Oral Anticoagulants and Precision Medicine: Something Old, Something New

Meta-analysis of outcomes following intravenous thrombolysis in patients with ischemic stroke on direct oral anticoagulants

BACKGROUND

There has been debate on the use of intravenous thrombolysis (IVT) in patients with ischemic stroke and the recent use of direct oral anticoagulants (DOACs). Studies have compared these patients with non-DOAC groups in terms of outcomes. Herein, we aimed to systematically investigate the association between DOAC use and IVT's efficacy and safety outcomes.

RESULTS

A comprehensive systematic search was performed in PubMed, Embase, Scopus, and the Web of Science for the identification of relevant studies. After screening and data extraction, a random-effect meta-analysis was performed to calculate the odds ratio (OR) and 95% confidence interval (CI) for comparison of outcomes between patients on DOAC and controls. Six studies were included in the final review. They investigated a total of 254,742 patients, among which 3,499 had recent use of DOACs. The most commonly used DOACs were rivaroxaban and apixaban. The patients on DOAC had significantly higher rates of atrial fibrillation, hypertension, diabetes, and smoking. Good functional outcome defined by modified Rankin Scale (mRS) 0-2 was significantly lower in patients who received DOACs (OR 0.71, 95% CI 0.62 to 0.81, P < 0.01). However, in the subgroup analysis of 90-day mRS 0-2, there was no significant difference between groups (OR 0.71, 95% 0.46 to 1.11, P = 0.14). All-cause mortality was not different between the groups (OR 1.02, 95% CI 0.68 to 1.52, P = 0.93). Similarly, there was no significant difference in either of the in-hospital and 90-day mortality subgroups. Regarding symptomatic intracranial hemorrhage (sICH), the previous DOAC use was not associated with an increased risk of bleeding (OR 0.98, 95% CI 0.69 to 1.39, P = 0.92). A similar finding was observed for the meta-analysis of any ICH (OR 1.15, 95% CI 0.94 to 1.40, P = 0.18).

CONCLUSIONS

Based on our findings, IVT could be considered as a treatment option in ischemic stroke patients with recent use of DOACs since it was not associated with an increased risk of sICH, as suggested by earlier studies. Further larger studies are needed to confirm these findings and establish the safety of IVT in patients on DOAC.

Biomarkers for the Prediction and Judgement of Sepsis and Sepsis Complications: A Step towards precision medicine?

Sepsis and septic shock are a major public health concern and are still associated with high rates of morbidity and mortality. Whilst there is growing understanding of different phenotypes and endotypes of sepsis, all too often treatment strategies still only employ a “one-size-fits-all” approach. Biomarkers offer a unique opportunity to close this gap to more precise treatment approaches by providing insight into clinically hidden, yet complex, pathophysiology, or by individualizing treatment pathways. Predicting and evaluating systemic inflammation, sepsis or septic shock are essential to improve outcomes for these patients. Besides opportunities to improve patient care, employing biomarkers offers a unique opportunity to improve clinical research in patients with sepsis. The high rate of negative clinical trials in this field may partly be explained by a high degree of heterogeneity in patient cohorts and a lack of understanding of specific endotypes or phenotypes. Moving forward, biomarkers can support the selection of more homogeneous cohorts, thereby potentially improving study conditions of clinical trials. This may finally pave the way to a precision medicine approach to sepsis, septic shock and complication of sepsis in the future.

PPDTS: Predicting potential drug-target interactions based on network similarity

Identification of drug–target interactions (DTIs) has great practical importance in the drug discovery process for known diseases. However, only a small proportion of DTIs in these databases has been verified experimentally, and the computational methods for predicting the interactions remain challenging. As a result, some effective computational models have become increasingly popular for predicting DTIs. In this work, the authors predict potential DTIs from the local structure of drug–target associations' network, which is different from the traditional global network similarity methods based on structure and ligand. A novel method called PPDTS is proposed to predict DTIs. First, according to the DTIs’ network local structure, the known DTIs are converted into a binary network. Second, the Resource Allocation algorithm is used to obtain a drug–drug similarity network and a target–target similarity network. Third, a Collaborative Filtering algorithm is used with the known drug–target topology information to obtain similarity scores. Fourth, the linear combination of drug–target similarity model and the target–drug similarity model are innovatively proposed to obtain the final prediction results. Finally, the experimental performance of PPDTS has proved to be higher than that of the previously mentioned four popular network‐based similarity methods, which is validated in different experimental datasets. Some of the predicted results can be supported in UniProt and DrugBank databases.

Machine Learning for Prediction of Stable Warfarin Dose in US Latinos and Latin Americans

Populations used to create warfarin dose prediction algorithms largely lacked participants reporting Hispanic or Latino ethnicity. While previous research suggests nonlinear modeling improves warfarin dose prediction, this research has mainly focused on populations with primarily European ancestry. We compare the accuracy of stable warfarin dose prediction using linear and nonlinear machine learning models in a large cohort enriched for US Latinos and Latin Americans (ULLA). Each model was tested using the same variables as published by the International Warfarin Pharmacogenetics Consortium (IWPC) and using an expanded set of variables including ethnicity and warfarin indication. We utilized a multiple linear regression model and three nonlinear regression models: Bayesian Additive Regression Trees, Multivariate Adaptive Regression Splines, and Support Vector Regression. We compared each model’s ability to predict stable warfarin dose within 20% of actual stable dose, confirming trained models in a 30% testing dataset with 100 rounds of resampling. In all patients (n = 7,030), inclusion of additional predictor variables led to a small but significant improvement in prediction of dose relative to the IWPC algorithm (47.8 versus 46.7% in IWPC, p = 1.43 × 10⁻¹⁵). Nonlinear models using IWPC variables did not significantly improve prediction of dose over the linear IWPC algorithm. In ULLA patients alone (n = 1,734), IWPC performed similarly to all other linear and nonlinear pharmacogenetic algorithms. Our results reinforce the validity of IWPC in a large, ethnically diverse population and suggest that additional variables that capture warfarin dose variability may improve warfarin dose prediction algorithms.