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. 2020 Jul 27;5(4):e831.
doi: 10.1097/PR9.0000000000000831. eCollection 2020 Jul-Aug.

Predicting poor postoperative acute pain outcome in adults: an international, multicentre database analysis of risk factors in 50,005 patients

Alexander Schnabel  1 , Maryam Yahiaoui-Doktor  2 , Winfried Meissner  3 , Peter Konrad Zahn  4 , Esther Miriam Pogatzki-Zahn  1
Affiliations

Predicting poor postoperative acute pain outcome in adults: an international, multicentre database analysis of risk factors in 50,005 patients

Alexander Schnabel et al. Pain Rep. .

Abstract

Background: The aim of this study was to determine simple risk factors for severe pain intensity (≥7 points on a numeric rating scale [NRS]), to analyse their relation to other patient-reported outcome measures and to develop a simple prediction model.

Methods: We used data from 50,005 patients from the PAIN-OUT project. Within a first data set (n = 33,667), relevant risk factors were identified by logistic binary regression analysis, assessed for additional patient-reported outcome measures beyond pain intensity and summed up for developing a simple risk score. Finally, sum of factors was plotted against postoperative pain outcomes within a validation data set (n = 16,338).

Results: Odds ratios (OR) for the following risk factors were identified: younger age (<54 years, OR: 1.277), preoperative chronic pain at the site of surgery (OR: 1.195), female sex (OR: 1.433), duration of surgery (>90 minutes, OR: 1.308), preoperative opioid intake (OR: 1.250), feeling anxious (OR: 1.239) and feeling helpless due to pain (OR: 1.198), and the country of the recruiting centre (OR: 1.919). Patients with ≥3 risk factors had more severe pain intensity scores, spent a longer time in severe pain, and wished to have received more pain treatment (P < 0.001). A simple risk score was created with 4 risk factors showing a moderate prediction level.

Conclusions: Patients with ≥3 risk factors are at higher risk for poor postoperative acute pain outcome after surgery. Future studies using this score might show that preventive strategies might decrease pain intensity, pain-related postoperative dysfunction, and the development of chronic pain.

Keywords: Chronification of pain; Database analysis; Postoperative pain; Risk factors; Risk prediction.

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Conflict of interest statement

The authors have no conflicts of interest to declare. This work was supported by the European Commission's Seventh Framework Program (FP7/2009-2013 under Grant Agreement No. 223590) to E.M. Pogatzki-Zahn and to W. Meissner (coordinator).Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Figures

Figure 1.
Figure 1.
Comparison of the number of risk factors in patients suffering from pain scores < or ≥7 points on a numeric rating scale (NRS).
Figure 2.
Figure 2.
Area under the curve for the prediction of patients (original risk factors) within the validation cohort (n = 16338) suffering from severe postoperative pain.
Figure 3.
Figure 3.
Comparison of the number of risk factors within the group of patients suffering from more than the 20% of time spending in severe pain in the last 24 hours.
Figure 4.
Figure 4.
Comparison of the number of risk factors within the group of patients wishing to have received more pain treatment or not.
See this image and copyright information in PMC

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