Drug and alcohol dependence · 2026
Applying decision curve analysis to evaluate a patient-centered opioid prescribing strategy among patients with laparoscopic cholecystectomy in the US.
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Huang Yongmei, Jacobson Judith S, Li Guohua, Mauro Pia M, Guschwan Marianne T, Tergas Ana I, Hou June Y, Xu Xiao, Martins Silvia S, Elkin Elena B, Hershman Dawn L, Wright Jason D
Аннотация
BACKGROUND: With evolving prescription policies, the clinical utility of patient-centered opioid prescribing remains unclear. We evaluated whether a patient-centered opioid prescribing approach improves outcomes compared to default strategies: "treat all" (emulating opioid over-prescribing practices in the late 1990s and 2000s) or "treat none" (reflecting prescribing restrictions in Centers for Disease Control and Prevention 2016 guideline).
METHODS: We conducted a decision curve analysis using electronic medical data from 1136 adult patients undergoing laparoscopic cholecystectomy for benign indications (October 2015 - December 2018) at an urban tertiary care center. Perioperative opioid needs were categorized as "none/low" or "medium/high" based on pain scores and opioid prescriptions/administrations from 30 days pre-operative to two weeks postoperative.
RESULTS: Of 803 patients in the training dataset, 63.9 % required medium/high opioid levels. Key predictors included sex, preoperative medication use (opioids, acetaminophen, other nonsteroidal anti-inflammatory drug, antidepressants), emergent surgery, anesthesia type, and surgical indications. The prediction model's c-statistic was 0.65 (95 %CI: 0.58-0.71) and Brier score was 0.20 (95 % CI: 0.009-0.71) in the testing dataset. At an optimal threshold probability of 0.60, the prediction model correctly identified 16 more true positive cases per 100 patients needing medium/high opioid prescriptions than the "treat all" strategy, accounting for false positives. Across a threshold range of 0.18-0.91, the model-based strategy constantly outperformed both default strategies.
CONCLUSION: Prediction-based, patient-centered opioid prescribing provides greater clinical utility across most thresholds.
METHODS: We conducted a decision curve analysis using electronic medical data from 1136 adult patients undergoing laparoscopic cholecystectomy for benign indications (October 2015 - December 2018) at an urban tertiary care center. Perioperative opioid needs were categorized as "none/low" or "medium/high" based on pain scores and opioid prescriptions/administrations from 30 days pre-operative to two weeks postoperative.
RESULTS: Of 803 patients in the training dataset, 63.9 % required medium/high opioid levels. Key predictors included sex, preoperative medication use (opioids, acetaminophen, other nonsteroidal anti-inflammatory drug, antidepressants), emergent surgery, anesthesia type, and surgical indications. The prediction model's c-statistic was 0.65 (95 %CI: 0.58-0.71) and Brier score was 0.20 (95 % CI: 0.009-0.71) in the testing dataset. At an optimal threshold probability of 0.60, the prediction model correctly identified 16 more true positive cases per 100 patients needing medium/high opioid prescriptions than the "treat all" strategy, accounting for false positives. Across a threshold range of 0.18-0.91, the model-based strategy constantly outperformed both default strategies.
CONCLUSION: Prediction-based, patient-centered opioid prescribing provides greater clinical utility across most thresholds.