealth of real-world longitudinal patient health data including demographics, diagnoses, procedures and other clinical information. These data can be used to address many of the existing challenges in the design and conduct of clinical trials by optimising trial design and simplifying the assessment of adherence, safety, and outcomes. When a trial concept is initiated, researchers may use HES data to explore the hypothesis and assess trial feasibility. The large-volume patient data enables a detailed understanding of the characteristics of the target patient populations and the estimation of the real-world treatment effects across different patient groups thus enabling identification of targeted populations for specific interventions. By tapping into this resource and using advanced statistical and machine learning methods, the investigators can emulate the trial and thus obtain the key trial parameters required for designing a clinical trial, minimise the number of assumptions imputed and make the design and development of clinical trials quicker, simpler, and more reliable.

Eligibility criteria: - The target trial protocol will be used to develop an emulated trial protocol, which will give the best approximation of the trial protocol, given the limitations and constraints of the observational data. To mimic an actual trial population, the target trial population will be matched with individual patient data from a historical trial, targeting the same population.

Primary outcomes:
measure: Major adverse cardiovascular events (MACE) and their components
timeFrame: 1-Year
measure: Major adverse cardiovascular events (MACE) and their components
timeFrame: 3-Year
measure: Major adverse cardiovascular events (MACE) and their components
timeFrame: 5-Year
Secondary outcomes:
measure: Adverse Events (Arrhythmias, Dialysis, Infection, Coronary Revascularization, Reintervention)
timeFrame: 1-Year
measure: Adverse Events (Arrhythmias, Dialysis, Infection, Coronary Revascularization, Reintervention)
timeFrame: 3-Year
measure: Adverse Events (Arrhythmias, Dialysis, Infection, Coronary Revascularization, Reintervention)
timeFrame: 5-Year
measure: Length of Hospital Stay
timeFrame: 1-Year
measure: Length of Hospital Stay
timeFrame: 3-Year
measure: Length of Hospital Stay
timeFrame: 5-Year
measure: Rehospitalisation
timeFrame: 1-Year
measure: Rehospitalisation
timeFrame: 3-Year
measure: Rehospitalisation
timeFrame: 5-Year
Other outcomes:
Locations:
city: Leicester
country: United Kingdom
facility: Department of Cardiovascular Sciences, University of Leicester, Clinical Sciences Wing,
state: Leicestershire
Study officials:
affiliation: University of Leicester
name: Gavin Murphy
role: PRINCIPAL_INVESTIGATOR
References:
citation: Pathak S, Lai FY, Miksza J, Petrie MC, Roman M, Murray S, Dearling J, Perera D, Murphy GJ. Surgical or percutaneous coronary revascularization for heart failure: an in silico model using routinely collected health data to emulate a clinical trial. Eur Heart J. 2023 Feb 1;44(5):351-364. doi: 10.1093/eurheartj/ehac670.