title: Value of functional capacity evaluation information in a clinical setting for predicting return to work.
abstract: OBJECTIVE
  To evaluate the quality of Functional Capacity Evaluation (FCE) information in predicting return to work (RTW).
  
  
  METHODS
  Prospective cohort study.
  
  
  METHODS
  Inpatient rehabilitation clinic.
  
  
  METHODS
  Patients (N=220) with chronic musculoskeletal disorders (MSD) conducting a medical rehabilitation.
  
  
  METHODS
  Not applicable.
  
  
  METHODS
  Patients filled in questionnaires at admission and 1-year follow-up. An FCE was performed on admission. RTW was defined as a combination of employment at 1-year follow-up with a maximum of 6 weeks sick leave because of MSD in the postrehabilitation year. As predictive FCE information, the physical capacity (Dictionary of Occupational Titles categories 1-5), the number of test results not meeting work demands (0-25), and the tester's recommendation of work ability in the actual job (> or =6h/d) were analyzed. Logistic regression models (crude and adjusted for the concurrent predictors employment, preadmission sick leave, and patient's prognosis of RTW) were created to predict RTW.
  
  
  RESULTS
  Complete data were obtained for 145 patients. The sample showed a non-RTW at 1-year follow-up for 37.9%. All FCE information showed significant relations to RTW (r=.28-.43; P<.05). In the crude as well as in the adjusted regression models, all FCE information predicted RTW, but the models' quality was low. The integration of FCE information led to an increase of 5%. The predictive efficiency was poor. The adjusted model for failed tests showed a substantial improvement compared with the reference model (concurrent predictors only).
  
  
  CONCLUSIONS
  There was a significant relation between FCE information and RTW with and without concurrent predictors, but the predictive efficiency is poor. Primarily, the number of failed tests seemed to be of significance for patients with ambiguous RTW prognosis. A first proposal for a prediction rule was discussed.
authors.affiliation: Department of Health Care Research and Quality Management in Rehabilitation, Charité University Berlin, Germany. marco.streibelt@charite.de
authors.forename: Marco
authors.initials: M
authors.lastname: Streibelt
authors.forename: Carsten
authors.initials: C
authors.lastname: Blume
authors.forename: Karsten
authors.initials: K
authors.lastname: Thren
authors.forename: Michiel F
authors.initials: MF
authors.lastname: Reneman
authors.forename: Werner
authors.initials: W
authors.lastname: Mueller-Fahrnow
mesh_terms: D002908:Chronic Disease; D015331:Cohort Studies; D004651:Employment / Q000706:statistics & numerical data; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D009140:Musculoskeletal Diseases / Q000534:rehabilitation*; D011159:Population Surveillance; D011237:Predictive Value of Tests; D011446:Prospective Studies; D014938:Work Capacity Evaluation