Evidence map›Paper›PMID 35177890›Full record

ArticleFederal practitioner : for the health care professionals of the VA, DoD, and PHS2021

Multimodal Pain Management With Adductor Canal Block Decreases Opioid Consumption Following Total Knee Arthroplasty.

Neil Thomas Soehnlen, Eric Donald Erb, Eric McKee Kiskaddon, Uthona Green, Andrew Froehle, Anil Krishnamurthy

Open access · bronzeAbstract read
In one paragraph

Article in Federal practitioner : for the health care professionals of the VA, DoD, and PHS, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 38% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Neil Thomas SoehnlenDayton Veterans Affairs Medical Center, Ohio.
Eric Donald ErbDayton Veterans Affairs Medical Center, Ohio.
Eric McKee KiskaddonDayton Veterans Affairs Medical Center, Ohio.
Uthona GreenDayton Veterans Affairs Medical Center, Ohio.
Andrew FroehleWright State University, Dayton, Ohio.
Anil KrishnamurthyDayton Veterans Affairs Medical Center, Ohio.
Wright State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEase of access to opioids in the perioperative period is a risk factor for subsequent opioid misuse. The purpose of this study was to quantify a decrease in opioid consumption following implementation of a new analgesic protocol after total knee arthroplasty (TKA).

methodsA retrospective cohort study was performed analyzing patients who underwent TKA at a US Department of Veterans Affairs medical center. Patients were divided into 2 groups by multimodal analgesic regimen: Analgesia with intraoperative general anesthesia, a patient-controlled analgesia pump, and oral opioids (control group) or analgesia with intraoperative spinal anesthesia, a multimodal medication regimen, and an adductor canal block (protocol group).

resultsA total of 533 TKAs were included. The mean (SD) IV morphine equivalent dose (MED) requirement was 178.2 (98.0) for the control and 12.0 (24.6) for the protocol group (

conclusionsThe described analgesic protocol resulted in significant decreases in IV and total opioid requirement, and lower rates of opioid prescriptions at the first postoperative visit. These findings demonstrate a decrease in opioid utilization with modern perioperative analgesia protocols and reinforce multiple recommendations to decrease opioid exposure and access.

Identifiers

PMID35177890
PMCPMC8843006
OpenAlexW4205797113

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.