The First 110,593 COVID-19 Patients Hospitalised in Lombardy: A Regionwide Analysis of Case Characteristics, Risk Factors and Clinical Outcomes

Deaths induced by compassionate use of hydroxychloroquine during the first COVID-19 wave: an estimate

BACKGROUND

During the first wave of COVID-19, hydroxychloroquine (HCQ) was used off-label despite the absence of evidence documenting its clinical benefits. Since then, a meta-analysis of randomised trials showed that HCQ use was associated with an 11% increase in the mortality rate. We aimed to estimate the number of HCQ-related deaths worldwide.

METHODS AND FINDINGS

We estimated the worldwide in-hospital mortality attributable to HCQ use by combining the mortality rate, HCQ exposure, number of hospitalised patients, and the increased relative risk of death with HCQ. The mortality rate in hospitalised patients for each country was calculated using pooled prevalence estimated by a meta-analysis of published cohorts. The HCQ exposure was estimated using median and extreme estimates from the same systematic review. The number of hospitalised patients during the first wave was extracted from dedicated databases. The systematic review included 44 cohort studies (Belgium: k = 1, France: k = 2, Italy: k = 12, Spain: k = 6, Turkey: k = 3, USA: k = 20). HCQ prescription rates varied greatly from one country to another (range 16-84%). Overall, using median estimates of HCQ use in each country, we estimated that 16,990 HCQ-related in-hospital deaths (range 6267-19256) occurred in the countries with available data. The median number of HCQ-related deaths in Belgium, Turkey, France, Italy, Spain, and the USA was 240 (range not estimable), 95 (range 92-128), 199 (range not estimable), 1822 (range 1170-2063), 1895 (range 1475-2094) and 12739 (3244- 15570), respectively.

CONCLUSIONS

Although our estimates are limited by their imprecision, these findings illustrate the hazard of drug repurposing with low-level evidence.

Impact of COVID-19 on Cardiovascular Disease

Coronavirus disease 2019 (COVID-19) is a viral infection with the novel severe acute respiratory distress syndrome corona virus 2 (SARS-CoV-2). Until now, more than 670 million people have suffered from COVID-19 worldwide, and roughly 7 million death cases were attributed to COVID-19. Recent evidence suggests an interplay between COVID-19 and cardiovascular disease (CVD). COVID-19 may serve as a yet underappreciated CVD risk modifier, including risk factors such as diabetes mellitus or arterial hypertension. In addition, recent data suggest that previous COVID-19 may increase the risk for many entities of CVD to an extent similarly observed for traditional cardiovascular (CV) risk factors. Furthermore, increased CVD incidence and worse clinical outcomes in individuals with preexisting CVD have been observed for myocarditis, acute coronary syndrome, heart failure (HF), thromboembolic complications, and arrhythmias. Direct and indirect mechanisms have been proposed by which COVID-19 may impact CVD and CV risk, including viral entry into CV tissue or by the induction of a massive systemic inflammatory response. In the current review, we provide an overview of the literature reporting an interaction between COVID-19 and CVD, review potential mechanisms underlying this interaction, and discuss preventive and treatment strategies and their interference with CVD that were evaluated since the onset of the COVID-19 pandemic.

A Calculator for COVID-19 Severity Prediction Based on Patient Risk Factors and Number of Vaccines Received

Vaccines have allowed for a significant decrease in COVID-19 risk, and new antiviral medications can prevent disease progression if given early in the course of the disease. The rapid and accurate estimation of the risk of severe disease in new patients is needed to prioritize the treatment of high-risk patients and maximize lives saved. We used electronic health records from 101,039 individuals infected with SARS-CoV-2, since the beginning of the pandemic and until 30 November 2021, in a national healthcare organization in Israel to build logistic models estimating the probability of subsequent hospitalization and death of newly infected patients based on a few major risk factors (age, sex, body mass index, hemoglobin A1C, kidney function, and the presence of hypertension, pulmonary disease, and malignancy) and the number of BNT162b2 mRNA vaccine doses received. The model’s performance was assessed by 10-fold cross-validation: the area under the curve was 0.889 for predicting hospitalization and 0.967 for predicting mortality. A total of 50%, 80%, and 90% of death events could be predicted with respective specificities of 98.6%, 95.2%, and 91.2%. These models enable the rapid identification of individuals at high risk for hospitalization and death when infected, and they can be used to prioritize patients to receive scarce medications or booster vaccination. The calculator is available online.