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. 2022 Mar 11;14(3):e23079.
doi: 10.7759/cureus.23079. eCollection 2022 Mar.

Predictive Analytics for Inpatient Postoperative Opioid Use in Patients Undergoing Mastectomy

Isabella M Dolendo  1 , Anne M Wallace  2 , Ava Armani  2 , Ruth S Waterman  3 , Engy T Said  1 , Rodney A Gabriel  1
Affiliations

Predictive Analytics for Inpatient Postoperative Opioid Use in Patients Undergoing Mastectomy

Isabella M Dolendo et al. Cureus. .

Abstract

Introduction: The use of opioids in mastectomy patients is a particular challenge, having to balance the management of acute pain while minimizing risks of continuous opioid use postoperatively. Despite attempts to decrease postmastectomy opioid use, including regional anesthetics, gabapentinoids, topical anesthetics, and nonopioid anesthesia, prolonged opioid use remains clinically significant among these patients. The goal of this study is to identify risk factors and develop machine-learning-based models to predict patients who are at higher risk for postoperative opioid use after mastectomy.

Methods: In this retrospective cohort study, we collected data from patients that underwent mastectomy procedures. The primary outcome of interest was defined as oxycodone milligram equivalents (OME) greater than or equal to the 75% of OME use on a postoperative day 1. Model performance (area under the receiver-operating characteristics curve (AUC)) of various machine learning approaches was calculated via 10-fold cross-validation. Odds ratio (OR) and 95% confidence intervals (CI) were reported.

Results: There were a total of 148 patients that underwent mastectomy and were included. The medium (quartiles) postoperative day 1 opioid use was 5 mg OME (0.25 mg OME). Using multivariable logistic regression, the most protective factors against higher opioid use was being postmenopausal (OR: 0.13, 95% CI: 0.03-0.61, p = 0.009) and cancer diagnosis (OR: 0.19, 95% CI: 0.05-0.73, p = 0.01). The AUC was 0.725 (95% CI: 0.572-0.876). There was no difference in the performance of other machine-learning-based approaches.

Conclusions: The ability to predict patients' postoperative pain could have a significant impact on preoperative counseling and patient satisfaction.

Keywords: acute pain; analgesia; machine learning; mastectomy; opioids.

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Conflict of interest statement

Rodney Gabriel serves on the advisory board for Avanos and Acacia Pharmaceuticals. Rodney Gabriel and Engy Said’s institution has received research funding from Epimed, Teleflex, and SPR Therapeutics.

Figures

Figure 1
Figure 1. Performance of the multivariable logistic regression with variable selection predicting patients at risk for higher acute opioid use on postoperative day 1: A) area under the receiver operating characteristics curve and B) calibration plot illustration goodness-of-fit.
AUC: area under the receiver-operating characteristics curve; CI: confidence interval; HL: Hosmer–Lemeshow; ROC: receiver-operating characteristics
See this image and copyright information in PMC

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