Length of disability prognosis in acute occupational low back pain: development and testing of a practical approach.

STUDY DESIGN: Retrospective cohort study. OBJECTIVES: Practical evaluation of a rapid prognostic screening method to predict length of disability after acute occupational low back pain (OLBP). SUMMARY OF BACKGROUND DATA: Few studies have evaluated the prognostic value of administrative data and selected clinical variables in typical practice settings. METHODS: Nurse case manager (NCM) input for 16 variables and 7 administrative data variables were collected for 494 OLBP cases with at least 30 days of disability. Length of disability (LOD) was ascertained by individual indemnity payment analysis. Cases were censored after accumulating 365 days of temporary total disability or if they received a lump sum settlement. Prognostic variables were evaluated by Cox proportional hazards modeling. RESULTS: In a multivariate model, prolonged LOD was associated with older age, shorter job tenure, female gender, presence of language barriers, comorbidity, prior work absence, delayed referral, attorney involvement nonsupportive of return to work (RTW), and low RTW motivation. Although only 12% of overall variance in LOD was explained by the model, high-risk and low-risk terciles were readily distinguished. CONCLUSIONS: In a typical setting, data collection and risk prediction by nurses or case managers are feasible and provide specific information that can be used to identify who should receive intervention, as well as some guidance on factors that should be addressed.

Prediction of prolonged work disability in occupational low-back pain based on nurse case management data.

This study evaluated whether risk prediction data captured by nurse case managers (NCMs) could predict prolonged disability from occupational low back pain. Cases of uncomplicated occupational low back pain referred to NCMs, with dates of injury between January 1, 1997 and June 30, 1998, were selected (n = 986). Predictive information was obtained from the NCM screening checklist of potential risk factors and other administrative claims data. Data were collected on 23 variables thought to predict work status at 6 months postinjury. In a multivariate logistic regression model, being out of work was significantly associated with older age, language barriers, earlier referral to NCM, and neutral or negative attorney attitude toward return to work. The overall predictive power of the model was low (area under curve = 0.6). Although NCM risk factor data collection is feasible and practical in insurance settings, future studies should explore additional variables and refined data collection methods in order to achieve a more accurate prediction.