Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital

Genetic association of ACE2 and TMPRSS2 polymorphisms with COVID-19 severity; a single centre study from Egypt

BACKGROUND

Since the emergence of the COVID-19 infection in China, it has caused considerable morbidity, mortality, and economic burden. It causes the vast majority of clinical manifestations, ranging from mild or even no symptoms to severe respiratory failure. There are many risk factors for severe COVID-19, such as old age, male gender, and associated comorbidities. A major role for genetic factors may exist. The SARS-CoV-2 virus enters the cell primarily through ACE2 receptors. rs2285666 is one of many polymorphisms found in the ACE2 receptor gene. To enable endosome-independent entry into target cells, the transmembrane protease serine-type 2 (TMPRSS2) is necessary to cleave the virus' spike (S) glycoprotein. TMPRSS2 is characterized by an androgen receptor element. The rs12329760 polymorphism in TMPRSS2 may explain different genetic susceptibilities to COVID-19.

METHOD

This cross-sectional study was held in Mansoura University Hospitals during the period from June 2020 to April 2022 on patients who had mild and severe COVID-19. Demographic, clinical, and laboratory data were collected, and the TaqMan real-time polymerase chain was used for allelic discrimination in the genotyping of rs2285666 and rs12329760.

RESULTS

This study included 317 Egyptian patients, aged from 0.2 to 87 years. Males were 146, while females were 171. They were divided into mild and severe groups (91 and 226 patients, respectively) based on their clinical symptoms. There was a significant association between COVID-19 severity and male gender, hypertension, diabetes mellitus, and high CRP. The genotype and allele frequency distributions of the ACE2 rs2285666 polymorphism showed no significant association with the severity of COVID-19 in both. In contrast, in TMPRSS2 rs12329760 minor T allele and CT, TT genotypes were significantly associated with a reduced likelihood of developing severe COVID-19.

CONCLUSION

Our study indicates that the ACE2 rs2285666 polymorphism is not related to the severity of COVID-19, whether genotypes or alleles. In TMPRSS2 rs12329760, the dominant model and T allele showed significantly lower frequencies in severe cases, with a protective effect against severity. The discrepancies with previous results may be due to variations in other ACE2 receptor-related genes, inflammatory mediators, and coagulation indicators. Haplotype blocks and differences in racial makeup must be taken into consideration. Future research should be done to clarify how ethnicity affects these polymorphisms and how other comorbidities combine to have an additive effect.

Identified Factors in COVID-19 Patients in Predicting Mortality

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has led to a significant increase in global mortality rates. Numerous studies have been conducted to identify the factors associated with mortality in COVID-19 cases. In these studies, overall mortality was evaluated in patients, and no distinction was made as ward or intensive care mortality.

AIM

This study aims to determine mortality-related factors in patients who died while in the ward. This could enable us to review the indications for intensive care hospitalization in possible pandemics.

MATERIALS AND METHOD

This retrospective study was conducted on a cohort of 237 patients who applied to our institution between January 2020 and December 2021 with the diagnosis of COVID-19. Demographic characteristics, length of stay, type of admission (emergency ward or outpatient clinic), presence of comorbidities, thoracic computerized tomography (CT) findings, and laboratory findings were extracted from the hospital database. The demographic and laboratory results of both deceased and recovered patients were compared.

RESULTS

While many demographic and laboratory findings were statistically significant in the initial analysis, multiple logistic regression analysis showed that decreased albumin levels (adjusted OR = 0.23, 95% CI = 0.09 - 0.57), increased troponin (adjusted OR = 1.03, 95% CI = 1.02 - 1.05), and procalcitonin (adjusted OR = 3.46, 95% CI = 1.04 - 11.47) levels and higher partial thromboplastin time (PTT) (adjusted OR = 1.18, 95% CI = 1.09 - 1.28) values, presence of diabetes mellitus (DM) in patients (adjusted OR = 2.18, 95% CI = 1.01 - 4.69, P = 0.047), and admission to hospital from the emergency department (adjusted OR = 5.15, 95% CI = 1.45 - 18.27, P = 0.011) were significantly associated with mortality when adjusted for age. When a predictive model is constructed with these variables, this model predicted mortality statistically significant (AUC = 0.904, 95% CI = 0.856 - 0.938, P < 0.001), with a sensitivity of 77.2% (95% CI, 67.8 - 85), a specificity of 91.2% (95% CI, 85.1 - 95.4), a positive predictive value (PPV) of 86.7% (95% CI, 72 - 85.3), and an negative predictive value (NPV) of 84.4% (95% CI, 79.4 - 89.6).

CONCLUSION

In this study, we may predict mortality among COVID-19-diagnosed patients admitted to the ward via this model which has the potential to provide guidance for reconsidering the indications for intensive care unit (ICU) admission.

Development and validation of a prediction model based on comorbidities to estimate the risk of in-hospital death in patients with COVID-19

Background

Most existing prognostic models of COVID-19 require imaging manifestations and laboratory results as predictors, which are only available in the post-hospitalization period. Therefore, we aimed to develop and validate a prognostic model to assess the in-hospital death risk in COVID-19 patients using routinely available predictors at hospital admission.

Methods

We conducted a retrospective cohort study of patients with COVID-19 using the Healthcare Cost and Utilization Project State Inpatient Database in 2020. Patients hospitalized in Eastern United States (Florida, Michigan, Kentucky, and Maryland) were included in the training set, and those hospitalized in Western United States (Nevada) were included in the validation set. Discrimination, calibration, and clinical utility were evaluated to assess the model's performance.

Results

A total of 17 954 in-hospital deaths occurred in the training set (n = 168 137), and 1,352 in-hospital deaths occurred in the validation set (n = 12 577). The final prediction model included 15 variables readily available at hospital admission, including age, sex, and 13 comorbidities. This prediction model showed moderate discrimination with an area under the curve (AUC) of 0.726 (95% confidence interval [CI]: 0.722-0.729) and good calibration (Brier score = 0.090, slope = 1, intercept = 0) in the training set; a similar predictive ability was observed in the validation set.

Conclusion

An easy-to-use prognostic model based on predictors readily available at hospital admission was developed and validated for the early identification of COVID-19 patients with a high risk of in-hospital death. This model can be a clinical decision-support tool to triage patients and optimize resource allocation.