A survival analysis of COVID-19 in the Mexican population

Application of Mixture and Non-mixture Cure Models in Survival Analysis of Patients With COVID-19

Background Due to the emergence of new COVID-19 mutations and an increase in re-infection rates, it has become an important priority for the medical community to identify the factors affecting the short- and long-term survival of patients. This study aimed to determine the risk factors of short- and long-term survival in patients with COVID-19 based on mixture and non-mixture cure models. Methodology In this study, the data of 880 patients with COVID-19 confirmed with polymerase chain reaction in Fereydunshahr city (Isfahan, Iran) from February 20, 2020, to December 21, 2021, were gathered, and the vital status of these patients was followed for at least one year. Due to the high rate of censoring, mixture and non-mixture cure models were applied to estimate the survival. Akaike information criterion values were used to evaluate the fit of the models. Results In this study, the mean age of the patients was 48.9 ± 21.23 years, and the estimated survival rates on the first day, the fourth day, the first week, the first month, and at one year were 0.997, 0.982, 0.973, 0.936, and 0.928, respectively. Among the parametric models, the log-logistic mixed cure model with the logit link, which showed the lowest Akaike information criterion value, demonstrated the best fit. The variables of age and prescribed medication type were significant predictors of long-term survival, while occupation was influential in the short-term survival of patients. Conclusions According to the results of this study, it can be concluded that elderly patients should observe health protocols more strictly and consider receiving booster vaccine doses. The log-logistic cure model with a logit link can be used for survival analysis in COVID-19 patients, a fraction of whom have long-term survival.

Assessing the hazard of death, cardiac tamponade, and pericardial constriction among HIV and tuberculosis pericarditis patients using the extended Cox-hazard model: Intervention study

Background and Aims

Tuberculous (TB) pericarditis (TBP), a TB of the heart, is linked to significant morbidity and mortality rates. Administering glucocorticoid therapy to individuals with TBP might enhance overall results and lower the likelihood of fatality. However, the actual clinical effectiveness of supplementary glucocorticoids remains uncertain. This study specifically evaluated the effects of prednisolone, prednisolone-antiretroviral therapy (ART) interaction, and other potential risk factors in reducing the hazard of the composite outcome, death, cardiac tamponade, and constriction, among TBP and human immunodeficiency virus (HIV) patients.

Methods

The data used in this study were obtained from the investigation of the Management of Pericarditis trial, a multicentre international randomized double-blind placebo-controlled 2 × 2 factorial study that investigated the effects of two TB treatments, prednisolone and Mycobacterium indicus pranii  immunotherapy in patients with TBP in Africa. This study used a sample size of 587 TBP and HIV-positive patients randomized into prednisolone and its corresponding placebo arm. We used the extended Cox-proportional hazard model to evaluate the effects of the covariates on the hazard of the survival outcomes. Models fitting and parameter estimation were carried out using R version 4.3.1.

Results

Prednisolone reduces the hazard of composite outcome (hazrad ratio [HR] = 0.32, 95% confidence interval [CI] = 0.19 , 0.54 , p < 0.001), cardiac tamponade (HR = 0.14, 95% CI = 0.05, 0.42, p < 0.001) and constriction (HR = 0.81, 95% CI = 0.41, 1.61, p = 0.55). However, prednisolone increases the hazard of death (HR = 1.58, 95% CI = 1.11, 2.24, p = 0.01). Consistent usage of ART reduces the hazard of composite outcome, death, and constriction but insignificantly increased the hazard of cardiac tamponade.

Conclusion

The study offers valuable insights into how prednisolone impact the hazard of different outcomes in patients with TBP and HIV. The findings hold potential clinical significance, particularly in guiding treatment decisions and devising strategies to enhance outcomes in this specific patient group. However, there are concerns about prednisolone potentially increasing the risk of death due to HIV-related death.

Spatial Survival Model for COVID-19 in México

A spatial survival analysis was performed to identify some of the factors that influence the survival of patients with COVID-19 in the states of Guerrero, México, and Chihuahua. The data that we analyzed correspond to the period from 28 February 2020 to 24 November 2021. A Cox proportional hazards frailty model and a Cox proportional hazards model were fitted. For both models, the estimation of the parameters was carried out using the Bayesian approach. According to the DIC, WAIC, and LPML criteria, the spatial model was better. The analysis showed that the spatial effect influences the survival times of patients with COVID-19. The spatial survival analysis also revealed that age, gender, and the presence of comorbidities, which vary between states, and the development of pneumonia increase the risk of death from COVID-19.

Death and Venous Thromboembolism Analyses among Hospitalized COVID-19-Positive Patients: A Multicenter Study

BACKGROUND

Coagulation disorders are frequently encountered among patients infected with coronavirus disease 2019 (COVID-19), especially among admitted patients with more severe symptoms. This study aims to determine the mortality rate and incidence and risk factors for venous thromboembolism (VTE) in hospitalized patients with COVID-19.

METHODS

This retrospective observational cohort study was conducted from March to July 2020 using a hospital database. All adult patients (>18 years old) with laboratory-confirmed COVID-19 were included. Laboratory data and the real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) for SARS-CoV-2 were obtained from medical records. The mortality rate and the incidence of VTE were established as study results. A multivariate logistic regression analysis was performed to identify predictors of thrombotic events.

RESULTS

rA total of 1024 confirmed COVID-19 patients were treated, of whom 110 (10.7%) were deceased and 58 patients (5.7%) developed VTE. Death occurred more frequently in patients older than 50 years and those admitted to the intensive care unit (ICU, 95%) and who received mechanical ventilation (62.7%). Multivariate analysis revealed that cancer patients were two times more likely to have VTE (adjusted odds ratio = 2.614; 95% CI = (1.048-6.519); p = 0.039). Other chronic diseases, such as diabetes, hypertension, and chronic kidney disease, were not associated with an increased risk of VTE.

CONCLUSIONS

One-tenth of hospitalized COVID-19 patients were deceased, and VTE was prevalent among patients with chronic conditions, such as cancer, despite anticoagulation therapy. Healthcare professionals should closely monitor individuals with a high risk of developing VTE to prevent unwanted complications.

Acute respiratory distress syndrome in patients with COVID-19 vs. Non-COVID-19: clinical characteristics and outcomes in a tertiary care setting in Mexico City

BACKGROUND

Acute Respiratory Distress Syndrome (ARDS) due tocoronavirus disease (COVID-19) infection has a unique phenotype generating a growing need to determine the existing differences that can alter existing evidence-based management strategies for ARDS.

RESEARCH QUESTION

What differences does the clinical profile of patients with ARDS due to COVID 19 and Non-COVID 19 have?

STUDY DESIGN AND METHODS

We conducted a comparative, observational, retrospective study in the Intensive Care Unit (ICU)of a third-level hospital in Mexico City, from March 2020 through March 2022. Clinical, echocardiographic, and laboratory variables were compared between patients with ARDS due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and those due to other etiologies.

RESULTS

We enrolled 140 patients with a diagnosis of ARDS. The study group of COVID-19 etiology were younger males, higher body mass index, progressed to organ dysfunction, required more frequently renal replacement therapy, and higher SOFA score. There was no difference in rates of right ventricular dysfunction.

INTERPRETATION

COVID-19 ARDS exhibit much greater severity that led to higher admission and mortality rates, whilst being younger and less comorbid.

Age and Comorbidities as Risk Factors for Severe COVID-19 in Mexico, before, during and after Massive Vaccination

During 2020-2023, Mexico had a large COVID-19 emergency with >331,000 adult deaths and one of the highest excess mortalities worldwide. Age at COVID-19 death has been lower in Mexico than in high-income countries, presumably because of the young demographics and high prevalence of chronic metabolic diseases in young and middle-aged adults. SARS-CoV-2 vaccination covered 85% of adults with at least one dose and 50% with booster(s) up to April 2022. No new vaccination efforts or updated boosters were introduced until October 2023; thus, we explored the public health impact of massive SARS-CoV-2 vaccination against ancestral strains and asked whether their real-world protection has persisted through time. We compared three periods with respect to vaccine roll-outs: before, during and after vaccine introduction in a national retrospective cohort of >7.5 million COVID-19 cases. The main findings were that after vaccination, COVID-19 mortality decreased, age at COVID-19 death increased by 5-10 years, both in populations with and without comorbidities; obesity stopped being a significant risk factor for COVID-19 death and protection against severe disease persisted for a year after boosters, including at ages 60-79 and 80+. Middle-aged adults had the highest protection from vaccines/hybrid immunity and they more than halved their proportions in COVID-19 deaths.

A probabilistic approach for the study of epidemiological dynamics of infectious diseases: Basic model and properties

The dynamics of epidemiological phenomena associated to infectious diseases have long been modelled taking different approaches. However, recent pandemic events exposed many areas of opportunity to improve the existing models. We develop a stochastic model based on the idea that transitions between epidemiological stages are alike sampling processes that may involve more than one subset of the population or may be mostly dependent on time intervals defined by pathological or clinical criteria. We apply the model to simulate epidemics, analyse the final distribution of the case fatality ratio, and define a basic reproductive number to determine the existence of a probabilistic phase transition for the dynamics. The resulting modelling scheme is robust, easy to implement, and can readily lend itself for extensions aimed at answering questions that emerge from close examination of data trends, such as those emerging from the COVID-19 pandemic, and other infectious diseases.

[Prognostic scale for in-hospital mortality in patients with COVID-19 viral pneumonia]

Background

The COVID-19 pandemic represented a challenge in medical care. A tool would be very useful to establish the prognosis of in-hospital death that is reliable and can be applied to the Mexican population entitled to the IMSS.

Objective

To propose a prognostic scale to stratify patients with viral pneumonia COVID-19 in the emergency services.

Material and methods

A nested case-control study was conducted in a cohort of patients who were consecutively admitted to the emergency department with viral pneumonia COVID-19. The cases were those patients who died, and the controls were those who were discharged due to health improvement. An association analysis was performed between the variables with significant differences between groups. Subsequently, the association was adjusted using a multivariate logistic regression model, from which the prognostic scale was developed.

Results

A total of 70 subjects with COVID-19 were included, 34 cases and 36 controls. Chronic diseases, smoking, severe pulmonary involvement diagnosed by tomography, leukocytosis, and pulse oximetry less than 80% with were associated with in-hospital mortality; Odds Ratio (OR) of >1.1. Vaccination was a protective factor (OR = 0.04, CI95%: 0.01-0.16). A score greater than 3 points on the prognostic scale predicts in-hospital mortality with a specificity of 0.86 and a sensitivity of 0.73.

Conclusions

The proposed prognostic scale can be a useful tool in the classification of patients with COVID-19 viral pneumonia in the emergency room services of secondary care level Hospitals.

COVID-19 infection and mortality among non-pregnant indigenous adults in Mexico 2020-2022: Impact of marginalisation

Background

Indigenous individuals have higher rates of mortality and poverty in Mexico and more than half are marginalised, and COVID-19 pandemic aggravated the existing burden of health disparities. We aimed to analyse the effects of being indigenous and marginalised on coronavirus (COVID-19) infection fatality in Mexico.

Methods

We identified 3 424 690 non-pregnant, COVID-19 positive adults ≥19 years in the Mexico national COVID-19 database with known date of symptom. We used demographic information, indigenous status, marginalisation status, and co-morbidities in binary logistic regression to predict mortality, adjusting for covariates, including hospitalisation, admission to the intensive care unit (ICU), and mechanical ventilation use. We also assessed the interaction between indigenous status and marginalisation.

Results

Marginalisation was much higher among indigenous (53.7%) compared to non-indigenous individuals (4.8%). COVID-19 fatalities were approximately 20 years older (64.4 and 63.0 years) than survivors (44.7 and 41.2 years) among indigenous vs non-indigenous individuals, respectively. The unadjusted risk of COVID-19 fatality among indigenous individuals was nearly two-fold (odds ratio (OR) = 1.92)) compared to non-indigenous individuals (OR = 1.05). COVID-19 fatality was higher among highly marginalised individuals (upper quartile) (OR = 1.51; 95% confidence interval (CI) = 1.49-1.54). Marginalised indigenous individuals had a significantly lower likelihood of ICU admission compared to non-indigenous non-marginalised individuals. The likelihood of mechanical ventilation for indigenous individuals was 4% higher compared to non-indigenous individuals. Indigenous marginalised individuals had a significantly lower probability of mechanical ventilation compared to non-indigenous non-marginalised individuals. COVID-19 comorbidity risks of fatality significantly differed between the two groups in the Cox survival analysis. In the fully adjusted model, indigenous individuals were 4% more likely to die from COVID-19 compared to non-indigenous.

Conclusions

Indigenous, marginalised individuals with COVID-19 had higher risk of hospitalisation and ICU admission than non-indigenous patients. Marginalised, indigenous individuals were less likely to receive mechanical ventilation compared to non-indigenous, but had a higher risk of COVID-19. Indigenous individuals had a 4% higher COVID-19 mortality risk COVID-19 compared to non-indigenous individuals. Improved community medical care and augmented health services in rural hospitals could mitigate barriers to health care access in indigenous, marginalised populations.

Improved Survival Analyses Based on Characterized Time-Dependent Covariates to Predict Individual Chronic Kidney Disease Progression

Kidney diseases can cause severe morbidity, mortality, and health burden. Determining the risk factors associated with kidney damage and deterioration has become a priority for the prevention and treatment of kidney disease. This study followed 497 patients with stage 3-5 chronic kidney disease (CKD) who were treated at the ward of Taipei Veterans General Hospital from January 2006 to 2019 in Taiwan. The patients underwent 3-year-long follow-up sessions for clinical measurements, which occurred every 3 months. Three time-dependent survival models, namely the Cox proportional hazard model (Cox PHM), random survival forest (RSF), and an artificial neural network (ANN), were used to process patient demographics and laboratory data for predicting progression to renal failure, and important features for optimal prediction were evaluated. The individual prediction of CKD progression was validated using the Kaplan-Meier estimation method, based on patients' true outcomes during and beyond the study period. The results showed that the average concordance indexes for the cross-validation of the Cox PHM, ANN, and RSF models were 0.71, 0.72, and 0.89, respectively. RSF had the best predictive performances for CKD patients within the 3 years of follow-up sessions, with a sensitivity of 0.79 and specificity of 0.88. Creatinine, age, estimated glomerular filtration rate, and urine protein to creatinine ratio were useful factors for predicting the progression of CKD patients in the RSF model. These results may be helpful for instantaneous risk prediction at each follow-up session for CKD patients.

Probabilistic survival modeling in health research: an assessment using cohort data from hospitalized patients with COVID-19 in a Latin American city

Probabilistic survival methods have been used in health research to analyze risk factors and adverse health outcomes associated with COVID-19. The aim of this study was to employ a probabilistic model selected among three distributions (exponential, Weibull, and lognormal) to investigate the time from hospitalization to death and determine the mortality risks among hospitalized patients with COVID-19. A retrospective cohort study was conducted for patients hospitalized due to COVID-19 within 30 days in Londrina, Brazil, between January 2021 and February 2022, registered in the database for severe acute respiratory infections (SIVEP-Gripe). Graphical and Akaike Information Criterion (AIC) methods were used to compare the efficiency of the three probabilistic models. The results from the final model were presented as hazard and event time ratios. Our study comprised of 7,684 individuals, with an overall case fatality rate of 32.78%. Data suggested that older age, male sex, severe comorbidity score, intensive care unit admission, and invasive ventilation significantly increased risks for in-hospital mortality. Our study highlights the conditions that confer higher risks for adverse clinical outcomes attributed to COVID-19. The step-by-step process for selecting appropriate probabilistic models may be extended to other investigations in health research to provide more reliable evidence on this topic.

A survival analysis of socio-demographic and clinical predictors among hospitalized COVID-19 patients in Southern Iran

BACKGROUND

This study aimed to evaluate the socio-demographic, clinical, and laboratory risk factors in hospitalized COVID-19 patients during the first 6 months of the SARS-CoV-2 epidemic.

METHOD

This retrospective hospital-based cross-sectional study included all laboratory-confirmed cases of the COVID-19 virus that were admitted to the Shohadaye-Khalije-Fars Hospital in Bushehr, Iran, from February 22, 2020 to September 21, 2020. The patients' records were reviewed during the hospitalization period. The global COVID-19 clinical platform, i.e., the World Health Organization Rapid Case Report Form was used as the data collection tool. We conducted the survival analysis using the Kaplan-Meier and the Stepwise Cox regression analyses.

RESULTS

The analysis included 2108 confirmed cases of COVID-19 with a mean age of 47.81 years (SD 17.78); 56.8% men, 43.2% women and 6.3% (n = 133) deaths. After adjustment, it was found that factors associated with an increased risk of death consisted of chronic kidney disease, intensive care unit admission, cancer, and hemoptysis. The 7-day survival rate was 95.8%, which decreased to 95.1%, 94.0%, and 93.8% on days 14, 21, and 28 of hospitalization, respectively.

DISCUSSION AND CONCLUSION

Older COVID-19 patients with manifestation of hemoptysis and a past medical history of chronic kidney disease and cancer, should be closely monitored to prevent disease deterioration and death, and also should be admitted to the intensive care unit.

Risk Factors for Mortality of Hospitalized Adult Patients with COVID-19 Pneumonia: A Two-Year Cohort Study in a Private Tertiary Care Center in Mexico

During the COVID-19 pandemic, the high prevalence of comorbidities and the disparities between the public and private health subsystems in Mexico substantially contributed to the severe impact of the disease. The objective of this study was to evaluate and compare the risk factors at admission for in-hospital mortality of patients with COVID-19. A 2-year retrospective cohort study of hospitalized adult patients with COVID-19 pneumonia was conducted at a private tertiary care center. The study population consisted of 1258 patients with a median age of 56 ± 16.5 years, of whom 1093 recovered (86.8%) and 165 died (13.1%). In the univariate analysis, older age (p < 0.001), comorbidities such as hypertension (p < 0.001) and diabetes (p < 0.001), signs and symptoms of respiratory distress, and markers of acute inflammatory response were significantly more frequent in non-survivors. The multivariate analysis showed that older age (p < 0.001), the presence of cyanosis (p = 0.005), and previous myocardial infarction (p = 0.032) were independent predictors of mortality. In the studied cohort, the risk factors present at admission associated with increased mortality were older age, cyanosis, and a previous myocardial infarction, which can be used as valuable predictors for patients' outcomes. To our knowledge, this is the first study analyzing predictors of mortality in COVID-19 patients attended in a private tertiary hospital in Mexico.

A competing risk survival analysis of the sociodemographic factors of COVID-19 in-hospital mortality in Ecuador

This study aimed to analyze the effect of sociodemographic characteristics on COVID-19 in-hospital mortality in Ecuador from March 1 to December 31, 2020. This retrospective longitudinal study was performed with data from publicly accessible registries of the Ecuadorian National Institute of Statistics and Censuses (INEC). Data underwent a competing risk analysis with estimates of the cumulative incidence function (CIF). The effect of covariates on CIFs was estimated using the Fine-Gray model and results were expressed as adjusted subdistribution hazard ratios (SHR). The analysis included 30,991 confirmed COVID-19 patients with a mean age of 56.57±18.53 years; 60.7% (n = 18,816) were men and 39.3% (n = 12,175) were women. Being of advanced age, especially older than or equal to 75 years (SHR = 17.97; 95%CI: 13.08-24.69), being a man (SHR = 1.29; 95%CI: 1.22-1.36), living in rural areas (SHR = 1.18; 95%CI: 1.10-1.26), and receiving care in a public health center (SHR = 1.64; 95%CI: 1.51-1.78) were factors that increased the incidence of death from COVID-19, while living at an elevation higher than 2,500 meters above sea level (SHR = 0.69; 95%CI: 0.66-0.73) decreased this incidence. Since the incidence of death for individuals living in rural areas and who received medical care from the public sector was higher, income and poverty are important factors in the final outcome of this disease.

Clinical features, coagulation and inflammatory biomarkers associated with poor in-hospital outcomes in a Honduran population with RT-PCR confirmed COVID-19

Background

SARS-COV-2, in most cases, only generates a mild acute respiratory disease. However, patients with severe disease show an exaggerated response of the immune system, creating a pro-inflammatory state, which could cause abnormalities in the coagulation system that increases mortality. Latin American countries, specially those with limited resources, have few studies about clinical features, coagulation and inflammatory biomarkers that could be useful at admission to assess poor outcomes.

Objective

The objective of this study is to describe the clinical features, coagulation, and inflammatory biomarkers, and identify risk factors at admission that are associated poor outcomes in Honduran population.

Methods

A cohort study was conducted. 210 patients were included, which 105 died during hospitalization due to COVID-19 and 105 were discharged alive, between September 2020 and January 2021. Clinical and laboratorial data was retrospectively collected.

Results

57,6% of the population were male. The median age was 58 years. The median time between symptom onset and hospital admission was 6 days. D-dimer median was higher in the dead group compared with the alive group. Poor prognosis factors in the Cox multivariable model were male gender, age, symptom's duration, obesity and an elevated d dimer at admission.

Conclusion

In low-middle income countries, the assessment of these clinical and laboratory tools, especially in those with risk factors for prothrombotic states, could help clinicians to correctly stratify disease prognosis, establish a baseline to evaluate further evolution, and also predict outcomes, thus improving patient management.

Risk factors and survival in patients with COVID-19 in northeastern Brazil

BACKGROUND

Knowledge about the epidemiology and risk factors surrounding COVID-19 contributes to developing better health strategies to combat the disease.

OBJECTIVE

This study aimed to establish a survival analysis and identify the risk factors for patients with COVID-19 in an upper middle-income city in Brazil.

METHODS

A retrospective cohort study was conducted with 280 hospitalized patients with COVID-19. The eCOVID platform provided data to monitor COVID-19 cases and help the communication between professionals.

RESULTS

Age ≥ 65 years was associated with decreased survival (54.8%), and females had a lower survival rate than males (p = 0.01). Regarding risk factors, urea concentration (p<0.001), hospital length of stay (p = 0.002), oxygen concentration (p = 0.005), and age (p = 0.02) were associated with death.

CONCLUSION

Age, hospital length of stay, high blood urea concentration, and low oxygen concentration were associated with death by COVID-19 in the studied population. These findings corroborate with studies conducted in research centers worldwide.

A Weibull-Beta Prime Distribution to Model COVID-19 Data with the Presence of Covariates and Censored Data

Motivated by the recent popularization of the beta prime distribution, a more flexible generalization is presented to fit symmetrical or asymmetrical and bimodal data, and a non-monotonic failure rate. Thus, the Weibull-beta prime distribution is defined, and some of its structural properties are obtained. The parameters are estimated by maximum likelihood, and a new regression model is proposed. Some simulations reveal that the estimators are consistent, and applications to censored COVID-19 data show the adequacy of the models.

Epidemiological Profile and Social Welfare Index as Factors Associated with COVID-19 Hospitalization and Severity in Mexico City: A Retrospective Analysis

Epidemiological data indicate that Mexico holds the 19th place in cumulative cases (5506.53 per 100,000 inhabitants) of COVID-19 and the 5th place in cumulative deaths (256.14 per 100,000 inhabitants) globally and holds the 4th and 3rd place in cumulative cases and deaths in the Americas region, respectively, with Mexico City being the most affected area. Several modifiable and non-modifiable risk factors have been linked to a poor clinical outcome in COVID-19 infection; however, whether socioeconomic and welfare factors are associated with clinical outcome has been scanty addressed. This study tried to investigate the association of Social Welfare Index (SWI) with hospitalization and severity due to COVID-19. A retrospective analysis was conducted at the Centro Médico Nacional "20 de Noviembre"-ISSSTE, based in Mexico City, Mexico. A total of 3963 patients with confirmed or suspected COVID-19, registered from March to July 2020, were included, retrieved information from the Virology Analysis and Reference Unit Database. Demographic, symptoms and clinical data were analyzed, as well as the SWI, a multidimensional parameter based on living and household conditions. An adjusted binary logistic regression model was performed in order to compare the outcomes of hospitalization, mechanical ventilation requirement (MVR) and mortality between SWI categories: Very high (VHi), high (Hi), medium (M) and low (L). The main findings show that lower SWI were independently associated with higher probability for hospital entry: VHi vs. Hi vs. M vs. L-SWI (0 vs. +0.24 [OR = 1.24, CI95% 1.01-1.53] vs. +0.90 [OR = 1.90, CI95% 1.56-2.32] vs. 0.73 [OR = 1.73, CI95% 1.36-2.19], respectively); Mechanical Ventilation Requirement: VHi vs. M vs. L-SWI (0 vs. +0.45 [OR = 1.45, CI95% 1.11-1.87] vs. +0.35 [OR = 1.35, CI95% 1.00-1.82]) and mortality: VHi vs. Hi vs. M (0 vs. +0.54 [OR = 1.54, CI95% 1.22-1.94] vs. +0.41 [OR = 1.41, CI95% 1.13-1.76]). We concluded that SWI was independently associated with the poor clinical outcomes in COVID-19, beyond demographic, epidemiological and clinical characteristics.

Delirium in COVID-19 and post-liver transplant patients: an observational study

OBJECTIVE

Delirium is a major complication in hospitalised patients. This study aimed to compare the mortality trends of patients with delirium according to the underlying physical condition.

METHODS

Hospitalised patients diagnosed with delirium by the Modena Consultation-Liaison Psychiatry Service (Italy) during 2020 were enrolled. Three groups were identified: COVID; after orthotopic liver transplant (OLT); other conditions. The full medical records were screened to retrieve socio-demographic and clinical data. INTERMED score and Delirium Rating Scale were used to retrospectively rate bio-social-complexity and delirium severity. Early (20 days) and end of study (31^st January 2021) mortality were ascertained for each subject.

RESULTS

A total of 103 patients were enrolled. Patients hospitalised for COVID showed higher INTERMED scores (two-tailed t-test, p = 0.019) and higher 20-day mortality (HR = 3.68, p = 0.014). When considering a 1-year follow-up, the main predictor of mortality was patients' age in all three subgroups (HR = 1.06; p = 0.003).

CONCLUSION

Our results suggest that patients hospitalised for COVID-19 with delirium showed higher bio-psycho-social complexity and higher short-term mortality, regardless of the severity of delirium. OLT patients showed lower mortality and bio-psycho-social complexity, despite being still considered as 'complex', according to the INTERMED score. Future research should focus on understanding the underlying mechanisms in the relationship between delirium and mortality.Key pointsPatients hospitalised for COVID-19 with delirium were found at risk of higher short-term mortality and higher bio-psycho-social complexity.OLT patients showed lower overall mortality and lower bio-psycho-social complexity than the other two groups, despite being still in the 'complex' range according to the INTERMED score.Future research should assess the areas of impact of delirium in patients affected by COVID-19, considering short- and long-term outcomes.

Survival, mortality, and related comorbidities among COVID-19 patients in Saudi Arabia: A hospital-based retrospective cohort study

OBJECTIVES

To assess the survival of COVID-19 patients in Saudi Arabia and to investigate possible mortality predictors.

METHODS

This is a retrospective cohort study involving 248 patients with severe acute respiratory syndrome coronavirus-2 who were admitted to the primary COVID-19 referral hospital in Jeddah between March and June of 2020. Socio-demographic characteristics, comorbidities, laboratory investigations, management protocols, complications, treatment options, and mortality data were extracted from electronic medical records. The time analysis began at the first signs of illness thorough discharge or death.

RESULTS

Our study showed that in-hospital complications including heart failure followed by acute renal failure had the largest effect size on mortality (p<0.001). Elderly patients and those with comorbid asthma had a higher risk of death. Non-survivors presented more commonly with shortness of breath and fever than survivors. High D-Dimer level was a marginally significant indicator of mortality in the studied population (p=0.05). We did not find a significant benefit in relation to any treatment option.

CONCLUSION

Age, asthma, some in-hospital complications are important survival indicators in hospitalized COVID-19 patients. The controllable co-factors should be monitored and managed by healthcare workers to reduce mortality rates in those hospitalized with COVID-19.

The Effects of Biological Sex and Cardiovascular Disease on COVID-19 Mortality

Cardiovascular disease (CVD) is a common comorbidity observed in patients with COVID-19 that is associated with increased severity and mortality. However, the effects of biological sex on CVD associated mortality in COVID-19 patients is poorly established particularly among Hispanic/Latin Americans. We examined the association of preexisting CVD with COVID-19 mortality in hospitalized Latin American men and women. This multicenter study included hospitalized Mexican patients with a positive diagnosis of COVID-19. The main outcome was in-hospital mortality. Multivariable regression analyses were used to calculate the adjusted odd ratios with 95% confidence interval for mortality in women and men. Of 81,400 patients with a positive diagnosis for SARS-CoV-2 infection, 28,929 (35.54%) hospitalized patients were evaluated. Of these, the 35.41% (10,243) were women. In-hospital death was higher in men than in women. In relation to CVD between the sexes, women had a higher incidence of CVD than men (4.69% vs 3.93%. P=0.0023). The adjusted logistic regression analyses showed that CVD was significantly associated with COVID-19 mortality in women but not men. We then stratified by sex according to age <52 and ≥52 years old. Similar significant association was also found in pre-specified analysis in women ≥52 years old but not in men of similar age. We conclude that CVD's effect on mortality among COVID-19 hospitalized patients is dependent on biological sex and age in this Latin American cohort. These results suggest that therapeutic strategies for Latin American women with CVD and COVID-19 should include particular attention to their cardiovascular health.

Analysis of Survival of Patients Hospitalized with COVID-19 in Espírito Santo, Brazil

Objective: To analyze the survival of patients hospitalized with COVID-19 and its associated factors. Methods: Retrospective study of survival analysis in individuals notified and hospitalized with COVID-19 in the state of Espírito Santo, Brazil. As data source, the reports of hospitalized patients in the period from 1 March 2020, to 31 July 2021 were used. The Cox regression analysis plus the proportional risk assessment (assumption) were used to compare hospitalization time until the occurrence of the event (death from COVID-19) associated with possible risk factors. Results: The sample comprised 9806 notifications of cases, with the occurrence of 1885 deaths from the disease (19.22%). The mean age of the group was 58 years (SD ± 18.3) and the mean hospital length of stay was 10.5 days (SD ± 11.8). The factors that presented a higher risk of death from COVID-19, associated with a lower survival rate, were non-work-related infection (HR = 4.33; p < 0.001), age group 60−79 years (HR: 1.62; p < 0.001) and 80 years or older (HR = 2.56; p < 0.001), presence of chronic cardiovascular disease (HR = 1.18; p = 0.028), chronic kidney disease (HR = 1.5; p = 0.004), smoking (HR = 1.41; p < 0.001), obesity (HR = 2.28; p < 0.001), neoplasms (HR = 1.81; p < 0.001) and chronic neurological disease (HR = 1.68; p < 0.001). Conclusion: It was concluded that non-work-related infection, age group above or equal to 60 years, presence of chronic cardiovascular disease, chronic kidney disease, chronic neurological disease, smoking, obesity and neoplasms were associated with a higher risk of death, and, therefore, a lower survival in Brazilian patients hospitalized with COVID-19. The identification of priority groups is crucial for Health Surveillance and can guide prevention, control, monitoring, and intervention strategies against the new coronavirus.

In Vitro Exposure of Primary Human T Cells and Monocytes to Polyclonal Stimuli Reveals a Basal Susceptibility to Display an Impaired Cellular Immune Response and Develop Severe COVID-19

The contribution of the cellular immune response to the severity of coronavirus disease 2019 (COVID-19) is still uncertain because most evidence comes from patients receiving multiple drugs able to change immune function. Herein, we conducted a prospective cohort study and obtained blood samples from 128 unvaccinated healthy volunteers to examine the in vitro response pattern of CD4+ and CD8+ T cells and monocyte subsets to polyclonal stimuli, including anti-CD3, anti-CD28, poly I:C, severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) recombinant spike S1 protein, and lipopolysaccharide. Then, we started a six-month follow-up and registered 12 participants who got SARS-CoV-2 infection, from whom we retrospectively analyzed the basal immune response pattern of T cells and monocytes. Of the 12 participants infected, six participants developed mild COVID-19 with self-limiting symptoms such as fever, headache, and anosmia. Conversely, six other participants developed severe COVID-19 with pneumonia, respiratory distress, and hypoxia. Two severe COVID-19 cases required invasive mechanical ventilation. There were no differences between mild and severe cases for demographic, clinical, and biochemical baseline characteristics. In response to polyclonal stimuli, basal production of interleukin-2 (IL-2) and interferon (IFN-) gamma significantly decreased, and the programmed cell death protein 1 (PD-1) increased in CD4+ and CD8+ T cells from participants who posteriorly developed severe COVID-19 compared to mild cases. Likewise, CD14++CD16- classical and CD14+CD16+ non-classical monocytes lost their ability to produce IFN-alpha in response to polyclonal stimuli in participants who developed severe COVID-19 compared to mild cases. Of note, neither the total immunoglobulin G serum titers against the virus nor their neutralizing ability differed between mild and severe cases after a month of clinical recovery. In conclusion, using in vitro polyclonal stimuli, we found a basal immune response pattern associated with a predisposition to developing severe COVID-19, where high PD-1 expression and low IL-2 and IFN-gamma production in CD4+ and CD8+ T cells, and poor IFN-alpha expression in classical and non-classical monocytes are linked to disease worsening. Since antibody titers did not differ between mild and severe cases, these findings suggest cellular immunity may play a more crucial role than humoral immunity in preventing COVID-19 progression.

Association between chronic kidney disease and mortality in patients with a confirmed COVID-19 diagnosis

Objective

To determine the association between chronic kidney disease (CKD) and mortality in persons with a confirmed coronavirus disease 2019 (COVID-19) diagnosis.

Methods

Cross-sectional secondary baseline study. The study population consisted of 243,065 patients confirmed to have COVID-19 during May-December 2020. Stata 16.0 was used for statistical analysis, Chi-square test was used for bivariate analysis, and Poisson regression with robust variances was used for multiple analysis.

Results

The prevalence of patients with a confirmed COVID-19 diagnosis who had CKD and died was 1.42 times the prevalence of mortality in those without CKD. The comorbidities combined with CKD that presented the highest probability of mortality were diabetes mellitus and hypertension.

Conclusions

CKD is associated with a high mortality rate in patients with a confirmed COVID-19 diagnosis. Patients with CKD, diabetes mellitus, and arterial hypertension have a higher prevalence of mortality than those without comorbidities.

Survival analysis of COVID-19 patients in Ethiopia: A hospital-based study

BACKGROUND

COVID-19 is a global public health problem causing high mortality worldwide. This study aimed to assess time to death and predictors of mortality among patients hospitalized for COVID-19 in the Arsi zone treatment center.

METHOD

We performed a retrospective observational cohort study using medical records of laboratory-confirmed COVID-19 cases hospitalized at Bokoji Hospital COVID-19 treatment center from 1st July 2020 to 5th March 2021. We extracted data on the patients' sociodemographic and clinical characteristics from medical records of hospitalized patients retrospectively. We carried out Kaplan Meier and Cox regression analysis to estimate survival probability and investigate predictors of COVID-19 death 5% level of significance. The Adjusted Hazard Ratio (aHR) with 95% Confidence Interval (CI) was estimated and interpreted for predictors of time to death in the final cox model.

RESULT

A total of 422 COVID-19 patients treated were analyzed, of these more than one tenth (11.14%) deaths, with a mortality rate of 6.35 cases per 1000 person-days. The majority (87.2%) of deaths occurred within the first 14 days of admission, with a median time-to-death of nine (IQR: 8-12) days. We found patients that age between 31 and 45 years (aHR = 2.55; 95% CI: (1.03, 6.34), older than 46 years (aHR = 2.59 (1.27, 5.30), chronic obstructive pulmonary disease (aHR = 4.60, 95%CI: (2.37, 8.91), Chronic kidney disease (aHR = 5.58, 95%CI: (1.70, 18.37), HIV/AIDS (aHR = 3.66, 95%CI: (1.20, 11.10), admission to the Intensive care unit(aHR = 7.44, 95%CI: (1.82, 30.42), and being on intranasal oxygen care (aHR = 6.27, 95%CI: (2.75, 4.30) were independent risk factors increasing risk of death from COVID-19 disease than their counterparts.

CONCLUSION

The risk of dying due to COVID-19 disease was higher among patients with HIV/AIDS, chronic obstructive pulmonary disease, and chronic kidney diseases. We also found that older people, those admitted to ICU, and patients who received intranasal oxygen care had a higher risk of dying due to COVID-19 disease. Therefore, close monitoring hospitalized patients that are old aged and those with comorbidities after hospitalization is crucial within the first ten days of admission.

Factors associated with survival of Iranian patients with COVID-19: comparison of Cox regression and mixture cure model

BACKGROUNDS

SARS-CoV-2 is almost the most problematic virus of this century. It has caused extensive damage to various economic, social, and health aspects worldwide. Nowadays, coronavirus disease 2019 (COVID-19) is the most dangerous threat to human survival. Therefore, this study aimed to investigate factors associated with the survival of Iranian patients with SARS-CoV-2.

METHODS

This retrospective hospital-based cohort study was conducted on 870 COVID-19 patients with blood oxygen levels of less than 93%. Cox regression and mixture cure model were used and compared to analyze the patients' survival. It is worth noting that no similar study has been previously conducted using mixture cure regression to model the survival of Iranian patients with COVID-19.

RESULT

The cure rate and median survival time were respectively 81.5% and 20 days. Cox regression identified that respiratory distress, history of heart disease and hypertension, and older age were shown to increase the hazard. The Incidence and Latency parts of the mixture cure model respectively revealed that respiratory distress, history of hypertension, diabetes and cardiovascular diseases (CVDs), cough, fever, and older age reduced the cure odds; also, respiratory distress, history of hypertension, and CVDs, and older age increased the hazard.

CONCLUSION

The findings of our study revealed that priority should be given to older patients with a history of diabetes, hypertension, and CVDs in receiving intensive care and immunization. Also, the lower cure odds for patients with respiratory distress, fever, and cough favor early hospitalization before the appearance of severe symptoms.

Clinical characteristics and outcomes in a cohort of oncologic patients with COVID-19 during the first year of the pandemic in Mexico

Background

Literature on severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection in cancer patients is scarce in Latin America. This population seems to have a higher risk for adverse outcomes. This study aims to correlate clinical characteristics with outcomes in patients with cancer.

Methods

We included all patients with cancer and confirmed SARS‐CoV‐2 infection from April 19 to December 31, 2020, at the Instituto Nacional de Cancerologia, Mexico. Clinical information was obtained from medical and epidemiological records. For the association between variables and hospitalization, invasive mechanical ventilation (IMV), and mortality, univariate and multivariate logistic regression were performed; odds ratios and 95% confidence intervals were calculated.

Results

Four hundred thirty‐three patients were included; 268 (62%) were female, the median age was 55 years. One hundred thirty‐five (31%), 131 (30%), and 93 (21%) patients had obesity, hypertension, and diabetes mellitus (DM), respectively. Three hundred forty‐one (79%) had solid cancer. One hundred seventy (39%) had advanced cancer. Two hundred (46%) patients were hospitalized. Age (p < 0.01), male gender (p = 0.03), hematological malignancies (HM) (p = 0.04) and advanced cancer (p = 0.03) increased the risk for hospital admission. Forty‐five (10%) patients required IMV. Age (p = 0.02); DM (p = 0.04); high C‐reactive protein (p < 0.01), and lactate dehydrogenase (p = 0.03) were associated with IMV. Mortality within 30 days after diagnosis was 18% (76 cases). Associated characteristics were age (p = 0.04) and low albumin (p < 0.01).

Conclusions

In this study, patients with cancer showed higher mortality, need for hospitalization, and IMV compared with other non‐cancer cohorts. We did not find an increased risk in mortality for HM. Although our cohort was younger than others previously reported, age was a strong predictor of adverse outcomes. Variables associated with IMV and death were similar to those previously described in cancer patients with COVID‐19.

Emotional State of Mexican University Students in the COVID-19 Pandemic

Background: Since the WHO declared the COVID-19 crisis a pandemic in March 2020, the young population is suffering from a range of psychological symptoms. The present study measured symptoms of stress, anxiety and depression in university students of Saltillo, Mexico, using the Depression and Anxiety Stress Scale-21 (DASS-21). Methods. The DASS-21 scale and an ad hoc questionnaire were used to collect sociodemographic information. Results: The results show that the students who participated in this study suffer from high levels of stress, anxiety and depression. In terms of sociodemographic variables, women, people suffering from a chronic disease and people living with a chronic disease had the highest levels of stress, anxiety and depression, and people who live with a chronically ill person, people who have had the COVID-19 disease and those who have had someone close to them fall sick have had more stress, anxiety and depression than the rest. Another finding of the present study is that university students who believe that others comply with COVID-19 safety measures have significantly lower anxiety and depression than those who believe that others do not comply. Conclusions: It is concluded that university students are a psychologically vulnerable group in the face of the pandemic.

Novel application of survival models for predicting microbial community transitions with variable selection for eDNA

Survival analysis is a prolific statistical tool in medicine for inferring risk and time to disease-related events. However, it is under-utilized in microbiome research to predict microbial community mediated events, partly due to the sparsity and high dimensional nature of the data. We advance the application of Cox proportional hazards (Cox PH) survival models to environmental DNA (eDNA) data with feature selection suitable for filtering irrelevant and redundant taxonomic variables. Selection methods are compared in terms of false positives, sensitivity, and survival estimation accuracy in simulation and in a real data setting to forecast harmful cyanobacterial blooms. A novel extension of a method for selecting microbial biomarkers with survival data (SuRFCox) reliably outperforms other methods. We determine Cox PH models with SuRFCox selected predictors are more robust to varied signal, noise, and data correlation structure. SuRFCox also yields the most accurate and consistent prediction of blooms according to cross-validated testing by year over eight different bloom seasons. Identification of common biomarkers among validated survival forecasts over changing conditions has clear biological significance. Survival models with such biomarkers inform risk assessment and provide insight into the causes of critical community transitions. Importance In this paper, we report on a novel approach of selecting microorganisms for model-based prediction of the time to critical microbially-modulated events (e.g., harmful algal blooms, clinical outcomes, community shifts, etc.). Our novel method for identifying biomarkers from large, dynamic communities of microbes has broad utility to environmental and ecological impact risk assessment and public health. Results will also promote theoretical and practical advancements relevant to the biology of specific organisms. To address the unique challenge posed by diverse environmental conditions and sparse microbes, we developed a novel method of selecting predictors for modelling time-to-event data. Competing methods for selecting predictors are rigorously compared to determine which is the most accurate and generalizable. Model forecasts are applied to show suitable predictors can precisely quantify the risk over time of biological events like harmful cyanobacterial blooms.

Subphenotyping of COVID-19 patients at pre-admission towards anticipated severity stratification: an analysis of 778 692 Mexican patients through an age-sex unbiased meta-clustering technique

Background

The COVID-19 pandemic has led to an unprecedented global health care challenge for both medical institutions and researchers. Recognizing different COVID-19 subphenotypes—the division of populations of patients into more meaningful subgroups driven by clinical features—and their severity characterization may assist clinicians during the clinical course, the vaccination process, research efforts, the surveillance system, and the allocation of limited resources.

Objective

We aimed to discover age-sex unbiased COVID-19 patient subphenotypes based on easily available phenotypical data before admission, such as pre-existing comorbidities, lifestyle habits, and demographic features, to study the potential early severity stratification capabilities of the discovered subgroups through characterizing their severity patterns, including prognostic, intensive care unit (ICU), and morbimortality outcomes.

Methods

We used the Mexican Government COVID-19 open data, including 778,692 SARS-CoV-2 population-based patient-level data as of September 2020. We applied a meta-clustering technique that consists of a 2-stage clustering approach combining dimensionality reduction (ie, principal components analysis and multiple correspondence analysis) and hierarchical clustering using the Ward minimum variance method with Euclidean squared distance.

Results

In the independent age-sex clustering analyses, 56 clusters supported 11 clinically distinguishable meta-clusters (MCs). MCs 1-3 showed high recovery rates (90.27%-95.22%), including healthy patients of all ages, children with comorbidities and priority in receiving medical resources (ie, higher rates of hospitalization, intubation, and ICU admission) compared with other adult subgroups that have similar conditions, and young obese smokers. MCs 4-5 showed moderate recovery rates (81.30%-82.81%), including patients with hypertension or diabetes of all ages and obese patients with pneumonia, hypertension, and diabetes. MCs 6-11 showed low recovery rates (53.96%-66.94%), including immunosuppressed patients with high comorbidity rates, patients with chronic kidney disease with a poor survival length and probability of recovery, older smokers with chronic obstructive pulmonary disease, older adults with severe diabetes and hypertension, and the oldest obese smokers with chronic obstructive pulmonary disease and mild cardiovascular disease. Group outcomes conformed to the recent literature on dedicated age-sex groups. Mexican states and several types of clinical institutions showed relevant heterogeneity regarding severity, potentially linked to socioeconomic or health inequalities.

Conclusions

The proposed 2-stage cluster analysis methodology produced a discriminative characterization of the sample and explainability over age and sex. These results can potentially help in understanding the clinical patient and their stratification for automated early triage before further tests and laboratory results are available and even in locations where additional tests are not available or to help decide resource allocation among vulnerable subgroups such as to prioritize vaccination or treatments.

Predictive determinants of overall survival among re-infected COVID-19 patients using the elastic-net regularized Cox proportional hazards model: a machine-learning algorithm

BACKGROUND

Narrowing a large set of features to a smaller one can improve our understanding of the main risk factors for in-hospital mortality in patients with COVID-19. This study aimed to derive a parsimonious model for predicting overall survival (OS) among re-infected COVID-19 patients using machine-learning algorithms.

METHODS

The retrospective data of 283 re-infected COVID-19 patients admitted to twenty-six medical centers (affiliated with Shiraz University of Medical Sciences) from 10 June to 26 December 2020 were reviewed and analyzed. An elastic-net regularized Cox proportional hazards (PH) regression and model approximation via backward elimination were utilized to optimize a predictive model of time to in-hospital death. The model was further reduced to its core features to maximize simplicity and generalizability.

RESULTS

The empirical in-hospital mortality rate among the re-infected COVID-19 patients was 9.5%. In addition, the mortality rate among the intubated patients was 83.5%. Using the Kaplan-Meier approach, the OS (95% CI) rates for days 7, 14, and 21 were 87.5% (81.6-91.6%), 78.3% (65.0-87.0%), and 52.2% (20.3-76.7%), respectively. The elastic-net Cox PH regression retained 8 out of 35 candidate features of death. Transfer by Emergency Medical Services (EMS) (HR=3.90, 95% CI: 1.63-9.48), SpO2≤85% (HR=8.10, 95% CI: 2.97-22.00), increased serum creatinine (HR=1.85, 95% CI: 1.48-2.30), and increased white blood cells (WBC) count (HR=1.10, 95% CI: 1.03-1.15) were associated with higher in-hospital mortality rates in the re-infected COVID-19 patients.

CONCLUSION

The results of the machine-learning analysis demonstrated that transfer by EMS, profound hypoxemia (SpO2≤85%), increased serum creatinine (more than 1.6 mg/dL), and increased WBC count (more than 8.5 (×109 cells/L)) reduced the OS of the re-infected COVID-19 patients. We recommend that future machine-learning studies should further investigate these relationships and the associated factors in these patients for a better prediction of OS.

COVID-19 and chronic kidney disease: an updated overview of reviews

BACKGROUND

Coronavirus disease (COVID-19) has resulted in the death of more than 3.5 million people worldwide. While COVID-19 mostly affects the lungs, different comorbidities can have an impact on its outcomes. We performed an overview of reviews to assess the effect of Chronic Kidney Disease (CKD) on contracting COVID-19, hospitalization, mortality, and disease severity.

METHODS

We searched published and preprint databases. We updated the reviews by searching for primary studies published after August 2020, and prioritized reviews that are most updated and of higher quality using the AMSTAR tool.

RESULTS

We included 69 systematic reviews and 66 primary studies. Twenty-eight reviews reported on the prevalence of CKD among patients with COVID-19, which ranged from 0.4 to 49.0%. One systematic review showed an increased risk of hospitalization in patients with CKD and COVID-19 (RR = 1.63, 95% CI 1.03-2.58) (Moderate certainty). Primary studies also showed a statistically significant increase of hospitalization in such patients. Thirty-seven systematic reviews assessed mortality risk in patients with CKD and COVID-19. The pooled estimates from primary studies for mortality in patients with CKD and COVID-19 showed a HR of 1.48 (95% CI 1.33-1.65) (Moderate certainty), an OR of 1.77 (95% CI 1.54-2.02) (Moderate certainty) and a RR of 1.6 (95% CI 0.88-2.92) (Low certainty).

CONCLUSIONS

Our review highlights the impact of CKD on the poor outcomes of COVID-19, underscoring the importance of identifying strategies to prevent COVID-19 infection among patients with CKD.

A Comprehensive Descriptive Epidemiological and Clinical Analysis of SARS-CoV-2 in West-Mexico during COVID-19 Pandemic 2020

This study aimed to summarize the epidemiological and clinical characteristics of COVID-19 from Western Mexico people during 2020. A retrospective analysis from an electronic database of people visiting a sentinel center for molecular SARS-CoV-2 confirmatory diagnosis by RT-PCR from April to December 2020 was carried out for epidemiological and clinical description of COVID-19. Out of 23,211 patients evaluated, 6918 (29.8%) were confirmed for SARS-CoV-2 infection (mean age 38.5 ± 13.99), mostly females (53.8%). Comorbidities, such as diabetes (34.7%), obesity (31.15%), and hypertension (31.8%), presented an increased odds OR = 1.27, CI = 1.14-1.41; OR = 1.08, CI = 1.01-1.16; and OR = 1.09, CI = 0.99-1.19, respectively, for viral-infection. Moreover, fever, headache, and dry cough were the most frequent symptoms. No infection difference among sex was found. Those patients >60 years old were prone to COVID-19 severity (OR = 3.59, CI = 2.10-6.14), evaluated by the number of manifested symptoms, increasing with age. In conclusion, a high SARS-CoV-2 prevalence was found in Western Mexico. Comorbidities were frequent in infected people; nevertheless, no association with disease outcomes was observed, in contrast with the highest disease severity risk found in older patients; however, continuous monitoring should be carried since comorbidities have been reported as aggravating factors. This study can help the health officials for the elaboration of planning efforts of the disease management and others in the future.

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.

Disability-Adjusted Life Years for the COVID-19 Pandemic in the Mexican Population

Mexico is one of the countries most affected by the COVID-19 disease. Although there is vast information on the disease, there still are unknown data on the societal and economic cost of the pandemic. To estimate this impact, the disability-adjusted life years (DALYs) can be a useful tool. Objective: To assess the DALYs due to COVID-19 in Mexico. Methods: We used the data released by the Mexican Ministry of Health to estimate the DALYs by the sum of the years of life lived with disability (YLDs) and the years of life lost (YLLs). Results: A total of 1,152,885 confirmed cases and 324,570 suspected cases of COVID-19 have been registered. Half of the cases were men, with a median age of 43.4 ± 16.9 years. About 8.3% died. A total of 39,202 YLDs were attributable to COVID-19. The total YLLs caused by COVID-19 were 2,126,222. A total of 2,165,424.5 DALYs for COVID-19 were estimated. The total DALYs were the highest in people between 50 and 59 years. The DALYs for each COVID-19 case were the highest in individuals between 60 and 79 years. Conclusion: The DALYs generated by the COVID-19 represent a more significant disease burden than that reported for other causes, such as the 2009 H1N1 influenza pandemic. Although it impacts all age groups in terms of disability, the most affected group are people over 50 years of age, whose risk of death is higher.

Evaluation of Factors Related to the Survival of Hospitalized Patients With COVID-19: Survival Analysis With Frailty Approach

OBJECTIVE

Considering that coronavirus disease 2019 (COVID-19) is an emerging disease and results in very different outcomes, from complete recovery to death, it is important to determine the factors affecting the survival of patients. Given the lack of knowledge about effective factors and the existence of differences in the outcome of individuals with similar values of the observed covariates, this study aimed to investigate the factors affecting the survival of patients with COVID-19 by the parametric survival model with the frailty approach.

METHODS

The data of 139 patients with COVID-19 hospitalized in Imam Reza Hospital in Tabriz were analyzed by the Gompertz survival model with gamma frailty effect. At first, variables with P < 0.1 in univariable analysis were included in the multivariable analysis, and then the stepwise method was used for variable selection.

RESULTS

Diabetes mellitus was significantly related to the survival of hospitalized patients (P = 0.021). The rest of the investigated variables were not significant. The frailty effect was significant (P = 0.019).

CONCLUSIONS

In the investigated sample of patients with COVID-19, diabetes was an important variable related to patient survival. Also, the significant frailty effect indicates the existence of unobserved heterogeneity that causes individuals with a similar value of the observed covariates to have different survival distributions.

Coronavirus Disease-2019 Survival in Mexico: A Cohort Study on the Interaction of the Associated Factors

The pandemic caused by the new coronavirus Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is currently affecting more than 200 countries. The most lethal clinical presentation is respiratory insufficiency, requiring attention in intensive care units (ICU). The most susceptible people are over 60 years old with comorbidities. The health systems organization may represent a transcendental role in survival. Objective: To analyze the correlation of sociodemographic factors, comorbidities and health system organization variables with survival in cases infected by SARS-CoV-2 during the first 7 months of the pandemic in Mexico. Methods: The cohort study was performed in a health system public basis from March 1st to September 30th, 2020. The included subjects were positive for the SARS-CoV-2 test, and the target variable was mortality in 60 days. The risk variables studied were: age, sex, geographic distribution, comorbidities, health system, hospitalization, and access to ICU. Bivariate statistics (X2-test), calculation of fatality rates, survival analyses and adjustment of confusing variables with Cox proportional-hazards were performed. Results: A total of 753,090 subjects were analyzed, of which the 52% were men. There were 78,492 deaths (10.3% of general fatality and 43% inpatient). The variables associated with a higher risk of hospital mortality were age (from 60 years onwards), care in public sectors, geographic areas with higher numbers of infection and endotracheal intubation without management in the ICU. Conclusions: The variables associated with a lower survival in cases affected by SARS-CoV-2 were age, comorbidities, and respiratory insufficiency (with endotracheal intubation without care in the ICU). Additionally, an interaction was observed between the geographic location and health sector where they were treated.

An analysis of mortality and survival of COVID 19 patients admitted to a tertiary care hospital in Maharashtra, India

Background

After nine months of responding to the coronavirus disease-19 (COVID-19) pandemic, the scientific fraternity is yet to unravel the mystery of those who are at most risk from mortality. Despite resistance to wear masks, the global public health response has beaten the grimmer projections of millions of deaths. The present study seeks to analyze the survival of COVID-19 patients at a tertiary care hospital and identify the risk factors of mortality.

Methods

Medical records of 1233 RT PCR confirmed COVID-19 patients admitted in a tertiary care hospital between 01 April and 30 September 2020 were retrospectively analyzed for calculating overall survival and to investigate the independent predictors of survival of COVID-19 patients.

Results

There were 72 (5.8%) deaths; which occurred in 24.9% of the elderly (age > 60yrs) people (P < 0.001), 76.0% in people with multiple comorbidities (having more than one comorbidity) (P < 0.001), 75.6% in people with diabetes (P < 0.001), and 75.5% in people with hypertension (P < 0.001). A significantly higher risk of mortality was observed in elderly patients, patients with comorbidities, and patients requiring oxygen while admitted in the hospital.

Conclusion

Survival reflects the cure rates and is used by health professionals and policymakers to plan and implement disease control measures. The insights provided by the study would help facilitate the identification of patients at risk and timely provision of specialized care for the prevention of adverse outcomes in the hospital setting.

Treatment profiles and clinical outcomes of COVID-19 patients at private hospital in Jakarta

BACKGROUND

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a virus that causes COVID-19, which has become a worldwide pandemic. However, until now, there is no vaccine or specific drug to prevent or treat COVID-19.

OBJECTIVES

To find out the effective treatment as an antiviral agent for COVID-19, to determine the correlation between sociodemography with clinical outcomes and duration of treatment, and to determine the relationship between comorbidities with clinical outcomes and duration of treatment for COVID-19 patients.

METHODS

A prospective cohort study was conducted in this study. This study included only confirmed COVID-19 patients who were admitted to the hospital during April-May 2020. Convenience sampling was used to select 103 patients, but only 72 patients were suitable for inclusion.

RESULTS

The survival analysis for COVID-19 patients using the Kaplan Meier method showed that patients receiving Oseltamivir + Hydroxychloroquine had an average survival rate of about 83% after undergoing treatment of about ten days. Gender (p = 0.450) and age (p = 0.226) did not have a significant correlation with the duration of treatment for COVID-19 patients. Gender (p = 0.174) and age (p = 0.065) also did not have a significant correlation with clinical outcome of COVID-19 patients. Comorbidities showed a significant correlation with duration of treatment (p = 0.002) and clinical outcome (p = 0.014) of COVID-19 patients.

CONCLUSION

The most effective antiviral agent in this study based on treatment duration was the combination of Oseltamivir + Hydroxychloroquine. The higher the patient's average treatment duration is, the lower the average survival rate for COVID-19 patients.

A Study of Factors Affecting the Length of Hospital Stay of COVID-19 Patients by Cox-Proportional Hazard Model in a South Indian Tertiary Care Hospital

Background:

The objective of the study was to identify the factors that alter the length of hospital stay of COVID-19 patients so we have an estimate of the duration of hospitalization of patients. To achieve this, we used a time to event analysis to arrive at factors that could alter the length of hospital stay, aiding in planning additional beds for any future rise in cases.

Methods:

Information about COVID-19 patients was collected between June and August 2020. The response variable was the time from admission to discharge of patients. Cox proportional hazard model was used to identify the factors that were associated with the length of hospital stay.

Results:

A total of 730 COVID-19 patients were included, of which 675 (92.5%) recovered and 55 (7.5%) were considered to be right-censored, that is, the patient died or was discharged against medical advice. The median length of hospital stay of COVID-19 patients who were hospitalized was found to be 7 days by the Kaplan Meier curve. The covariates that prolonged the length of hospital stay were found to be abnormalities in oxygen saturation (HR = 0.446, P < .001), neutrophil-lymphocyte ratio (HR = 0.742, P = .003), levels of D-dimer (HR = 0.60, P = .002), lactate dehydrogenase (HR = 0.717, P = .002), and ferritin (HR = 0.763, P = .037). Also, patients who had more than 2 chronic diseases had a significantly longer length of stay (HR = 0.586, P = .008) compared to those with no comorbidities.

Conclusion:

Factors that are associated with prolonged length of hospital stay of patients need to be considered in planning bed strength on a contingency basis.