Abstract

BackgroundPostoperative pain is the most prominent concern among surgical patients. It has previously been reported that venous cannulation-induced pain (VCP) can be used to predict postoperative pain after laparoscopic cholecystectomy within 90 mins in the recovery room. Its potential in predicting postoperative pain in patients with patient-controlled intravenous analgesia (PCIA) is worth establishing. The purpose of this prospective observational study was to investigate the application of VCP in predicting postoperative pain in patients with PCIA during the first 24 h after laparoscopic nephrectomy.MethodsOne hundred twenty patients scheduled for laparoscopic nephrectomy were included in this study. A superficial vein on the back of the hand was cannulated with a standard-size peripheral venous catheter (1.1 × 3.2 mm) by a nurse in the preoperative areas. Then the nurse recorded the VAS score associated with this procedure estimated by patients, and dichotomized the patients into low response group (VAS scores < 2.0) or high response group (VAS scores ≥2.0). After general anesthesia and surgery, all the patients received the patient-controlled intravenous analgesia (PCIA) with sufentanil. The VAS scores at rest and on coughing at 2 h, 4 h, 8 h, 12 h, 24 h, the effective number of presses and the number of needed rescue analgesia within 24 h after surgery were recorded.ResultsPeripheral venous cannulation-induced pain score was significantly correlated with postoperative pain intensity at rest (rs = 0.64) and during coughing (rs = 0.65), effective times of pressing (rs = 0.59), additional consumption of sufentanil (rs = 0.58). Patients with venous cannulation-induced pain intensity ≥2.0 VAS units reported higher levels of postoperative pain intensity at rest (P < 0.0005) and during coughing (P < 0.0005), needed more effective times of pressing (P < 0.0005) and additional consumption of sufentanil (P < 0.0005), and also needed more rescue analgesia (P = 0.01) during the first 24 h. The odds of risk for moderate or severe postoperative pain (OR 3.5, 95% CI 1.3–9.3) was significantly higher in patients with venous cannulation-induced pain intensity ≥2.0 VAS units compared to those <2.0 VAS units.ConclusionsPreoperative assessment of pain induced by venous cannulation can be used to predict postoperative pain intensity in patients with PCIA during the first 24 h after laparoscopic nephrectomy.Trial registrationWe registered this study in a Chinese Clinical Trial Registry (ChiCTR) center on July 6 2019 and received the registration number: ChiCTR1900024352.

Highlights

  • Postoperative pain is the most prominent concern among surgical patients

  • Preoperative assessment of pain induced by venous cannulation can be used to predict postoperative pain intensity in patients with patient-controlled intravenous analgesia (PCIA) during the first 24 h after laparoscopic nephrectomy

  • Bivariate correlations between peripheral venous cannulation-induced pain score and outcome variables are shown in Fig. 2 and Table 2

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Summary

Introduction

Postoperative pain is the most prominent concern among surgical patients. It has previously been reported that venous cannulation-induced pain (VCP) can be used to predict postoperative pain after laparoscopic cholecystectomy within 90 mins in the recovery room. Its potential in predicting postoperative pain in patients with patient-controlled intravenous analgesia (PCIA) is worth establishing. The purpose of this prospective observational study was to investigate the application of VCP in predicting postoperative pain in patients with PCIA during the first 24 h after laparoscopic nephrectomy. Current postoperative pain management strategies apply a standardized, one-size-fits-all approach to all patients. These standardized protocols are not suitable for the significant difference in patient’s pain and may lead to insufficient analgesia in patients with high analgesic needs, or excessive analgesia, which is accompanied by increasing analgesic-related side effects. Preoperative pain prediction methods are highly relevant in this regard

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