Multivariate prognostic modeling of persistent pain following lumbar discectomy

Persistent postsurgical pain (PPSP) affects between 10% and 50% of surgical patients, the development of which is a complex and poorly understood process. To date, most studies on PPSP have focused on specific surgical procedures where individuals do not suffer from chronic pain before the surgical intervention. Individuals who have a chronic nerve injury are likely to have established peripheral and central sensitization which may increase the risk of developing PPSP. Concurrent analyses of the possible factors contributing to the development of PPSP following lumbar discectomy have not been examined.

The aim of this study is to identify risk and protective factors that predict the course of recovery following lumbar discectomy and to develop an easily applicable preoperative multivariate prognostic model for the occurrence of PPSP in this patient cohort.

A prospective study of elective lumbar discectomy with a 3 month follow-up.

University setting in Ireland.

All ASA I-II patients, (n = 53, 18-65 years old), undergoing elective lumbar discectomy at a single institute were included and followed for a 3 month period postsurgery. Preoperative potential predictors were collected: age, gender, pain intensity (McGill score, visual analog scale [VAS], Present Pain Intensity), degree of dysfunction (Roland-Morris Function score), psychological status (pain catastrophizing, anxiety, and depression scores), health-related quality of life (SF-36), quantitative sensory testing (QST), inflammatory biomarkers, and a genetic pain profile. The proposed primary outcome was significant pain reduction (VAS > 70%) 3 months following surgery compared to the preoperative pain intensity.

A final prediction model was obtained using a multivariate logistic regression in combination with bootstrapping techniques for internal validation. Twenty (37.7%) patients developed PPSP. Independent predictor factors included age (odds ratio [OR] = 1.0 per year), present pain intensity (OR = 0.6), and degree of dysfunction (OR = 1.2). The concordance index C (.658) supports a good monotonic association (where perfect prediction is 1) and the Akaike's information criteria indicated a good fit of the model. Inclusion of additional measured parameters (QST, biomarker, or genotyping) did not improve the model.

Before this internally validated model can be integrated into clinical practice, and used for patient counselling and quality assurance purposes, external validation studies are necessary.

We demonstrated that the occurrence of PPSP can be predicted using a small set of variables easily obtained at the preoperative visit. This a prediction rule that could further optimize perioperative pain treatment and reduce attendant complications by allowing the preoperative classification of surgical patients according to their risk of developing PPSP.