Evidence map›Paper›PMID 28018756›Full record

ArticleCureus2016

Incidence of Potential Drug-Drug Interactions in a Limited and Stereotyped Prescription Setting - Comparison of Two Free Online Pharmacopoeias.

Bhaskar Kannan, Amrutha Bindu Nagella, A Sathia Prabhu, Gopalakrishnan M Sasidharan, A S Ramesh, Venkatesh Madhugiri

Open access · diamondAbstract read
In one paragraph

Article in Cureus, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 25% 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. The prevalence and severity of potential drug-drug interactions among adult polypharmacy patients at outpatient clinics in Jordan.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2020
    Article
  6. 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

6 authors at 2 institutions in 1 country.

Bhaskar KannanDepartment of Neurosurgery, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, India.
Amrutha Bindu NagellaDepartment of Anesthesiology and Critical Care, Mahatma Gandhi Medical College and Research Institute, Pondicherry, India.
A Sathia PrabhuDepartment of Neurosurgery, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, India.
Gopalakrishnan M SasidharanDepartment of Neurosurgery, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, India.
A S RameshDepartment of Neurosurgery, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, India.
Venkatesh MadhugiriDivision of Neurosurgery, Tata Memorial Hospital, Mumbai, India.
Jawaharlal Institute of Post Graduate Medical Education and Research · INMahatma Gandhi Medical College and Research Institute · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDrug-drug interactions (DDIs) are very common adverse events in health care delivery settings. The use of electronic pharmacopeias can potentially reduce the incidence of DDIs, but they are often thought to be cumbersome to use. This study is aimed at studying the incidence of potential DDIs in a surgical department, where a limited number of drugs are used in stereotyped combinations. We also compared two popular drug compendia in detecting potential DDIs.

methodsThe prescriptions of selected patients were entered into Epocrates® and Medscape® for Android smartphones. Potential DDIs were generated and their categories were noted. The warnings generated by Epocrates® were compared with those generated by Medscape® and an agreement index was calculated.

resultsThree hundred and thirty-one patients were included for analysis who had received a total of 2,878 drug orders. The incidence of potential DDIs was very high - 89% of all prescriptions. Phenytoin was the drug most commonly implicated, followed by furosemide. Of the DDIs detected, 0.14% were potentially serious and the drug combinations were contraindicated. There was a significant discrepancy between the categories of potential DDIs detected by Epocrates® and Medscape®. No clinically significant DDI was detected in any patient in this cohort.

conclusionsDespite routinely using only a limited number of drugs in stereotyped combinations, prescriptions in surgical departments may not be immune from a significant incidence of DDIs. The use of free apps could reduce the incidence of DDIs, enhance patient safety, and also aid in educating trainees.

Indexed as

adverse drug reactionsdrug interactionsepocratesmedscapepatient safety

Identifiers

PMID28018756
PMCPMC5179105
OpenAlexW2551560337

What OpenQuestion holds

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LicenceCC BY
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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.