Kaplan-Meier Type Survival Curves for COVID-19: A Health Data Based Decision-Making Tool

Assessing the cardiovascular events and clinical outcomes of COVID-19 on patients with primary aldosteronism

BACKGROUND

Primary Aldosteronism (PA) is a common subtype of hypertension that increases the risk of adverse cardiovascular and kidney events. The impact of COVID-19 on patients with PA is not well understood. This study aimed to investigate the impact of COVID-19 on patients with PA and compare their outcomes with hypertensive patients with essential hypertension.

METHODS

A cohort study was conducted using data from the Trinetx platform, including 9,817,307 participants enrolled between January 1, 2020, and July 31, 2022. The study group consisted of participants who tested positive for PCR SARS-CoV-2. The primary outcome was critical care and all-cause mortality, while the secondary outcomes were major adverse cardiac events (MACE) or major adverse kidney events (MAKE). The study included 4814 patients with PA and 4814 hypertensive controls.

RESULTS

Patients with PA had a higher risk of critical outcomes than the hypertensive control group (adjusted hazard ratio [aHR] 1.14, p = 0.001). Moreover, they had higher risks of MACE (aHR 1.32, p < 0.001) and MAKE (aHR 1.36, p < 0.001) for up to 180 days after COVID-19. The analysis of the aHR as a horizon plot after discharge showed that patients with pre-existing PA and COVID-19 had the highest risk of critical outcomes at 7 months (aHR = 1.21), MACE (aHR = 1.35) at 9 months, and MAKE (aHR = 1.47) at 10 months compared to those with EH.

CONCLUSIONS

This study provides a comprehensive analysis of the cardiovascular impact of the COVID-19 pandemic on individuals with PA. The findings underscore the increased risk of mortality, critical care, MACE, and MAKE among patients with PA and COVID-19. The study highlights the need for continued optimization of strategies for follow-up care for patients with PA after SARS-CoV-2 infections.

Vaccination Status and In-hospital Mortality Among Adults With COVID-19 in Jakarta, Indonesia: A Retrospective Hospital-based Cohort Study

OBJECTIVES

Prospective studies on vaccination status and mortality related to coronavirus disease 2019 (COVID-19) in low-resource settings are still limited. We assessed the association between vaccination status (full, partial, or none) and in-hospital mortality among COVID-19 patients at most hospitals in Jakarta, Indonesia during the Delta predomination wave.

METHODS

We conducted a retrospective cohort study among hospitalized COVID-19 patients who met the study criteria (>18 years old and admitted for inpatient treatment because of laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection). We linked individual-level data in the hospital admission database with vaccination records. Several socio-demographic and clinical characteristics were also analyzed. A Cox proportional hazards regression model was used to explore the association between vaccination status and in-hospital mortality in this patient group.

RESULTS

In total, 40 827 patients were included in this study. Of these, 70% were unvaccinated (n=28 543) and 19.3% (n=7882) died during hospitalization. The mean age of the patients was 49 years (range, 35-59), 53.2% were female, 22.0% had hypertension, and 14.2% were treated in the intensive care unit, and the median hospital length of stay across the group was 9 days. Our study showed that the risk of in-hospital mortality among fully and partially vaccinated patients was lower than among unvaccinated adults (adjusted hazard ratio [aHR], 0.43; 95% confidence interval [CI], 0.40 to 0.47 and aHR, 0.70; 95% CI, 0.64 to 0.77, respectively).

CONCLUSIONS

Vaccinated patients had fewer severe outcomes among hospitalized adults during the Delta wave in Jakarta. These features should be carefully considered by healthcare professionals in treating adults within this patient group.

Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases

The mortality rate from cervical cancer (CESC), a malignant tumor that affects women, has increased significantly globally in recent years. The discovery of biomarkers points to a direction for the diagnosis of cervical cancer with the advancement of bioinformatics technology. The goal of this study was to look for potential biomarkers for the diagnosis and prognosis of CESC using the GEO and TCGA databases. Because of the high dimension and small sample size of the omic data, or the use of biomarkers generated from a single omic data, the diagnosis of cervical cancer may be inaccurate and unreliable. The purpose of this study was to search the GEO and TCGA databases for potential biomarkers for the diagnosis and prognosis of CESC. We begin by downloading CESC (GSE30760) DNA methylation data from GEO, then perform differential analysis on the downloaded methylation data and screen out the differential genes. Then, using estimation algorithms, we score immune cells and stromal cells in the tumor microenvironment and perform survival analysis on the gene expression profile data and the most recent clinical data of CESC from TCGA. Then, using the 'limma' package and Venn plot in R language to perform differential analysis of genes and screen out overlapping genes, these overlapping genes were then subjected to GO and KEGG functional enrichment analysis. The differential genes screened by the GEO methylation data and the differential genes screened by the TCGA gene expression data were intersected to screen out the common differential genes. A protein-protein interaction (PPI) network of gene expression data was then created in order to discover important genes. The PPI network's key genes were crossed with previously identified common differential genes to further validate them. The Kaplan-Meier curve was then used to determine the prognostic importance of the key genes. Survival analysis has shown that CD3E and CD80 are important for the identification of cervical cancer and can be considered as potential biomarkers for cervical cancer.

Neutralizing Antibody Response, Safety, and Efficacy of mRNA COVID-19 Vaccines in Pediatric Patients with Inflammatory Bowel Disease: A Prospective Multicenter Case-Control Study

The vaccination of immunocompromised children against coronavirus disease 2019 is an important public health issue. We evaluated the serological response, safety, and efficacy of the BNT162b2 vaccine in children with and without inflammatory bowel disease (IBD). A prospective, multicenter, case–control study was conducted in a pediatric population, including patients with IBD, aged 12–18 years. Clinical characteristics, safety profile, and serum samples for surrogate virus-neutralizing antibody testing pre- and post-BNT162b2 vaccination were assessed. The breakthrough infection rate during the Omicron outbreak was calculated to evaluate efficacy. Fifteen controls and twenty-three patients with IBD were enrolled. After two vaccine doses, the median level of percentage inhibition was highly increased, without significant differences between the groups (control 96.9 and IBD 96.3). However, it was significantly reduced in IBD patients receiving combination therapy (anti-tumor necrosis factor-α + immunomodulators) relative to those in other therapies and controls. Serious adverse events were not observed. The breakthrough infection rate was 42.1%, without statistical differences between the groups. Immunization with BNT162b2 in patients with IBD was comparable with that in healthy adolescents in terms of immunogenicity and safety. Nevertheless, the efficacy of BNT162b2 in preventing infection caused by the Omicron variant in the pediatric population was insufficient.

Modeling Hospital Resource Management during the COVID-19 Pandemic: An Experimental Validation

One of the main challenges posed by the healthcare crisis generated by COVID-19 is to avoid hospital collapse. The occupation of hospital beds by patients diagnosed by COVID-19 implies the diversion or suspension of their use for other specialities. Therefore, it is useful to have information that allows efficient management of future hospital occupancy. This article presents a robust and simple model to show certain characteristics of the evolution of the dynamic process of bed occupancy by patients with COVID-19 in a hospital by means of an adaptation of Kaplan-Meier survival curves. To check this model, the evolution of the COVID-19 hospitalization process of two hospitals between 11 March and 15 June 2020 is analyzed. The information provided by the Kaplan-Meier curves allows forecasts of hospital occupancy in subsequent periods. The results shows an average deviation of 2.45 patients between predictions and actual occupancy in the period analyzed.