Differential associations of beta-blockers with hemorrhagic events for chronic heart failure patients on warfarin

The association between warfarin usage and international normalized ratio increase: systematic analysis of FDA Adverse Event Reporting System (FAERS)

Introduction

Elevated international normalized ratio (INR) has been commonly reported as an adverse drug event (ADE) for patients taking warfarin for anticoagulant therapy.

Aim

The purpose of this study was to determine the association between increased INR and the usage of warfarin by using the pharmacovigilance data from the FDA Adverse Event Reporting System (FAERS).

Methods

The ADEs in patients who took warfarin (N = 77,010) were analyzed using FAERS data. Association rule mining was applied to identify warfarin-related ADEs that were most associated with elevated INR (n = 15,091) as well as possible drug-drug interactions (DDIs) associated with increased INR. Lift values were used to identify ADEs that were most commonly reported alongside elevated INR based on the correlation between both item sets. In addition, this study sought to determine if the increased INR risk was influenced by sex, age, temporal distribution, and geographic distribution and were reported as reporting odds ratios (RORs).

Results

The top 5 ADEs most associated with increased INR in patients taking warfarin were decreased hemoglobin (lift = 2.31), drug interactions (lift = 1.88), hematuria (lift = 1.58), asthenia (lift = 1.44), and fall (lift = 1.32). INR risk increased as age increased, with individuals older than 80 having a 63% greater likelihood of elevated INR compared to those younger than 50. Males were 9% more likely to report increased INR as an ADE compared to females. Individuals taking warfarin concomitantly with at least one other drug were 43% more likely to report increased INR. The top 5 most frequently identified DDIs in patients taking warfarin and presenting with elevated INR were acetaminophen (lift = 1.81), ramipril (lift = 1.71), furosemide (lift = 1.64), bisoprolol (lift = 1.58), and simvastatin (lift = 1.58).

Conclusion

The risk of elevated INR increased as patient age increased, particularly among those older than 80. Elevated INR frequently co-presented with decreased hemoglobin, drug interactions, hematuria, asthenia, and fall in patients taking warfarin. This effect may be less pronounced in women due to the procoagulatory effects of estrogen signaling. Multiple possible DDIs were identified, including acetaminophen, ramipril, and furosemide.

Drug-drug Interactions with Warfarin: A Systematic Review and Meta-analysis

AIM

The objective of this paper is to systematically review the literature on drug-drug interactions with warfarin, with a focus on patient-important clinical outcomes.

METHODS

MEDLINE, EMBASE, and the International Pharmaceutical Abstract (IPA) databases were searched from January 2004 to August 2019. We included studies describing drug-drug interactions between warfarin and other drugs. Screening and data extraction were conducted independently and in duplicate. We synthesized pooled odds ratios (OR) with 95% confidence intervals (CIs), comparing warfarin plus another medication to warfarin alone. We assessed the risk of bias at the study level and evaluated the overall certainty of evidence using GRADE.

RESULTS

Of 42,013 citations identified, a total of 72 studies reporting on 3,735,775 patients were considered eligible, including 11 randomized clinical trials and 61 observational studies. Increased risk of clinically relevant bleeding when added to warfarin therapy was observed for antiplatelet (AP) regimens (OR=1.74; 95% CI 1.56, 1.94), many antimicrobials (OR=1.63; 95% CI 1.45, 1.83), NSAIDs including COX-2 NSAIDs (OR=1.83; 95% CI 1.29, 2.59), SSRIs (OR=1.62; 95% CI 1.42, 1.85), mirtazapine (OR=1.75; 95% CI 1.30, 2.36), loop diuretics (OR=1.92; 95% CI 1.29, 2.86), and others. We found a protective effect of proton pump inhibitors (PPIs) against warfarin-related gastrointestinal (GI) bleedings (OR=0.69; 95% CI 0.64, 0.73). No significant effect on thromboembolic events or mortality of any drug group used with warfarin was found, including single or dual AP regimens.

CONCLUSIONS

This review found low to moderate certainty evidence supporting the interaction between warfarin and a small group of medications, which result in increased bleeding risk. PPIs are associated with reduced hospitalization for upper GI bleeding for patients taking warfarin. Further studies are required to better understand drug-drug interactions leading to thromboembolic outcomes or death.

Polypharmacy, Adverse Outcomes, and Treatment Effectiveness in Patients ≥75 With Atrial Fibrillation

Background

Polypharmacy is highly prevalent in elderly people with chronic conditions, including atrial fibrillation (AF). The impact of polypharmacy on adverse outcomes and on treatment effectiveness in elderly patients with AF remains unaddressed.

Methods and Results

We studied 338 810 AF patients ≥75 years of age enrolled in the MarketScan Medicare Supplemental database in 2007–2015. Polypharmacy was defined as ≥5 active prescriptions at AF diagnosis (defined by the presence of International Classification of Diseases, Ninth Revision, Clinical Modification [ICD‐9‐CM] codes) based on outpatient pharmacy claims. AF treatments (oral anticoagulation, rhythm and rate control) and cardiovascular end points (ischemic stroke, bleeding, heart failure) were defined based on inpatient, outpatient, and pharmacy claims. Multivariable Cox models were used to estimate associations of polypharmacy with cardiovascular end points and the interaction between polypharmacy and AF treatments in relation to cardiovascular end points. Prevalence of polypharmacy was 52%. Patients with polypharmacy had increased risk of major bleeding (hazard ratio [HR], 1.16; 95% CI, 1.12–1.20) and heart failure (HR, 1.33; 95% CI, 1.29–1.36) but not ischemic stroke (HR, 0.96; 95% CI, 0.92–1.00), compared with those not receiving polypharmacy. Polypharmacy status did not consistently modify the effectiveness of oral anticoagulants. Rhythm control (versus rate control) was more effective in preventing heart failure hospitalization in patients not receiving polypharmacy (HR, 0.87; 95% CI, 0.76–0.99) than among those with polypharmacy (HR, 0.98; 95% CI, 0.91–1.07; P=0.02 for interaction).

Conclusion

Polypharmacy is common among patients ≥75 with AF, is associated with adverse outcomes, and may modify the effectiveness of AF treatments. Optimizing management of polypharmacy in AF patients ≥75 may lead to improved outcomes.

Improving drug safety: From adverse drug reaction knowledge discovery to clinical implementation

Adverse drug reactions (ADRs) are a major public health concern, causing over 100,000 fatalities in the United States every year with an annual cost of $136 billion. Early detection and accurate prediction of ADRs is thus vital for drug development and patient safety. Multiple scientific disciplines, namely pharmacology, pharmacovigilance, and pharmacoinformatics, have been addressing the ADR problem from different perspectives. With the same goal of improving drug safety, this article summarizes and links the research efforts in the multiple disciplines into a single framework from comprehensive understanding of the interactions between drugs and biological system and the identification of genetic and phenotypic predispositions of patients susceptible to higher ADR risks and finally to the current state of implementation of medication-related decision support systems. We start by describing available computational resources for building drug-target interaction networks with biological annotations, which provides a fundamental knowledge for ADR prediction. Databases are classified by functions to help users in selection. Post-marketing surveillance is then introduced where data-driven approach can not only enhance the prediction accuracy of ADRs but also enables the discovery of genetic and phenotypic risk factors of ADRs. Understanding genetic risk factors for ADR requires well organized patient genetics information and analysis by pharmacogenomic approaches. Finally, current state of clinical decision support systems is presented and described how clinicians can be assisted with the integrated knowledgebase to minimize the risk of ADR. This review ends with a discussion of existing challenges in each of disciplines with potential solutions and future directions.

Role of text mining in early identification of potential drug safety issues

Drugs are an important part of today's medicine, designed to treat, control, and prevent diseases; however, besides their therapeutic effects, drugs may also cause adverse effects that range from cosmetic to severe morbidity and mortality. To identify these potential drug safety issues early, surveillance must be conducted for each drug throughout its life cycle, from drug development to different phases of clinical trials, and continued after market approval. A major aim of pharmacovigilance is to identify the potential drug-event associations that may be novel in nature, severity, and/or frequency. Currently, the state-of-the-art approach for signal detection is through automated procedures by analyzing vast quantities of data for clinical knowledge. There exists a variety of resources for the task, and many of them are textual data that require text analytics and natural language processing to derive high-quality information. This chapter focuses on the utilization of text mining techniques in identifying potential safety issues of drugs from textual sources such as biomedical literature, consumer posts in social media, and narrative electronic medical records.

Adverse drug events with hyperkalaemia during inpatient stays: evaluation of an automated method for retrospective detection in hospital databases

BACKGROUND

Adverse drug reactions and adverse drug events (ADEs) are major public health issues. Many different prospective tools for the automated detection of ADEs in hospital databases have been developed and evaluated. The objective of the present study was to evaluate an automated method for the retrospective detection of ADEs with hyperkalaemia during inpatient stays.

METHODS

We used a set of complex detection rules to take account of the patient's clinical and biological context and the chronological relationship between the causes and the expected outcome. The dataset consisted of 3,444 inpatient stays in a French general hospital. An automated review was performed for all data and the results were compared with those of an expert chart review. The complex detection rules' analytical quality was evaluated for ADEs.

RESULTS

In terms of recall, 89.5% of ADEs with hyperkalaemia "with or without an abnormal symptom" were automatically identified (including all three serious ADEs). In terms of precision, 63.7% of the automatically identified ADEs with hyperkalaemia were true ADEs.

CONCLUSIONS

The use of context-sensitive rules appears to improve the automated detection of ADEs with hyperkalaemia. This type of tool may have an important role in pharmacoepidemiology via the routine analysis of large inter-hospital databases.

Data mining methodologies for pharmacovigilance

Medicines are designed to cure, treat, or prevent diseases; however, there are also risks in taking any medicine - particularly short term or long term adverse drug reactions (ADRs) can cause serious harm to patients. Adverse drug events have been estimated to cause over 700,000 emergency department visits each year in the United States. Thus, for medication safety, ADR monitoring is required for each drug throughout its life cycle, including early stages of drug design, different phases of clinical trials, and postmarketing surveillance. Pharmacovigilance (PhV) is the science that concerns with the detection, assessment, understanding and prevention of ADRs. In the pre-marketing stages of a drug, PhV primarily focuses on predicting potential ADRs using preclinical characteristics of the compounds (e.g., drug targets, chemical structure) or screening data (e.g., bioassay data). In the postmarketing stage, PhV has traditionally involved in mining spontaneous reports submitted to national surveillance systems. The research focus is currently shifting toward the use of data generated from platforms outside the conventional framework such as electronic medical records (EMRs), biomedical literature, and patient-reported data in online health forums. The emerging trend of PhV is to link preclinical data from the experimental platform with human safety information observed in the postmarketing phase. This article provides a general overview of the current computational methodologies applied for PhV at different stages of drug development and concludes with future directions and challenges.

Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records

OBJECTIVE

Medication  safety requires that each drug be monitored throughout its market life as early detection of adverse drug reactions (ADRs) can lead to alerts that prevent patient harm. Recently, electronic medical records (EMRs) have emerged as a valuable resource for pharmacovigilance. This study examines the use of retrospective medication orders and inpatient laboratory results documented in the EMR to identify ADRs.

METHODS

Using 12 years of EMR data from Vanderbilt University Medical Center (VUMC), we designed a study to correlate abnormal laboratory results with specific drug administrations by comparing the outcomes of a drug-exposed group and a matched unexposed group. We assessed the relative merits of six pharmacovigilance measures used in spontaneous reporting systems (SRSs): proportional reporting ratio (PRR), reporting OR (ROR), Yule's Q (YULE), the χ(2) test (CHI), Bayesian confidence propagation neural networks (BCPNN), and a gamma Poisson shrinker (GPS).

RESULTS

We systematically evaluated the methods on two independently constructed reference standard datasets of drug-event pairs. The dataset of Yoon et al contained 470 drug-event pairs (10 drugs and 47 laboratory abnormalities). Using VUMC's EMR, we created another dataset of 378 drug-event pairs (nine drugs and 42 laboratory abnormalities). Evaluation on our reference standard showed that CHI, ROR, PRR, and YULE all had the same F score (62%). When the reference standard of Yoon et al was used, ROR had the best F score of 68%, with 77% precision and 61% recall.

CONCLUSIONS

Results suggest that EMR-derived laboratory measurements and medication orders can help to validate previously reported ADRs, and detect new ADRs.

Are the risk factors listed in warfarin prescribing information associated with anticoagulation-related bleeding? A systematic literature review

Warfarin significantly reduces thromboembolic risk, but perceptions of associated bleeding risk limit its use. The evidence supporting the association between bleeding and individual patient risks factors is unclear. This systematic review aims to determine the strength of evidence supporting an accentuated bleeding risk when patients with risk factors listed in the warfarin prescribing information are prescribed the drug. A systematic literature search of MEDLINE and Cochrane CENTRAL was conducted to identify studies reporting multivariate relationships between prespecified covariates and the risk of bleeding in patients receiving warfarin. The prespecified covariates were identified based on patient characteristics for bleeding listed in the warfarin package insert. Each covariate was evaluated for its association with specific types of bleeding. The quality of individual evaluations was rated as 'good', 'fair' or 'poor' using methods consistent with those recommended by the Agency for Healthcare Research and Quality (AHRQ). Overall strength of evidence was determined using the Grading of Recommendations Assessment, Development (GRADE) criteria and categorised as 'insufficient', 'very low', 'low', 'moderate' or 'high'. Thirty-four studies, reporting 134 multivariate evaluations of the association between a covariate and bleeding risk were identified. The majority of evaluations had a low strength of evidence for the association between covariates and bleeding and none had a high strength of evidence. Malignancy and renal insufficiency were the only two covariates that had a moderate strength of evidence for their association with major and minor bleeding respectively. The associations between covariates listed in the warfarin prescribing information and increased bleeding risk are not well supported by the medical literature.

Association between CHADS₂risk factors and anticoagulation-related bleeding: a systematic literature review

OBJECTIVE

To determine the strength of evidence supporting an accentuated bleeding risk when patients with CHADS(2) risk factors (chronic heart failure, hypertension, advanced age, diabetes, and prior stroke/transient ischemic attack) receive warfarin.

METHODS

A systematic literature search of MEDLINE (January 1, 1950, through December 22, 2009) and Cochrane CENTRAL (through December 22, 2009) was conducted to identify studies that reported multivariate results on the association between CHADS(2) covariates and risk of bleeding in patients receiving warfarin. Each covariate was evaluated for its association with a specific type of bleeding. Individual evaluations were rated as good, fair, or poor using methods consistent with those recommended by the Agency for Healthcare Research and Quality. The strength of the associations between each CHADS(2) covariate and a specific type of bleeding was determined using Grading of Recommendations Assessment, Development and Evaluation criteria as insufficient, very low, low, moderate, or high for the entire body of evidence.

RESULTS

Forty-one studies were identified, reporting 127 multivariate evaluations of the association between a CHADS(2) covariate and bleeding risk. No CHADS(2) covariate had a high strength of evidence for association with any bleeding type. For the vast majority of evaluations, the strength of evidence between covariates and bleeding was low. Advanced age was the only covariate that had a moderate strength of evidence for association; this was the strongest independent positive predictor for major bleeding. Similar findings were observed regardless of whether all included studies, or only those evaluating patients with atrial fibrillation, were assessed.

CONCLUSION

The associations between CHADS(2) covariates and increased bleeding risk were weak, with the exception of age. Given the known association of the CHADS(2) score and stroke risk, the decision to prescribe warfarin should be driven more by patients' risk of stroke than by the risk of bleeding.

Electronic health records: Implications for drug discovery

Electronic health records (EHRs) have increased in popularity in many countries. Pushed by legal mandates, EHR systems have seen substantial progress recently, including increasing adoption of standards, improved medical vocabularies and enhancements in technical infrastructure for data sharing across healthcare providers. Although the progress is directly beneficial to patient care in a hospital or clinical setting, it can also aid drug discovery. In this article, we review three specific applications of EHRs in a drug discovery context: finding novel relationships between diseases, re-evaluating drug usage and discovering phenotype-genotype associations. We believe that in the near future EHR systems and related databases will impact significantly how we discover and develop safe and efficacious medicines.

Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study

OBJECTIVE It is vital to detect the full safety profile of a drug throughout its market life. Current pharmacovigilance systems still have substantial limitations, however. The objective of our work is to demonstrate the feasibility of using natural language processing (NLP), the comprehensive Electronic Health Record (EHR), and association statistics for pharmacovigilance purposes. DESIGN Narrative discharge summaries were collected from the Clinical Information System at New York Presbyterian Hospital (NYPH). MedLEE, an NLP system, was applied to the collection to identify medication events and entities which could be potential adverse drug events (ADEs). Co-occurrence statistics with adjusted volume tests were used to detect associations between the two types of entities, to calculate the strengths of the associations, and to determine their cutoff thresholds. Seven drugs/drug classes (ibuprofen, morphine, warfarin, bupropion, paroxetine, rosiglitazone, ACE inhibitors) with known ADEs were selected to evaluate the system. RESULTS One hundred thirty-two potential ADEs were found to be associated with the 7 drugs. Overall recall and precision were 0.75 and 0.31 for known ADEs respectively. Importantly, qualitative evaluation using historic roll back design suggested that novel ADEs could be detected using our system. CONCLUSIONS This study provides a framework for the development of active, high-throughput and prospective systems which could potentially unveil drug safety profiles throughout their entire market life. Our results demonstrate that the framework is feasible although there are some challenging issues. To the best of our knowledge, this is the first study using comprehensive unstructured data from the EHR for pharmacovigilance.

Novel statistical tools for monitoring the safety of marketed drugs

Robust tools for monitoring the safety of marketed therapeutic products are of paramount importance to public health. In recent years, innovative statistical approaches have been developed to screen large post-marketing safety databases for adverse events (AEs) that occur with disproportionate frequency. These methods, known variously as quantitative signal detection, disproportionality analysis, or safety data mining, facilitate the identification of new safety issues or possible harmful effects of a product. In this article, we describe the statistical concepts behind these methods, as well as their practical application to monitoring the safety of pharmaceutical products using spontaneous AE reports. We also provide examples of how these tools can be used to identify novel drug interactions and demographic risk factors for adverse drug reactions. Challenges, controversies, and frontiers for future research are discussed.