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  • B310 Development and internal validation of a multivariable risk prediction model for severe rebound pain after foot and ankle surgery involving single-shot popliteal sciatic nerve blockade

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Postoperative pain management
B310 Development and internal validation of a multivariable risk prediction model for severe rebound pain after foot and ankle surgery involving single-shot popliteal sciatic nerve blockade
  1. TTH Jen1,2,
  2. JXC Ke1,2,3,
  3. K Wing4,
  4. J Denomme1,
  5. DI McIsaac5,6,7,
  6. S-C Huang2,8,
  7. RM Ree1,2,
  8. C Prabhakar1,2,
  9. SKW Schwarz1,2 and
  10. CH Yarnold1,2
  1. 1Department of Anesthesia, St. Paul’s Hospital/Providence Health Care, Vancouver, Canada
  2. 2Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, Canada
  3. 3Department of Anesthesia, Pain Management and Perioperative Medicine, Dalhousie University, Halifax, Canada
  4. 4Department of Orthopedics, The University of British Columbia, Vancouver, Canada
  5. 5Department of Anesthesiology and Pain Medicine, University of Ottawa, Ottawa, Canada
  6. 6Department of Anesthesiology and Pain Medicine, The Ottawa Hospital, Ottawa, Canada
  7. 7Ottawa Hospital Research Institute, Ottawa, Canada
  8. 8Faculty of Medicine, The University of British Columbia, Vancouver, Canada

Abstract

Background and Aims Rebound pain occurs after 50% of ambulatory surgeries with regional anaesthesia. (1) To assist with risk stratification, we aimed to develop a model to predict severe rebound pain after foot and ankle surgery involving single-shot popliteal sciatic nerve blockade.

Methods After ethics approval, we performed a retrospective cohort study at St. Paul’s Hospital, a tertiary care centre in Vancouver, Canada. Patients undergoing lower limb surgery with popliteal sciatic nerve blockade from January 2016 to November 2019 were included. Exclusion criteria were uncontrolled pain in recovery room, perineural catheters, and loss-to-follow-up. We developed and internally validated a multivariable logistic regression model for severe rebound pain, defined as transition from well-controlled pain in recovery room (numerical rating scale [NRS]≤3) to severe pain (NRS≥7) within 48 hours. (1) A prioripredictors were age, sex, surgery type, planned admission, local anaesthetic type, dexamethasone use, and intraoperative anaesthesia type. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC), Nagelkerke’s R2, scaled Brier score, and calibration slope.

Results The cohort included 1365 patients (50 [16] years). Primary outcome was collected in 1311 (96%) patients, with severe rebound pain in 652 (50%). Internal validation revealed poor model performance, with AUROC 0.632 (95% CI 0.602, 0.661; Bootstrap optimism 0.021), Nagelkerke’s R20.063, and scaled Brier score 0.047 (Table 1). Calibration slope was 0.832 (95% CI 0.623, 1.041; Figure 1).

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Abstract B310 Table 1
Abstract B310 Figure 1
Abstract B310 Figure 1

Conclusions A model developed using routinely collected clinical data has poor predictive performance for rebound pain. Prospective studies involving other patient-related predictors are needed.

http://dx.doi.org/10.1136/rapm-2022-ESRA.384

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