Evidence map›Paper›PMID 26615182›Full record

Trial reportJournal of the American Medical Informatics Association : JAMIA2016

The impact of real-time alerting on appropriate prescribing in kidney disease: a cluster randomized controlled trial.

Linda Awdishu, Carrie R Coates, Adam Lyddane, Kim Tran, Charles E Daniels, Joshua Lee, Robert El-Kareh

Open access · bronzeAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 9 pooled it
3.8field-weighted citation impact, top 6% 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

35 citing papers in PubMed, 9 syntheses or guidelines pooled it, 67 citations in OpenAlex.

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  8. Electronic Alerts for Acute Kidney Injury.Deutsches Arzteblatt international · 2017
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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

7 authors at 5 institutions in 1 country.

Linda AwdishuUC San Diego Skaggs School of Pharmacy and Pharmaceutical Sciences, San Diego, CA, USA UC San Diego School of Medicine, San Diego, CA, USA lawdishu@ucsd.edu.
Carrie R CoatesUC San Diego Health System, San Diego, CA, USA.
Adam LyddaneUC San Diego Health System, San Diego, CA, USA.
Kim TranNortheast Georgia Diagnostic Clinic, Gainsville, GA, USA.
Charles E DanielsUC San Diego Skaggs School of Pharmacy and Pharmaceutical Sciences, San Diego, CA, USA UC San Diego Health System, San Diego, CA, USA.
Joshua LeeKeck Medicine, University of Southern California, Los Angeles, CA, USA.
Robert El-KarehUC San Diego School of Medicine, San Diego, CA, USA.
UC San Diego Health System · USUniversity of California, San Diego · USNortheast Georgia Medical Center · USUniversity of Montana · USUniversity of Southern California · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients with kidney disease are at risk for adverse events due to improper medication prescribing. Few randomized controlled trials of clinical decision support (CDS) utilizing dynamic assessment of patients' kidney function to improve prescribing for patients with kidney disease have been published.

methodsWe developed a CDS tool for 20 medications within a commercial electronic health record. Our system detected scenarios in which drug discontinuation or dosage adjustment was recommended for adult patients with impaired renal function in the ambulatory and acute settings - both at the time of the initial prescription ("prospective" alerts) and by monitoring changes in renal function for patients already receiving one of the study medications ("look-back" alerts). We performed a prospective, cluster randomized controlled trial of physicians receiving clinical decision support for renal dosage adjustments versus those performing their usual workflow. The primary endpoint was the proportion of study prescriptions that were appropriately adjusted for patients' kidney function at the time that patients' conditions warranted a change according to the alert logic. We employed multivariable logistic regression modeling to adjust for glomerular filtration rate, gender, age, hospitalized status, length of stay, type of alert, time from start of study, and clustering within the prescribing physician on the primary endpoint.

resultsA total of 4068 triggering conditions occurred in 1278 unique patients; 1579 of these triggering conditions generated alerts seen by physicians in the intervention arm and 2489 of these triggering conditions were captured but suppressed, so as not to generate alerts for physicians in the control arm. Prescribing orders were appropriate adjusted in 17% of the time vs 5.7% of the time in the intervention and control arms, respectively (odds ratio: 1.89, 95% confidence interval, 1.45-2.47, P < .0001). Prospective alerts had a greater impact than look-back alerts (55.6% vs 10.3%, in the intervention arm).

conclusionsThe rate of appropriate drug prescribing in kidney impairment is low and remains a patient safety concern. Our results suggest that CDS improves drug prescribing, particularly when providing guidance on new prescriptions.

Indexed as

Electronic PrescribingMedical Order Entry SystemsPractice Patterns, Physicians'AdultDrug Therapy, Computer-AssistedFemaleHospitalizationHumansMaleRenal InsufficiencyAKIappropriate prescribingCKDclinical decision supportmedication dosingrenal insufficiency

Identifiers

PMID26615182
PMCPMC11740753
OpenAlexW2267084137

What OpenQuestion holds

Textmetadata
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.