Evidence map›Paper›PMID 33234119›Full record

Observational studyBMC medicine2020

Multimorbidity, polypharmacy, and drug-drug-gene interactions following a non-ST elevation acute coronary syndrome: analysis of a multicentre observational study.

R M Turner, E M de Koning, V Fontana, A Thompson, M Pirmohamed

Open access · goldAbstract readMulticenter StudyObservational Study
In one paragraph

Observational study in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.7field-weighted citation impact, top 10% 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

14 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Association ofPharmaceuticals (Basel, Switzerland) · 2025
    Article
  3. Article
  4. Article
  5. Article
  6. Oral P2YEuropean cardiology · 2025
    Review
  7. Article
  8. Review
  9. Frontiers in pharmacology · 2023
    Article
  10. The role ofJournal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques · 2023
    Article
  11. Drug-Drug-Gene Interactions in Cardiovascular Medicine.Pharmacogenomics and personalized medicine · 2022
    Review
  12. Review
  13. Review
  14. 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

5 authors at 2 institutions in 2 countries.

R M TurnerWolfson Centre for Personalised Medicine, University of Liverpool, Liverpool, L69 3GL, UK. Richard.Turner@liverpool.ac.uk.
E M de KoningFranciscus Gasthuis & Vlietland, Rotterdam, the Netherlands.
V FontanaWolfson Centre for Personalised Medicine, University of Liverpool, Liverpool, L69 3GL, UK.
A ThompsonWolfson Centre for Personalised Medicine, University of Liverpool, Liverpool, L69 3GL, UK.
M PirmohamedWolfson Centre for Personalised Medicine, University of Liverpool, Liverpool, L69 3GL, UK.
University of Liverpool · GBSint Franciscus Gasthuis · NL

Funding

Department of Health [UK]Medical Research Council G1000417Medical Research Council MR/L006758/1Medical Research Council MR/S000933/1
6 · The paper itself

Abstract

backgroundThe number of patients living with co-existing diseases is growing. This study aimed to assess the extent of multimorbidity, medication use, and drug- and gene-based interactions in patients following a non-ST elevation acute coronary syndrome (NSTE-ACS).

methodsIn 1456 patients discharged from hospital for a NSTE-ACS, comorbidities and multimorbidity (≥ 2 chronic conditions) were assessed. Of these, 698 had complete drug use recorded at discharge, and 652 (the 'interaction' cohort) had drug use and actionable genotypes available for CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A5, DPYD, F5, SLCO1B1, TPMT, UGT1A1, and VKORC1. The following drug interactions were investigated: pharmacokinetic drug-drug (DDIs) involving CYPs (CYPs above, plus CYP1A2, CYP2C8, CYP3A4), SLCO1B1, and P-glycoprotein; drug-gene (DGIs); drug-drug-gene (DDGIs); and drug-gene-gene (DGGIs). Interactions predicted to be 'substantial' were defined as follows: DDIs due to strong inhibitors/inducers, DGIs due to variant homozygous/compound heterozygous genotypes, and DDGIs/DGGIs where the constituent DDI/DGI(s) both influenced the victim drug in the same direction.

resultsIn the whole cohort, 727 (49.9%) patients had multimorbidity. Non-linear relationships between age and increasing comorbidities and decreasing coronary intervention were observed. There were 98.1% and 39.8% patients on ≥ 5 and ≥ 10 drugs, respectively (from n = 698); women received more non-cardiovascular drugs than men (median (IQR) 3 (1-5) vs 2 (1-4), p = 0.014). Overall, 98.7% patients had at least one actionable genotype. Within the interaction cohort, 882 interactions were identified in 503 patients (77.1%), of which 346 in 252 patients (38.7%) were substantial: 59.2%, 11.6%, 26.3%, and 2.9% substantial interactions were DDIs, DGIs, DDGIs, and DGGIs, respectively. CYP2C19 (49.5% of all interactions) and SLCO1B1 (18.4%) were involved in the largest number of interactions. Multimorbidity (p = 0.019) and number of drugs (p = 9.8 × 10

conclusionsMultimorbidity, polypharmacy, and drug interactions are common after a NSTE-ACS. Replication of results is required; however, the high prevalence of DDGIs suggests integrating co-medications with genetic data will improve medicines optimisation.

Indexed as

Acute Coronary SyndromeAgedCohort StudiesDrug InteractionsFemaleHumansMaleMiddle AgedMultimorbidityNon-ST Elevated Myocardial InfarctionPharmacogeneticsPolypharmacyProspective StudiesAcute coronary syndromeInteractionsMultimorbidityPharmacogenomicsPolypharmacy

Identifiers

PMID33234119
PMCPMC7687685
OpenAlexW3106624682

What OpenQuestion holds

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LicenceCC BY
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.