Prediction of recovery in trauma patients using Latent Markov models

Purpose Patients' expectations during recovery after a trauma can affect the recovery. The aim of the present study was to identify different physical recovery trajectories based on Latent Markov Models (LMMs) and predict these recovery states based on individual patient characteristics. Methods The data of a cohort of adult trauma patients until the age of 75 years with a length of hospital stay of 3 days and more were derived from the Brabant Injury Outcome Surveillance (BIOS) study. The EuroQol-5D 3-level version and the Health Utilities Index were used 1 week, and 1, 3, 6, 12, and 24 months after injury. Four prediction models, for mobility, pain, self-care, and daily activity, were developed using LMMs with ordinal latent states and patient characteristics as predictors for the latent states. Results In total, 1107 patients were included. Four models with three ordinal latent states were developed, with different covariates in each model. The prediction of the (ordinal) latent states in the LMMs yielded pseudo-R-2 values between 40 and 53% and between 21 and 41% (depending of the type R-2 used) and classification errors between 24 and 40%. Most patients seem to recover fast as only about a quarter of the patients remain with severe problems after 1 month. Conclusion The use of LMMs to model the development of physical function post-injury is a promising way to obtain a prediction of the physical recovery. The step-by-step prediction fits well with the outpatient follow-up and it can be used to inform the patients more tailor-made to manage the expectations.

Automated summary

This study identified different physical recovery trajectories using Latent Markov Models (LMMs) and predicted these recovery states based on individual patient characteristics. Most patients recovered quickly, with only about a quarter experiencing severe problems after 1 month.