Controlling the worldwide chaotic spreading of COVID-19 through vaccinations

The striking differences and similarities between the Spanish-flu of 1918 and the Coronavirus disease of 2019 (COVID-19) are analyzed. Progress in medicine and technology and in particular the availability of vaccines has decreased the death probability from about 2% of the affected for the Spanish-flu, to about 10-5 in the UK and 10-3 in Italy, USA, Canada, San Marino and other countries for COVID-19. The logistic map reproduces most features of the disease and may be of guidance for predictions and future steps to be taken in order to contrast the virus. We estimate 6.4 107 deaths worldwide without the vaccines, this value decreases to 2.4 107 with the current vaccination rate. In August 2021, the number of deceased worldwide was 4.4 106. To reduce the fatalities further, it is imperative to increase the vaccination rate worldwide to at least 120 millions/day.

Retrospective analysis of cohort risk-factors and feeding phase timing associated with non-infectious heart disease deaths in US fed cattle

Heart disease, specifically, congestive heart failure has become of increased interest to geneticists and cattle feeders. Data on cohort associations of risk factors related to heart disease and when heart disease deaths occur in US feedlot cattle are limited. The study objectives were to 1) determine potential associations between feedlot cohort demographics and the risk of at least one non-infectious heart disease (NIHD) death occurrence and 2) determine potential association between feedlot cohort demographics and the timing of NIHD deaths during the feeding phase. Data were downloaded from commercial feedyard software and analyzed by constructing a generalized linear mixed model for both analyses. A binomial and Gaussian distribution for risk of NIHD death and timing of NIHD were utilized as link functions for their respective models. Our study population consisted of 28,950 cohorts (representing 4,596,205 cattle) that were placed in 22 US commercial feedlots from January 01, 2016, to January 01, 2019. There were 3,282 cases of NIHD deaths from a population of 75,963 cattle that died during the three-year study period. Average cohort arrival weight’s effect on NIHD probability was influenced by arrival quarter and arrival year of placement (P < 0.01). Cohorts with steers were associated with a greater probability of at least one NIHD death (2.38%) compared to heifers (1.95%; P < 0.01). Increasing cohort size was associated with an increased probability of a cohort having at least one NIHD death (P < 0.01). The probability of at least one NIHD death in a cohort increased from 1.51%, to 2.12%, and 2.87% in d on feed categories 100-175, 176-250, and 251-326 respectively. Cattle > 326 d on feed were no different in the probability of a NIHD death compared to the other feeding categories. Timing of a NIHD death had a mean and median occurrence of 110 d on feed with an interquartile range of 64 to 153 d on feed. The effect of arrival weight on d at death was influenced by year placed with heavier cattle generally decreasing the model adjusted means of d on feed at NIHD death. Arrival quarter was influenced by year placed on model adjusted means on the timing of a NIHD death. Steers with NIHD died later compared to heifers (P < 0.01) diagnosed with NIHD. In conclusion, multiple factors are associated with probability and timing of a NIHD death. Probability of having at least one NIHD death within a cohort was low and half of the deaths occurred before 110 d on feed.

The impact of the incorporation of a feasible postoperative mortality model at the Post-Anaesthestic Care Unit (PACU) on postoperative clinical deterioration: A pragmatic trial with 5,353 patients

Background Practical use of risk predictive tools and the assessment of their impact on outcome reduction is still a challenge. This pragmatic study of quality improvement (QI) describes the preoperative adoption of a customised postoperative death probability model (SAMPE model) and the evaluation of the impact of a Postoperative Anaesthetic Care Unit (PACU) pathway on the clinical deterioration of high-risk surgical patients. Methods A prospective cohort of 2,533 surgical patients compared with 2,820 historical controls after the adoption of a quality improvement (QI) intervention. We carried out quick postoperative high-risk pathways at PACU when the probability of postoperative death exceeded 5%. As outcome measures, we used the number of rapid response team (RRT) calls within 7 and 30 postoperative days, in-hospital mortality, and non-planned Intensive Care Unit (ICU) admission. Results Not only did the QI succeed in the implementation of a customised risk stratification model, but it also diminished the postoperative deterioration evaluated by RRT calls on very high-risk patients within 30 postoperative days (from 23% before to 14% after the intervention, p = 0.05). We achieved no survival benefits or reduction of non-planned ICU. The small group of high-risk patients (13% of the total) accounted for the highest proportion of RRT calls and postoperative death. Conclusion Employing a risk predictive tool to guide immediate postoperative care may influence postoperative deterioration. It encouraged the design of pragmatic trials focused on feasible, low-technology, and long-term interventions that can be adapted to diverse health systems, especially those that demand more accurate decision making and ask for full engagement in the control of postoperative morbi-mortality.

Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique

Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management.

Central Nervous System Involvement in Trichinellosis: A Systematic Review

We reviewed the evidence on features of central nervous system (CNS) involvement in trichinellosis, systematically searching five databases (to January 2021). We categorized clinical features based on their diagnostic value as warning signs for severe CNS infection (with outcome death) or non-specific signs (outcome improvement). They were suggestive of severe infection if they substantially raised death probability. The review included 87 papers published from 1906 through 2019, with data on 168 patients. Mydriasis, paraparesis, dysphagia, psychomotor seizures, or delirium present a 30–45% increased death likelihood. The best poor prognosis predictor is mydriasis (positive likelihood ratio 9.08). Slow/absent light reflex, diminished/absent knee reflexes, globally decreased tendon reflexes present a moderate increase (20–25%) of death risk. Anisocoria, acalculia, or seizures could also indicate an increased death risk. We provided a detailed presentation of clinical and paraclinical signs that alert physicians of a possible neurotrichinellosis, emphasizing signs that might indicate a poor prognosis.

An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning

COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable, and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on a dataset made public by Yan et al. in [1] of 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high-sensitivity C-reactive protein, and age (LNLCA)—acquired at hospital admission—were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate-, and high-risk groups using LNLCA cutoff values of 10.4 and 12.65 with the death probability less than 5%, 5–50%, and above 50%, respectively. The prognostic model, nomogram, and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.

Modeling the Consequence of Vinyl Chloride Accidental Release from Tanks in a Petrochemical Plant

Introduction: Each year, many accidents occur in processing industries such as oil, gas, and petrochemicals. Processing industries mostly work with hazardous chemicals and units in high temperature and high-pressure conditions like reactors and storage tanks. The study aimed to model the consequences of a complete tank rapture (explosion and fire) and specify the intensity caused by the events. Materials and methods: The applied method in this study was based on the Quantitative Risk Assessment method. This method is used for risk assessment in chemical, petroleum, gas, and petrochemical processes and transport industries. Initially, the process associated with the monomer vinyl-chloride storage tank was identified. At the next stage, the scenarios and probable hazards were identified and defined and the PHAST Risk 7.11 was run for modeling the consequences. Results: The most dangerous consequences of vinyl-chloride storage tanks include sudden fire and explosion in a complete tank rapture. In a full tank-explosion, the radiation of the explosion wave was once recorded as 79 meters with the death probability of 99 percent. Conclusion: Each explosion or probable rapture in monomer vinyl-chloride tanks may cause terrible consequences. The vinyl-chloride monomer storage process is a high-risk process that is not tolerable. To reduce the risk, the consequence intensity, the consequence probability, and the exposure amount should be reduced. To this end, it is highly recommended to use smaller tanks, modify operational variables (capacity, pressure, temperature, etc.), and reduce the level of exposure in similar projects.

Determinants on COVID-19 Case Fatality Rates of Cities in China: A Logit-NB Hurdle Model Analysis

Background: The ongoing Coronavirus Disease 2019 (COVID-19), a global pandemic with high infectiousness and high mortality, has seriously threatened human health, life safety and caused enormous economic losses. This study investigates the influencing factors on the case fatality rate (CFR) of COVID-19 at the city level in China. Methods: A logistic-negative binomial (Logit-NB) hurdle model is employed to examine the determinants on the probability of death and the value of CFR with COVID-19, based on confirmed cases and deaths by 13 March 2020 and 25 January 2021 at the city level in China and related environmental, demographic, and socioeconomic data.Results: We found that the probability of death from COVID-19 will increase by 1% with 1 newly increased confirmed case and increase by 4% in response to a rise of 1 unit in the air quality index. CFR will feebly increase with the number of confirmed cases, with the estimator being 2.81E-05. As the number of doctors increases by 10,000, CFR will decrease by 0.18%. Each 1% increase in the humidity leads to a 0.02% decrease in CFR, and each 1-unit increase in the population density causes a 0.09% decline in CFR. The comparison between the two research periods confirms the robustness of the results.Conclusions: The number of confirmed cases and the air quality are closely associated with the death probability, while the number of confirmed cases, the medical resources, the humidity, and the population density significantly affect the CFR. Furthermore, the air quality and population density stand out in the first wave of epidemic outbreak, while they become non-significant in the second wave.