A case of thrombocytopenia associated with the use of hydroxychloroquine following open heart surgery

Prognosing the risk of COVID-19 death through a machine learning-based routine blood panel: A retrospective study in Brazil

BACKGROUND

Despite an extensive network of primary care availability, Brazil has suffered profoundly during the COVID-19 pandemic, experiencing the greatest sanitary collapse in its history. Thus, it is important to understand phenotype risk factors for SARS-CoV-2 infection severity in the Brazilian population in order to provide novel insights into the pathogenesis of the disease.

OBJECTIVE

This study proposes to predict the risk of COVID-19 death through machine learning, using blood biomarkers data from patients admitted to two large hospitals in Brazil.

METHODS

We retrospectively collected blood biomarkers data in a 24-h time window from 6,979 patients with COVID-19 confirmed by positive RT-PCR admitted to two large hospitals in Brazil, of whom 291 (4.2%) died and 6,688 (95.8%) were discharged. We then developed a large-scale exploration of risk models to predict the probability of COVID-19 severity, finally choosing the best performing model regarding the average AUROC. To improve generalizability, for each model five different testing scenarios were conducted, including two external validations.

RESULTS

We developed a machine learning-based panel composed of parameters extracted from the complete blood count (lymphocytes, MCV, platelets and RDW), in addition to C-Reactive Protein, which yielded an average AUROC of 0.91 ± 0.01 to predict death by COVID-19 confirmed by positive RT-PCR within a 24-h window.

CONCLUSION

Our study suggests that routine laboratory variables could be useful to identify COVID-19 patients under higher risk of death using machine learning. Further studies are needed for validating the model in other populations and contexts, since the natural history of SARS-CoV-2 infection and its consequences on the hematopoietic system and other organs is still quite recent.

Safety of Short-Term Treatments with Oral Chloroquine and Hydroxychloroquine in Patients with and without COVID-19: A Systematic Review

Chloroquine (CQ) and hydroxychloroquine (HCQ) have recently become the focus of global attention as possible treatments for Coronavirus Disease 2019 (COVID-19). The current systematic review aims to assess their safety in short treatments (≤14 days), whether used alone or in combination with other drugs. Following the PRISMA and SWiM recommendations, a search was conducted using four health databases for all relevant English-, Chinese-, and Spanish-language studies from inception through 30 July 2021. Patients treated for any condition and with any comparator were included. The outcomes of interest were early drug adverse effects and their frequency. A total of 254 articles met the inclusion criteria, including case and case-control reports as well as cross-sectional, cohort, and randomised studies. The results were summarised either qualitatively in table or narrative form or, when possible (99 studies), quantitatively in terms of adverse event frequencies. Quality evaluation was conducted using the CARE, STROBE, and JADAD tools. This systematic review showed that safety depended on drug indication. In COVID-19 patients, cardiac adverse effects, such as corrected QT interval prolongation, were relatively frequent (0–27.3% and up to 33% if combined with azithromycin), though the risk of torsade de pointes was low. Compared to non-COVID-19 patients, COVID-19 patients experienced a higher frequency of cardiac adverse effects regardless of the regimen used. Dermatological adverse effects affected 0–10% of patients with autoimmune diseases and COVID-19. A broad spectrum of neuropsychiatric adverse effects affected patients treated with CQ for malaria with variable frequencies and some cases were reported in COVID-19 patients. Gastrointestinal adverse effects occurred regardless of drug indication affecting 0–50% of patients. In conclusion, CQ and HCQ are two safe drugs widely used in the treatment of malaria and autoimmune diseases. However, recent findings on their cardiac and neuropsychiatric adverse effects should be considered if these drugs were to be proposed as antivirals again.

Are platelet volume indices of clinical use in COVID-19? A systematic review

Background

The incidence of thrombotic complications is high in COVID-19 patients with severe disease. As key regulators of thrombus formation, platelets likely play a crucial role as mediators of severe acute respiratory syndrome coronavirus 2 associated pathogenesis. Studies have reported that parameters reflecting platelet size, known as platelet volume indices (PVI), are raised in patients with thrombosis and can predict poor outcomes. This systematic review evaluates the potential for PVI to be used as a predictor of COVID-19 morbidity and mortality.

Methods

English and Chinese databases were searched electronically to identify studies reporting data on mean platelet volume, platelet distribution width or platelet-large cell ratio in COVID-19 patients. Included articles underwent a quality rating and descriptive narrative analysis.

Results

Thirty-two studies were included in the systematic review. The results show a general trend for PVI to be raised in severe COVID-19 patients and non-survivors, with 14 studies reporting significant differences of baseline PVI between severe and mild disease. Nonetheless, longitudinal studies showed varying PVI trends over the course of the disease and evidence for PVI to be associated with disease progression was limited. The quality rating of 12 studies was poor, 16 were rated fair and four were good. Most studies were retrospective in design, used small study populations and did not consider confounding factors that influence platelet volume. Studies also contained technical flaws in PVI measurement, limiting the reliability of the results.

Conclusion

The evidence on the clinical usefulness of PVI is greatly limited by the lack of prospective evaluation, together with technical problems in measuring PVI. Carefully designed prospective studies are warranted.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=304305, identifier CRD42022304305.

Identifying side effects of commonly used drugs in the treatment of Covid 19

To increase the success in Covid 19 treatment, many drug suggestions are presented, and some clinical studies are shared in the literature. There have been some attempts to use some of these drugs in combination. However, using more than one drug together may cause serious side effects on patients. Therefore, detecting drug-drug interactions of the drugs used will be of great importance in the treatment of Covid 19. In this study, the interactions of 8 drugs used for Covid 19 treatment with 645 different drugs and possible side effects estimates have been produced using Graph Convolutional Networks. As a result of the experiments, it has been found that the hematopoietic system and the cardiovascular system are exposed to more side effects than other organs. Among the focused drugs, Heparin and Atazanavir appear to cause more adverse reactions than other drugs. In addition, as it is known that some of these 8 drugs are used together in Covid-19 treatment, the side effects caused by using these drugs together are shared. With the experimental results obtained, it is aimed to facilitate the selection of the drugs and increase the success of Covid 19 treatment according to the targeted patient.

Effect of COVID-19 on platelet count and its indices

BACKGROUND

Easily accessible, inexpensive, and widely used laboratory tests that demonstrate the severity of COVID-19 are important. Therefore, in this study, we aimed to investigate the relationship between mortality in COVID-19 and platelet count, Mean Platelet Volume (MPV), and platelet distribution width.

METHODS

In total, 215 COVID-19 patients were included in this study. The patients were divided into two groups. Patients with room air oxygen saturation < 90% were considered as severe COVID-19, and patients with ≥90% were considered moderate COVID-19. Patient medical records and the electronic patient data monitoring system were examined retrospectively. Analyses were performed using the SPSS statistical software. A p-value <0.05 was considered significant.

RESULTS

The patients' mean age was 64,32 ± 16,07 years. According to oxygen saturation, 81 patients had moderate and 134 had severe COVID-19. Our findings revealed that oxygen saturation at admission and the MPV difference between the first and third days of hospitalization were significant parameters in COVID-19 patients for predicting mortality. While mortality was 8.4 times higher in patients who had oxygen saturation under 90 % at hospital admission, 1 unit increase in MPV increased mortality 1.76 times.

CONCLUSION

In addition to the lung capacity of patients, the mean platelet volume may be used as an auxiliary test in predicting the mortality in COVID-19 patients.