Impact of Antibacterials on the Quality of Anticoagulation Control in Patients Initiating Warfarin Therapy

BACKGROUND

Warfarin interacts with antibacterials to prolong the prothrombin time international normalized ratio (PT-INR) and increase the risk of bleeding. Patients initiating warfarin therapy often undergo precise dosage adjustments; however, the clinical implications of these interactions with antibacterials remain unclear. This study aimed to clarify the effect of antibacterials on PT-INR during the warfarin induction phase.

METHODS

This was a retrospective, observational study. Patients who were newly treated with warfarin after cardiovascular surgery were included. The primary endpoint was the comparison of the maximum PT-INR and time in therapeutic range (TTR) after warfarin initiation between the antibacterial-treated (ABx) and non-treated (non-ABx) groups.

RESULTS

The maximum PT-INR was significantly higher in the ABx group (which included β-lactams, glycopeptides, quinolones, tetracyclines, and aminoglycosides) than in the non-ABx group (median [interquartile range] 2.37 [2.03-2.71] vs. 2.08 [1.93-2.33]; P = 0.005); however, the TTR did not differ significantly (65% [44-76] vs. 71% [43-85]; P = 0.150). The odds ratio for maximum PT-INR > 2.6 with antimicrobial therapy was 2.51 (95% confidence interval 1.21-5.21).

DISCUSSION

Antibacterial therapy was a risk factor for a maximum PT-INR >2.6. However, there was no association with the TTR, which is a marker of good outcomes. This was due to the strict warfarin dosing regimen according to the algorithm, which immediately and appropriately adjusted for PT-INR overexpansion.

CONCLUSIONS

Antibacterials have been suggested to increase PT-INR during the induction phase of warfarin. However, with strict dose adjustments, the clinical impact on the PT-INR and TTR is likely limited.

Construction of warfarin population pharmacokinetics and pharmacodynamics model in Han population based on Bayesian method

The purpose of this paper is to study the genetic polymorphisms of related gene loci (CYP2C9*3, VKORC1-1639G > A) based on demographic and clinical factors, and use the maximum a posterior Bayesian method to construct a warfarin individualized dose prediction model in line with the Chinese Han population. Finally, the built model is compared and analyzed with the widely used models at home and abroad. In this study, a total of 5467 INR measurements are collected from 646 eligible subjects in our hospital, and the maximum a posterior Bayesian method is used to construct a warfarin dose prediction that conforms to the Chinese Han population on the basis of the Hamberg model. The model is verified and compared with foreign models. This study finds that body weight and concomitant use of amiodarone have a significant effect on the anticoagulant effect of warfarin. The model can provide an effective basis for individualized and rational dosing of warfarin in Han population more accurately. In the performance of comparison with different warfarin dose prediction models, the new model has the highest prediction accuracy, and the prediction percentage is as high as 72.56%. The dose predicted by the Huang model is the closest to the actual dose of warfarin. The population pharmacokinetics and pharmacodynamics model established in this study can better reflect the distribution characteristics of INR values after warfarin administration in the Han population, and performs better than the models reported in the literature.

Managing the clinical effects of drug-induced intestinal dysbiosis with a focus to antibiotics: Challenges and opportunities

The term "intestinal dysbiosis" is used for indicating change(s) of the intestinal microbiota which have been associated with the development of diseases and the deterioration of disease treatments in humans. In this review, documented clinical effects of drug-induced intestinal dysbiosis are briefly presented, and methodologies which could be considered for the management of drug-induced intestinal dysbiosis based on clinical data are critically reviewed. Until relevant methodologies are optimized and/or their effectiveness to the general population is confirmed, and, since drug-induced intestinal dysbiosis refers predominantly to antibiotic-specific intestinal dysbiosis, a pharmacokinetically-based approach for mitigating the impact of antimicrobial therapy on intestinal dysbiosis is proposed.

Predicting drug-microbiome interactions with machine learning

Pivotal work in recent years has cast light on the importance of the human microbiome in maintenance of health and physiological response to drugs. It is now clear that gastrointestinal microbiota have the metabolic power to promote, inactivate, or even toxify the efficacy of a drug to a level of clinically relevant significance. At the same time, it appears that drug intake has the propensity to alter gut microbiome composition, potentially affecting health and response to other drugs. Since the precise composition of an individual's microbiome is unique, one's drug-microbiome relationship is similarly unique. Thus, in the age of evermore personalised medicine, the ability to predict individuals' drug-microbiome interactions is highly sought. Machine learning (ML) offers a powerful toolkit capable of characterising and predicting drug-microbiota interactions at the individual patient level. ML techniques have the potential to learn the mechanisms operating drug-microbiome activities and measure patients' risk of such occurrences. This review will outline current knowledge at the drug-microbiota interface, and present ML as a technique for examining and forecasting personalised drug-microbiome interactions. When harnessed effectively, ML could alter how the pharmaceutical industry and healthcare professionals consider the drug-microbiome axis in patient care.