Personalised Outcomes Forecasts of Supervised Exercise Therapy in Intermittent Claudication: An Application of Neighbours Based Prediction Methods with Routinely Collected Clinical Data

Objective: Insights regarding individual patient prognosis may improve exercise therapy by informing patient expectations, promoting exercise adherence, and facilitating tailored care. Therefore, the aim was to develop and evaluate personalised outcomes forecasts for functional claudication distance over six months of supervised exercise therapy for patients with intermittent claudication. Methods: Data of 5 940 patients were eligible for analysis. Neighbours based predictions were generated via an adaptation of predictive mean matching. Data from the nearest 223 matches (a.k.a. neighbours) for an index patient were modelled via Generalised Additive Model for Location Scale and Shape (GAMLSS). The realised outcome measures were then evaluated against the GAMLSS model, and the average bias, coverage, and precision were calculated. Model calibration was analysed via within sample and of sample analyses. Results: Neighbours based predictions demonstrated small average bias (– 0.04 standard deviations; ideal = 0) and accurate average coverage (48.7% of realised data within 50% prediction interval; ideal = 50%). Moreover, neighbours based predictions improved prediction precision by 24%, compared with estimates derived from the whole sample. Both within sample and of sample testing showed predictions to be well calibrated. Conclusion: Neighbours based prediction is a method for generating accurate personalised outcomes forecasts for patients with intermittent claudication undertaking supervised exercise therapy. Future work should examine the influence of personalised outcomes forecasts on clinical decisions and patient outcomes.