Decreased mortality and increased side effects in COVID-19 patients treated with IL-6 receptor antagonists: systematic review and meta-analysis

There is controversy whether IL-6 (receptor) antagonists are beneficial in treating COVID-19 patients. We therefore update our systematic review to answer the following research questions: (1) Do patients hospitalized for COVID-19 treated with IL-6 (receptor) antagonists have lower mortality compared to standard of care? (2) Do patients hospitalized for COVID-19 treated with IL-6 (receptor) antagonists have more side effects compared to standard of care? The following databases were search up to December 1st 2020: PubMed, PMC PubMed Central, MEDLINE, WHO COVID-19 Database, Embase, Web-of-Science, COCHRANE LIBRARY, Emcare and Academic Search Premier. In order to pool the risk ratio (RR) and risk difference of individual studies we used random effects meta-analysis. The search strategy retrieved 2975 unique titles of which 71 studies (9 RCTs and 62 observational) studies comprising 29,495 patients were included. Mortality (RR 0.75) and mechanical ventilation (RR 0.78) were lower and the risk of neutropenia (RR 7.3), impaired liver function (RR 1.67) and secondary infections (RR 1.26) were higher for patients treated with IL-6 (receptor) antagonists compared to patients not treated with treated with IL-6 (receptor) antagonists. Our results showed that IL-6 (receptor) antagonists are effective in reducing mortality in COVID-19 patients, while the risk of side effects was higher. The baseline risk of mortality was an important effect modifier: IL-6 (receptor) antagonists were effective when the baseline mortality risk was high (e.g. ICU setting), while they could be harmful when the baseline mortality risk was low.

Estimating excess mortality in Canada during the COVID-19 pandemic: Statistical methods adapted for rapid response in an evolving crisis

This paper presents the approach adopted at Statistics Canada to produce timely and accurate estimates of excess mortality during the ongoing COVID-19 pandemic. It focuses primarily on the two models involved in the estimation of excess mortality: the model used to estimate the expected number of deaths in the absence of the pandemic (baseline mortality), and the model used to adjust provisional death counts for undercoverage. We describe both, including how the models were adapted to fit our specific criteria as well as the various limitations they both possess. We conclude by presenting selected results from Statistics Canada’s official release of excess mortality estimates from February 8th, 2021.

All-cause excess mortality in the State of Gujarat, India, during the COVID-19 pandemic (March 2020-April 2021)

Official COVID-19 mortality statistics are strongly influenced by the local diagnostic capacity, strength of the healthcare system, and the recording and reporting capacities on causes of death. This can result in significant undercounting of COVID-19 attributable deaths, making it challenging to understand the total mortality burden of the pandemic. Excess mortality, which is defined as the increase in observed death counts compared to a baseline expectation, provides an alternate measure of the mortality shock of the COVID-19 pandemic. Here, we use data from civil death registers for 54 municipalities across the state of Gujarat, India, to estimate the impact of the COVID-19 pandemic on all-cause mortality. Using a model fit to monthly data from January 2019 to February 2020, we estimate excess mortality over the course of the pandemic from March 2020 to April 2021. We estimated 16,000 [95% CI: 14,000, 18,000] excess deaths across these municipalities since March 2020. The sharpest increase in deaths was observed in April 2021, with an estimated 480% [95% CI: 390%, 580%] increase in mortality from expected counts for the same period. Females and the 40 to 60 age groups experienced a greater increase from baseline mortality compared to other demographic groups. Our excess mortality estimate for these 54 municipalities, representing approximately 5% of the state population, exceeds the official COVID-19 death count for the entire state of Gujarat.

Survey evidence of excess mortality in Bihar in the second COVID-19 surge

The second surge of COVID-19 had a large mortality impact in India. However, there are few reliable estimates of the magnitude of this impact for India’s poorer states. This note presents results of a small-scale phone survey in Bihar which interviewed a random sample of beneficiaries of the state’s Public Distribution System. This pilot survey was conducted in June 2021 and asked more than 500 respondents about any deaths in their household since April 1, 2021.We observe an annualized Crude Death Rate of 24.3 deaths per 1,000 [95% CI 13.0-37.4] during the second surge of the pandemic in Bihar. The observed death rate is more than four times baseline mortality (5.8 deaths per 1,000 per year). The probability that mortality during the second surge was at least thrice the level of baseline mortality is 0.88. This large surge in mortality warrants an urgent public discussion on state priorities in Bihar. It also suggests the viability of and need for continuous large-scale mortality surveys.

Factors associated with baseline mortality in Norwegian Atlantic salmon farming

In 2019, it was estimated that more than 50 million captive Atlantic salmon in Norway died in the final stage of their production in marine cages. This mortality represents a significant economic loss for producers and a need to improve welfare for farmed salmon. Single adverse events, such as algal blooms or infectious disease outbreaks, can explain mass mortality in salmon cages. However, little is known about the production, health, or environmental factors that contribute to their baseline mortality during the sea phase. Here we conducted a retrospective study including 1627 Atlantic salmon cohorts put to sea in 2014–2019. We found that sea lice treatments were associated with Atlantic salmon mortality. In particular, the trend towards non-medicinal sea lice treatments, including thermal delousing, increases Atlantic salmon mortality in the same month the treatment is applied. There were differences in mortality among production zones. Stocking month and weight were other important factors, with the lowest mortality in smaller salmon stocked in August–October. Sea surface temperature and salinity also influenced Atlantic salmon mortality. Knowledge of what affects baseline mortality in Norwegian aquaculture can be used as part of syndromic surveillance and to inform salmon producers on farming practices that can reduce mortality.

Association of BMI, comorbidities and all-cause mortality by using a baseline mortality risk model

Objective The association of body mass index (BMI) and all-cause mortality is controversial, frequently referred to as a paradox. Whether the cause is metabolic factors or statistical biases is still controversial. We assessed the association of BMI and all-cause mortality considering a wide range of comorbidities and baseline mortality risk. Methods Retrospective cohort study of Olmsted County residents with at least one BMI measurement between 2000–2005, clinical data in the electronic health record and minimum 8 year follow-up or death within this time. The cohort was categorized based on baseline mortality risk: Low, Medium, Medium-high, High and Very-high. All-cause mortality was assessed for BMI intervals of 5 and 0.5 Kg/m2. Results Of 39,739 subjects (average age 52.6, range 18–89; 38.1% male) 11.86% died during 8-year follow-up. The 8-year all-cause mortality risk had a “U” shape with a flat nadir in all the risk groups. Extreme BMI showed higher risk (BMI <15 = 36.4%, 15 to <20 = 15.4% and ≥45 = 13.7%), while intermediate BMI categories showed a plateau between 10.6 and 12.5%. The increased risk attributed to baseline risk and comorbidities was more obvious than the risk based on BMI increase within the same risk groups. Conclusions There is a complex association between BMI and all-cause mortality when evaluated including comorbidities and baseline mortality risk. In general, comorbidities are better predictors of mortality risk except at extreme BMIs. In patients with no or few comorbidities, BMI seems to better define mortality risk. Aggressive management of comorbidities may provide better survival outcome for patients with body mass between normal and moderate obesity.

Excess mortality analysis for Germany for all three COVID-19 waves in 2020 - 2021

Background and Aims: The reported case and death numbers of the coronavirus disease (COVID-19) are often used to estimate the impact of COVID-19. We observe that during the second half of the first and second waves, the COVID-19 deaths are significantly higher than the excess mortality. We attribute the difference to the pre-dying effect. We then compare the excess mortality to the official COVID-19 death numbers and calculate the infection fatality rates (IFRs) and the percentage of infected individuals from excess mortality for different age bands. We also compare the impact of COVID-19 to past influenza waves and analyze the vaccination effect on excess mortality. Methods: We forecast the baseline mortality from official data on deaths in Germany. Distributing a part of excess mortality into the near future, we lower the baseline simulating the pre-dying effect. From there, we compare the excess mortality to official COVID-19 deaths. From the observed mortality deficit, we estimate the percentage of infected individuals and then estimate the age-dependent IFRs. Results: In the first wave, we find an overall excess mortality of ca. 8 000. For the second wave, the overall excess mortality adds up to ca. 56 000. We find, that the pre-dying effect explains the difference between the official COVID-19 deaths and excess mortality in the second half of the waves to a high degree. Attributing the whole excess mortality to COVID-19, we find that the IFRs are significantly higher in the second wave. In the third wave, we find an excess mortality in mid-age bands which cannot be explained by the official COVID-19 deaths. For the senior band 80+, we find results in favor of a strong and positive vaccination effect for the third COVID-19 wave. Conclusions: We conclude that in the first and second COVID-19 waves, the COVID-19 deaths explain almost all excess mortality when the pre-dying effect is taken into account. In the third wave in 2021, the excess mortality is not very pronounced for the 80+ age band, probably due to vaccination. The partially unvaccinated 40-80 age group experiences a pronounced excess mortality in the third wave while there are too few official COVID-19 deaths to explain the excess. The no-vaccination scenario for the 80+ age band results in a similarly high excess mortality as for the more younger age bands, suggesting a very positive vaccination effect on reduction of COVID-19 deaths.

Use of the first National Early Warning Score recorded within 24 hours of admission to estimate the risk of in-hospital mortality in unplanned COVID-19 patients: a retrospective cohort study

ObjectivesAlthough the National Early Warning Score (NEWS) and its latest version NEWS2 are recommended for monitoring deterioration in patients admitted to hospital, little is known about their performance in COVID-19 patients. We aimed to compare the performance of the NEWS and NEWS2 in patients with COVID-19 versus those without during the first phase of the pandemic.DesignA retrospective cross-sectional study.SettingTwo acute hospitals (Scarborough and York) are combined into a single dataset and analysed collectively.ParticipantsAdult (≥18 years) non-elective admissions discharged between 11 March 2020 and 13 June 2020 with an index or on-admission NEWS2 electronically recorded within ±24 hours of admission to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) in COVID-19 versus non-COVID-19 admissions.ResultsOut of 6480 non-elective admissions, 620 (9.6%) had a diagnosis of COVID-19. They were older (73.3 vs 67.7 years), more often male (54.7% vs 50.1%), had higher index NEWS (4 vs 2.5) and NEWS2 (4.6 vs 2.8) scores and higher in-hospital mortality (32.1% vs 5.8%). The c-statistics for predicting in-hospital mortality in COVID-19 admissions was significantly lower using NEWS (0.64 vs 0.74) or NEWS2 (0.64 vs 0.74), however, these differences reduced at 72hours (NEWS: 0.75 vs 0.81; NEWS2: 0.71 vs 0.81), 48 hours (NEWS: 0.78 vs 0.81; NEWS2: 0.76 vs 0.82) and 24hours (NEWS: 0.84 vs 0.84; NEWS2: 0.86 vs 0.84). Increasing NEWS2 values reflected increased mortality, but for any given value the absolute risk was on average 24% higher (eg, NEWS2=5: 36% vs 9%).ConclusionsThe index or on-admission NEWS and NEWS2 offers lower discrimination for COVID-19 admissions versus non-COVID-19 admissions. The index NEWS2 was not proven to be better than the index NEWS. For each value of the index NEWS/NEWS2, COVID-19 admissions had a substantially higher risk of mortality than non-COVID-19 admissions which reflects the increased baseline mortality risk of COVID-19.

A heart failure phenotype stratified model for predicting 1-year mortality in patients admitted with acute heart failure: results from an individual participant data meta-analysis of four prospective European cohorts

Background Prognostic models developed in general cohorts with a mixture of heart failure (HF) phenotypes, though more widely applicable, are also likely to yield larger prediction errors in settings where the HF phenotypes have substantially different baseline mortality rates or different predictor-outcome associations. This study sought to use individual participant data meta-analysis to develop an HF phenotype stratified model for predicting 1-year mortality in patients admitted with acute HF. Methods Four prospective European cohorts were used to develop an HF phenotype stratified model. Cox model with two rounds of backward elimination was used to derive the prognostic index. Weibull model was used to obtain the baseline hazard functions. The internal-external cross-validation (IECV) approach was used to evaluate the generalizability of the developed model in terms of discrimination and calibration. Results 3577 acute HF patients were included, of which 2368 were classified as having HF with reduced ejection fraction (EF) (HFrEF; EF < 40%), 588 as having HF with midrange EF (HFmrEF; EF 40–49%), and 621 as having HF with preserved EF (HFpEF; EF ≥ 50%). A total of 11 readily available variables built up the prognostic index. For four of these predictor variables, namely systolic blood pressure, serum creatinine, myocardial infarction, and diabetes, the effect differed across the three HF phenotypes. With a weighted IECV-adjusted AUC of 0.79 (0.74–0.83) for HFrEF, 0.74 (0.70–0.79) for HFmrEF, and 0.74 (0.71–0.77) for HFpEF, the model showed excellent discrimination. Moreover, there was a good agreement between the average observed and predicted 1-year mortality risks, especially after recalibration of the baseline mortality risks. Conclusions Our HF phenotype stratified model showed excellent generalizability across four European cohorts and may provide a useful tool in HF phenotype-specific clinical decision-making.

Credible learning of hydroxychloroquine and dexamethasone effects on COVID-19 mortality outside of randomized trials

ObjectivesTo investigate the effectiveness of hydroxychloroquine and dexamethasone on coronavirus disease (COVID-19) mortality using patient data outside of randomized trials.DesignPhenotypes derived from electronic health records were analyzed using the stability-controlled quasi-experiment (SCQE) to provide a range of possible causal effects of hydroxy-chloroquine and dexamethasone on COVID-19 mortality.Setting and participantsData from 2,007 COVID-19 positive patients hospitalized at a large university hospital system over the course of 200 days and not enrolled in randomized trials were analyzed using SCQE. For hyrdoxychloroquine, we examine a high-use cohort (n=766, days 1 to 43) and a later, low-use cohort (n=548, days 44 to 82). For dexamethasone, we examine a low-use cohort (n=614, days 44 to 101) and high-use cohort (n=622, days 102 to 200).Outcome measure14-day mortality, with a secondary outcome of 28-day mortality.ResultsHydroxycholoroquine could only have been significantly (p<0.05) beneficial if baseline mortality was at least 6.4 percentage points (55%) lower among patients in the later (low-use) than the earlier (high-use) cohort. Hydroxychloroquine instead proves significantly harmful if baseline mortality rose from one cohort to the next by just 0.3 percentage points. Dexamethasone significantly reduced mortality risk if baseline mortality in the later (high-use) cohort (days 102-200) was higher than, the same as, or up to 1.5 percentage points lower than that in the earlier (low-use) cohort (days 44-101). It could only prove significantly harmful if mortality improved from one cohort to the next by 6.8 percentage points due to other causes—an assumption implying an unlikely 84% reduction in mortality due to other causes, leaving an in-hospital mortality rate of just 1.3%.ConclusionsThe assumptions required for a beneficial effect of hydroxychloroquine on 14 day mortality are difficult to sustain, while the assumptions required for hydroxychloroquine to be harmful are difficult to reject with confidence. Dexamethasone, by contrast, was beneficial under a wide range of plausible assumptions, and was only harmful if a nearly impossible assumption is met. More broadly, the SCQE reveals what inferences can be credibly supported by evidence from non-randomized uses of experimental therapies, making it a useful tool when randomized trials have not yet produced clear evidence or to provide corroborative evidence from different populations.