TY - JOUR
T1 - Patient and in-hospital predictors of post-discharge opioid utilization
T2 - Individualizing prescribing after radical prostatectomy based on the ORIOLES initiative
AU - Su, Zhuo T.
AU - Becker, Russell E.N.
AU - Huang, Mitchell M.
AU - Biles, Michael J.
AU - Harris, Kelly T.
AU - Koo, Kevin
AU - Han, Misop
AU - Pavlovich, Christian P.
AU - Allaf, Mohamad E.
AU - Herati, Amin S.
AU - Patel, Hiten D.
N1 - Funding Information:
The authors thank Greta Stoianovici for her time and assistance with creating the online prescribing calculator. Funding source: None.
Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2022/3
Y1 - 2022/3
N2 - Objective: Judicious opioid stewardship would match each patient's prescription to their true medical necessity. However, most prescribing paradigms apply preset quantities and clinical judgment without objective data to predict individual use. We evaluated individual patient and in-hospital parameters as predictors of post-discharge opioid utilization after radical prostatectomy (RP) to provide evidence-based guidance for individualized prescribing. Methods: A prospective cohort of patients who underwent open or robotic RP were followed in the Opioid Reduction Intervention for Open, Laparoscopic, and Endoscopic Surgery (ORIOLES) initiative. Baseline demographics, in-hospital parameters, and inpatient and post-discharge pain medication utilization were tabulated. Opioid medications were converted to oral morphine equivalents (OMEQ). Predictive factors for post-discharge opioid utilization were analyzed by univariable and multivariable linear regression, adjusting for opioid reduction interventions performed in ORIOLES. Results: Of 443 patients, 102 underwent open and 341 underwent robotic RP. The factors most strongly associated with post-discharge opioid utilization included inpatient opioid utilization in the final 12 hours before discharge (+39.6 post-discharge OMEQ if inpatient OMEQ was >15 vs. 0), maximum patient-reported pain score (range 0–10) in the 12 hours before discharge (+27.6 OMEQ for pain score ≥6 vs. ≤1), preoperative opioid use (+76.2 OMEQ), and body mass index (BMI; +1.4 OMEQ per 1 kg/m2). A final predictive calculator to guide post-discharge opioid prescribing was constructed. Conclusions: Following RP, inpatient opioid use, patient-reported pain scores, prior opioid use, and BMI are correlated with post-discharge opioid utilization. These data can help guide individualized opioid prescribing to reduce risks of both overprescribing and underprescribing.
AB - Objective: Judicious opioid stewardship would match each patient's prescription to their true medical necessity. However, most prescribing paradigms apply preset quantities and clinical judgment without objective data to predict individual use. We evaluated individual patient and in-hospital parameters as predictors of post-discharge opioid utilization after radical prostatectomy (RP) to provide evidence-based guidance for individualized prescribing. Methods: A prospective cohort of patients who underwent open or robotic RP were followed in the Opioid Reduction Intervention for Open, Laparoscopic, and Endoscopic Surgery (ORIOLES) initiative. Baseline demographics, in-hospital parameters, and inpatient and post-discharge pain medication utilization were tabulated. Opioid medications were converted to oral morphine equivalents (OMEQ). Predictive factors for post-discharge opioid utilization were analyzed by univariable and multivariable linear regression, adjusting for opioid reduction interventions performed in ORIOLES. Results: Of 443 patients, 102 underwent open and 341 underwent robotic RP. The factors most strongly associated with post-discharge opioid utilization included inpatient opioid utilization in the final 12 hours before discharge (+39.6 post-discharge OMEQ if inpatient OMEQ was >15 vs. 0), maximum patient-reported pain score (range 0–10) in the 12 hours before discharge (+27.6 OMEQ for pain score ≥6 vs. ≤1), preoperative opioid use (+76.2 OMEQ), and body mass index (BMI; +1.4 OMEQ per 1 kg/m2). A final predictive calculator to guide post-discharge opioid prescribing was constructed. Conclusions: Following RP, inpatient opioid use, patient-reported pain scores, prior opioid use, and BMI are correlated with post-discharge opioid utilization. These data can help guide individualized opioid prescribing to reduce risks of both overprescribing and underprescribing.
KW - Individualized prescribing
KW - Opioid utilization
KW - Predictive model
KW - Radical prostatectomy
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U2 - 10.1016/j.urolonc.2021.10.007
DO - 10.1016/j.urolonc.2021.10.007
M3 - Article
C2 - 34857445
AN - SCOPUS:85122937446
SN - 1078-1439
VL - 40
SP - 104.e9-104.e15
JO - Urologic Oncology: Seminars and Original Investigations
JF - Urologic Oncology: Seminars and Original Investigations
IS - 3
ER -