Musculoskeletal disorders including as back and neck pain are leading causes of work disability. Effective interventions exist (i.e. functional restoration, multidisciplinary biopsychosocial rehabilitation, workplace-based interventions, etc.), but it is difficult to select the optimal intervention for specific patients. The Work Assessment Triage Tool (WATT) is a clinical decision support tool developed using machine learning to help select interventions. The WATT algorithm categorizes patients based on individual, occupational, and clinical characteristics according to likelihood of successful return-to-work following rehabilitation. Internal validation showed acceptable classification accuracy, but WATT has not been tested beyond the original development sample. Our purpose was to externally validate the WATT. A population-based cohort design was used, with administrative and clinical data extracted from a Canadian provincial compensation database. Data were available on workers being considered for rehabilitation between January 2013 and December 2016. Data was obtained on patient characteristics (ie. age, sex, education level), clinical factors (ie. diagnosis, part of body affected, pain and disability ratings), occupational factors (ie. occupation, employment status, modified work availability), type of rehabilitation program undertaken, and return-to-work outcomes (receipt of wage replacement benefits 30 days after assessment). Analysis included classification accuracy statistics of WATT recommendations for selecting interventions that lead to successful RTW outcomes. The sample included 5296 workers of which 33% had spinal conditions. Sensitivity of the WATT was 0.35 while specificity was 0.83. Overall accuracy was 73%.Purposes and Background
Methods and Results
Musculoskeletal disorders are leading causes of work disability. Our purpose was to develop a predictive model in a cohort from 2012 and validate the model in 2016 data. Prospectively collected data was used to identify inception cohorts in 2012 (n=1652) and 2016 (n=199). Data from back pain claimants receiving treatment in physiotherapy clinics and the Ontario workers' compensation database were linked. Patients were followed for 1 year. Variables from a back pain questionnaire and clinical, demographic and administrative factors were assessed for predictive value. The outcome was cumulative number of calendar days receiving wage-replacement benefits. Cox regression revealed 8 significant predictors of shorter time on benefits in the 2012 cohort: early intervention (HR=1.51), symptom duration < 31 days (HR=0.88), not in construction industry (HR=1.89), high Low Back Outcome Score (HR=1.03), younger age (HR=0.99), higher benefit rate (HR=1.00), intermittent pain (HR=1.15), no sleep disturbance (HR=1.15). The 2012 model c-statistic was 0.73 with a calibration slope of 0.90 (SE=0.19, p=0.61) in the 2016 data, meaning not significantly different. The c-statistic in the 2016 data was 0.69. Median duration on benefits of those with a high risk score was 129 days in 2012 and 45 days in 2016.Purposes and Background
Methods and Results
