In Silico Trials of Surgical Interventions

In Silico Trials of Surgical Interventions - Using Routinely Collected Data to Model Trial Feasibility and Design Efficiency In Vivo Randomised Controlled Trials

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.

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.

- 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.