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Burns ML, Hilliard P, Vandervest J, Mentz G, Josifoski A, Varghese J, Fisher C, Kheterpal S, Shah N, Bicket MC. Variation in Intraoperative Opioid Administration by Patient, Clinician, and Hospital Contribution. JAMA Netw Open 2024; 7:e2351689. [PMID: 38227311 DOI: 10.1001/jamanetworkopen.2023.51689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024] Open
Abstract
Importance The opioid crisis has led to scrutiny of opioid exposures before and after surgical procedures. However, the extent of intraoperative opioid variation and the sources and contributing factors associated with it are unclear. Objective To analyze attributable variance of intraoperative opioid administration for patient-, clinician-, and hospital-level factors across surgical and analgesic categories. Design, Setting, and Participants This cohort study was conducted using electronic health record data collected from a national quality collaborative database. The cohort consisted of 1 011 268 surgical procedures at 46 hospitals across the US involving 2911 anesthesiologists, 2291 surgeons, and 8 surgical and 4 analgesic categories. Patients without ambulatory opioid prescriptions or use history undergoing an elective surgical procedure between January 1, 2014, and September 11, 2020, were included. Data were analyzed from January 2022 to July 2023. Main Outcomes and Measures The rate of intraoperative opioid administration as a continuous measure of oral morphine equivalents (OMEs) normalized to patient weight and case duration was assessed. Attributable variance was estimated in a hierarchical structure using patient, clinician, and hospital levels and adjusted intraclass correlations (ICCs). Results Among 1 011 268 surgical procedures (mean [SD] age of patients, 55.9 [16.2] years; 604 057 surgical procedures among females [59.7%]), the mean (SD) rate of intraoperative opioid administration was 0.3 [0.2] OME/kg/h. Together, clinician and hospital levels contributed to 20% or more of variability in intraoperative opioid administration across all analgesic and surgical categories (adjusting for surgical or analgesic category, ICCs ranged from 0.57-0.79 for the patient, 0.04-0.22 for the anesthesiologist, and 0.09-0.26 for the hospital, with the lowest ICC combination 0.21 for anesthesiologist and hosptial [0.12 for the anesthesiologist and 0.09 for the hospital for opioid only]). Comparing the 95th and fifth percentiles of opioid administration, variation was 3.3-fold among anesthesiologists (surgical category range, 2.7-fold to 7.7-fold), 4.3-fold among surgeons (surgical category range, 3.4-fold to 8.0-fold), and 2.2-fold among hospitals (surgical category range, 2.2-fold to 4.3-fold). When adjusted for patient and surgical characteristics, mean (square error mean) administration was highest for cardiac surgical procedures (0.54 [0.56-0.52 OME/kg/h]) and lowest for orthopedic knee surgical procedures (0.19 [0.17-0.21 OME/kg/h]). Peripheral and neuraxial analgesic techniques were associated with reduced administration in orthopedic hip (51.6% [95% CI, 51.4%-51.8%] and 60.7% [95% CI, 60.5%-60.9%] reductions, respectively) and knee (48.3% [95% CI, 48.0%-48.5%] and 60.9% [95% CI, 60.7%-61.1%] reductions, respectively) surgical procedures, but reduction was less substantial in other surgical categories (mean [SD] reduction, 13.3% [8.8%] for peripheral and 17.6% [9.9%] for neuraxial techniques). Conclusions and Relevance In this cohort study, clinician-, hospital-, and patient-level factors had important contributions to substantial variation of opioid administrations during surgical procedures. These findings suggest the need for a broadened focus across multiple factors when developing and implementing opioid-reducing strategies in collaborative quality-improvement programs.
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Affiliation(s)
- Michael L Burns
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor
| | - Paul Hilliard
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor
| | - John Vandervest
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor
| | - Graciela Mentz
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor
| | - Ace Josifoski
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor
| | - Jomy Varghese
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor
| | - Clark Fisher
- Department of Anesthesiology, Yale School of Medicine, New Haven, Connecticut
| | - Sachin Kheterpal
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor
| | - Nirav Shah
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor
| | - Mark C Bicket
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor
- Opioid Prescribing Engagement Network, Institute for Healthcare Innovation and Policy, University of Michigan, Ann Arbor
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Liu R, Gutiérrez R, Mather RV, Stone TAD, Santa Cruz Mercado LA, Bharadwaj K, Johnson J, Das P, Balanza G, Uwanaka E, Sydloski J, Chen A, Hagood M, Bittner EA, Purdon PL. Development and prospective validation of postoperative pain prediction from preoperative EHR data using attention-based set embeddings. NPJ Digit Med 2023; 6:209. [PMID: 37973817 PMCID: PMC10654400 DOI: 10.1038/s41746-023-00947-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 10/13/2023] [Indexed: 11/19/2023] Open
Abstract
Preoperative knowledge of expected postoperative pain can help guide perioperative pain management and focus interventions on patients with the greatest risk of acute pain. However, current methods for predicting postoperative pain require patient and clinician input or laborious manual chart review and often do not achieve sufficient performance. We use routinely collected electronic health record data from a multicenter dataset of 234,274 adult non-cardiac surgical patients to develop a machine learning method which predicts maximum pain scores on the day of surgery and four subsequent days and validate this method in a prospective cohort. Our method, POPS, is fully automated and relies only on data available prior to surgery, allowing application in all patients scheduled for or considering surgery. Here we report that POPS achieves state-of-the-art performance and outperforms clinician predictions on all postoperative days when predicting maximum pain on the 0-10 NRS in prospective validation, though with degraded calibration. POPS is interpretable, identifying comorbidities that significantly contribute to postoperative pain based on patient-specific context, which can assist clinicians in mitigating cases of acute pain.
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Affiliation(s)
- Ran Liu
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Rodrigo Gutiérrez
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Rory V Mather
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, US
| | - Tom A D Stone
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Laura A Santa Cruz Mercado
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Kishore Bharadwaj
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jasmine Johnson
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Proloy Das
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Gustavo Balanza
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Ekenedilichukwu Uwanaka
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Justin Sydloski
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Andrew Chen
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Mackenzie Hagood
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Edward A Bittner
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Patrick L Purdon
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
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