External Validation and Modification of a Predictive Model for Acute Postsurgical Pain at Home After Day Surgery

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Abstract

Objectives:

In 2009, Gramke and colleagues have described predictive factors to preoperatively detect those at risk for moderate to severe acute postsurgical pain (APSP) after day surgery. The aim of the present study is to externally validate this initial model and to improve and internally validate a modified version of this model.

Materials and Methods:

Elective patients scheduled for day surgery were prospectively enrolled from November 2008 to April 2010. Model discrimination was quantified using the area under the receiver operating characteristic curve (AUC). Model calibration was assessed by visual inspection of the calibration plot. Subsequently, we modified (different assignment of type of surgery, different cutoff for moderate to severe APSP, continuous of dichotomized variables and testing of additional variables) and internally validated this model by standard bootstrapping techniques.

Results:

A total of 1118 patients were included. The AUC for the original model was 0.81 in the derivation data set and 0.72 in our validation data set. The model showed poorly calibrated risk predictions. The AUC of the modified model was 0.82 (optimism-corrected AUC=0.78). This modified model showed good calibration.

Conclusions:

The original prediction model of Gramke and colleagues performed insufficiently on our cohort of outpatients with respect to discrimination and calibration. Internal validation of a modified model shows promising results. In this model, preoperative pain, patient derived expected pain, and different types of surgery are the strongest predictors of moderate to severe APSP after day surgery.

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