A comparison of methodological approaches to developing clinical prediction models for individuals living with multiple long-term conditions: a protocol for a systematic review

Introduction: Multiple long-term conditions (MLTCs) are being made a priority by funding bodies as prevalence rates increase. Improving early detection of individuals at high risk of developing MLTCs may delay or prevent complications and poor health outcomes. Predicting MLTCs remains a challenge, and methods for singular outcomes have been proven to be inappropriate for MLTC research. The aim of this paper is to present the protocol for a systematic review to identify all published models for prediction of MLTCs, and to summarise methods used for model development.

Methods and analysis: MEDLINE (Ovid), Embase (Ovid), CINAHL (EBSCOHost) and CENTRAL (Cochrane Library) will be searched from September 2015 to identify relevant clinical prediction models which predict the development of MLTCs.

Screening, data extraction and the risk of bias will be undertaken by two reviewers independently. Data extraction will include primary items for methodology and model outcomes and secondary items including study descriptors, population information, measured outcomes, methodology, model performance measures, clinical usefulness measures and risk of bias. A narrative synthesis will be conducted to summarise current methodological practice and to identify areas for improvement to inform future methodological and model development.

Ethics and dissemination: Ethical approval is not required for this systematic review as it will use published literature only. The findings of the review will be submitted for publication in a peer reviewed journal.

Strengths and limitations of this study:
• A comprehensive review of all methodologies used in clinical prediction modelling for MLTCs will be undertaken.

• Methodologies will be included regardless of the outcome conditions predicted.

• The protocol is reported according to the Preferred Reporting Items for Systematic Reviews Protocol (PRISMA – P).

• Screening, data extraction and risk of bias will be conducted independently by two reviewers.

• Only English language papers will be included.