Evidence map›Paper›PMID 41044473›Full record

ArticleBMC pediatrics2025

Evaluation of potential drug-drug interactions in the neonatal intensive care unit with two different decision support software: a retrospective study.

Yunus Emre Ayhan, Ercan Tutak, Burak Özdemir

Abstract read
In one paragraph

Article in BMC pediatrics, 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. Observational
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.

Yunus Emre Ayhan *Prof. Dr. Cemil Taşcıoğlu City Hospital, Clinical Pharmacy, Istanbul, Türkiye. eczyunusemreayhan@gmail.com.ORCID 0000-0001-6093-0849
Ercan Tutak *Prof. Dr. Cemil Taşcıoğlu City Hospital, Neonatal Intensive Care Unit, Istanbul, Türkiye.ORCID 0000-0002-5076-8400
Burak ÖzdemirProf. Dr. Cemil Taşcıoğlu City Hospital, Department of Pediatrics, Istanbul, Türkiye.ORCID 0009-0003-3794-2309

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDrug-drug interactions (DDIs) are a critical concern in neonatal intensive care units (NICUs) due to polypharmacy and the physiological vulnerability of neonates. This study aims to evaluate potential DDIs (pDDIs) using two different decision support software (DSS), Lexidrug™ and Micromedex

methodsThis retrospective study included neonates admitted to a tertiary NICU between January 1, 2022, and January 1, 2023, who received at least two systemically administered medications and stayed for ≥ 24 h. Patients with missing prescriptions or readmissions were excluded. pDDIs were assessed using two different DSS: Lexidrug™ and Micromedex

resultsThe study included 280 patients. The gestational age median (interquartile range [IQR]) was 36 (34-38) weeks, and birth weight median (IQR) was 2745 (2181.2-3250) g. According to Lexidrug™ and Micromedex

conclusionpDDIs were detected in a significant proportion of patients in the NICU. This study revealed weak agreement between different DSS used to assess pDDIs in the NICU. The findings underscore the importance of collaboration between clinical pharmacists and neonatologists when interpreting DSS results. Further prospective, multicenter cohort studies are warranted to validate these findings and improve DDI management strategies in neonatal populations.

trial registrationNot applicable.

Indexed as

Decision Support Systems, ClinicalIntensive Care Units, NeonatalSoftwareDrug InteractionsFemaleHumansInfant, NewbornMaleRetrospective StudiesClinical pharmacistDecision support softwareDrug-drug interactionNeonatal intensive care unitNeonatalogist

Identifiers

PMID41044473
PMCPMC12495852

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LicenceCC BY-NC-ND
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Registered trials

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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.