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Predicting prolonged sick leave among trauma survivors
Erik von Oelreich1,2, Mikael Eriksson3,4, Olof Brattström3,4
1Perioperative Medicine and Intensive Care, Karolinska University Hospital, Solna, Stockholm, Sweden. erik.vonoelreich@sll.se.
Scientific Reports
|January 13, 2019
Summary
This study developed prediction tools to identify trauma survivors at high risk for long-term sick leave. Pre-injury sick leave was the strongest predictor, aiding early intervention targeting.
Area of Science:
- Trauma surgery
- Public health
- Occupational medicine
Background:
- Trauma survivors frequently experience long-term morbidity, impacting their ability to work.
- Early identification of individuals at risk for prolonged sick leave is crucial for resource allocation and intervention.
- Existing prediction tools for post-trauma outcomes often lack comprehensive validation or focus on sick leave.
Purpose of the Study:
- To develop and validate prognostic prediction models for assessing full-time sick leave one year after trauma.
- To identify key predictors, including injury-related and non-injury-related factors, associated with long-term sick leave.
- To create both a comprehensive and a simplified model for practical clinical application.
Main Methods:
- An observational cohort study combining data from a trauma register and national health registers.
- Logistic regression and stepwise backward elimination were used to develop two prediction models (comprehensive and simplified).
- A total of 4458 individuals were included, with 488 experiencing full-time sick leave at 12 months post-trauma.
Main Results:
- Both developed models demonstrated excellent discrimination (AUC 0.81) in predicting one-year full-time sick leave.
- The comprehensive model showed very good calibration, while the simplified model exhibited good calibration.
- Pre-injury sick leave emerged as the single most significant predictor for long-term sick leave post-trauma.
Conclusions:
- The developed prediction models effectively assess post-trauma sick leave risk using a combination of injury and non-injury variables.
- These tools can facilitate efficient resource allocation and targeted follow-up interventions to improve patient outcomes.
- External validation is recommended to confirm the generalizability of the prediction models.
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