Development and validation of a preoperative risk assessment scale for postoperative pain in laparoscopic gynecologic surgery: A prospective observational study

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Abstract

BACKGROUND: Postoperative pain remains a major challenge in gynecologic surgery, contributing to the development of chronic pain syndromes and negatively affecting recovery. Recent studies have underscored the role of baroreflex sensitivity in pain modulation, opening new perspectives for predicting and managing postoperative pain.

AIM: To develop and validate a preoperative risk assessment scale for predicting postoperative pain in patients who underwent laparoscopic gynecologic surgery, based on the assessment of cardiopulmonary functional status.

METHODS: This prospective observational study included 546 patients (median age: 36 [30–45] years) who underwent laparoscopic gynecologic procedures at the Kuban State Medical University Clinic. Preoperative parameters, functional characteristics of the cardiopulmonary system (voluntary threshold apnea duration), and postoperative outcomes were assessed, including: pain intensity on the Numeric Rating Scale (NRS) at various time points (0, 15, 30 min; 2, 6, and 24 h); incidence of severe postoperative pain (NRS ≥7); frequency and severity of postoperative nausea and vomiting; quality of recovery measured by the QoR-15 scale.

RESULTS: Severe postoperative pain (NRS ≥7) was observed in 146 patients (29.9%). Logistic regression identified the following significant predictors: voluntary threshold apnea duration (OR=0.95, 95% CI: 0.93–0.97, p <0.0001), high anxiety level by GAD-7 scale (OR=1.15, 95% CI: 1.08–1.22, p <0.0001), pain catastrophizing (OR=1.08, 95% CI: 1.03–1.14, p=0.0008), diagnosis of endometriosis (OR=2.45, 95% CI: 1.79–3.34, p <0.0001), and laparoscopic procedure type (uterine surgery: OR=2.82, p=0.0002; adnexal surgery: OR=1.99, p=0.0177). Internal validation demonstrated high predictive accuracy of the model (AUC=0.90, sensitivity 87%, specificity 82.2%). High-risk patients experienced more intense postoperative pain, higher rates of postoperative nausea and vomiting (63.1% vs. 35.8%, p <0.0001), sleep disturbances (mean PSQI score 6.7±2.9 vs. 4.9±2.5, p <0.001), and lower recovery quality (QoR-15: 92.4±12.6 vs. 104.8±14.2, p <0.0001).

CONCLUSION: The developed model demonstrated high predictive accuracy and sensitivity, allowing for effective prediction of the risk of severe postoperative pain. Its clinical implementation can optimize postoperative pain management and enhance recovery in patients.

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About the authors

Aleksey A. Dmitriev

Kuban State Medical University

Author for correspondence.
Email: dmitrievalex-79@yandex.ru
ORCID iD: 0000-0002-5195-3149
SPIN-code: 7737-8639
Russian Federation, Krasnodar

Nikita V. Trembach

Kuban State Medical University; Regional Clinical Hospital No. 2, Krasnodar

Email: trembachnv@mail.ru
ORCID iD: 0000-0002-0061-0496
SPIN-code: 1675-4895

MD, Dr. Sci. (Medicine)

Russian Federation, Krasnodar; Krasnodar

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Supplementary files

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2. Fig. 1. Study design diagram. ЧРШ — Numeric Rating Scale.

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3. Fig. 2. ROC curve for the postoperative pain prediction model.

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4. Fig. 3. Calibration curve for the model’s predicted probabilities.

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