Evidence map›Paper›PMID 40612773›Full record

ArticleArthroplasty today2025

Toward Opioid-Free Total Hip Arthroplasty: A Retrospective Study of a Targeted Opioid Reduction Program in 229 Patients.

Mary L Noyes, Kamli N W Faour, Zoë A Walsh, Catherine M Call, Johanna A Mackenzie, Adam J Rana

Abstract read
In one paragraph

Article in Arthroplasty today, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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.

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.

Mary L NoyesTufts University School of Medicine, Boston, MA, USA.
Kamli N W FaourTufts University School of Medicine, Boston, MA, USA.
Zoë A WalshMMP Orthopedics & Sports Medicine, Maine Medical Center, Portland, ME, USA.
Catherine M CallTufts University School of Medicine, Boston, MA, USA.
Johanna A MackenzieMMP Orthopedics & Sports Medicine, Maine Medical Center, Portland, ME, USA.
Adam J RanaTufts University School of Medicine, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Prescription opioids leftover following arthroplasty surgery pose risks to patients and communitys. The purpose of this study was to capture opioid utilization patterns following primary total hip arthroplasty before and after a targeted intervention to decrease postoperative opioid prescription quantity. We hypothesized that reducing discharge pill count would not impact pain or functional outcomes. Methods: Primary total hip arthroplasties performed by a high-volume, fellowship-trained arthroplasty surgeon between October 2022 and January 2024 were retrospectively evaluated for study inclusion; 229 patients met inclusion criteria. Beginning in April 2023, the surgeon gradually implemented a 38% reduction in postoperative opioid prescribing from 40 to 24 pills. Opioid consumption was evaluated by patient-reported pill count at the first postoperative visit. Patients were sorted into 2 groups: "preintervention" (n = 157) and "postintervention" (poI) (n = 72). Preintervention patients received between 300 and 420 oral morphine equivalents and poI patients received between 240 and 299.99 oral morphine equivalents. Demographics, pill counts, refills, 30-day emergency department visits, function (Hip Disability and Osteoarthritis Outcome Score Joint Replacement), pain (visual analog scale), and satisfaction scores were analyzed. Results: Proportion of discharge prescription remaining at 2-week postoperative visit did not differ significantly between intervention groups ( Conclusions: Results support that arthroplasty surgeons can prescribe smaller quantities of opioids without compromising care. Such interventions can help reduce the number of prescription opioids available for misuse and diversion.

Indexed as

OpioidPatient-reported outcome measureTotal hip arthroplastyTotal joint arthroplasty

Identifiers

PMID40612773
PMCPMC12221641

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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.