Evidence map›Paper›PMID 26342216›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2016

Patient and physician predictors of patient receipt of therapies recommended by a computerized decision support system when initially prescribed broad-spectrum antibiotics: a cohort study.

Angela L P Chow, David C Lye, Onyebuchi A Arah

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PubMed Full text at PMC Publisher
In one paragraph

Article 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 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 16% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

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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

3 authors at 2 institutions in 2 countries.

Angela L P ChowDepartment of Clinical Epidemiology, Institute of Infectious Disease and Epidemiology, Tan Tock Seng Hospital, Singapore Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles (UCLA), Los Angeles, United States Angela_Chow@ttsh.com.sg.
David C LyeDepartment of Infectious Diseases, Institute of Infectious Disease and Epidemiology, Tan Tock Seng Hospital, Singapore Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Onyebuchi A ArahDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles (UCLA), Los Angeles, United States Center for Health Policy Research, University of California, Los Angeles (UCLA), Los Angeles, United States.
Tan Tock Seng Hospital · SGUniversity of California, Los Angeles · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveAntibiotic computerized decision support systems (CDSSs) were developed to guide antibiotic decisions, yet prescriptions of CDSS-recommended antibiotics have remained low. Our aim was to identify predictors of patients' receipt of empiric antibiotic therapies recommended by a CDSS when the prescribing physician had an initial preference for using broad-spectrum antibiotics.

methodsWe conducted a prospective cohort study in a 1 500-bed tertiary-care hospital in Singapore. We included all patients admitted from October 1, 2011 through September 30, 2012, who were prescribed piperacillin-tazobactam or carbapenem for empiric therapy and auto-triggered to receive antibiotic recommendations by the in-house antibiotic CDSS. Relevant data on the patient, prescribing and attending physicians were collected via electronic linkages of medical records and administrative databases. To account for clustering, we used multilevel logistic regression models to explore factors associated with receipt of CDSS-recommended antibiotic therapy.

resultsOne-quarter of the 1 886 patients received CDSS-recommended antibiotics. More patients treated for pneumonia (33.2%) than sepsis (12.1%) and urinary tract infections (7.1%) received CDSS-recommended antibiotic therapies. The prescribing physician - but not the attending physician or clinical specialty - accounted for some (13.3%) of the variation. Prior hospitalization (odds ratio [OR] 1.32, 95% CI, 1.01-1.71), presumed pneumonia (OR 6.77, 95% CI, 3.28-13.99), intensive care unit (ICU) admission (OR 0.38, 95% CI, 0.21-0.66), and renal impairment (OR 0.70, 95% CI, 0.52-0.93) were factors associated with patients' receipt of CDSS-recommended antibiotic therapies.

conclusionsWe observed that ICU admission and renal impairment were negative predictors of patients' receipt of CDSS-recommended antibiotic therapies. Patients admitted to ICU and those with renal impairment might have more complex clinical conditions that require a physician's assessment in addition to antibiotic CDSS.

Indexed as

Decision Support Systems, ClinicalGuideline AdherenceAcademic Medical CentersAgedAnti-Bacterial AgentsCarbapenemsCohort StudiesDrug Therapy, Computer-AssistedDrug UtilizationFemaleHumansInappropriate PrescribingIntensive Care UnitsKidney DiseasesMaleMiddle AgedAnti-Bacterial AgentsCarbapenemsPenicillanic AcidPiperacillinPiperacillin, Tazobactam Drug Combinationantibiotic prescribingantibiotic resistancedecision support systemempiric therapypatient factorsphysician factors

Identifiers

PMID26342216
PMCPMC4954629
OpenAlexW1866588926

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.