Exploring the Contribution of Patient-Reported and Clinician Based Variables for the Prediction of Low Back Work Status

Abstract

Background Successful management of workers on sick leave due to low back pain by the general physician and physiotherapist depends on reliable prognostic information on the course of low back pain and work resumption. Methods Retrospective cohort study in 194 patients who were compensated because of chronic low back pain and who were treated by a physiotherapy functional restoration program. Patient-reported and clinician based prognostic indicators were assessed at baseline before patients entered the functional restoration program. We investigated the predictive value of these indicators on work status at 6 months. Relationships were studied using logistic regression analysis in a 2-step bootstrap modelling approach and a nomogram was developed. Discrimination and calibration of the nomogram was evaluated internally and the explained variation of the nomogram calculated. Results Seventy percent of workers were back to work at 6 months. We found that including duration of complaints, functional disability, disc herniation and fear avoidance beliefs resulted in the ''best'' prognostic model. All these factors delayed work resumption. This model was used to construct a nomogram. The explained variation of the nomogram was 23.7%. Discrimination was estimated by the area under 383-397 DOI 10.1007/s10926-007-9084-1 123 the receiver operating characteristic curve and was 0.76 and for calibration we used the slope estimate that was 0.91. The positive predictive values of the nomogram at different cut-off levels of predicted probability were good. Conclusions Knowledge of the predictive value of these indicators by physicians and physiotherapists will help to identify subgroups of patients and will thus enhance clinical decision-making.

Outpatient rehabilitation of workers with musculoskeletal disorders using structured workplace description

“…The presence of this increase indicates the necessity to improve the work ability in workers with back complaints. The sick leave reduction shown here exceeds the eVects of non-invasive therapy regimen in musculoskeletal diseases Weiler et al 2006;Heymans et al 2007); in addition, we had a 97% return-to-work ratio 1 year post-treatment, whereas Anema et al (2004) and Heymans et al (2007) reported a 70-75% rtw rate as normal. Our results suggest that sick leave duration before rehabilitation is more predictive than the reintegration model with or without graded work, but this result may be biased by the fact that workers with high sick leave preferably got a graded work rtw-schedule.…”

Return to Work in a Cohort of Low Back Pain Patients: Development and Validation of a Clinical Prediction Rule

“…Dionne et al [42] did not present a value for the explained variance. The explained variance of the prediction model in the study of Heymans et al [43] was, 23.7%. Other studies that developed prognostic models for RTW in an occupational setting reported explained variances of 18-30% [39,44].…”

“…Dionne et al reported PPVs of 33-57% and NPVs for their model of 74-91%. The study of Heymans et al [43] reported PPVs in the range of 70-95% and NPVs in the range of 33-100%. With respect to the practical implication of our prediction rule we are aware that prudence has to be taken when using the prediction rule in practice.…”

“…Pransky et al [41] developed a practical screening model to predict length of disability after acute occupational LBP. Heymans et al [43] developed a nomogram to predict work status in chronic LBP patients. Their nomogram had presented a slope index of 0.91 and a c-index of 0.76.…”

The Added Value of Collecting Information on Pain Experience When Predicting Time on Benefits for Injured Workers with Back Pain

“…Were we to have built the model within the R-RTW cohort only, using the variables identified in the larger data (age, physical demands, opioid prescription, employer doubt about work-relatedness of injury, and healthcare provider's poor recovery expectations, union membership, availability of a return-to-work program, participation in a rehabilitation program, and communication of functional ability Both the model fit of the prediction rule (derived from the larger dataset) as validated in the smaller sample as well as the duration on benefits (median of 57 days) were comparable between the two samples, which confirms earlier 6,657 workers on full benefits on first day of injury 15 not accessible due to security restrictions 1,796 workers still on benefits (either/both total or/and partial benefits) at 4 weeks -1,615 cases Form 6 prior to 4 weeks -1,776 cases Form 7 prior to 4 weeks -1,685 cases Form 8 prior to 4weeks -1,796 cases with pharma data prior to 4 weeks Focus on those assessed at 4 weeks and still off work: 1,442 total benefits (100% wage replacement benefits) -113 also part of R-RTW cohort study -1310 cases Form 6 prior to 4 weeks -1424 cases Form 7 prior to 4 weeks -1354 cases Form 8 prior to 4 weeks -1442 cases with pharma data prior to 4 weeks 0 lost to follow up (benefits data only) analyses that the R-RTW is a representative sample of injured workers [7]. Still, a prediction rule developed in a small dataset is likely not applicable to the general population.The model fit of the improved prediction rule for time on benefits was better compared to the fit presented in studies in similar settings, which reported a discriminative ability of 0.80 [34] and 0.76 [35]. It was better compared to others reporting an AUC of 0.63 [36] and 0.69 when validating the Orebro Musculoskeletal Screening Questionnaire in a Canadian workers' compensation setting [37].…”