# Predicting prolonged work absence due to musculoskeletal disorders: development, validation, and clinical usefulness of prognostic prediction models

## TL;DR

This study developed and validated models to predict prolonged work absence due to musculoskeletal disorders, aiming to help prevent work disability.

## Contribution

The study introduces three validated prediction models for work absence duration in individuals with musculoskeletal disorders.

## Key findings

- All three models showed good predictive performance with c-statistics above 0.76 in external validation.
- The model predicting more than 180 sick days had the best performance with a c-statistic of 0.79 and good net benefit.
- The models may support secondary prevention and clinical trials but require further validation in larger samples.

## Abstract

Given the lack of robust prognostic models for early identification of individuals at risk of work disability, this study aimed to develop and externally validate three models for prolonged work absence among individuals on sick leave due to musculoskeletal disorders.

We developed three multivariable logistic regression models using data from 934 individuals on sick leave for 4–12 weeks due to musculoskeletal disorders, recruited through the Norwegian Labour and Welfare Administration. The models predicted three outcomes: (1) > 90 consecutive sick days, (2) > 180 consecutive sick days, and (3) any new or increased work assessment allowance or disability pension within 12 months. Each model was externally validated in a separate cohort of participants (8–12 weeks of sick leave) from a different geographical region in Norway. We evaluated model performance using discrimination (c-statistic), calibration, and assessed clinical usefulness using decision curve analysis (net benefit). Bootstrapping was used to adjust for overoptimism.

All three models showed good predictive performance in the external validation sample, with c-statistics exceeding 0.76. The model predicting > 180 days performed best, demonstrating good calibration and discrimination (c-statistic 0.79 (95% CI 0.73–0.85), and providing net benefit across a range of decision thresholds from 0.10 to 0.80.

These models, particularly the one predicting > 180 days, may facilitate secondary prevention strategies and guide future clinical trials. Further validation and refinement are necessary to optimise the models and to test their performance in larger samples.

The online version contains supplementary material available at 10.1007/s00420-025-02129-8.