Completed Mental Health NIHR-supported project Psychology & Behaviour

UK Multiple Long-Term Conditions Trials Collaborative (UK MTC) Team

In plain English

AI plain-English summary

A single patient with multiple long-term conditions—such as diabetes, heart disease, and depression—is typically excluded from clinical trials, leaving doctors with no reliable evidence on which treatments work best for them. The problem is that most clinical trials study one disease at a time, so people with multimorbidity are routinely left out. This means the drugs and therapies they receive are based on data from healthier, simpler patients. The UK Multiple Long-Term Conditions Trials Collaborative aims to fix this by pooling raw, individual-level data from many past trials—a technique called Individual Participant Data Meta-Analysis (IPDMA). This gives researchers enough statistical power to ask questions that single trials cannot answer: how do different combinations of conditions, frailty, ethnicity, or deprivation affect treatment outcomes? If successful, this work could reshape how the NHS designs and personalises care for the growing number of people living with multiple conditions. Instead of one-size-fits-all guidelines, clinicians might tailor treatments based on a patient’s specific cluster of illnesses and social circumstances. The project is building the network, skills, and data infrastructure to make this possible, with the ultimate goal of launching transformative trials that directly improve patient care.

View original technical description
We aim to develop the UK Multiple Long-Term Conditions Trials Collaborative (UK MTC) Team, to transform understanding of treatment effects and prognosis for people with multiple long-term conditions (multimorbidity) (MLTC) using individual participant data (IPD) from trials. Individual Participant Data Meta-Analysis (IPDMA) is a new frontier in MLTC research. It involves analysis of original individual-level trial data from multiple trials, substantially increasing statistical power to investigate questions of major strategic importance in MLTC research for personalised healthcare. For example, IPDMA has potential to transform understanding of how different combinations of long-term conditions, MLTC clusters, frailty, sex/gender, ethnicity and deprivation are associated with individual-outcomes (prognosis) and how they interact with the effect of a range of pharmacological and non-pharmacological interventions. This knowledge has the potential to redefine the way services are designed, delivered and personalised for people with MLTC. We will use this award to advance capacity for IPDMA in MLTC by developing a network and resources that harness existing IPD repositories, gathering new IPD , enhance IPDMA research skills and knowledge, and developing transformative grants to improve patient care.

Researchers

Tom Crocker (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

Using artificial intelligence (AI) to characterize the dynamic inter-relationships between MUltiple Long-term condiTIons and PoLYpharmacy and across diverse UK populations and inform health care pathways (AI-MULTIPLY)
Complex Multiple long-term conditions (MLTC) Phenotypes, Trends, and Endpoints (CoMPuTE)
NIHR Global Health Research Centre for Multiple Long-Term Conditions
Characterising the dynamic inter-relationships between polypharmacy and multiple long-term conditions. Using artificial intelligence (AI) to map patient journeys into multimorbidity clusters across the UK
Using Artificial Intelligence to Tackle Multiple Long-Term Conditions - Multi-morbidity in South Yorkshire

Original classification

Older People with Frailty

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.