Evidence map›Paper›PMID 33382802›Full record

ArticlePloS one2020

Evaluation of a novel metric for personalized opioid prescribing after hospitalization.

Nicholas R Iverson, Catherine Y Lau, Yumiko Abe-Jones, Margaret C Fang, Kirsten N Kangelaris, Priya Prasad, Sachin J Shah, Nader Najafi

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.4field-weighted citation impact, top 31% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. 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

8 authors at 1 institution in 1 country.

Nicholas R IversonDepartment of Medicine, University of California, San Francisco, San Francisco, California, United States of America.ORCID 0000-0001-6492-7110
Catherine Y LauDepartment of Medicine, University of California, San Francisco, San Francisco, California, United States of America.
Yumiko Abe-JonesDepartment of Medicine, University of California, San Francisco, San Francisco, California, United States of America.
Margaret C FangDepartment of Medicine, University of California, San Francisco, San Francisco, California, United States of America.
Kirsten N KangelarisDepartment of Medicine, University of California, San Francisco, San Francisco, California, United States of America.
Priya PrasadDepartment of Medicine, University of California, San Francisco, San Francisco, California, United States of America.
Sachin J ShahDepartment of Medicine, University of California, San Francisco, San Francisco, California, United States of America.
Nader NajafiDepartment of Medicine, University of California, San Francisco, San Francisco, California, United States of America.
University of California, San Francisco · US

Funding

Use and outcomes of anticoagulants for the treatment and prevention of thrombosis among hospitalized patientsK24HL141354 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FANG, MARGARET C. · 2018 to 2022
$731k
NHLBI NIH HHS K24 HL141354
6 · The paper itself

Abstract

backgroundThe duration of an opioid prescribed at hospital discharge does not intrinsically account for opioid needs during a hospitalization. This discrepancy may lead to patients receiving much larger supplies of opioids on discharge than they truly require.

objectiveAssess a novel discharge opioid supply metric that adjusts for opioid use during hospitalization, compared to the conventional discharge prescription signature. DESIGN, SETTING, &

participantsRetrospective study using electronic health record data from June 2012 to November 2018 of adults who received opioids while hospitalized and after discharge from a single academic medical center. MEASURES & ANALYSIS: We ascertained inpatient opioids received and milligrams of opioids supplied after discharge, then determined days of opioids supplied after discharge by the conventional prescription signature opioid-days ("conventional days") and novel hospital-adjusted opioid-days ("adjusted days") metrics. We calculated descriptive statistics, within-subject difference between measurements, and fold difference between measures. We used multiple linear regression to determine patient-level predictors associated with high difference in days prescribed between measures.

resultsThe adjusted days metric demonstrates a 2.4 day median increase in prescription duration as compared to the conventional days metric (9.4 vs. 7.0 days; P<0.001). 95% of all adjusted days measurements fall within a 0.19 to 6.90-fold difference as compared to conventional days measurements, with a maximum absolute difference of 640 days. Receiving a liquid opioid prescription accounted for an increased prescription duration of 135.6% by the adjusted days metric (95% CI 39.1-299.0%; P = 0.001). Of patients who were not on opioids prior to admission and required opioids during hospitalization but not in the last 24 hours, 325 (8.6%) were discharged with an opioid prescription.

conclusionsThe adjusted days metric, based on inpatient opioid use, demonstrates that patients are often prescribed a supply lasting longer than the prescription signature suggests, though with marked variability for some patients that suggests potential under-prescribing as well. Adjusted days is more patient-centered, reflecting the reality of how patients will take their prescription rather than providers' intended prescription duration.

Indexed as

Drug PrescriptionsPractice Patterns, Physicians'AdultAgedAnalgesics, OpioidElectronic Health RecordsFemaleHospitalizationHumansMaleMiddle AgedPatient DischargePostoperative PainPrecision MedicineRetrospective StudiesAnalgesics, Opioid

Identifiers

PMID33382802
PMCPMC7774844
OpenAlexW3116586671

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.