Overhydration Assessed Using Bioelectrical Impedance Vector Analysis Adversely Affects 90-Day Clinical Outcome among SARS-CoV2 Patients: A New Approach.

BACKGROUND: COVID-19 has taken on pandemic proportions with growing interest in prognostic factors. Overhydration is a risk factor for mortality in several medical conditions with its role in COVID-19, assessed with bioelectrical impedance (BI), gaining research interest. COVID-19 affects hydration status. The aim was to determine the hydration predictive role on 90 d survival COVID-19 and to compare BI assessments with traditional measures of hydration. METHODS: We studied 127 consecutive COVID-19 patients. Hydration status was estimated using a 50 kHz phase-sensitive BI and estimated, compared with clinical scores and laboratory markers to predict mortality. RESULTS: Non-surviving COVID-19 patients had significantly higher hydration 85.2% (76.9-89.3) vs. 73.7% (73.2-82.1) and extracellular water/total body water (ECW/TBW) 0.67 (0.59-0.75) vs. 0.54 (0.48-0.61) (p = 0.001, respectively), compared to surviving. Patients in the highest hydration tertile had increased mortality (p = 0.012), Intensive Care Unit (ICU) admission (p = 0.027), COVID-19 SEIMC score (p = 0.003), and inflammation biomarkers [CRP/prealbumin (p = 0.011)]. Multivariate analysis revealed that hydration status was associated with increased mortality. HR was 2.967 (95%CI, 1.459-6.032, p < 0.001) for hydration and 2.528 (95%CI, 1.664-3.843, p < 0.001) for ECW/TBW, which were significantly greater than traditional measures: CRP/prealbumin 3.057(95%CI, 0.906-10.308, p = 0.072) or BUN/creatinine 1.861 (95%CI, 1.375-2.520, p < 0.001). Hydration > 76.15% or ECW/TBW > 0.58 were the cut-off values predicting COVID-19 mortality with 81.3% and 93.8% sensitivity and 64 and 67.6% specificity, respectively. Hydration status offers a sensitive and specific prognostic test at admission, compared to established poor prognosis parameters. CONCLUSIONS AND RELEVANCE: Overhydration, indicated as high hydration (>76.15%) and ECW/TBW (>0.58), were significant predictors of COVID-19 mortality. These findings suggest that hydration evaluation with 50 kHz phase-sensitive BI measurements should be routinely included in the clinical assessment of COVID-19 patients at hospital admission, to identify increased mortality risk patients and assist medical care.

Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques.

The new pandemic caused by the COVID-19 virus has generated an overload in the quality of medical care in clinical centers around the world. Causes that originate this fact include lack of medical personnel, infrastructure, medicines, among others. The rapid and exponential increase in the number of patients infected by COVID-19 has required an efficient and speedy prediction of possible infections and their consequences with the purpose of reducing the health care quality overload. Therefore, intelligent models are developed and employed to support medical personnel, allowing them to give a more effective diagnosis about the health status of patients infected by COVID-19. This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico. Different prediction models such as KNN, logistic regression, random forests, ANN and majority vote were evaluated and compared. The models use risk factors as variables to predict the mortality of patients from COVID-19. The most successful scheme is the proposed ANN-based model, which obtained an accuracy of 90% and an F1 score of 89.64%. Data analysis reveals that pneumonia, advanced age and intubation requirement are the risk factors with the greatest influence on death caused by virus in Mexico.

Clinicoepidemiological Features and Mortality Analysis of Deceased Patients with COVID-19 in a Tertiary Care Center.

BACKGROUND AND OBJECTIVE: A large number of studies describing the clinicoepidemiological features of coronavirus disease-2019 (COVID-19) patients are available but very few studies have documented similar features of the deceased. This study was aimed to describe the clinicoepidemiological features and the causes of mortality of COVID-19 deceased patients admitted in a dedicated COVID center in India. METHODOLOGY: This was a retrospective study done in adult deceased patients admitted in COVID ICU from April 4 to July 24, 2020. The clinical features, comorbidities, complications, and causes of mortality in these patients were analyzed. Pediatric deceased were analyzed separately. RESULTS: A total of 654 adult patients were admitted in the ICU during the study period and ICU mortality was 37.7% (247/654). Among the adult deceased, 65.9% were males with a median age of 56 years [interquartile range (IQR), 41.5-65] and 94.74% had one or more comorbidities, most common being hypertension (43.3%), diabetes mellitus (34.8%), and chronic kidney disease (20.6%). The most common presenting features in these deceased were fever (75.7%), cough (68.8%), and shortness of breath (67.6%). The mean initial sequential organ failure assessment score was 9.3 ± 4.7 and 24.2% were already intubated at the time of admission. The median duration of hospital stay was 6 days (IQR, 3-11). The most common cause of death was sepsis with multi-organ failure (55.1%) followed by severe acute respiratory distress syndrome (ARDS) (25.5%). All pediatric deceased had comorbid conditions and the most common cause of death in this group was severe ARDS. CONCLUSION: In this cohort of adult deceased, most were young males with age less than 65 years with one or more comorbidities, hypertension being the most common. Only 5% of the deceased had no comorbidities. Sepsis with multi-organ dysfunction syndrome was the most common cause of death. HOW TO CITE THIS ARTICLE: Aggarwal R, Bhatia R, Kulshrestha K, Soni KD, Viswanath R, Singh AK, et al. Clinicoepidemiological Features and Mortality Analysis of Deceased Patients with COVID-19 in a Tertiary Care Center. Indian J Crit Care Med 2021; 25(6):622-628.

Prediction Model for Mortality Analysis of Pregnant Women Affected with COVID-19

COVID-19 pandemic is an ongoing global pandemic which has caused unprecedented disruptions in the public health sector and global economy. The virus, SARS-CoV-2 is responsible for the rapid transmission of coronavirus disease. Due to its contagious nature, the virus can easily infect an unprotected and exposed individual from mild to severe symptoms. The study of the virus's effects on pregnant mothers and neonatal is now a concerning issue globally among civilians and public health workers considering how the virus will affect the mother and the neonate's health. This paper aims to develop a predictive model to estimate the possibility of death for a COVID-diagnosed mother based on documented symptoms: dyspnea, cough, rhinorrhea, arthralgia, and the diagnosis of pneumonia. The machine learning models that have been used in our study are support vector machine, decision tree, random forest, gradient boosting, and artificial neural network. The models have provided impressive results and can accurately predict the mortality of pregnant mother's with a given input. The precision rate for 3 models(ANN, Gradient Boost, Random Forest) is 100% The highest accuracy score(Gradient Boosting, ANN) is 95%, highest recall(Support Vector Machine) is 92.75% and highest f1 score(Gradient Boosting, ANN) is 94.66%. Due to the accuracy of the model, pregnant mother can expect immediate medical treatment based on their possibility of death due to the virus. The model can be utilized by health workers globally to list down emergency patients, which can ultimately reduce the death rate of COVID-19 diagnosed pregnant mothers. © 2021 IEEE.

Medical certification of cause of death during COVID-19 pandemic - a challenging scenario.

Death certificate data is used to monitor local, regional and national mortality trend and is helpful in improving public health as well as public safety. Accurate and reliable information about the cause of death in a population is useful for understanding disease burden estimation and trends in the health of populations; moreover, the information provided by such data is vital in terms of public health planning as well. With the continuous upsurge in mortality due to coronavirus disease 19 (COVID-19), mortality analysis could be valuable in addressing the current pandemic and implementing the epidemic control strategies effectively and efficiently. Given that COVID-19 death certification substantially affects the local and national responses towards disease prevention and transmission, the importance of the accuracy and quality of information in these certificates cannot be understated. Hence, accurate death certification related to COVID-19 is vital to understand the extent and progression of the current pandemic.