Optimizing prediction of back pain outcomes

Abstract

An accurate means of identifying patients at high risk for chronic disabling pain could lead to more cost-effective care, with more intensive interventions targeted to those likely to benefit most. The Chronic Pain Risk Score is a tool developed to predict risk for chronic pain. The aim of this study was to examine whether its predictive ability could be enhanced by: (1) improved measures of the constructs it assesses (Improved Chronic Pain Risk Model); and (2) adding other predictors (Expanded Chronic Pain Risk Model). Patients initiating primary care for back pain (N=571) completed measures used in the Chronic Pain Risk Score, Improved Model, and Expanded Model, then completed the Graded Chronic Pain Scale (GCPS) 4months later (n=521; 91% response rate). In predicting 4-month GCPS grade III or IV (moderate or severe pain-related activity interference), the Improved Model performed better than did the Chronic Pain Risk Score (Net Reclassification Index [NRI]=0.32, P=0.003). The Expanded Model improved significantly on the prediction of the Improved Model (NRI=0.56, P<0.001) and demonstrated excellent discriminative ability (AUC=0.84, 95% CI=0.79-0.88). The Improved Model (AUC=0.79, 95% CI=0.75-0.84) and the Chronic Pain Risk Score (AUC=0.76, 95% CI=0.71-0.81) showed acceptable discriminative ability. A limited set of measures may be used to predict risk for future clinically significant pain in patients initiating primary care for back pain, but further evaluation of prognostic models is needed.