Research Note: COVID-19 Is Not an Independent Cause of Death.

The COVID-19 pandemic has had overwhelming global impacts with deleterious social, economic, and health consequences. To assess the COVID-19 death toll, researchers have estimated declines in 2020 life expectancy at birth (e0). When data are available only for COVID-19 deaths, but not for deaths from other causes, the risks of dying from COVID-19 are typically assumed to be independent of those from other causes. In this research note, we explore the soundness of this assumption using data from the United States and Brazil, the countries with the largest number of reported COVID-19 deaths. We use three methods: one estimates the difference between 2019 and 2020 life tables and therefore does not require the assumption of independence, and the other two assume independence to simulate scenarios in which COVID-19 mortality is added to 2019 death rates or is eliminated from 2020 rates. Our results reveal that COVID-19 is not independent of other causes of death. The assumption of independence can lead to either an overestimate (Brazil) or an underestimate (United States) of the decline in e0, depending on how the number of other reported causes of death changed in 2020.

Simultaneous hypothesis testing for multiple competing risks in comparative clinical trials.

Competing risks data are commonly encountered in randomized clinical trials or observational studies. Ignoring competing risks in survival analysis leads to biased risk estimates and improper conclusions. Often, one of the competing events is of primary interest and the rest competing events are handled as nuisances. These approaches can be inadequate when multiple competing events have important clinical interpretations and thus of equal interest. For example, in COVID-19 in-patient treatment trials, the outcomes of COVID-19 related hospitalization are either death or discharge from hospital, which have completely different clinical implications and are of equal interest, especially during the pandemic. In this paper we develop nonparametric estimation and simultaneous inferential methods for multiple cumulative incidence functions (CIFs) and corresponding restricted mean times. Based on Monte Carlo simulations and a data analysis of COVID-19 in-patient treatment clinical trial, we demonstrate that the proposed method provides global insights of the treatment effects across multiple endpoints.

A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19.

We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate the probability of admission to intensive care unit, the probability of death in hospital for patients before and after intensive care unit admission, the lengths of stay in hospital, and how all these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks. A less commonly used framework defines partially-latent subpopulations who will experience each subsequent event, and uses a mixture model to estimate the probability that an individual will experience each event, and the distribution of the time to the event given that it occurs. We compare the advantages and disadvantages of these two frameworks, in the context of the COVID-19 example. The issues include the interpretation of the model parameters, the computational efficiency of estimating the quantities of interest, implementation in software and assessing goodness of fit. In the example, we find that some groups appear to be at very low risk of some events, in particular intensive care unit admission, and these are best represented by using 'cure-rate' models to define transition-specific hazards. We provide general-purpose software to implement all the models we describe in the flexsurv R package, which allows arbitrarily flexible distributions to be used to represent the cause-specific hazards or times to events.

Genomic Transcriptome Benefits and Potential Harms of COVID-19 Vaccines Indicated from Optimized Genomic Biomarkers.

COVID-19 vaccines can be the tugboats for preventing SARS-CoV-2 infections when they are practical and, more importantly, without adverse effects. However, the reality is that they may result in short-term or long-term impacts on COVID-19-related diseases and even trigger the formation of new variants of SARS-CoV-2. Using published data, we use a set of optimized-performance COVID-19 genomic biomarkers (MND1, CDC6, ZNF282) to study the benefits and adverse effects of the BNT162b2 vaccine. We found that the vaccine lowered the expression values of genes MND1 and CDC6 while heightening the expression values of ZNF282 in individuals who are SARS-CoV-2 naïve, which is expected and satisfies the biological equivalence between the COVID-19 disease and the genomic signature patterns established in the literature. However, we also found that COVID-19-convalescent octogenarians responded reversely. The vaccine heightened the expression values of MND1 and CDC6. In addition, it lowered the expression values of ZNF282. Such adverse effects raise outstanding concerns about whether or not COVID-19-convalescent individuals should take the current vaccine or when they can take it. These findings are new at the genomic level and can provide insights into developing next-generation vaccines, antiviral drugs, and pandemic management guidance.

Genomic Biomarker Heterogeneities between SARS-CoV-2 and COVID-19.

Genes functionally associated with SARS-CoV-2 infection and genes functionally related to the COVID-19 disease can be different, whose distinction will become the first essential step for successfully fighting against the COVID-19 pandemic. Unfortunately, this first step has not been completed in all biological and medical research. Using a newly developed max-competing logistic classifier, two genes, ATP6V1B2 and IFI27, stand out to be critical in the transcriptional response to SARS-CoV-2 infection with differential expressions derived from NP/OP swab PCR. This finding is evidenced by combining these two genes with another gene in predicting disease status to achieve better-indicating accuracy than existing classifiers with the same number of genes. In addition, combining these two genes with three other genes to form a five-gene classifier outperforms existing classifiers with ten or more genes. These two genes can be critical in fighting against the COVID-19 pandemic as a new focus and direction with their exceptional predicting accuracy. Comparing the functional effects of these genes with a five-gene classifier with 100% accuracy identified and tested from blood samples in our earlier work, the genes and their transcriptional response and functional effects on SARS-CoV-2 infection, and the genes and their functional signature patterns on COVID-19 antibodies, are significantly different. We will use a total of fourteen cohort studies (including breakthrough infections and omicron variants) with 1481 samples to justify our results. Such significant findings can help explore the causal and pathological links between SARS-CoV-2 infection and the COVID-19 disease, and fight against the disease with more targeted genes, vaccines, antiviral drugs, and therapies.

Joint inference for competing risks data using multiple endpoints.

Competing risks data are commonly encountered in randomized clinical trials and observational studies. This paper considers the situation where the ending statuses of competing events have different clinical interpretations and/or are of simultaneous interest. In clinical trials, often more than one competing event has meaningful clinical interpretations even though the trial effects of different events could be different or even opposite to each other. In this paper, we develop estimation procedures and inferential properties for the joint use of multiple cumulative incidence functions (CIFs). Additionally, by incorporating longitudinal marker information, we develop estimation and inference procedures for weighted CIFs and related metrics. The proposed methods are applied to a COVID-19 in-patient treatment clinical trial, where the outcomes of COVID-19 hospitalization are either death or discharge from the hospital, two competing events with completely different clinical implications.

The Existence of at Least Three Genomic Signature Patterns and at Least Seven Subtypes of COVID-19 and the End of the Disease.

Hoping to find genomic clues linked to COVID-19 and end the pandemic has driven scientists' tremendous efforts to try all kinds of research. Signs of progress have been achieved but are still limited. This paper intends to prove the existence of at least three genomic signature patterns and at least seven subtypes of COVID-19 driven by five critical genes (the smallest subset of genes) using three blood-sampled datasets. These signatures and subtypes provide crucial genomic information in COVID-19 diagnosis (including ICU patients), research focuses, and treatment methods. Unlike existing approaches focused on gene fold-changes and pathways, gene-gene nonlinear and competing interactions are the driving forces in finding the signature patterns and subtypes. Furthermore, the method leads to high accuracy with hospitalized patients, showing biological and mathematical equivalences between COVID-19 status and the signature patterns and a methodological advantage over other methods that cannot lead to high accuracy. As a result, as new biomarkers, the new findings and genomic clues can be much more informative than other findings for interpreting biological mechanisms, developing the second (third) generation of vaccines, antiviral drugs, and treatment methods, and eventually bringing new hopes of an end to the pandemic.

A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort.

Having a complete and reliable list of risk factors from routine laboratory blood test for COVID-19 disease severity and mortality is important for patient care and hospital management. It is common to use meta-analysis to combine analysis results from different studies to make it more reproducible. In this paper, we propose to run multiple analyses on the same set of data to produce a more robust list of risk factors. With our time-to-event survival data, the standard survival analysis were extended in three directions. The first is to extend from tests and corresponding p-values to machine learning and their prediction performance. The second is to extend from single-variable to multiple-variable analysis. The third is to expand from analyzing time-to-decease data with death as the event of interest to analyzing time-to-hospital-release data to treat early recovery as a meaningful event as well. Our extension of the type of analyses leads to ten ranking lists. We conclude that 20 out of 30 factors are deemed to be reliably associated to faster-death or faster-recovery. Considering correlation among factors and evidenced by stepwise variable selection in random survival forest, 10 ~ 15 factors seem to be able to achieve the optimal prognosis performance. Our final list of risk factors contain calcium, white blood cell and neutrophils count, urea and creatine, d-dimer, red cell distribution widths, age, ferritin, glucose, lactate dehydrogenase, lymphocyte, basophils, anemia related factors (hemoglobin, hematocrit, mean corpuscular hemoglobin concentration), sodium, potassium, eosinophils, and aspartate aminotransferase.

Predicting Hospital Length of Stay at Admission Using Global and Country-Specific Competing Risk Analysis of Structural, Patient, and Nutrition-Related Data from nutritionDay 2007-2015.

Hospital length of stay (LOS) is an important clinical and economic outcome and knowing its predictors could lead to better planning of resources needed during hospitalization. This analysis sought to identify structure, patient, and nutrition-related predictors of LOS available at the time of admission in the global nutritionDay dataset and to analyze variations by country for countries with n > 750. Data from 2006-2015 (n = 155,524) was utilized for descriptive and multivariable cause-specific Cox proportional hazards competing-risks analyses of total LOS from admission. Time to event analysis on 90,480 complete cases included: discharged (n = 65,509), transferred (n = 11,553), or in-hospital death (n = 3199). The median LOS was 6 days (25th and 75th percentile: 4-12). There is robust evidence that LOS is predicted by patient characteristics such as age, affected organs, and comorbidities in all three outcomes. Having lost weight in the last three months led to a longer time to discharge (Hazard Ratio (HR) 0.89; 99.9% Confidence Interval (CI) 0.85-0.93), shorter time to transfer (HR 1.40; 99.9% CI 1.24-1.57) or death (HR 2.34; 99.9% CI 1.86-2.94). The impact of having a dietician and screening patients at admission varied by country. Despite country variability in outcomes and LOS, the factors that predict LOS at admission are consistent globally.

Probabilities of ICU admission and hospital discharge according to patient characteristics in the designated COVID-19 hospital of Kuwait.

BACKGROUND: Subsequent epidemic waves have already emerged in many countries and in the absence of highly effective preventive and curative options, the role of patient characteristics on the development of outcomes needs to be thoroughly examined, especially in middle-east countries where such epidemiological studies are lacking. There is a huge pressure on the hospital services and in particular, on the Intensive Care Units (ICU). Describing the need for critical care as well as the chance of being discharged from hospital according to patient characteristics, is essential for a more efficient hospital management. The objective of this study is to describe the probabilities of admission to the ICU and the probabilities of hospital discharge among positive COVID-19 patients according to demographics and comorbidities recorded at hospital admission. METHODS: A prospective cohort study of all patients with COVID-19 found in the Electronic Medical Records of Jaber Al-Ahmad Al-Sabah Hospital in Kuwait was conducted. The study included 3995 individuals (symptomatic and asymptomatic) of all ages who tested positive from February 24th to May 27th, 2020, out of which 315 were treated in the ICU and 3619 were discharged including those who were transferred to a different healthcare unit without having previously entered the ICU. A competing risk analysis considering two events, namely, ICU admission and hospital discharge using flexible hazard models was performed to describe the association between event-specific probabilities and patient characteristics. RESULTS: Results showed that being male, increasing age and comorbidities such as chronic kidney disease (CKD), asthma or chronic obstructive pulmonary disease and weakened immune system increased the risk of ICU admission within 10 days of entering the hospital. CKD and weakened immune system decreased the probabilities of discharge in both females and males however, the age-related pattern differed by gender. Diabetes, which was the most prevalent comorbid condition, had only a moderate impact on both probabilities (18% overall) in contrast to CKD which had the largest effect, but presented only in 7% of those admitted to ICU and in 1% of those who got discharged. For instance, within 5 days a 50-year-old male had 19% (95% C.I.: [15,23]) probability of entering the ICU if he had none of these comorbidities, yet this risk jumped to 31% (95% C.I.: [20,46]) if he had also CKD, and to 27% in the presence of asthma/COPD (95% C.I.: [19,36]) or of weakened immune system (95% C.I.: [16,42]). CONCLUSIONS: This study provides useful insight in describing the probabilities of ICU admission and hospital discharge according to age, gender, and comorbidities among confirmed COVID-19 cases in Kuwait. A web-tool is also provided to allow the user to estimate these probabilities for any combination of these covariates. These probabilities enable deeper understanding of the hospital demand according to patient characteristics which is essential to hospital management and useful for developing a vaccination strategy.

Correlates of In-Hospital COVID-19 Deaths: A Competing Risks Survival Time Analysis of Retrospective Mortality Data.

INTRODUCTION: Several aspects of the coronavirus disease 2019 (COVID-19) pandemic remain ambiguous, including its transmission, severity, geographic, and racial differences in mortality. These variations merit elaboration of local patterns to inform wider national policies. METHODS: In a retrospective analysis, data of patients treated at a dedicated COVID hospital with moderate and severe illness during 8 wk of the pandemic were reviewed with attention to mortality in a competing risks framework. RESULTS: A total of 1147 patients were hospitalized, and 312 (27.2%) died in hospital. Those who died were older (56.5 vs 47.6 y; P < 0.0001). Of these, 885 (77.2%) had tested positive on reverse transcriptase polymerase chain reaction (RT-PCR), with 219 (24.2%) deaths (incidence rate, 1.9 per 100 person-days). Median time from onset of symptoms to death was 11 days. A competing risks analysis for in-hospital death revealed an adjusted cause-specific hazard ratio of 1.4 for each decade increase in age. CONCLUSIONS: This retrospective analysis provides broad patterns of disease presentation and mortality. Even COVID test-negative patients will receive treatment at dedicated facilities, and 33% presenting cases may die within the first 72 h, most with comorbid illness. This should be considered while planning distribution of services for effective health-care delivery.

[Impact of the COVID-19 epidemic in Friuli Venezia Giulia Region (Northern Italy): assessment of factors associated with the risk of death by competing risks analysis]. / Impatto dell'epidemia da COVID-19 in Friuli Venezia Giulia: valutazione dei fattori associati al rischio di decesso mediante l'analisi dei rischi competitivi.

OBJECTIVES: to investigate the role of gender, age, province of residence, and nursing home residency on the risk of death for residents in the Friuli Venezia Giulia (FVG) Region (Northern Italy) tested positive for Covid-19, considering recovery as a competing event. The secondary objective is to describe the impact of the Covid-19 epidemic in FVG and in the Regions of Northern and Central Italy in terms of incidence and mortality compared to the national data. DESIGN: retrospective cohort study. SETTING AND PARTICIPANTS: resident population in FVG in the period between 29 February and 25 June 2020. MAIN OUTCOME MEASURES: in order to describe the impact of the Covid-19 outbreak in FVG, in terms of incidence and mortality compared to the national data, the standardized incidence (SIR) and mortality (SMR) ratios and their respective 95% confidence intervals (95%CI) were calculated compared to the Italian population for the northern and central Regions of Italy and the autonomous Provinces (PA) of Trento and Bolzano. A retrospective cohort study was conducted on subjects residing in FVG to whom at least one naso-oropharyngeal swab (hereafter, named swab) resulted positive for Covid-19. For each subject included in the cohort, the observation period started with the first positive swab and ended with the first of the following events: death, recovery or censored, which means that at the end of the observation period the subject was still alive and positive. The cause of death was assigned to Covid-19 if a subject had not yet recovered at the time when the event occurred. Cohort members were considered recovered after two negative consecutive swabs. The sub-hazard ratio (SHR) was estimated by applying the regression model of competing risks by Fine and Gray, in which the event of interest was the death caused by Covid-19 and the competing event was recovery. The explanatory variables included in the multiple models are: gender, age at the beginning of the observation period, the Province of residence, and nursing home residency. The cause-specific hazard was estimated using Cox proportional hazard regression. RESULTS: during the observation period, 3,305 cases and 345 deaths were recorded in FVG; SIR and SMR resulted, respectively, equal to 0.64 (95%CI 0.61-0.68) and 0.43 (95%CI 0.37-0.50). The FVG was the Northern Region one with the lowest incidence and mortality. The cohort consisted of 3,121 residents in FVG with at least one swab with a positive Covid-19 result during the study period. The SHR of dying for Covid-19 is equal to 16.13 (95%CI 9.73-26.74) for people with age 70-79 years and 35.58 (95%CI 21.77-58.15) with age >=80 years respect those with age <70 years. It is higher in males (SHR 1.71; 95%CI 1.34-2.17). There is no evidence that being resident in a nursing home affects the SHR (SHR 0.91 and 95%CI 0.69-1.20). As regards the province as an explanatory variable, the sub-hazard of death in the province of Trieste appears to overlap to the sub-hazard of Pordenone used as a reference; for the provinces of Udine and Gorizia the sub-hazards seem lower than the reference. CONCLUSIONS: while other Northern Regions and autonomous Provinces show higher standardized incidence and mortality compared with Italy, FVG and Veneto do not. In FVG, male gender and age are important determinants of death while there is no evidence that the condition of guest in a nursing home increases the sub-hazard of death.

Joint analysis of duration of ventilation, length of intensive care, and mortality of COVID-19 patients: a multistate approach.

BACKGROUND: The clinical progress of patients hospitalized due to COVID-19 is often associated with severe pneumonia which may require intensive care, invasive ventilation, or extracorporeal membrane oxygenation (ECMO). The length of intensive care and the duration of these supportive therapies are clinically relevant outcomes. From the statistical perspective, these quantities are challenging to estimate due to episodes being time-dependent and potentially multiple, as well as being determined by the competing, terminal events of discharge alive and death. METHODS: We used multistate models to study COVID-19 patients' time-dependent progress and provide a statistical framework to estimate hazard rates and transition probabilities. These estimates can then be used to quantify average sojourn times of clinically important states such as intensive care and invasive ventilation. We have made two real data sets of COVID-19 patients (n = 24* and n = 53**) and the corresponding statistical code publically available. RESULTS: The expected lengths of intensive care unit (ICU) stay at day 28 for the two cohorts were 15.05* and 19.62** days, while expected durations of mechanical ventilation were 7.97* and 9.85** days. Predicted mortality stood at 51%* and 15%**. Patients mechanically ventilated at the start of the example studies had a longer expected duration of ventilation (12.25*, 14.57** days) compared to patients non-ventilated (4.34*, 1.41** days) after 28 days. Furthermore, initially ventilated patients had a higher risk of death (54%* and 20%** vs. 48%* and 6%**) after 4 weeks. These results are further illustrated in stacked probability plots for the two groups from time zero, as well as for the entire cohort which depicts the predicted proportions of the patients in each state over follow-up. CONCLUSIONS: The multistate approach gives important insights into the progress of COVID-19 patients in terms of ventilation duration, length of ICU stay, and mortality. In addition to avoiding frequent pitfalls in survival analysis, the methodology enables active cases to be analyzed by allowing for censoring. The stacked probability plots provide extensive information in a concise manner that can be easily conveyed to decision makers regarding healthcare capacities. Furthermore, clear comparisons can be made among different baseline characteristics.

Adapting care for older cancer patients during the COVID-19 pandemic: Recommendations from the International Society of Geriatric Oncology (SIOG) COVID-19 Working Group.

The COVID-19 pandemic poses a barrier to equal and evidence-based management of cancer in older adults. The International Society of Geriatric Oncology (SIOG) formed a panel of experts to develop consensus recommendations on the implications of the pandemic on several aspects of cancer care in this age group including geriatric assessment (GA), surgery, radiotherapy, systemic treatment, palliative care and research. Age and cancer diagnosis are significant predictors of adverse outcomes of the COVID-19 infection. In this setting, GA is particularly valuable to drive decision-making. GA may aid estimating physiologic reserve and adaptive capability, assessing risk-benefits of either providing or temporarily withholding treatments, and determining patient preferences to help inform treatment decisions. In a resource-constrained setting, geriatric screening tools may be administered remotely to identify patients requiring comprehensive GA. Tele-health is also crucial to ensure adequate continuity of care and minimize the risk of infection exposure. In general, therapeutic decisions should favor the most effective and least invasive approach with the lowest risk of adverse outcomes. In selected cases, this might require deferring or omitting surgery, radiotherapy or systemic treatments especially where benefits are marginal and alternative safe therapeutic options are available. Ongoing research is necessary to expand knowledge of the management of cancer in older adults. However, the pandemic presents a significant barrier and efforts should be made to ensure equitable access to clinical trials and prospective data collection to elucidate the outcomes of COVID-19 in this population.