Drug–drug interactions with warfarin: A systematic review and meta‐analysis

Wang, Mei; Zeraatkar, Dena; Obeda, Michael; Lee, Munil; Garcia, Michael; Nguyen, Laura; Agarwal, Arnav; Al-Shalabi, Farah; Benipal, Harsukh; Ahmad, Afreen; Abbas, Momina; Vidug, Kristina; Holbrook, Anne

doi:10.1111/bcp.14833

Cited by 49 publications

(43 citation statements)

References 106 publications

(415 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Predicting optimal warfarin dose is influenced by many factors including demographic, clinical, environmental, and genetic factors, and multiple drug interactions, such as age, weight, height, vitamin K epoxide reductase complex subunit 1 (VKORC1) genotype, cytochrome P450 family 2 subfamily C member 9 (CYP2C9) genotype, coadministration of antiplatelet drugs, antimicrobials, nonsteroidal anti-inflammatory drugs (NSAIDs), and proton pump inhibitors, etc. [ 3 , 4 , 5 , 6 , 7 ]. Therefore, a tailored warfarin dose may help clinicians to select the appropriate dose for individualized warfarin treatment.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Machine Learning in Warfarin Dose Prediction: Insights from Contemporary Modelling Studies

Zhang

Liu

et al. 2022

JPM

View full text Add to dashboard Cite

Objective: This study aimed to systematically assess the characteristics and risk of bias of previous studies that have investigated nonlinear machine learning algorithms for warfarin dose prediction. Methods: We systematically searched PubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), China Biology Medicine (CBM), China Science and Technology Journal Database (VIP), and Wanfang Database up to March 2022. We assessed the general characteristics of the included studies with respect to the participants, predictors, model development, and model evaluation. The methodological quality of the studies was determined, and the risk of bias was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST). Results: From a total of 8996 studies, 23 were assessed in this study, of which 23 (100%) were retrospective, and 11 studies focused on the Asian population. The most common demographic and clinical predictors were age (21/23, 91%), weight (17/23, 74%), height (12/23, 52%), and amiodarone combination (11/23, 48%), while CYP2C9 (14/23, 61%), VKORC1 (14/23, 61%), and CYP4F2 (5/23, 22%) were the most common genetic predictors. Of the included studies, the MAE ranged from 1.47 to 10.86 mg/week in model development studies, from 2.42 to 5.18 mg/week in model development with external validation (same data) studies, from 12.07 to 17.59 mg/week in model development with external validation (another data) studies, and from 4.40 to 4.84 mg/week in model external validation studies. All studies were evaluated as having a high risk of bias. Factors contributing to the risk of bias include inappropriate exclusion of participants (10/23, 43%), small sample size (15/23, 65%), poor handling of missing data (20/23, 87%), and incorrect method of selecting predictors (8/23, 35%). Conclusions: Most studies on nonlinear-machine-learning-based warfarin prediction models show poor methodological quality and have a high risk of bias. The analysis domain is the major contributor to the overall high risk of bias. External validity and model reproducibility are lacking in most studies. Future studies should focus on external validity, diminish risk of bias, and enhance real-world clinical relevance.

show abstract

Section: Introductionmentioning

confidence: 99%

Nonlinear Machine Learning in Warfarin Dose Prediction: Insights from Contemporary Modelling Studies

Zhang

Liu

et al. 2022

JPM

View full text Add to dashboard Cite

show abstract

“…7 Gastrointestinal bleeding (GIB) risk is one of the most serious adverse effects of warfarin, which could be affected by the concomitant use of other drugs that influence the effect of warfarin through reduction of gastrointestinal absorption of warfarin or inhibition of cytochrome P450 (CYP) enzymes (particularly CYP2C9, which is responsible for metabolizing the more potent warfarin enantiomer). 7 Among drugs that could increase the risk of bleeding when taken along with warfarin are antiplatelets, antimicrobials such as azole antifungals, cephalosporins, sulfonamides, penicillins and quinolones, opioid analgesics, and nonsteroidal anti-inflammatory drugs. [7][8][9] Recent evidence from multiple individual studies examining the association between use of statins and the risk of GIB reported conflicting findings, [10][11][12][13] with variable risks reported relative to duration of statin exposure.…”

Section: Introductionmentioning

confidence: 99%

“…7 Among drugs that could increase the risk of bleeding when taken along with warfarin are antiplatelets, antimicrobials such as azole antifungals, cephalosporins, sulfonamides, penicillins and quinolones, opioid analgesics, and nonsteroidal anti-inflammatory drugs. [7][8][9] Recent evidence from multiple individual studies examining the association between use of statins and the risk of GIB reported conflicting findings, [10][11][12][13] with variable risks reported relative to duration of statin exposure. [14][15][16] Thus, whereas the risk of GIB among users of warfarin is well known, 5 there is a lack of conclusive evidence about the GIB risk associated with the use of statins alone or in combination with warfarin.…”

Section: Introductionmentioning

confidence: 99%

“…5 This is further complicated by its narrow therapeutic index and extensive interactions with food and coadministered drugs, which necessitates the need for close monitoring of international normalized ratio for dose adjustment. 7 Gastrointestinal bleeding (GIB) risk is one of the most serious adverse effects of warfarin, which could be affected by the concomitant use of other drugs that influence the effect of warfarin through reduction of gastrointestinal absorption of warfarin or inhibition of cytochrome P450 (CYP) enzymes (particularly CYP2C9, which is responsible for metabolizing the more potent warfarin enantiomer). 7 Among drugs that could increase the risk of bleeding when taken along with warfarin are antiplatelets, antimicrobials such as azole antifungals, cephalosporins, sulfonamides, penicillins and quinolones, opioid analgesics, and nonsteroidal anti-inflammatory drugs.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Risk of Gastrointestinal Bleeding on Treatment With Statin Alone or With Concomitant Administration of Warfarin: A Systematic Review and Meta-analysis of 5.3 Million Participants

et al. 2021

View full text Add to dashboard Cite

Objective: This study aimed to comprehensively evaluate the risk of gastrointestinal bleeding (GIB) with statin monotherapy or with concomitant warfarin use. Data Sources: PubMed, Web of Science, and EMBASE (via Scopus) were searched for observational studies that reported the risk of GIB in adults on statin therapy or with concomitant warfarin use until August 28, 2021. Study Selection and Data Extraction: Observational studies evaluating the risk of GIB in adults (age >18 years) on statin medication or concomitant use with warfarin were included. Data Synthesis: In all, 14 studies with a total of 5 235 123 participants, reporting 48 677 GIB events (43 734 from statin users and 4943 from users of statin combined with warfarin), were included in the analyses. The pooled analysis revealed no difference in the risk of GIB with statin monotherapy (relative risk [RR]: 0.65; 95% CI: 0.42-1.02) or concomitant statin + warfarin use (RR: 0.97; 95% CI: 0.91-1.02). Prior use of statin was not associated with GIB risk (RR: 0.88; 95% CI: 0.63-1.22), whereas a shorter duration of statin use (<5 years) was associated with a lower risk of GIB (RR: 0.42; 95% CI: 0.18-0.97). Relevance to Patient Care and Clinical Practice: This analysis provides strong evidence on the association between statin use (with/without warfarin) and risk of GIB. Conclusion: Statin alone or combined with warfarin was not significantly associated with either an increased or decreased risk of GIB. The GIB risk was significantly lower when statins were used for a short duration (<5 years). The putative relationship between statins and GIB in warfarin users warrant further investigation.

show abstract

Predictors of bleeding events in acute decompensated heart failure patients with antithrombotic therapy: AURORA study

et al. 2022

View full text Add to dashboard Cite

Aims Heart failure (HF) is reported to be one of the major risks of bleeding events. On the other hand, HF patients frequently receive anticoagulants or antiplatelet therapy to manage various co‐morbidities. However, predictors of bleeding events in patients with HF have rarely been reported. This study aimed to evaluate the predictors of bleeding events and relationship between bleeding events and HF re‐hospitalizations. Methods and results We included 1660 acute decompensated heart failure (ADHF) patients from the AURORA registry between January 2015 and December 2020. A total of 1429 patients were excluded because of history of HF admission, missing echocardiographic data at discharge, lost to follow‐up, haemodialysis and no antithrombotic drugs. Finally, we evaluated 231 patients from AURORA registry. The bleeding events were defined as Type 2 to 5 bleeding according to the Bleeding Academic Research Consortium definition. We divided our patients into the bleeding group and non‐bleeding group. We compared the baseline characteristics, medications, laboratory data, and echocardiographic data between the two groups. Median age was 78 (IQR 71–82) years old and male accounted for 59%. Approximately half of the patients had an antiplatelet therapy and 70% had an anticoagulant therapy. During a median follow‐up of 651 (IQR 357–1139) days, 32 patients (13.8%) suffered from bleeding events. The major driver of the registered events was gastrointestinal bleeding ( n = 21, 65.6%), and the other events were cerebral bleeding ( n = 4, 12.5%), intraarticular bleeding ( n = 2, 6.3%), urogenital bleeding ( n = 2, 6.3%), haemorrhagic pericardial effusions ( n = 1, 3.1%), subcutaneous hematomas ( n = 1, 3.1%), and haemothorax ( n = 1, 3.1%). There was a significantly lower haemoglobin level ( P < 0.01), higher proportion of inferior vena cava (IVC) diameter ≥21 mm ( P < 0.01), and higher furosemide equivalent doses per kilogram ( P < 0.01) in the bleeding group than non‐bleeding group. A multivariate analysis revealed an equivalent dose of furosemide per kilogram ≥0.66 mg/kg (hazard ratios (HR) of 2.64, 95% confidence interval (CI) 1.26–5.68, P = 0.01), haemoglobin ≤10.3 g/dL (HR of 2.43, 95% CI 1.14–5.03, P = 0.02), and IVC diameter ≥21 mm (HR of 2.79, 95% CI 1.16–6.29, P = 0.02) were independently associated with bleeding events. The Kaplan–Meier analysis showed that HF re‐hospitalization rates were higher in the bleeding group than non‐bleeding group ( P = 0.04). Conclusions High doses of oral loop diuretics, IVC dilatation, and anaemia were predictors of b...

show abstract

Drug–drug interactions with warfarin: A systematic review and meta‐analysis

Cited by 49 publications

References 106 publications

Nonlinear Machine Learning in Warfarin Dose Prediction: Insights from Contemporary Modelling Studies

Nonlinear Machine Learning in Warfarin Dose Prediction: Insights from Contemporary Modelling Studies

Risk of Gastrointestinal Bleeding on Treatment With Statin Alone or With Concomitant Administration of Warfarin: A Systematic Review and Meta-analysis of 5.3 Million Participants

Predictors of bleeding events in acute decompensated heart failure patients with antithrombotic therapy: AURORA study

Contact Info

Product

Resources

About