Evidence map›Paper›PMID 35486411›Full record

ArticleClinical pharmacology and therapeutics2022

Contextualized Drug-Drug Interaction Management Improves Clinical Utility Compared With Basic Drug-Drug Interaction Management in Hospitalized Patients.

Arthur T M Wasylewicz, Britt W M van de Burgt, Thomas Manten, Marieke Kerskes, Wilma N Compagner, Erik H M Korsten, Toine C G Egberts, Rene J E Grouls

Abstract read
In one paragraph

Article in Clinical pharmacology and therapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Evaluation of an electronic health record Drug Interaction Customization Editor (DICE).American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists · 2024
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
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.

Arthur T M WasylewiczDepartment of Healthcare Intelligence, Catharina Hospital, Eindhoven, The Netherlands.ORCID 0000-0002-7900-3583
Britt W M van de BurgtDepartment of Healthcare Intelligence, Catharina Hospital, Eindhoven, The Netherlands.ORCID 0000-0002-6882-3586
Thomas MantenDepartment of Clinical Pharmacy, Catharina Hospital, Eindhoven, The Netherlands.ORCID 0000-0002-9123-9863
Marieke KerskesDepartment of Clinical Pharmacy, Catharina Hospital, Eindhoven, The Netherlands.ORCID 0000-0001-5858-6049
Wilma N CompagnerDepartment of Healthcare Intelligence, Catharina Hospital, Eindhoven, The Netherlands.ORCID 0000-0002-2619-3264
Erik H M KorstenDepartment of Healthcare Intelligence, Catharina Hospital, Eindhoven, The Netherlands.ORCID 0000-0002-1610-6593
Toine C G EgbertsDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.ORCID 0000-0003-1758-7779
Rene J E GroulsDepartment of Clinical Pharmacy, Catharina Hospital, Eindhoven, The Netherlands.ORCID 0000-0002-6788-9874

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drug-drug interactions (DDIs) frequently trigger adverse drug events or reduced efficacy. Most DDI alerts, however, are overridden because of irrelevance for the specific patient. Basic DDI clinical decision support (CDS) systems offer limited possibilities for decreasing the number of irrelevant DDI alerts without missing relevant ones. Computerized decision tree rules were designed to context-dependently suppress irrelevant DDI alerts. A crossover study was performed to compare the clinical utility of contextualized and basic DDI management in hospitalized patients. First, a basic DDI-CDS system was used in clinical practice while contextualized DDI alerts were collected in the background. Next, this process was reversed. All medication orders (MOs) from hospitalized patients with at least one DDI alert were included. The following outcome measures were used to assess clinical utility: positive predictive value (PPV), negative predictive value (NPV), number of pharmacy interventions (PIs)/1,000 MOs, and the median time spent on DDI management/1,000 MOs. During the basic DDI management phase 1,919 MOs/day were included, triggering 220 DDI alerts/1,000 MOs; showing 57 basic DDI alerts/1,000 MOs to pharmacy staff; PPV was 2.8% with 1.6 PIs/1,000 MOs costing 37.2 minutes/1,000 MOs. No DDIs were missed by the contextualized CDS system (NPV 100%). During the contextualized DDI management phase 1,853 MOs/day were included, triggering 244 basic DDI alerts/1,000 MOs, showing 9.6 contextualized DDIs/1,000 MOs to pharmacy staff; PPV was 41.4% (P < 0.01), with 4.0 PIs/1,000 MOs (P < 0.01) and 13.7 minutes/1,000 MOs. The clinical utility of contextualized DDI management exceeds that of basic DDI management.

Indexed as

Decision Support Systems, ClinicalDrug-Related Side Effects and Adverse ReactionsMedical Order Entry SystemsCross-Over StudiesDrug InteractionsHumans

Identifiers

PMID35486411
PMCPMC9540177

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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.