Exposing and Overcoming Limitations of Clinical Laboratory Tests in COVID-19 by Adding Immunological Parameters; A Retrospective Cohort Analysis and Pilot Study.

Background: Two years since the onset of the COVID-19 pandemic no predictive algorithm has been generally adopted for clinical management and in most algorithms the contribution of laboratory variables is limited. Objectives: To measure the predictive performance of currently used clinical laboratory tests alone or combined with clinical variables and explore the predictive power of immunological tests adequate for clinical laboratories. Methods: Data from 2,600 COVID-19 patients of the first wave of the pandemic in the Barcelona area (exploratory cohort of 1,579, validation cohorts of 598 and 423 patients) including clinical parameters and laboratory tests were retrospectively collected. 28-day survival and maximal severity were the main outcomes considered in the multiparametric classical and machine learning statistical analysis. A pilot study was conducted in two subgroups (n=74 and n=41) measuring 17 cytokines and 27 lymphocyte phenotypes respectively. Findings: 1) Despite a strong association of clinical and laboratory variables with the outcomes in classical pairwise analysis, the contribution of laboratory tests to the combined prediction power was limited by redundancy. Laboratory variables reflected only two types of processes: inflammation and organ damage but none reflected the immune response, one major determinant of prognosis. 2) Eight of the thirty variables: age, comorbidity index, oxygen saturation to fraction of inspired oxygen ratio, neutrophil-lymphocyte ratio, C-reactive protein, aspartate aminotransferase/alanine aminotransferase ratio, fibrinogen, and glomerular filtration rate captured most of the combined statistical predictive power. 3) The interpretation of clinical and laboratory variables was moderately improved by grouping them in two categories i.e., inflammation related biomarkers and organ damage related biomarkers; Age and organ damage-related biomarker tests were the best predictors of survival, and inflammatory-related ones were the best predictors of severity. 4) The pilot study identified immunological tests (CXCL10, IL-6, IL-1RA and CCL2), that performed better than most currently used laboratory tests. Conclusions: Laboratory tests for clinical management of COVID 19 patients are valuable but limited predictors due to redundancy; this limitation could be overcome by adding immunological tests with independent predictive power. Understanding the limitations of tests in use would improve their interpretation and simplify clinical management but a systematic search for better immunological biomarkers is urgent and feasible.

Immune-mediated adverse events post-COVID vaccination and types of vaccines: a systematic review and meta-analysis.

Background: In late 2019, Coronavirus disease 2019 has been declared as a global emergency by World Health Organization. Hopefully, recent reports of effective and safe vaccines were welcomed, and approved on emergency base. Millions of recipients had received one of the approved COVID 19 vaccines, with lots of adverse events recorded global wide. Objective: To assess post-COVID vaccination immune-mediated adverse events and evaluate its association to specific type of vaccine global wide. Methods: Systematic literature review and meta-analysis of published reports (since December 2020 till December 2021) on immune-mediated adverse events post-COVID vaccination. Results: We evaluated 34 published studies; 460 cases with various adverse events post-COVID vaccination. Studies in current literature are primarily retrospective case series, isolated case reports or narrative studies. Different COVID vaccines were involved. Results' data was subcategorized according to associated vaccine. Adverse effects of COVID-19 vaccinations included thrombotic, neurological, myocarditis, ocular, dermatological, renal, hematological events timely linked to inoculation. Each vaccine type was linked to adverse profile that differ from others. Conclusion: High suspicion of post-vaccination adverse events is mandatory to provoke earlier detection, better understanding, optimum prevention, and management. Specific vaccine/patient risk profile is needed to selectively categorize target population to reduce morbidity and mortality post-vaccination.

A Concise Appraisal of Cameroon’s Hazard Risk Profile: Multi-Hazard Inventories, Causes, Consequences and Implications for Disaster Management

The paucity of a comprehensive document on Cameroon’s hazard/disaster risk profile is a limitation to the country wide risk assessment and adequate disaster resilience. This article narrows this gap by retrospectively exploring Cameroon’s hazard/disaster profile. This has been achieved through an investigative approach that applies a set of qualitative methods to derive and articulate an inventory and analysis of hazards/disasters in Cameroon. The findings indicate that Cameroon has a wide array and high incidence/frequency of hazards that have had devastating consequences. The hazards have been structured along four profiles: a classification of all hazard types plaguing Cameroon into natural, potentially socio-natural, technological, and social and anthropogenic hazards;occurrence/origin of the hazards;their impacts/effects to the ‘at risk’ communities/populace and potential disaster management or mitigation measures. In-depth analysis indicate that natural hazards have the lowest frequency but the potential to cause the highest fatalities in a single incident;potentially socio-natural hazards affect the largest number of people and the widest geographical areas, technological hazards have the highest frequency of occurrence;while social/anthropogenic hazards are the newest in the country but have caused the highest population displacement. Arguably, the multi-hazard/disaster inventory presented in this article serves as a vital preliminary step to a more comprehensive profile of Cameroon’s disaster risk profile.

An Adaptive algorithm to predict bad debts during COVID-19 Pandemic

Loan recovery during the COVID-19 pandemic is anxious. Automated decision-making would boost the identification of bad debts while issuing loans. The objective of the proposed work is thus to design and implement an adaptive algorithm, which will be used to predict bad debts. Machine learning is an artificial intelligence technology, which gives systems the ability to automatically learn and improve from experience without explicit programming. The adaptive algorithm proposed is deterministic, uses two parameters known as neighborhood distance and minimum support threshold value for the risk profile, and can be very useful in predicting bad debts. It produces overlapped as well as non-overlapped clusters. This algorithm can detect the outliers with the help of an adaptive threshold value for the object's risk profile attribute. Objects with a moderately high or high value of risk profile attribute may emerge as outliers, and these outliers can be known as bad debts. The clusters generated are labeled as paid fully, not paid fully, and not paid. It can also generate clusters of different sizes. The proposed adaptive deterministic algorithm clusters the dataset without knowing the number of clusters. Many clusters are generated using this algorithm, but the parameter risk profile minimum threshold value prunes the clusters being formed. This proposed adaptive algorithm is testedusing real and artificial data sets and shows 83% accuracy in bad debt prediction. © 2022 IEEE

Neurological Manifestations and Outcomes in a Retrospective Cohort of Mexican Inpatients with SARS-CoV-2 Pneumonia: Design of a Risk Profile.

We analyzed the neurological manifestations in Mexican patients hospitalized with pneumonia due to COVID-19 and investigated the association between demographic, clinical, and biochemical variables and outcomes, including death. A retrospective, analytical study was conducted using the electronic records of patients hospitalized between 1 April 2020 and 30 September 2020. Records of 1040 patients were analyzed: 31.25% died and 79.42% had neurological symptoms, including headache (80.62%), anosmia (32.20%), ageusia (31.96%), myopathy (28.08%), disorientation (14.89%), encephalopathy (12.22%), neuropathy (5.4%), stroke (1.3%), seizures (1.3%), cerebral hemorrhage (1.08%), encephalitis (0.84%), central venous thrombosis (0.36%), and subarachnoid hemorrhage (0.24%). Patients also had comorbidities, such as hypertension (42.30%), diabetes mellitus (38.74%), obesity (61.34%), chronic obstructive pulmonary disease (3.17%), and asthma (2.01%). Factors associated with neurological symptoms were dyspnea, chronic obstructive pulmonary disease, advanced respiratory support, prolonged hospitalization, and worsening fibrinogen levels. Factors associated with death were older age, advanced respiratory support, amine management, chronic obstructive pulmonary disease, intensive care unit management, dyspnea, disorientation, encephalopathy, hypertension, neuropathy, diabetes, male sex, three or more neurological symptoms, and obesity grade 3. In this study we designed a profile to help predict patients at higher risk of developing neurological complications and death following COVID-19 infection.

A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine.

BACKGROUND: We performed a phenome-wide association study to identify pre-existing conditions related to Coronavirus disease 2019 (COVID-19) prognosis across the medical phenome and how they vary by race. METHODS: The study is comprised of 53,853 patients who were tested/diagnosed for COVID-19 between 10 March and 2 September 2020 at a large academic medical center. RESULTS: Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with intensive care unit (ICU) admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks. CONCLUSIONS: Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.

A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine.

BACKGROUND: We perform a phenome-wide scan to identify pre-existing conditions related to COVID-19 susceptibility and prognosis across the medical phenome and how they vary by race. METHODS: The study is comprised of 53,853 patients who were tested/positive for COVID-19 between March 10 and September 2, 2020 at a large academic medical center. RESULTS: Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with ICU admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks. CONCLUSIONS: Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.