Predictive modelling of major trauma PROMs using machine learning University of Leicester

Every year across England and Wales, 16,000 people die after injury and many tens of thousands are left disabled for life.

In the early stages after injury, some patients will have a good chance of recovery while others (for example, those with severe head injuries) will need a higher level of therapy input. However, for a large group of patients in between these extremes, it is difficult to know what the outcome will be.

There is already a well-developed system for collecting national data about major trauma patients – the Trauma Audit and Research Network (TARN) – and a more recent system for major trauma patient reported outcome measures (PROMs).

This project from the University of Leicester and partners will take major trauma PROMs data to the next stage by linking it to the data held within TARN to create a model to predict PROMs, which will enable national clinical audit to focus on outcomes that are important to patients.

Different modelling approaches will be used to identify predictors of quality of life after major trauma, and create an optimal model for assessing the quality of rehabilitation and reablement alongside acute care. The overall aim is to improve care for major trauma patients by enabling a ‘personalised medicine’ approach that focuses on rehabilitation resources.

The project will also define the best method for presenting PROMs information and model predictions to patients, therapists and commissioners.

A predictive model for PROMs will provide a new quality measure for national clinical audits, allow therapists to target interventions, enhance patient involvement in their recovery and enable researchers to identify high-risk groups for intervention studies.

Contact details

For more information about this project, please contact Timothy Coats, Professor of Emergency Medicine, University of Leicester.