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Featured Articles: Infographic

The Case for Big Data: Patterns of Opioid Utilization

Nathan, Naveen MD

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doi: 10.1213/ANE.0000000000005826

As we progressively try to define the optimal role of opiates in the perioperative setting, it is instructive to know what variability exists in their current use. A single-center evaluation of practice patterns may not be generalizable. As such, the use of large repositories of clinical data from a broad array of health care centers would better illuminate these patterns. Naik et al do precisely that through a retrospective, longitudinal study from 2012 to 2016 in which intraoperative opiate dosing was assessed from 10 hospitals by mining operative records from the Multicenter Perioperative Outcomes Group (MPOG) registry. Opiate usage was described as parenteral morphine equivalents (PME) and characterized as a function of multiple variables. These include age, anesthesia duration, year, peripheral block, neuraxial block, general anesthesia, emergency status, race, sex, remifentanil infusion, major surgery, ASA Physical status, nonopioid analgesic count, MPOG institution, and surgery category. More than 1 million cases were included in the analysis. As this infographic shows, the use of opioids declined from an average PME of 152 (151–153) mcg/kg in 2012 to 129 (129–130) mcg/kg in 2016. Male patients received less opioids compared to females. Finally, the variability in PME between institutions was high, ranging from a low of 80 (79–81) mcg/kg to as high as 186 (184–187) mcg/kg. The corresponding editorial by Grant and Anderson places this study in the broader context of how we understand studies derived from large-scale data banks, underscoring their advantages and complexities.


    1. Naik B, Kuck K, Saager L, et al. Practice patterns and variability in intraoperative opioid utilization: a report from the Multicenter Perioperative Outcomes Group. Anesth Analg. 2022;134:8–17.
    2. Grant MC, Anderson TA. Laying the first brick: a foundation for the medical investigation through Big Data. Anesth Analg. 2022;134:5–7.
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