Evidence map›Paper›PMID 41269691›Full record

ArticleJAMA network open2025

Leveraging the 72-Hour Rule Change to Support Transition From Hospital to Opioid Treatment Program.

Susan L Calcaterra, Melissa B Weimer, Eric Grimm, Yevgeniya Scherbak, Rawan Abdel Galil, Olivia Berger, Lindsay A Bowman, Suzanne A Nesbit, Alexandra Barany, Megan E Buresh

Abstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Susan L CalcaterraDepartment of Medicine, Division of Hospital Medicine, University of Colorado, Aurora.
Melissa B WeimerDepartment of Medicine, Yale School of Medicine, New Haven, Connecticut.
Eric GrimmDepartment of Medicine, Division of Hospital Medicine, University of Colorado, Aurora.
Yevgeniya ScherbakUCHealth, University of Colorado Hospital, Aurora.
Rawan Abdel GalilDivision of Addiction Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland.
Olivia BergerDepartment of Pharmacy, Johns Hopkins Bayview Medical Center, Baltimore, Maryland.
Lindsay A BowmanDepartment of Pharmacy, The Johns Hopkins Hospital, Baltimore, Maryland.
Suzanne A NesbitDepartment of Pharmacy, The Johns Hopkins Hospital, Baltimore, Maryland.
Alexandra BaranyDepartment of Pharmacy, The Johns Hopkins Hospital, Baltimore, Maryland.
Megan E BureshDivision of Addiction Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland.

Funding

Understanding Factors that Limit Access to Opioid Use Disorder Treatment in the Hospital to Inform Innovative Approaches to Expand Hospital-Based TreatmentK08DA049905 · NIDA · UNIVERSITY OF COLORADO DENVER · PI CALCATERRA, SUSAN L · 2020 to 2024
$945k
NIDA NIH HHS K08 DA049905
6 · The paper itself

Abstract

Importance: Changes have been made to the US federal 72-hour rule, which allows hospitals to administer or dispense but not prescribe up to 72 hours' worth of methadone for opioid withdrawal or opioid use disorder (OUD) at discharge (ie, take-home doses). Objective: To characterize acute care encounters involving take-home methadone and identify variables associated with completion of the transition from hospital to opioid treatment program (OTP) within 72 hours (hereafter 72-hour hospital to OTP linkage). Design, Setting, and Participants: This cohort study examined hospital and emergency department encounters for patients with OUD who received take-home methadone doses from 4 geographically diverse hospitals between November 2022 and April 2024. Exposure: Receipt of take-home methadone at hospital discharge. Main Outcomes and Measures: The primary outcome was documentation of 72-hour hospital to OTP linkage. Multinomial logistic regression was used to analyze the association between OTP linkage status (yes, no, or unknown) and discharge disposition, OTP enrollment at the time of the acute care encounter, co-use of opioids and stimulants, methadone dose, pregnancy, length of hospital stay, and psychiatric conditions. Results: Included in the analysis were 519 encounters for 456 individuals (median [IQR] age, 46 [36-56] years) who received take-home methadone at hospital discharge. Of these encounters, 280 (53.9%) involved patients who were male. Weekend discharge was the most common reason for dispensing take-home methadone doses (342 encounters [65.9%]). Overall, 231 encounters (44.5%) involved patients who completed a 72-hour hospital-to-OTP linkage, 67 encounters (12.9%) did not link to an OTP within 72 hours of discharge, and 221 (42.6%) had an unknown 72-hour OTP linkage status. Variables associated with completion of 72-hour hospital to OTP linkage included discharge to a postacute care facility (odds ratio [OR], 2.12; 95% CI, 1.12-4.21; P = .02), OTP enrollment at the time of the acute care encounter (OR, 2.63; 95% CI, 1.48-4.79; P < .001), and increasing methadone dose in 10-mg increments (OR, 1.20; 95% CI, 1.09-1.33; P < .001). Co-use of opioids and stimulants was inversely associated with completion of the 72-hour hospital to OTP linkage (OR, 0.48; 95% CI, 0.26-0.87; P = .01). Conclusions and Relevance: In this cohort study, preexisting relationships between hospitals, postacute care facilities, and OTPs, along with provision of take-home methadone, likely facilitated OTP linkage. Expansion of these relationships and processes could facilitate OUD treatment engagement during tenuous care transitions.

Indexed as

MethadoneOpiate Substitution TreatmentOpioid-Related DisordersPatient DischargeAdultAnalgesics, OpioidCohort StudiesFemaleHumansMaleMiddle AgedTime FactorsUnited StatesAnalgesics, OpioidMethadone

Identifiers

PMID41269691
PMCPMC12639479

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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.