BACKGROUND: From the viewpoint of cost prevention, it is necessary to identify patients that are of high risk for long-term work disability, production loss and sick-leave. METHODS: Secondary data analysis in a cohort of 628 workers on sick-leave between 3 and 6 weeks due to low back pain (LBP). The association of a broad set of demographic, work, LBP and psychosocial related factors on lasting return to work was studied using Cox regression analysis with backward selection. The most relevant factors were used to derive a clinical prediction rule to determine the risk of sick-leave of more than 6 months. Variable and model selection and clinical model performance were performed with bootstrapping techniques. Also the test characteristics of the clinical model were considered. RESULTS: Longer work absence is related to "moderate" to "poor" job satisfaction, a higher score of fear avoidance beliefs, higher pain intensity at baseline, a longer duration of complaints and being of female gender. Calibration and discrimination of the clinical prediction rule were 0.90 (slope) and 0.63 (c-index), respectively. The explained variance of 6% of the prediction rule was low and the clinical performance in terms of sensitivity, specificity, positive and negative predictive values at specific cut-off points was moderate. CONCLUSIONS: Our study confirmed the importance of demographic, work, LBP and psychosocial related factors on the prediction of long-term sick-leave. When these factors were used to derive a clinical prediction rule the performance was moderate. As a consequence, prudence has to be taken when using the prediction rule in practice.
BACKGROUND: From the viewpoint of cost prevention, it is necessary to identify patients that are of high risk for long-term work disability, production loss and sick-leave. METHODS: Secondary data analysis in a cohort of 628 workers on sick-leave between 3 and 6 weeks due to low back pain (LBP). The association of a broad set of demographic, work, LBP and psychosocial related factors on lasting return to work was studied using Cox regression analysis with backward selection. The most relevant factors were used to derive a clinical prediction rule to determine the risk of sick-leave of more than 6 months. Variable and model selection and clinical model performance were performed with bootstrapping techniques. Also the test characteristics of the clinical model were considered. RESULTS: Longer work absence is related to "moderate" to "poor" job satisfaction, a higher score of fear avoidance beliefs, higher pain intensity at baseline, a longer duration of complaints and being of female gender. Calibration and discrimination of the clinical prediction rule were 0.90 (slope) and 0.63 (c-index), respectively. The explained variance of 6% of the prediction rule was low and the clinical performance in terms of sensitivity, specificity, positive and negative predictive values at specific cut-off points was moderate. CONCLUSIONS: Our study confirmed the importance of demographic, work, LBP and psychosocial related factors on the prediction of long-term sick-leave. When these factors were used to derive a clinical prediction rule the performance was moderate. As a consequence, prudence has to be taken when using the prediction rule in practice.
Authors: Martijn W Heymans; Henrica C W de Vet; Dirk L Knol; Paulien M Bongers; Bart W Koes; Willem van Mechelen Journal: J Occup Rehabil Date: 2006-12
Authors: C A M Heitz; R Hilfiker; L M Bachmann; H Joronen; T Lorenz; D Uebelhart; A Klipstein; Florian Brunner Journal: Eur Spine J Date: 2009-06-30 Impact factor: 3.134
Authors: Marc H Jansen; Sophie E Veldhuijzen van Zanten; Esther Sanchez Aliaga; Martijn W Heymans; Monika Warmuth-Metz; Darren Hargrave; Erica J van der Hoeven; Corrie E Gidding; Eveline S de Bont; Omid S Eshghi; Roel Reddingius; Cacha M Peeters; Antoinette Y N Schouten-van Meeteren; Rob H J Gooskens; Bernd Granzen; Gabriel M Paardekooper; Geert O Janssens; David P Noske; Frederik Barkhof; Christof M Kramm; W Peter Vandertop; Gertjan J Kaspers; Dannis G van Vuurden Journal: Neuro Oncol Date: 2014-06-05 Impact factor: 12.300