The impact of SARS-CoV-2 treatment on the cardiovascular system: an updated review.

The coronavirus disease-2019 (COVID-19) pandemic has become a major global health problem. COVID-19 is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and exhibits pulmonary and extrapulmonary effects, including cardiovascular involvement. There are several attempts to identify drugs that could treat COVID-19. Moreover, many patients infected with COVID-19 have underlying diseases, particularly cardiovascular diseases. These patients are more likely to develop severe illnesses and would require optimized treatment strategies. The current study gathered information from various databases, including relevant studies, reviews, trials, or meta-analyses until April 2022 to identify the impact of SARS-CoV-2 treatment on the cardiovascular system. Studies have shown that the prognosis of patients with underlying cardiovascular disease is worsened by COVID-19, with some COVID-19 medications interfering with the cardiovascular system. The COVID-19 treatment strategy should consider many factors and parameters to avoid medication-induced cardiac injury, mainly in elderly patients. Therefore, this article provides a synthesis of evidence on the impact of different COVID-19 medications on the cardiovascular system and related disease conditions.

Plasma Metabolites as Predictors of Warfarin Outcome in Atrial Fibrillation.

BACKGROUND: Warfarin is prescribed as an oral anticoagulant to treat/prevent thromboembolism in conditions such as atrial fibrillation. As there is a narrow therapeutic window, treatment with warfarin is challenging. Pharmacometabonomics using nuclear magnetic resonance (NMR) spectroscopy may provide novel techniques for the identification of novel biomarkers of warfarin. PURPOSE: The aim was to determine the metabolic fingerprint that predicts warfarin response based on the international normalized ratio (INR) in patients who are already receiving warfarin (phase I: identification) and to ascertain the metabolic fingerprint that discriminates stable from unstable INR in patients starting treatment with warfarin (phase II: validation). EXPERIMENTAL APPROACH: A total of 94 blood samples were collected for phase I: 44 patients with stable INR and 50 with unstable INR. Meanwhile, 23 samples were collected for phase II: nine patients with stable INR and 14 with unstable INR. Data analysis was performed using multivariate analysis including principal component analysis and partial least square-discriminate analysis (PLS-DA), followed by univariate and multivariate logistic regression (MVLR) to develop a model to identify unstable INR biomarkers. KEY RESULTS: For phase I, the PLS-DA model showed the following results: sensitivity 93.18%, specificity 91.49% and accuracy 92.31%. In the MVLR analysis of phase I, ten regions were associated with unstable INR. For phase II, the PLS-DA model showed the following results: sensitivity 66.67%, specificity 61.54% and accuracy 63.64%. CONCLUSIONS AND IMPLICATIONS: We have shown that the pharmacometabonomics technique was able to differentiate between unstable and stable INR with good accuracy. NMR-based pharmacometabonomics has the potential to identify novel biomarkers in plasma, which can be useful in individualizing treatment and controlling warfarin side effects, thus, minimizing undesirable effects in the future.