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

Björn Stessel, Audrey A A Fiddelers, Marco A Marcus, Sander M J van Kuijk, Elbert A Joosten, Madelon L Peters, Wolfgang F F A Buhre, Hans-Fritz Gramke

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Abstract

OBJECTIVES: In 2009, Gramke et al have described predictive factors to pre-operatively detect those at risk for moderate to severe acute postoperative 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.

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 cut-off 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 dataset and 0.72 in our validation dataset. 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.

CONCLUSION: The original prediction model of Gramke et al performed insufficient 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.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially. http://creativecommons.org/licenses/by-nc-nd/4.0.

Original languageEnglish
Pages (from-to)405-413
Number of pages9
JournalClinical Journal of Pain
Volume33
Issue number5
DOIs
Publication statusPublished - May 2017

Keywords

  • day surgery
  • postoperative pain
  • predictive model
  • validation
  • modification
  • SEVERE POSTOPERATIVE PAIN
  • INTENSITY
  • MODERATE
  • OUTPATIENT
  • PREVALENCE
  • SEVERITY
  • IMPACT
  • SCALE
  • CARE

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