COVID-19 mortality risk assessment: An international multi-center study

Deep-Risk: Deep Learning-Based Mortality Risk Predictive Models for COVID-19

The SARS-CoV-2 virus has proliferated around the world and caused panic to all people as it claimed many lives. Since COVID-19 is highly contagious and spreads quickly, an early diagnosis is essential. Identifying the COVID-19 patients' mortality risk factors is essential for reducing this risk among infected individuals. For the timely examination of large datasets, new computing approaches must be created. Many machine learning (ML) techniques have been developed to predict the mortality risk factors and severity for COVID-19 patients. Contrary to expectations, deep learning approaches as well as ML algorithms have not been widely applied in predicting the mortality and severity from COVID-19. Furthermore, the accuracy achieved by ML algorithms is less than the anticipated values. In this work, three supervised deep learning predictive models are utilized to predict the mortality risk and severity for COVID-19 patients. The first one, which we refer to as CV-CNN, is built using a convolutional neural network (CNN); it is trained using a clinical dataset of 12,020 patients and is based on the 10-fold cross-validation (CV) approach for training and validation. The second predictive model, which we refer to as CV-LSTM + CNN, is developed by combining the long short-term memory (LSTM) approach with a CNN model. It is also trained using the clinical dataset based on the 10-fold CV approach for training and validation. The first two predictive models use the clinical dataset in its original CSV form. The last one, which we refer to as IMG-CNN, is a CNN model and is trained alternatively using the converted images of the clinical dataset, where each image corresponds to a data row from the original clinical dataset. The experimental results revealed that the IMG-CNN predictive model outperforms the other two with an average accuracy of 94.14%, a precision of 100%, a recall of 91.0%, a specificity of 100%, an F1-score of 95.3%, an AUC of 93.6%, and a loss of 0.22.

The relationship of laboratory parameters and mortality of patients followed in intensive care units with COVID-19

Aim: We aimed to evaluate the parameters associated with mortality in COVID-19 patients followed up in the intensive care unit. Material and Method: Three hundred twenty-one patients followed up with the diagnosis of COVID-19 were included in the study. Demographic characteristics, laboratory and clinical parameters were compared in patients with and without mortality. Results: A higher intubation rate (98.6% vs. 10.9%) and longer hospitalization (10.0 vs. 8.0 days) were detected in the non-survivor group (p

Characteristics of our hypoxemic COVID-19 pneumonia patients receiving corticosteroids and mortality-associated factors

BACKGROUND

COVID-19 is a disease associated with diffuse lung injury that has no proven effective treatment yet. It is thought that glucocorticoids may reduce inflammation-mediated lung injury, disease progression, and mortality. We aimed to evaluate our patient's characteristics and treatment outcomes who received corticosteroids for COVID-19 pneumonia.

METHODS

We conducted a multicenter retrospective study and reviewed 517 patients admitted due to COVID-19 pneumonia who were hypoxemic and administered steroids regarding demographic, laboratory, and radiological characteristics, treatment response, and mortality-associated factors.

RESULTS

Of our 517 patients with COVID-19 pneumonia who were hypoxemic and received corticosteroids, the mortality rate was 24.4% (n = 126). The evaluation of mortality-associated factors revealed that age, comorbidities, a CURB-65 score of ≥ 2, higher SOFA scores, presence of MAS, high doses of steroids, type of steroids, COVID-19 treatment, stay in the intensive care unit, high levels of d-dimer, CRP, ferritin, and troponin, and renal dysfunction were associated with mortality.

CONCLUSION

Due to high starting and average steroid doses are more associated with mortality, high-dose steroid administration should be avoided. We believe that knowing the factors associated with mortality in these cases is essential for close follow-up. The use of CURB-65 and SOFA scores can predict prognosis in COVID-19 pneumonia.

Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study

OBJECTIVE

The COVID-19 outbreak was first reported in Wuhan, China, and has been acknowledged as a pandemic due to its rapid spread worldwide. Predicting the trend of COVID-19 is of great significance for its prevention. A comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more accurate for anticipating the occurrence of COVID-19 in the USA.

DESIGN

Time-series study.

SETTING

The USA was the setting for this study.

MAIN OUTCOME MEASURES

Three accuracy metrics, mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE), were applied to evaluate the performance of the two models.

RESULTS

In our study, for the training set and the validation set, the MAE, RMSE and MAPE of the XGBoost model were less than those of the ARIMA model.

CONCLUSIONS

The XGBoost model can help improve prediction of COVID-19 cases in the USA over the ARIMA model.

In-hospital mortality and severe outcomes after hospital discharge due to COVID-19: A prospective multicenter study from Brazil

Background

We evaluated in-hospital mortality and outcomes incidence after hospital discharge due to COVID-19 in a Brazilian multicenter cohort.

Methods

This prospective multicenter study (RECOVER-SUS, NCT04807699) included COVID-19 patients hospitalized in public tertiary hospitals in Brazil from June 2020 to March 2021. Clinical assessment and blood samples were performed at hospital admission, with post-hospital discharge remote visits. Hospitalized participants were followed-up until March 31, 2021. The outcomes were in-hospital mortality and incidence of rehospitalization or death after hospital discharge. Kaplan–Meier curves and Cox proportional-hazard models were performed.

Findings

1589 participants [54.5% male, age=62 (IQR 50-70) years; BMI=28.4 (IQR,24.9–32.9) Kg/m² and 51.9% with diabetes] were included. A total of 429 individuals [27.0% (95%CI,24.8–29.2)] died during hospitalization (median time 14 (IQR,9–24) days). Older age [vs<40 years; age=60–69 years-aHR=1.89 (95%CI,1.08–3.32); age=70–79 years-aHR=2.52 (95%CI,1.42–4.45); age≥80-aHR=2.90 (95%CI 1.54–5.47)]; noninvasive or mechanical ventilation at admission [vs facial-mask or none; aHR=1.69 (95%CI 1.30–2.19)]; SAPS-III score≥57 [vs<57; aHR=1.47 (95%CI 1.13–1.92)] and SOFA score≥10 [vs <10; aHR=1.51 (95%CI 1.08–2.10)] were independently associated with in-hospital mortality. A total of 65 individuals [6.7% (95%CI 5.3–8.4)] had a rehospitalization or death [rate=323 (95%CI 250–417) per 1000 person-years] in a median time of 52 (range 1–280) days post-hospital discharge. Age ≥ 60 years [vs <60, aHR=2.13 (95%CI 1.15–3.94)] and SAPS-III ≥57 at admission [vs <57, aHR=2.37 (95%CI 1.22–4.59)] were independently associated with rehospitalization or death after hospital discharge.

Interpretation

High in-hospital mortality rates due to COVID-19 were observed and elderly people remained at high risk of rehospitalization and death after hospital discharge.

Funding

Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Programa INOVA-FIOCRUZ.

National geographical pattern of COVID-19 hospitalization, case fatalities, and associated factors in patients covered by Iran Health Insurance Organization

BACKGROUND

Understanding the Spatio-temporal distribution and interpersonal comparisons are important tools in etiological studies. This study was conducted to investigate the temporal and geographical distribution of COVID-19 hospitalized patients in the Iran Health Insurance Organization (IHIO) insured population (the second largest social health insurance organization) and the factors affecting their case fatality rate (CFR).

METHODS

In this descriptive-analytical cross-sectional study, the demographic and clinical data of all insured of the IHIO who were hospitalized with COVID-19 in hospitals across the country until March 2021 was extracted from the comprehensive system of handling the inpatient documents of this organization. The Excel 2019 and GeoDA software were used for descriptive reporting and geographical distribution of variables. A multiple logistic regression model was used to estimate the Odds Ratio (OR) of death in patients with COVID-19 using STATA 14 software.

RESULTS

During the first 14 months of the COVID-19 outbreak in Iran, 0.72% of the IHIO insured (303,887 individuals) were hospitalized with COVID-19. Hospitalization per 100,000 people varied from 192.51 in East Azerbaijan to 1,277.49 in Yazd province. The overall CFR in hospitalized patients was 14%. Tehran and Kohgiluyeh & BoyerAhmad provinces had the highest and lowest CFR with 19.39% and 5.19%, respectively. The highest odds of death were in those over 80 years old people (OR = 9.65), ICU-admitted (OR = 7.49), Hospitalized in governmental hospitals (OR = 2.08), Being a foreign national (OR = 1.45), hospitalized in November (OR = 1.47) and Residence in provinces such as Sistan & Baluchestan (OR = 1.47) and Razavi Khorasan (OR = 1.66) respectively. Furthermore, the odds of death were lower in females (OR = 0.81) than in males.

CONCLUSIONS

A sound understanding of the primary causes of COVID-19 death and severity in different groups can be the basis for developing programs focused on more vulnerable groups in order to manage the crisis more effectively and benefit from resources more efficiently.

Chest CT Total Severity Score on Admission to Predict In-Hospital Mortality in COVID-19 Patients with Acute and Chronic Renal Impairment

Aim: To identify the predictors of in-hospital mortality in patients with coronavirus disease of 2019 (COVID-19) and acute renal impairment (ARI) or chronic kidney disease (CKD), and to evaluate the performance and inter-reader concordance of chest CT total severity scores (TSSs). Methods: This retrospective single-center study was conducted on symptomatic COVID-19 patients with renal impairment (either acute or chronic) and a serum creatinine of >2 mg/dL at the time of admission. The patients’ demographic characteristics, clinical data, and laboratory data were extracted from the clinical computerized medical records. All chest CT images obtained at the time of hospital admission were analyzed. Two radiologists independently assessed the pulmonary abnormalities and scored the severity using CT chest total severity score (TSS). Univariate logistic regression analysis was used to determine factors associated with in-hospital mortality. A receiver operating characteristic (ROC) curve analysis was performed for the TSS in order to identify the cut-off point that predicts mortality. Bland−Altman plots were used to evaluate agreement between the two radiologists assessing TSS. Results: A total of 100 patients were included, with a mean age of 60 years, 54 were males, 53 had ARI, and 47 had CKD. In terms of in-hospital mortality, 60 patients were classified in the non-survivor group and 40 were classified in the survivor group. The mortality rate was higher for those with ARI compared to those with CKD (p = 0.033). The univariate regression analysis showed an increasing odds of in-hospital mortality associated with higher respiratory rate (OR 1.149, 95% CI 1.057−1.248, p = 0.001), total bilirubin (OR 2.532, 95% CI 1.099−5.836, p = 0.029), lactate dehydrogenase (LDH) (OR 1.001, 95% CI 1.000−1.003, p = 0.018), CRP (OR 1.010, 95% CI 1.002−1.017, p = 0.012), invasive mechanical ventilation (MV) (OR 7.667, 95% CI 2.118−27.755, p = 0.002), a predominant pattern of pulmonary consolidation (OR 21.714, 95% CI 4.799−98.261, p < 0.001), and high TSS (OR 2.082, 95% CI 1.579−2.745, p < 0.001). The optimum cut-off value of TSS used to predict in-hospital mortality was 8.5 with a sensitivity of 86.7% and a specificity of 87.5%. There was excellent interobserver agreement (ICC > 0.9) between the two independent radiologists in their quantitative assessment of pulmonary changes using TSS. Conclusions: In-hospital mortality is high in COVID-19 patients with ARI/CKD, especially for those with ARI. High serum bilirubin, a predominant pattern of pulmonary consolidation, and TSS are the most significant predictors of mortality in these patients. Patients with a higher TSS may require more intensive hospital care. TSS is a reliable and helpful auxiliary tool for risk stratification among COVID-19 patients with ARI/CKD.

Early taurine administration as a means for halting the cytokine storm progression in COVID-19 patients

Around the world, more than 6.2 million individuals have died as a result of coronavirus disease 2019 (COVID-19). According to a recent survey conducted among immunologists, epidemiologists, and virologists, this disease is expected to become endemic. This implies that the disease could have a continuous presence and/or normal frequency in the population. Pharmacological interventions to prevent infection, as well as to treat the patients at an early phase of illness to avoid hospitalization are essential additions to the vaccines. Taurine is known to inhibit the generation of all inflammatory mediators linked to the cytokine storm. It can also protect against lung injury by suppressing increased oxidants production and promoting the resolution of the inflammatory process. Neutrophil lactoferrin degranulation stimulated by taurine may have antiviral effects against SARS-CoV-2, limiting viral replication. It is hypothesized that if taurine is administered early in the onset of COVID-19 disease, it may stop the cytokine storm from progressing, lowering morbidity and mortality.

The Impact of World Trade Center Related Medical Conditions on the Severity of COVID-19 Disease and Its Long-Term Sequelae

The individuals who served our country in the aftermath of the attacks on the World Trade Center (WTC) following the attacks of 11 September 2001 have, since then, been diagnosed with a number of conditions as a result of their exposures. In the present study, we sought to determine whether these conditions were risk factors for increased COVID-19 disease severity within a cohort of N = 1280 WTC responders with complete information on health outcomes prior to and following COVID-19 infection. We collected data on responders diagnosed with COVID-19, or had evidence of receiving positive SARS-CoV-2 polymerase chain reaction or antigen testing, or were asymptomatic but had IgG positive antibody testing. The presence of post-acute COVID-19 sequelae was measured using self-reported symptom severity scales. Analyses revealed that COVID-19 severity was associated with age, Black race, obstructive airway disease (OAD), as well as with worse self-reported depressive symptoms. Similarly, post-acute COVID-19 sequelae was associated with initial analysis for COVID-19 severity, upper respiratory disease (URD), gastroesophageal reflux disease (GERD), OAD, heart disease, and higher depressive symptoms. We conclude that increased COVID-19 illness severity and the presence of post-acute COVID-19 sequelae may be more common in WTC responders with chronic diseases than in those responders without chronic disease processes resulting from exposures at the WTC disaster.

A Network Analysis of the Fear of COVID-19 Scale (FCV-19S): A Large-Scale Cross-Cultural Study in Iran, Bangladesh, and Norway

The rapid spread of the coronavirus disease 2019 (COVID-19) has led to high levels of fear worldwide. Given that fear is an important factor in causing psychological distress and facilitating preventive behaviors, assessing the fear of COVID-19 is important. The seven-item Fear of COVID-19 Scale (FCV-19S) is a widely used psychometric instrument to assess this fear. However, the factor structure of the FCV-19S remains unclear according to the current evidence. Therefore, the present study used a network analysis to provide further empirical evidence for the factor structure of FCV-19S. A total of 24,429 participants from Iran (n = 10,843), Bangladesh (n = 9906), and Norway (n = 3680) completed the FCV-19S in their local language. A network analysis (via regularized partial correlation networks) was applied to investigate the seven FCV-19S items. Moreover, relationships between the FCV-19S items were compared across gender (males vs. females), age groups (18−30 years, 31−50 years, and >50 years), and countries (Iran, Bangladesh, and Norway). A two-factor structure pattern was observed (three items concerning physical factors, including clammy hands, insomnia, and heart palpitations; four items concerning psychosocial factors, including being afraid, uncomfortable, afraid of dying, and anxious about COVID-19 news). Moreover, this pattern was found to be the same among men and women, across age groups and countries. The network analysis used in the present study verified the two-factor structure for the FCV-19S. Future studies may consider using the two-factor structure of FCV-19S to assess the fear of COVID-19 during the COVID-19 era.

Personalized Medicine Using Predictive Analytics: A Machine Learning-Based Prognostic Model for Patients Undergoing Hip Arthroscopy

BACKGROUND

Personalized medicine models to predict outcomes of orthopaedic surgery are scarce. Many have required data that are only available postoperatively, mitigating their usefulness in preoperative decision making.

PURPOSE

To establish a method for predictive modeling to enable individualized prognostication and shared decision making based on preoperative patient factors using data from a prospective hip preservation registry.

STUDY DESIGN

Cohort study (Prognosis); Level of evidence, 2.

METHODS

Preoperative data of 2415 patients undergoing hip arthroscopy for femoroacetabular impingement syndrome between February 2008 and November 2017 were retrospectively analyzed. Two machine-learning analyses were evaluated: Tree-structured survival analysis (TSSA) and Cox proportional hazards modeling for predicting time to event and for computing hazard ratios for survivorship after hip arthroscopy. The Fine-Gray model was similarly used for repeat hip arthroscopy. Variables were selected for inclusion using the minimum Akaike Information Criterion (AIC). The stepwise selection was used for the Cox and Fine-Gray models. A web-based calculator was created based on the final models.

RESULTS

Prognostic models were successfully created using Cox proportional hazards modeling and Fine-Gray models for survivorship and repeat hip arthroscopy, respectively. The Harrell C-statistics of the Cox model calculators for survivorship after hip arthroscopy and the Fine-Gray model for repeat hip arthroscopy were 0.848 and 0.662, respectively. Using the AIC, 13 preoperative variables were identified as predictors of survivorship, and 6 variables were identified as predictors for repeat hip arthroscopy. In contrast, the TSSA model performed poorly, resulting in a Harrell C-statistic <0.6, rendering it inaccurate and not interpretable. A web-based calculator was created based on these models.

CONCLUSION

This study successfully created an institution-specific machine learning-based prognostic model for predictive analytics in patients undergoing hip arthroscopy. Prognostic models to predict survivorship and the need for repeat surgeries were both adapted into web-based tools to assist the physician with shared decision making. This prognostic model may be useful at other institutions after performing external validation. Additionally, this study may serve as proof of concept for a methodology for the development of patient-specific prognostic models. This methodology may be used to create other predictive analytics models in different realms of orthopaedic surgery, contributing to the evolution from evidence-based medicine to personalized medicine.

An 18-Month Epidemiologic Survey of 3364 Deceased COVID-19 Cases; a Retrospective Cross-sectional Study

Introduction

The COVID-19 pandemic has been considered an international problem. This study aimed to survey the demographic and clinical characteristics of the deceased COVID-19 patients.

Methods

The present cross-sectional study was performed on all deceased COVID-19 patients who died in Imam Reza Hospital, Mashhad, Iran, from March 20, 2020, to September 23, 2021. Their data, including age, gender, complaints, and clinical symptoms at the time of admission, as well as information at the time of death (hour, shift, holiday/non-holiday) were analyzed and reported.

Results

3364 deaths due to COVID-19 have been registered during the study period (60.46% male). The patients' mean age was 66.99±16.97 (range: 1-101) years (92.7% of them were Iranian). The mortality at night shifts was less than day shifts (1643 vs. 1721). The average amount of deaths/day on holidays and workdays was (5.63 vs. 6.24). The number of deaths varied during the various hours of the day and night. Diabetes and cardiovascular diseases were the most common confounding factors, which were observed in 22.44% and 15.36% of the cases, respectively.

Conclusion

Based on the findings of this series, COVID-19 mortality was frequently observed in male patients, those with the mean age of 66.99 years, morning shifts, and workdays.

Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies. Two authors independently searched the literature from PubMed, Embase, Cochrane library, and CINAHL on 18 May 2021; removed duplicates; screened the titles, abstracts, and full texts by using criteria; and extracted data from the eligible articles. The variations among the studies were examined by using Cochrane, Q.; I2, and meta-regression. Out of 11,975 articles that were obtained from the databases and screened, 559 studies were abstracted, and then, where appropriate, were analyzed by meta-analysis (n = 542). COVID-19-related severe illness, admission to the ICU, and death were significantly correlated with comorbidities, male sex, and an age older than 60 or 65 years, although high heterogeneity was present in the pooled estimates. The study design, the study country, the sample size, and the year of publication contributed to this. There was publication bias among the studies that compared the odds of COVID-19-related deaths, severe illness, and admission to the ICU on the basis of the comorbidity status. While an older age and chronic diseases were shown to increase the risk of developing severe illness, admission to the ICU, and death among the COVID-19 patients in our analysis, a marked heterogeneity was present when linking the specific risks with the outcomes.

Development and Validation of a Two-Step Predictive Risk Stratification Model for Coronavirus Disease 2019 In-hospital Mortality: A Multicenter Retrospective Cohort Study

Objectives

An accurate prognostic score to predict mortality for adults with COVID-19 infection is needed to understand who would benefit most from hospitalizations and more intensive support and care. We aimed to develop and validate a two-step score system for patient triage, and to identify patients at a relatively low level of mortality risk using easy-to-collect individual information.

Design

Multicenter retrospective observational cohort study.

Setting

Four health centers from Virginia Commonwealth University, Georgetown University, the University of Florida, and the University of California, Los Angeles.

Patients

Coronavirus Disease 2019-confirmed and hospitalized adult patients.

Measurements and Main Results

We included 1,673 participants from Virginia Commonwealth University (VCU) as the derivation cohort. Risk factors for in-hospital death were identified using a multivariable logistic model with variable selection procedures after repeated missing data imputation. A two-step risk score was developed to identify patients at lower, moderate, and higher mortality risk. The first step selected increasing age, more than one pre-existing comorbidities, heart rate >100 beats/min, respiratory rate ≥30 breaths/min, and SpO₂ <93% into the predictive model. Besides age and SpO₂, the second step used blood urea nitrogen, absolute neutrophil count, C-reactive protein, platelet count, and neutrophil-to-lymphocyte ratio as predictors. C-statistics reflected very good discrimination with internal validation at VCU (0.83, 95% CI 0.79-0.88) and external validation at the other three health systems (range, 0.79-0.85). A one-step model was also derived for comparison. Overall, the two-step risk score had better performance than the one-step score.

Conclusions

The two-step scoring system used widely available, point-of-care data for triage of COVID-19 patients and is a potentially time- and cost-saving tool in practice.

Decline in Mental Health in the Beginning of the COVID-19 Outbreak Among European Older Adults-Associations With Social Factors, Infection Rates, and Government Response

Objective

Governments across the world have deployed a wide range of non-pharmaceutical interventions (NPI) to mitigate the spread of COVID-19. Certain NPIs, like limiting social contacts or lockdowns, had negative consequences for mental health in the population. Especially elder people are prone to mental illnesses during the current pandemic. This article investigates how social factors, infections rates, and stringency of NPIs are associated with a decline in mental health in different European countries.

Methods

Data stem from the eighth wave of the SHARE survey. Additional data sources were used to build macro indicators for infection rates and NPIs. Two subsamples of persons with mental health problems were selected (people who reported being depressed, n = 9.240 or nervous/anxious, n = 10.551). Decline in mental health was assessed by asking whether depressive symptoms or nervousness/anxiety have become worse since the beginning of the COVID-19 outbreak. For each outcome, logistic regression models with survey-design were used to estimate odds ratios (OR), using social factors (age, gender, education, living alone, and personal contacts) and macro indicators (stringency of NPIs and infection rates) as predictors.

Results

Higher age was associated with a lower likelihood of becoming more depressed (OR 0.87) or nervous/anxious (OR 0.88), while female gender increased the odds of a decline in mental health (OR 1.53 for being more depressed; OR 1.57 for being more nervous/anxious). Higher education was only associated with becoming more nervous/anxious (OR 1.59), while living alone or rare personal contacts were not statistically significant. People from countries with higher infection rates were more likely to become more depressed (OR 3.31) or nervous/anxious (OR 4.12), while stringency of NPIs showed inconsistent associations.

Conclusion

A majority of European older adults showed a decline in mental health since the beginning of the COVID-19 outbreak. This is especially true in countries with high prevalence rates of COVID-19. Among older European adults, age seems to be a protective factor for a decline in mental health while female gender apparently is a risk factor. Moreover, although NPIs are an essential preventative mechanism to reduce the pandemic spread, they might influence the vulnerability for elderly people suffering from mental health problems.

COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods

As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large.

Preclinical and randomized phase I studies of plitidepsin in adults hospitalized with COVID-19

Plitidepsin, a marine-derived cyclic-peptide, inhibits SARS-CoV-2 replication at nanomolar concentrations by targeting the host protein eukaryotic translation elongation factor 1A. Here, we show that plitidepsin distributes preferentially to lung over plasma, with similar potency against across several SARS-CoV-2 variants in preclinical studies. Simultaneously, in this randomized, parallel, open-label, proof-of-concept study (NCT04382066) conducted in 10 Spanish hospitals between May and November 2020, 46 adult hospitalized patients with confirmed SARS-CoV-2 infection received either 1.5 mg (n = 15), 2.0 mg (n = 16), or 2.5 mg (n = 15) plitidepsin once daily for 3 d. The primary objective was safety; viral load kinetics, mortality, need for increased respiratory support, and dose selection were secondary end points. One patient withdrew consent before starting procedures; 45 initiated treatment; one withdrew because of hypersensitivity. Two Grade 3 treatment-related adverse events were observed (hypersensitivity and diarrhea). Treatment-related adverse events affecting more than 5% of patients were nausea (42.2%), vomiting (15.6%), and diarrhea (6.7%). Mean viral load reductions from baseline were 1.35, 2.35, 3.25, and 3.85 log10 at days 4, 7, 15, and 31. Nonmechanical invasive ventilation was required in 8 of 44 evaluable patients (16.0%); six patients required intensive care support (13.6%), and three patients (6.7%) died (COVID-19-related). Plitidepsin has a favorable safety profile in patients with COVID-19.

Social network-based ethical analysis of COVID-19 vaccine supply policy in three Central Asian countries

Background

In the pandemic time, many low- and middle-income countries are experiencing restricted access to COVID-19 vaccines. Access to imported vaccines or ways to produce them locally became the principal source of hope for these countries. But developing a strategy for success in obtaining and allocating vaccines was not easy task. The governments in those countries have faced the difficult decision whether to accept or reject offers of vaccine diplomacy, weighing the price and availability of COVID-19 vaccines against the concerns over their efficacy and safety. We aimed to analyze public opinion regarding the governmental strategies to obtain COVID-19 vaccines in three Central Asian countries, focusing particularly on possible ethical issues.

Methods

We searched for opinions expressed either in Russian or in the respective national languages. We provided data on the debate within three countries, drawn from social media postings and other sources. The opinion data was not restricted by source and time. This allowed collecting a wide range of possible opinions that could be expressed regarding COVID-19 vaccine supply and human participation in the vaccine trial. We recognized ethical issues and possible questions concerning different ethical frameworks. We also considered scientific data and other information, in the process of reasoning.

Results

As a result, public views on their respective government policies on COVID-19 vaccine supply ranged from strongly negative to slightly positive. We extracted the most important issues from public debates, for our analysis. The first issue involved trade-offs between quantity, speed, price, freedom, efficacy, and safety in the vaccines. The second set of issues arose in connection with the request to site a randomized trial in one of the countries (Uzbekistan). After considering additional evidence, we weighed individual and public risks against the benefits to make specific judgements concerning every issue.

Conclusions

We believe that our analysis would be a helpful example of solving ethical issues that can arise concerning COVID-19 vaccine supply around the world. The public view can be highly critical, helping to spot such issues. An ignoring this view can lead to major problems, which in turn, can become a serious obstacle for the vaccine coverage and epidemics’ control in the countries and regions.

A simple risk score for mortality including the PCR Ct value upon admission in patients hospitalized due to COVID-19

Purpose

To develop a simple score for the outcomes from COVID-19 that integrates information obtained at the time of admission including the Ct value (cycle threshold) for SARS-CoV-2.

Methods

Patients with COVID-19 hospitalized from February 1st to May 31st 2021 in RoMed hospitals, Germany, were included. Clinical and laboratory parameters upon admission were recorded and patients followed until discharge or death. Logistic regression analysis was used to determine predictors of outcomes. Regression coefficients were used to develop a risk score for death.

Results

Of 289 patients (46% female, median age 66 years), 29% underwent high-flow nasal oxygen (HFNO) therapy, 28% were admitted to the Intensive Care Unit (ICU, 51% put on invasive ventilation, IV), and 15% died. Age > 70 years, oxygen saturation ≤ 90%, oxygen supply upon admission, eGFR ≤ 60 ml/min and Ct value ≤ 26 were significant (p < 0.05 each) predictors for death, to which 2, 2, 1, 1 and 2 score points, respectively, could be attributed. Sum scores of ≥ 4 or ≥ 5 points were associated with a sensitivity of 95.0% or 82.5%, and a specificity of 72.5% or 81.7% regarding death. The high predictive value of the score was confirmed using data obtained between December 15th 2020 and January 31st 2021 (n = 215).

Conclusion

In COVID-19 patients, a simple scoring system based on data available shortly after hospital admission including the Ct value had a high predictive value for death. The score may also be useful to estimate the likelihood for required interventions at an early stage.

Supplementary Information

The online version contains supplementary material available at 10.1007/s15010-022-01783-1.

Impact of Age and Sex on COVID-19 Severity Assessed From Radiologic and Clinical Findings

Background

Data on the epidemiological characteristics and clinical features of COVID-19 in patients of different ages and sex are limited. Existing studies have mainly focused on the pediatric and elderly population.

Objective

Assess whether age and sex interact with other risk factors to influence the severity of SARS-CoV-2 infection.

Material and Methods

The study sample included all consecutive patients who satisfied the inclusion criteria and who were treated from 24 February to 1 July 2020 in Dubai Mediclinic Parkview (560 cases) and Al Ain Hospital (605 cases), United Arab Emirates. We compared disease severity estimated from the radiological findings among patients of different age groups and sex. To analyze factors associated with an increased risk of severe disease, we conducted uni- and multivariate regression analyses. Specifically, age, sex, laboratory findings, and personal risk factors were used to predict moderate and severe COVID-19 with conventional machine learning methods.

Results

Need for O2 supplementation was positively correlated with age. Intensive care was required more often for men of all ages (p < 0.01). Males were more likely to have at least moderate disease severity (p = 0.0083). These findings were aligned with the results of biochemical findings and suggest a direct correlation between older age and male sex with a severe course of the disease. In young males (18-39 years), the percentage of the lung parenchyma covered with consolidation and the density characteristics of lesions were higher than those of other age groups; however, there was no marked sex difference in middle-aged (40-64 years) and older adults (≥65 years). From the univariate analysis, the risk of the non-mild COVID-19 was significantly higher (p < 0.05) in midlife adults and older adults compared to young adults. The multivariate analysis provided similar findings.

Conclusion

Age and sex were important predictors of disease severity in the set of data typically collected on admission. Sexual dissimilarities reduced with age. Age disparities were more pronounced if studied with the clinical markers of disease severity than with the radiological markers. The impact of sex on the clinical markers was more evident than that of age in our study.

Mortality Predictive Value of the C2HEST Score in Elderly Subjects with COVID-19-A Subanalysis of the COLOS Study

Senility has been identified among the strongest risk predictors for unfavorable COVID-19-outcome. However, even in the elderly population, the clinical course of infection in individual patients remains unpredictable. Hence, there is an urgent need for developing a simple tool predicting adverse COVID-19-outcomes. We assumed that the C2HEST-score could predict unfavorable clinical outcomes in the elderly subjects with COVID-19-subjects. Methods: We retrospectively analyzed 1047 medical records of patients at age > 65 years, hospitalized at the medical university center due to COVID-19. Subsequently, patients were divided into three categories depending on their C2HEST-score result. Results: We noticed significant differences in the in-hospital and 3-month and 6-month mortality-which was the highest in high-risk-C2HEST-stratum reaching 35.7%, 54.4%, and 65.9%, respectively. The medium-risk-stratum mortalities reached 24.1% 43.4%, and 57.6% and for low-risk-stratum 14.4%, 25.8%, and 39.2% respectively. In the C2HEST-score model, a change from the low to the medium category increased the probability of death intensity approximately two-times. Subsequently, transfer from the low-risk to the high-risk-stratum raised all-cause-death-intensity 2.7-times. Analysis of the secondary outcomes revealed that the C2HEST-score has predictive value for acute kidney injury, acute heart failure, and cardiogenic shock. Conclusions: C2HEST-score analysis on admission to the hospital may predict the mortality, acute kidney injury, and acute heart failure in elderly subjects with COVID-19.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Relationship between hyperglycemia, waist circumference, and the course of COVID-19: Mortality risk assessment

An observational study was conducted in Ukraine to determine the independent mortality risks among adult inpatients with COVID-19. The results of treatment of COVID-19 inpatients (n = 367) are presented, and waist circumference (WC) was measured. Logistic regression analysis was applied to evaluate the effects of factors on the risk of mortality. Odds ratios and 95% CIs for the association were calculated. One hundred and three of 367 subjects had fasting plasma glucose level that met the diabetes mellitus criteria (≥7.0 mmol/L), in 53 patients, diabetes mellitus was previously known. Two hundred and eleven patients did not have diabetes or hyperglycemia. Diabetes mellitus/hyperglycemia odds ratio 2.5 (CI 1.0-6.1), p = 0.045 loses statistical significance after standardization by age, waist circumference or fasting plasma glucose. No effect on gender, body mass index-determined obesity, or hypertension was found. The fasting plasma glucose (>8.5 mmol/L), age (≥61 years), and waist circumference (>105 cm) categories were associated with ORs 6.34 (CI 2.60-15.4); 4.12 (CI 1.37-12.4); 8.93 (CI 3.26-24.5), respectively. The optimal model of mortality risk with AUC 0.86 (CI 0.81-0.91) included the diabetes/heperglycemia and age categories as well as waist circumference as a continued variable. Waist circumference is an independent risk factor for mortality of inpatients with COVID-19.

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India. The proposed classification model is applied to the recent COVID-19 dataset for India, and a performance evaluation of various state-of-the-art classifiers to the proposed model is performed. The classifiers forecasted the patients’ infection status in different regions to better plan resources and response care systems. The appropriate classification of the output class based on the extracted input features is essential to achieve accurate results of classifiers. The experimental outcome exhibits that the proposed Hybrid Model reached a maximum F1-score of 94% compared to Ensembles and other classifiers like Support Vector Machine, Decision Trees, and Gaussian Naïve Bayes on a dataset of 5004 instances through 10-fold cross-validation for predicting the right class. The feasibility of automated prediction for COVID-19 infection cure and death rates in the Indian states was demonstrated.

Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes

The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset.

Procalcitonin and High APACHE (Acute Physiological and Chronic Health Evaluation) Level Are Associated with the Course of Acute Kidney Injury in Patients with SARS-CoV-2

BACKGROUND

Acute kidney injury (AKI) is associated with poor outcomes in patients infected with SARS-CoV-2. Sepsis, direct injury to kidney cells by the virus, and severe systemic inflammation are mechanisms implicated in its development. We investigated the association between inflammatory markers (C-reactive protein, procalcitonin, D-dimer, lactate dehydrogenase, and ferritin) in patients infected with SARS-CoV-2 and the development of AKI.

METHODS

A prospective cohort study performed at the Civil Hospital (Dr. Juan I. Menchaca) Guadalajara, Mexico, included patients aged >18 years with a diagnosis of SARS-CoV-2 pneumonia confirmed by RT-PCR and who did or did not present with AKI (KDIGO) while hospitalized. Biomarkers of inflammation were recorded, and kidney function was estimated using the CKD-EPI formula.

RESULTS

291 patients were included (68% males; average age, 57 years). The incidence of AKI was 40.5% (118 patients); 21% developed stage 1 AKI, 6% developed stage 2 AKI, and 14% developed stage 3 AKI. The development of AKI was associated with higher phosphate (p = 0.002) (RR 1.39, CI 95% 1.13-1.72), high procalcitonin levels at hospital admission (p = 0.005) (RR 2.09, CI 95% 1.26-3.50), and high APACHE scores (p = 0.011) (RR 2.0, CI 95% 1.17-3.40). The survival analysis free of AKI according to procalcitonin levels and APACHE scores demonstrated a lower survival in patients with procalcitonin >0.5 ng/ml (p = 0.001) and APACHE >15 points (p = 0.004).

CONCLUSIONS

Phosphate, high procalcitonin levels, and APACHE levels >15 were predictors of AKI development in patients hospitalized with COVID-19.

A Time-Series Feature-based Recursive Classification Model to Optimize Treatment Strategies for Improving Outcomes and Resource Allocations of COVID-19 Patients

This paper presents a novel Lasso Logistic Regression model based on feature-based time series data to determine disease severity and when to administer drugs or escalate intervention procedures in patients with coronavirus disease 2019 (COVID-19). Advanced features were extracted from highly enriched and time series vital sign data of hospitalized COVID-19 patients, including oxygen saturation readings, and with a combination of patient demographic and comorbidity information, as inputs into the dynamic feature-based classification model. Such dynamic combinations brought deep insights to guide clinical decision-making of complex COVID-19 cases, including prognosis prediction, timing of drug administration, admission to intensive care units, and application of intervention procedures like ventilation and intubation. The COVID-19 patient classification model was developed utilizing 900 hospitalized COVID-19 patients in a leading multi-hospital system in Texas, United States. By providing mortality prediction based on time-series physiologic data, demographics, and clinical records of individual COVID-19 patients, the dynamic feature-based classification model can be used to improve efficacy of the COVID-19 patient treatment, prioritize medical resources, and reduce casualties. The uniqueness of our model is that it is based on just the first 24 hours of vital sign data such that clinical interventions can be decided early and applied effectively. Such a strategy could be extended to prioritize resource allocations and drug treatment for future pandemic events.

Does COVID-19 Clinical Status Associate with Outcome Severity? An Unsupervised Machine Learning Approach for Knowledge Extraction

Since the beginning of the COVID-19 pandemic, 195 million people have been infected and 4.2 million have died from the disease or its side effects. Physicians, healthcare scientists and medical staff continuously try to deal with overloaded hospital admissions, while in parallel, they try to identify meaningful correlations between the severity of infected patients with their symptoms, comorbidities and biomarkers. Artificial intelligence (AI) and machine learning (ML) have been used recently in many areas related to COVID-19 healthcare. The main goal is to manage effectively the wide variety of issues related to COVID-19 and its consequences. The existing applications of ML to COVID-19 healthcare are based on supervised classifications which require a labeled training dataset, serving as reference point for learning, as well as predefined classes. However, the existing knowledge about COVID-19 and its consequences is still not solid and the points of common agreement among different scientific communities are still unclear. Therefore, this study aimed to follow an unsupervised clustering approach, where prior knowledge is not required (tabula rasa). More specifically, 268 hospitalized patients at the First Propaedeutic Department of Internal Medicine of AHEPA University Hospital of Thessaloniki were assessed in terms of 40 clinical variables (numerical and categorical), leading to a high-dimensionality dataset. Dimensionality reduction was performed by applying a principal component analysis (PCA) on the numerical part of the dataset and a multiple correspondence analysis (MCA) on the categorical part of the dataset. Then, the Bayesian information criterion (BIC) was applied to Gaussian mixture models (GMM) in order to identify the optimal number of clusters under which the best grouping of patients occurs. The proposed methodology identified four clusters of patients with similar clinical characteristics. The analysis revealed a cluster of asymptomatic patients that resulted in death at a rate of 23.8%. This striking result forces us to reconsider the relationship between the severity of COVID-19 clinical symptoms and the patient's mortality.

Clinical profile and short-term outcomes of RT-PCR- positive patients with COVID-19: a cross-sectional study in a tertiary care hospital in Dhaka, Bangladesh

OBJECTIVE

The COVID-19 pandemic is still raging worldwide. While there is significant published evidence on the attributes of patients with COVID-19 from lower-income and middle-income countries, there is a dearth of original research published from Bangladesh, a low-income country in Southeast Asia. Based on a case series from a tertiary healthcare centre, this observational study has explored the epidemiology, clinical profile of patients with COVID-19 and short-term outcomes in Dhaka, Bangladesh.

DESIGN AND SETTING

A total of 422 COVID-19-confirmed patients (via reverse transcription-PCR test) were enrolled in this study (male=271, female=150, 1 unreported). We have compiled medical records of the patients and descriptively reported their demographic, socioeconomic and clinical features, treatment history, health outcomes, and postdischarge complications.

RESULT

Patients were predominantly male (64%), between 35 and 49 years (28%), with at least one comorbidity (52%), and had COVID-19 symptoms for 1 week before hospitalisation (66%). A significantly higher proportion (p<0.05) of male patients had diabetes, hypertension and ischaemic heart disease, while female patients had asthma (p<0.05). The most common symptoms were fever (80%), cough (60%), dyspnoea (41%) and sore throat (21%). The majority of the patients received antibiotics (77%) and anticoagulant therapy (56%) and stayed in the hospital for an average of 12 days. Over 90% of patients were successfully weaned, while 3% died from COVID-19, and 41% reported complications after discharge.

CONCLUSION

The diversity of clinical and epidemiological characteristics and health outcomes of patients with COVID-19 across age groups and gender is noteworthy. Our result will inform the clinicians and epidemiologists of Bangladesh of their COVID-19 mitigation effort.

The association of blood urea nitrogen levels upon emergency admission with mortality in acute exacerbation of chronic obstructive pulmonary disease

Background and purpose

High blood urea nitrogen (BUN) is associated with an elevated risk of mortality in various diseases, such as heart failure and pneumonia. Heart failure and pneumonia are common comorbidities of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). However, data on the relationship of BUN levels with mortality in patients with AECOPD are sparse. The purpose of this study was to evaluate the correlation between BUN level and in-hospital mortality in a cohort of patients with AECOPD who presented at the emergency department (ED).

Methods

A total of 842 patients with AECOPD were enrolled in the retrospective observational study from January 2018 to September 2020. The outcome was all-cause in-hospital mortality. Receiver operating characteristic (ROC) curve analysis and logistic regression models were performed to evaluate the association of BUN levels with in-hospital mortality in patients with AECOPD. Propensity score matching was used to assemble a cohort of patients with similar baseline characteristics, and logistic regression models were also performed in the propensity score matching cohort.

Results

During hospitalization, 26 patients (3.09%) died from all causes, 142 patients (16.86%) needed invasive ventilation, and 190 patients (22.57%) were admitted to the ICU. The mean level of blood urea nitrogen was 7.5 ± 4.5 mmol/L. Patients in the hospital non-survivor group had higher BUN levels (13.48 ± 9.62 mmol/L vs. 7.35 ± 4.14 mmol/L, p < 0.001) than those in the survivor group. The area under the curve (AUC) was 0.76 (95% CI 0.73–0.79, p < 0.001), and the optimal BUN level cutoff was 7.63 mmol/L for hospital mortality. As a continuous variable, BUN level was associated with hospital mortality after adjusting respiratory rate, level of consciousness, pH, PCO_2, lactic acid, albumin, glucose, CRP, hemoglobin, platelet distribution width, D-dimer, and pro-B-type natriuretic peptide (OR 1.10, 95% CI 1.03–1.17, p=0.005). The OR of hospital mortality was significantly higher in the BUN level ≥7.63 mmol/L group than in the BUN level <7.63 mmol/L group in adjusted model (OR 3.29, 95% CI 1.05–10.29, p=0.041). Similar results were found after multiple imputation and in the propensity score matching cohort.

Conclusions

Increased BUN level at ED admission is associated with hospital mortality in patients with AECOPD who present at the ED. The level of 7.63 mmol/L can be used as a cutoff value for critical stratification.

Mental Well-Being during COVID-19: A Cross-Sectional Study of Fly-In Fly-Out Workers in the Mining Industry in Australia

Coronavirus disease 2019 (COVID-19) has devastated the world, and its mental health impact has been recognized in the general population. However, little is known about the mental health impact of COVID-19 on fly-in fly-out (FIFO) workers, who are flown to temporarily stay and work in remote areas, during this pandemic. This study examined the mental well-being of FIFO workers in the mining industry during COVID-19 restrictions in Western Australia. An online survey was conducted between May to November 2020 among (N = 842) FIFO workers who underwent COVID-19 screening at a large mining company in Western Australia. The mental well-being score among workers was higher than population norms. One-way ANOVA with Bonferroni post-hoc tests showed significant differences in mental well-being by age, being placed under travel quarantine, undertaking self-isolation, impact of social distance guidelines, and experience of COVID-19 related symptoms. Multiple linear regression analysis showed workers who were younger, placed under travel quarantine and experienced two or more COVID-19 related symptoms were more likely to have worse mental well-being. Acknowledging the negative emotions and distress experiences among the vulnerable groups could help in providing suitable support to help lessen these negative experiences in FIFO workers.

Mortality Prediction of COVID-19 Patients at Intensive Care Unit Admission

Background

Coronavirus-2019 (COVID-19) patients admitted to the intensive care unit (ICU) have mortality rates between 30%-50%. Identifying patient factors associated with mortality can help identify critical patients early and treat them accordingly.

Patients and methods

In this retrospective study, the records of patients admitted to the COVID-19 ICU in a single tertiary care hospital from April 2020 to September 2020 were analysed. The clinical and laboratory parameters between patients who were discharged from the hospital (survival cohort) and those who died in the hospital (mortality cohort) were compared. A multivariate logistic regression model was constructed to identify parameters associated with mortality. 

Results

A total of 147 patients were included in the study. The age of the patients was 55 (45, 64), median (IQR), years. At admission, 23 (16%) patients were on mechanical ventilation and 73 (50%) were on non-invasive ventilation. Sixty patients (40%, 95% CI: 32.8 to 49.2%) had died. Patients who died had a higher Charlson comorbidity index (CCI): 3 (2, 4) vs. 2 (1, 3), p = 0.0019, and a higher admission sequential organ failure assessment (SOFA) score: 5 (4, 7) vs. 4 (3, 4), p < 0.001. Serum urea, serum creatinine, neutrophils on differential leukocyte count, neutrophil to lymphocyte ratio (N/L ratio), D-dimer, serum lactate dehydrogenase (LDH), and C-reactive protein were higher in the mortality cohort. The ratio of partial pressure of arterial oxygen to fraction of inspired oxygen, platelet count, lymphocytes on differential leukocyte count, and absolute lymphocyte count was lower in the mortality cohort. The parameters and cut-off values used for the multivariate logistic regression model included CCI > 2, SOFA score > 4, D-dimer > 1346 ng/mL, LDH > 514 U/L and N/L ratio > 27. The final model had an area under the curve of 0.876 (95% CI: 0.812 to 0.925), p < 0.001 with an accuracy of 78%. All five parameters were found to be independently associated with mortality. 

Conclusions

CCI, SOFA score, D-dimer, LDH, and N/L ratio are independently associated with mortality. A model incorporating the combination of these clinical and laboratory parameters at admission can predict COVID-19 ICU mortality with good accuracy.

Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients

The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

Evaluating risk stratification scoring systems to predict mortality in patients with COVID-19

Background

The COVID-19 pandemic has necessitated efficient and accurate triaging of patients for more effective allocation of resources and treatment.

Objectives

The objectives are to investigate parameters and risk stratification tools that can be applied to predict mortality within 90 days of hospital admission in patients with COVID-19.

Methods

A literature search of original studies assessing systems and parameters predicting mortality of patients with COVID-19 was conducted using MEDLINE and EMBASE.

Results

589 titles were screened, and 76 studies were found investigating the prognostic ability of 16 existing scoring systems (area under the receiving operator curve (AUROC) range: 0.550–0.966), 38 newly developed COVID-19-specific prognostic systems (AUROC range: 0.6400–0.9940), 15 artificial intelligence (AI) models (AUROC range: 0.840–0.955) and 16 studies on novel blood parameters and imaging.

Discussion

Current scoring systems generally underestimate mortality, with the highest AUROC values found for APACHE II and the lowest for SMART-COP. Systems featuring heavier weighting on respiratory parameters were more predictive than those assessing other systems. Cardiac biomarkers and CT chest scans were the most commonly studied novel parameters and were independently associated with mortality, suggesting potential for implementation into model development. All types of AI modelling systems showed high abilities to predict mortality, although none had notably higher AUROC values than COVID-19-specific prediction models. All models were found to have bias, including lack of prospective studies, small sample sizes, single-centre data collection and lack of external validation.

Conclusion

The single parameters established within this review would be useful to look at in future prognostic models in terms of the predictive capacity their combined effect may harness.

COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal

More than a year has passed since the report of the first case of coronavirus disease 2019 (COVID), and increasing deaths continue to occur. Minimizing the time required for resource allocation and clinical decision making, such as triage, choice of ventilation modes and admission to the intensive care unit is important. Machine learning techniques are acquiring an increasingly sought-after role in predicting the outcome of COVID patients. Particularly, the use of baseline machine learning techniques is rapidly developing in COVID mortality prediction, since a mortality prediction model could rapidly and effectively help clinical decision-making for COVID patients at imminent risk of death. Recent studies reviewed predictive models for SARS-CoV-2 diagnosis, severity, length of hospital stay, intensive care unit admission or mechanical ventilation modes outcomes; however, systematic reviews focused on prediction of COVID mortality outcome with machine learning methods are lacking in the literature. The present review looked into the studies that implemented machine learning, including deep learning, methods in COVID mortality prediction thus trying to present the existing published literature and to provide possible explanations of the best results that the studies obtained. The study also discussed challenging aspects of current studies, providing suggestions for future developments.

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study

The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.

Identifying mortality factors from Machine Learning using Shapley values - a case of COVID19

In this paper we apply a series of Machine Learning models to a recently published unique dataset on the mortality of COVID19 patients. We use a dataset consisting of blood samples of 375 patients admitted to a hospital in the region of Wuhan, China. There are 201 patients who survived hospitalisation and 174 patients who died whilst in hospital. The focus of the paper is not only on seeing which Machine Learning model is able to obtain the absolute highest accuracy but more on the interpretation of what the Machine Learning models provides. We find that age, days in hospital, Lymphocyte and Neutrophils are important and robust predictors when predicting a patients mortality. Furthermore, the algorithms we use allows us to observe the marginal impact of each variable on a case-by-case patient level, which might help practicioneers to easily detect anomalous patterns. This paper analyses the global and local interpretation of the Machine Learning models on patients with COVID19.

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.

Social network-based ethical analysis of COVID-19 vaccine supply policy in three Central Asian countries

Background

In the pandemic time, many low- and middle-income countries are experiencing restricted access to COVID-19 vaccines. An access to imported vaccines or ways to produce them locally becomes the principal source of hope. But developing a strategy for success in obtaining and allocating vaccines is not easy task. The governments in those countries have faced difficult decision whether to accept or reject offers of vaccine diplomacy, weighing price and availability of COVID-19 vaccines against concerns over their efficacy and safety. Our aim was to analyze public opinion regarding the governmental strategies to obtain COVID-19 vaccines in three Central Asian countries, focusing particularly on possible ethical issues.

Methods

We searched opinions expressed either in Russian or in the respective national languages. We provided data of the debate within three countries, drawn from social media postings and other sources. The opinion data was not restricted by source and time. This allowed to collect a wide range of possible opinions that could be expressed regarding COVID-19 vaccine supply and public’s participation in vaccine trials. We recognized ethical issues and possible questions concerning different ethical frameworks. We also considered additional information or scientific data, in the process of reasoning.

Results

As a result, public views on their respective government policies on COVID-19 vaccine supply ranged from strongly negative to slightly positive. We extracted most important issues from public debates, for our analysis. The first issue involved trade-offs between quantity, speed, price, freedom, efficacy and safety in the vaccines. The second set of issues arouse in connection with the request to site a randomized trial in one of countries (Uzbekistan). After considering additional evidences, we weighed individual with public risks and benefits to make specffic judgements concerning every issue.

Conclusions

We believe that our analysis would be a helpful example of solving ethical issues that can rise concerning COVID-19 vaccine supply round the world. The public view can be highly critical, helping to spot such issues. An ignoring this view can lead to major problems, which in turn, can become a serious obstacle for the vaccine coverage and epidemics’ control in the countries and regions.

Absolute mortality risk assessment of COVID-19 patients: the Khorshid COVID Cohort (KCC) study

BACKGROUND

Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis.

METHODS

We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead.

RESULTS

Cause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC = 0.872 [CI 95%: 0.835-0.910]).

CONCLUSIONS

This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.

Predication of oxygen requirement in COVID-19 patients using dynamic change of inflammatory markers: CRP, hypertension, age, neutrophil and lymphocyte (CHANeL)

The objective of the study was to develop and validate a prediction model that identifies COVID-19 patients at risk of requiring oxygen support based on five parameters: C-reactive protein (CRP), hypertension, age, and neutrophil and lymphocyte counts (CHANeL). This retrospective cohort study included 221 consecutive COVID-19 patients and the patients were randomly assigned randomly to a training set and a test set in a ratio of 1:1. Logistic regression, logistic LASSO regression, Random Forest, Support Vector Machine, and XGBoost analyses were performed based on age, hypertension status, serial CRP, and neutrophil and lymphocyte counts during the first 3 days of hospitalization. The ability of the model to predict oxygen requirement during hospitalization was tested. During hospitalization, 45 (41.8%) patients in the training set (n = 110) and 41 (36.9%) in the test set (n = 111) required supplementary oxygen support. The logistic LASSO regression model exhibited the highest AUC for the test set, with a sensitivity of 0.927 and a specificity of 0.814. An online risk calculator for oxygen requirement using CHANeL predictors was developed. "CHANeL" prediction models based on serial CRP, neutrophil, and lymphocyte counts during the first 3 days of hospitalization, along with age and hypertension status, provide a reliable estimate of the risk of supplement oxygen requirement among patients hospitalized with COVID-19.

Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID)

In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the anonymized clinical and laboratory data at admission and determine the probability of Deaths at 7 and 28 days. Data of 1393 admitted patients (Expired-8.54%) was collected from six Apollo Hospital centers (from April to July 2020) using a standardized template and electronic medical records. 63 Clinical and Laboratory parameters were studied based on the patient's initial clinical state at admission and laboratory parameters within the first 24 h. The Machine Learning (ML) modelling was performed using eXtreme Gradient Boosting (XGB) Algorithm. 'Time to event' using Cox Proportional Hazard Model was used and combined with XGB Algorithm. The prospective validation cohort was selected of 977 patients (Expired-8.3%) from six centers from July to October 2020. The Clinical API for the Algorithm is  http://20.44.39.47/covid19v2/page1.php being used prospectively. Out of the 63 clinical and laboratory parameters, Age [adjusted hazard ratio (HR) 2.31; 95% CI 1.52-3.53], Male Gender (HR 1.72, 95% CI 1.06-2.85), Respiratory Distress (HR 1.79, 95% CI 1.32-2.53), Diabetes Mellitus (HR 1.21, 95% CI 0.83-1.77), Chronic Kidney Disease (HR 3.04, 95% CI 1.72-5.38), Coronary Artery Disease (HR 1.56, 95% CI - 0.91 to 2.69), respiratory rate > 24/min (HR 1.54, 95% CI 1.03-2.3), oxygen saturation below 90% (HR 2.84, 95% CI 1.87-4.3), Lymphocyte% in DLC (HR 1.99, 95% CI 1.23-2.32), INR (HR 1.71, 95% CI 1.31-2.13), LDH (HR 4.02, 95% CI 2.66-6.07) and Ferritin (HR 2.48, 95% CI 1.32-4.74) were found to be significant. The performance parameters of the current model is at AUC ROC Score of 0.8685 and Accuracy Score of 96.89. The validation cohort had the AUC of 0.782 and Accuracy of 0.93. The model for Mortality Risk Prediction provides insight into the COVID Clinical and Laboratory Parameters at admission. It is one of the early studies, reflecting on 'time to event' at the admission, accurately predicting patient outcomes.

Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients

The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.

Targeted Mitochondrial Therapy With Over-Expressed MAVS Protein From Mesenchymal Stem Cells: A New Therapeutic Approach for COVID-19

The SARS-CoV-2, the virus that causes COVID-19, has infected millions of people worldwide. The symptoms of this disease are primarily due to pulmonary involvement, uncontrolled tissue inflammation, and inadequate immune response against the invader virus. Impaired interferon (IFN) production is one of the leading causes of the immune system's inability to control the replication of the SARS-CoV-2. Mitochondria play an essential role in developing and maintaining innate cellular immunity and IFN production. Mitochondrial function is impaired during cellular stress, affecting cell bioenergy and innate immune responses. The mitochondrial antiviral-signaling protein (MAVS), located in the outer membrane of mitochondria, is one of the key elements in engaging the innate immune system and interferon production. Transferring healthy mitochondria to the damaged cells by mesenchymal stem cells (MSCs) is a proposed option for regenerative medicine and a viable treatment approach to many diseases. In addition to mitochondrial transport, these cells can regulate inflammation, repair the damaged tissue, and control the pathogenesis of COVID-19. The immune regulatory nature of MSCs dramatically reduces the probability of an immune rejection. In order to induce an appropriate immune response against the SARS-CoV-2, we hypothesize to donate mitochondria to the host cells of the virus. We consider MSCs as an appropriate biological carrier for mitochondria. Besides, enhancing the expression of MAVS protein in MSCs and promoting the expression of SARS-CoV-2 viral spike protein as a specific ligand for ACE2+ cells will improve IFN production and innate immune responses in a targeted manner.

Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study

Background

Early detection and intervention are the key factors for improving outcomes in patients with COVID-19.

Objective

The objective of this observational longitudinal study was to identify nonoverlapping severity subgroups (ie, clusters) among patients with COVID-19, based exclusively on clinical data and standard laboratory tests obtained during patient assessment in the emergency department.

Methods

We applied unsupervised machine learning to a data set of 853 patients with COVID-19 from the HM group of hospitals (HM Hospitales) in Madrid, Spain. Age and sex were not considered while building the clusters, as these variables could introduce biases in machine learning algorithms and raise ethical implications or enable discrimination in triage protocols.

Results

From 850 clinical and laboratory variables, four tests—the serum levels of aspartate transaminase (AST), lactate dehydrogenase (LDH), C-reactive protein (CRP), and the number of neutrophils—were enough to segregate the entire patient pool into three separate clusters. Further, the percentage of monocytes and lymphocytes and the levels of alanine transaminase (ALT) distinguished cluster 3 patients from the other two clusters. The highest proportion of deceased patients; the highest levels of AST, ALT, LDH, and CRP; the highest number of neutrophils; and the lowest percentages of monocytes and lymphocytes characterized cluster 1. Cluster 2 included a lower proportion of deceased patients and intermediate levels of the previous laboratory tests. The lowest proportion of deceased patients; the lowest levels of AST, ALT, LDH, and CRP; the lowest number of neutrophils; and the highest percentages of monocytes and lymphocytes characterized cluster 3.

Conclusions

A few standard laboratory tests, deemed available in all emergency departments, have shown good discriminative power for the characterization of severity subgroups among patients with COVID-19.

Plitidepsin has a positive therapeutic index in adult patients with COVID-19 requiring hospitalization

Plitidepsin is a marine-derived cyclic-peptide that inhibits SARS-CoV-2 replication at low nanomolar concentrations by the targeting of host protein eEF1A (eukaryotic translation-elongation-factor-1A). We evaluated a model of intervention with plitidepsin in hospitalized COVID-19 adult patients where three doses were assessed (1.5, 2 and 2.5 mg/day for 3 days, as a 90-minute intravenous infusion) in 45 patients (15 per dose-cohort). Treatment was well tolerated, with only two Grade 3 treatment-related adverse events observed (hypersensitivity and diarrhea). The discharge rates by Days 8 and 15 were 56.8% and 81.8%, respectively, with data sustaining dose-effect. A mean 4.2 log10 viral load reduction was attained by Day 15. Improvement in inflammation markers was also noted in a seemingly dose-dependent manner. These results suggest that plitidepsin impacts the outcome of patients with COVID-19.

ONE-SENTENCE SUMMARY

Plitidepsin, an inhibitor of SARS-Cov-2 in vitro , is safe and positively influences the outcome of patients hospitalized with COVID-19.

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

Mortality Rate of Patients With COVID-19 Based on Underlying Health Conditions

OBJECTIVE

The aim of this study was to evaluate the mortality rates of 566,602 patients with coronavirus disease (COVID-19) based on sex, age, and the presence or absence of underlying diseases and determine whether the underlying disease provides prognostic information specifically related to death.

METHODS

The mortality rate was evaluated using conditional probability to identify the significant factors, and adjusted odds ratios (ORs) using a multivariable logistic regression analysis were estimated.

RESULTS

The mortality rate of patients with underlying health conditions was 12%, which was 4 times higher than that of patients without underlying health conditions. Furthermore, the mortality rates of women and men with underlying health conditions were 5.5 and 3.4 times higher than the mortality rates of patients without underlying health conditions, respectively. In a multivariable logistic regression analysis including sex, age, and underlying health conditions, male sex (OR: 1.83), age ≥ 41 y (ORs > 2.70), and underlying health conditions (OR: 2.20) were confirmed as risk factors for death.

CONCLUSIONS

More attention should be paid to older patients with underlying diseases and male patients with underlying diseases as the probability of death in this population was significantly higher.