Factors Associated with Length of Hospital Stay among COVID-19 Patients in Saudi Arabia: A Retrospective Study during the First Pandemic Wave

Factors Affecting Hospitalization Length and in-Hospital Death Due to COVID-19 Infection in Saudi Arabia: A Single-Center Retrospective Analysis

Background

The emerging COVID-19 coronavirus disease has widely spread, causing a serious worldwide pandemic. Disease severity and mortality risk can be predicted using an analysis of COVID-19 clinical characteristics. Finding out what influences patients' hospitalization length and in-hospital mortality is crucial for decision-making and planning for emergencies. The goal of this study is to identify the factors that influence hospital stay length and in-hospital death due to COVID-19 infection.

Methods

This cross-sectional study was conducted from August to October 2020 and included 630 patients with a confirmed diagnosis of COVID-19 infection. Using odds ratios (OR) and 95% confidence intervals (CI), a multivariable logistic regression model was used to assess the variables that are linked to longer hospital stays and in-hospital deaths.

Results

Most patients were male (64.3%), and most were older than 40 years (81.4%). The mean length of hospital stay (LoHS) was 10.4±11.6 days. The overall death rate among these COVID-19 cases was 14.3%. Non-survivors were older, had more comorbidities, had prolonged LoHS with increased ICU admission rates and mechanical ventilation usage, and had a more severe condition than survivors. ICU admission, low serum albumin, and elevated LDH levels were associated with longer LoHS, while ICU admission, DM, and respiratory diseases as comorbidities, total leukocytic count, and serum albumin were predictors of mortality.

Conclusion

Longer LoHS due to COVID-19 infection was linked to ICU admission, low serum albumin, and elevated LDH levels, while the independent predictors of in-hospital death were ICU admission, DM, and respiratory diseases as comorbidities, total leukocytic count, and serum albumin.

Bayesian model averaging for predicting factors associated with length of COVID-19 hospitalization

INTRODUCTION

The length of hospital stay (LOHS) caused by COVID-19 has imposed a financial burden, and cost on the healthcare service system and a high psychological burden on patients and health workers. The purpose of this study is to adopt the Bayesian model averaging (BMA) based on linear regression models and to determine the predictors of the LOHS of COVID-19.

METHODS

In this historical cohort study, from 5100 COVID-19 patients who had registered in the hospital database, 4996 patients were eligible to enter the study. The data included demographic, clinical, biomarkers, and LOHS. Factors affecting the LOHS were fitted in six models, including the stepwise method, AIC, BIC in classical linear regression models, two BMA using Occam's Window and Markov Chain Monte Carlo (MCMC) methods, and GBDT algorithm, a new method of machine learning.

RESULTS

The average length of hospitalization was 6.7 ± 5.7 days. In fitting classical linear models, both stepwise and AIC methods (R 2 = 0.168 and adjusted R 2 = 0.165) performed better than BIC (R 2 = 0.160 and adjusted = 0.158). In fitting the BMA, Occam's Window model has performed better than MCMC with R 2 = 0.174. The GBDT method with the value of R 2 = 0.64, has performed worse than the BMA in the testing dataset but not in the training dataset. Based on the six fitted models, hospitalized in ICU, respiratory distress, age, diabetes, CRP, PO2, WBC, AST, BUN, and NLR were associated significantly with predicting LOHS of COVID-19.

CONCLUSION

The BMA with Occam's Window method has a better fit and better performance in predicting affecting factors on the LOHS in the testing dataset than other models.

Factors associated with prolonged hospitalization of patients with corona virus disease (COVID-19) in Uganda: a retrospective cohort study

INTRODUCTION

Identification of factors predicting prolonged hospitalization of patients with coronavirus disease (COVID-19) guides the planning, care and flow of patients in the COVID-19 Treatment Units (CTUs). We determined the length of hospital stay and factors associated with prolonged hospitalization among patients with COVID-19 at six CTUs in Uganda.

METHODS

We conducted a retrospective cohort study of patients admitted with COVID-19 between January and December 2021 in six CTUs in Uganda. We conducted generalized linear regression models of the binomial family with a log link and robust variance estimation to estimate risk ratios of selected exposure variables and prolonged hospitalization (defined as a hospital stay for 14 days or more). We also conducted negative binomial regression models with robust variance to estimate the rate ratios between selected exposures and hospitalization duration.

RESULTS

Data from 968 participants were analyzed. The median length of hospitalization was 5 (range: 1-89) days. A total of 136/968 (14.1%: 95% confidence interval (CI): 11.9-16.4%) patients had prolonged hospitalization. Hospitalization in a public facility (adjusted risk ratio (ARR) = 2.49, 95% CI: 1.65-3.76), critical COVID-19 severity scores (ARR = 3.24: 95% CI: 1.01-10.42), and malaria co-infection (adjusted incident rate ratio (AIRR) = 0.67: 95% CI: 0.55-0.83) were associated with prolonged hospitalization.

CONCLUSION

One out of seven COVID-19 patients had prolonged hospitalization. Healthcare providers in public health facilities should watch out for unnecessary hospitalization. We encourage screening for possible co-morbidities such as malaria among patients admitted for COVID-19.

Excess Mortality among Physicians and Dentists during COVID-19 in Italy: A Cross-Sectional Study Related to a High-Risk Territory

Background: Many studies previously reported epidemiological data on mortality due to COVID-19 among health workers. All these studies included a partial sample of the population with a substantial selection bias. The present study evaluates the trend of mortality among physicians and dentists operating in an area considered to be at high risk during the COVID-19 pandemic. Methods: Data relating to all physicians and dentists registered in the province of Pavia (Italy), a sample consisting of 5454 doctors in 2020 was analyzed. The mortality rates obtained were compared with those related to the 5-year period preceding the pandemic and with those related to the general population. Results: In the area considered, a mortality rate of 0.83% (+69% compared to 2015–2019) was observed in the entire sample in 2020 and 0.43% (−11% compared to 2015–2019) in 2021; among physicians, there was a mortality rate of 0.76% (+53% compared to 2015-2019) in 2020 and 0.35% (−29% compared to 2015–2019) in 2021; for dentists, there was a mortality rate of 1.27% (+185% compared to 2015–2019) in 2020 and 1.01% (+127% compared to 2015–2019) in 2021. Conclusions: These data report the global impact of the SARS-CoV-2 pandemic on physicians and dentists in a high-risk territory. In 2020, a significant increase in the mortality rate compared to the previous 5 years was observed for both physicians and dentists; in 2021, a significant increase in the mortality rate was observed only for dentists. These data are also significant in evaluating the impact of vaccination on physicians and dentists and indicate that dentists were among the professions most at risk during the pandemic.