What are the risk factors of hospital length of stay in the novel coronavirus pneumonia (COVID-19) patients? A survival analysis in southwest China

Evaluation of the relationship of treatment and vaccination with prognosis in patients with a diagnosis of COVID-19

PURPOSE

Coronavirus disease 2019 (COVID-19) has affected millions of people worldwide and caused mortality. Many factors have been reported to affect the prognosis of COVID-19. In this study, we aimed to investigate the effects of drug therapy and vaccination on prognosis in patients hospitalized with a COVID-19 diagnosis.

METHODS

In this single-center, cross-sectional study, data were retrospectively collected from patients receiving inpatient treatment at a university hospital with a diagnosis of COVID-19 between January 1, 2020, and April 30, 2022. The patients' demographic and clinical characteristics were recorded. The Chi-square, Cox and logistic regression was performed, P < 0.05 was considered statistically significant.

RESULTS

Total 1723 patients (50.1% were men, mean age: 60.6 ± 16.90) who had not been vaccinated rate was 27.0% (> 3 doses: 45.7%). Mortality rate was 17.0%. Increasing age, male, a high Charlson Comorbidity Index (CCI), and no vaccination significantly increased mortality (P < 0.05). The mortality rate was significantly lower in the chloroquine treatment group than in the other treatment groups. Increasing age, male, and a high CCI were determined to be factors that significantly increased the length of hospital stay (LOHS). LOHS found to be significantly lower in the favipiravir or chloroquine groups compared to the remaining treatment groups (P < 0.001). Both mortality and the LOHS significantly differed according to AST, d-dimer, ferritin, and GFR.

CONCLUSION

This study primarily investigated the effect of treatment and vaccination on the prognosis of COVID-19. This was determined to be prepared for another potential pandemic that may arise due to COVID-19.

Maternal Characteristics and Outcomes Affected by COVID-19 Pandemic in Indonesia

Aim

Compare the impact of the intervention on the outcome of pregnancy of patients receiving obstetric care with and non-COVID-19 diagnosis at a tertiary referral hospital located in the region of East Java, Indonesia.

Methods

This was a cross-sectional analysis of 694 pregnant women. These patients' information was acquired based on the medical data obtained from the hospital. The Mann-Whitney test was used to analyze the disparities among the factors examined in this research investigation.

Results

There was a statistically significant difference in length of stay (LOS). It has been shown that individuals diagnosed with COVID-19 often exhibit a longer length of stay (LOS) in healthcare facilities compared to those who do not have the infection. More than fifty percent of patients gave birth by cesarean section, 83 in COVID-19 group and 283 in non-COVID-19 group. The most prevalent complications among COVID-19 patients were maternal infectious and parasitic diseases (1.3 vs 0%), prolonged labor (12.3 vs 9.6%) and puerperal complications (0.6 vs 0%). In 40.9% of COVID-19 patients, acute respiratory distress syndrome (ARDS) was encountered. Infection due to COVID-19 had no discernible impact on the outcomes of pregnancy.

Conclusion

Numerous interventions, including cesarean delivery in COVID-19 and non-COVID-19 patients, require reevaluation. It is imperative to undertake a comprehensive reassessment of the health care delivery system, with particular emphasis on enhancing the efficacy of the referral system.

Association between the severity of periodontitis, COVID-19, C-reactive protein and interleukin-6 levels in hospitalized patients: a case‒control study

BACKGROUND

The COVID-19 pandemic is perhaps one of the most important events of the 21st century. Periodontitis is one of the most prevalent diseases of the oral cavity. Due to possible pathways of interaction between these two diseases, we investigated their association.

METHODS

The study population consisted of hospitalized patients with established COVID-19 diagnoses. Patients with mild to moderate COVID-19 were considered controls, while cases had severe to critical COVID-19. Periodontal examination and serum and saliva sampling were performed for each patient. Relevant medical data were extracted from patients' hospital files.

RESULTS

Of the enrolled patients, 122 were included in the statistical analyses. The severity of periodontitis was directly and significantly correlated with the severity of COVID-19 (P < 0.001). Patients with generalized stage III or IV periodontitis displayed an adjusted odds ratio of 4.24 for severe to critical COVID-19. Salivary and serum interleukin-6 levels were significantly associated with COVID-19 severity (P values: 0.002 and 0.004, respectively). Hospitalization length was significantly associated with the severity of periodontitis (P = 0.004). Clinical attachment level and gingival index were associated with increased odds for adverse events (P values: 0.004 and 0.035, respectively), while number of remaining teeth was associated with decreased odds for adverse events (P = 0.023).

CONCLUSIONS

This study showed that the severity of periodontitis is associated with the severity of COVID-19. This association might manifest as increased odds of adverse events. COVID-19 severity was associated with higher levels of salivary and serum interleukin-6 levels.

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.

Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties

Although the coronavirus disease (COVID-19) emergency status is easing, the COVID-19 pandemic continues to affect healthcare systems globally. It is crucial to have a reliable and population-wide prediction tool for estimating COVID-19-induced hospital admissions. We evaluated the feasibility of using wastewater-based epidemiology (WBE) to predict COVID-19-induced weekly new hospitalizations in 159 counties across 45 states in the United States of America (USA), covering a population of nearly 100 million. Using county-level weekly wastewater surveillance data (over 20 months), WBE-based models were established through the random forest algorithm. WBE-based models accurately predicted the county-level weekly new admissions, allowing a preparation window of 1-4 weeks. In real applications, periodically updated WBE-based models showed good accuracy and transferability, with mean absolute error within 4-6 patients/100k population for upcoming weekly new hospitalization numbers. Our study demonstrated the potential of using WBE as an effective method to provide early warnings for healthcare systems.

Hospital length of stay throughout bed pathways and factors affecting this time: A non-concurrent cohort study of Colombia COVID-19 patients and an unCoVer network project

Predictions of hospital beds occupancy depends on hospital admission rates and the length of stay (LoS) according to bed type (general ward -GW- and intensive care unit -ICU- beds). The objective of this study was to describe the LoS of COVID-19 hospital patients in Colombia during 2020-2021. Accelerated failure time models were used to estimate the LoS distribution according to each bed type and throughout each bed pathway. Acceleration factors and 95% confidence intervals were calculated to measure the effect on LoS of the outcome, sex, age, admission period during the epidemic (i.e., epidemic waves, peaks or valleys, and before/after vaccination period), and patients geographic origin. Most of the admitted COVID-19 patients occupied just a GW bed. Recovered patients spent more time in the GW and ICU beds than deceased patients. Men had longer LoS than women. In general, the LoS increased with age. Finally, the LoS varied along epidemic waves. It was lower in epidemic valleys than peaks, and decreased after vaccinations began in Colombia. Our study highlights the necessity of analyzing local data on hospital admission rates and LoS to design strategies to prioritize hospital beds resources during the current and future pandemics.

Potential drug-drug interactions and their associated factors in hospitalized COVID-19 patients with comorbidities

Background

Hospitalized COVID-19 patients with comorbidities receive more complex drug therapy. This increases the probability of potential drug-drug interactions (pDDIs). Studies on pDDIs in hospitalized patients with COVID-19 in countries with limited resources like Indonesia during the later period of the disease are still limited. This study aims to identify the pattern of pDDIs in hospitalized COVID-19 patients with comorbidities and their associated factors, especially in the second wave of the disease in Indonesia.

Methods

This study was a longitudinal-retrospective study observing hospitalized COVID-19 patients with comorbidities using medical record data in June-August 2021 at a public hospital in a region in Indonesia. pDDIs were identified using the Lexicomp^® database. Data were descriptively analyzed. Factors associated with important pDDIs were analyzed in multivariate logistic regression model.

Results

A total of 258 patients with a mean age of 56.99 ± 11.94 years met the inclusion criteria. Diabetes mellitus was the most common comorbidity experienced by 58.14% of the patients. More than 70% of the patients had one comorbidity and the average number of administered drugs was 9.55 ± 2.71 items per patient. Type D pDDIs, which required modification of therapeutic regimens, amounted to 21.55% of the total interactions. Only the number of drugs was significantly and independently associated with type D pDDIs (adjusted odds ratio 1.47 [1.23-1.75], p < 0.01).

Conclusion

The drugs involved in the pDDIs of hospitalized COVID-19 patients with comorbidities may differ depending on the disease periods, hospital settings, or countries. This study was small, single center, and of short duration. However, it may give a glimpse of important pDDIs during the delta variant of COVID-19 in a similar limited-resource setting. Further studies are needed to confirm the clinical significance of these pDDIs.

Clinical courses of 24,563 hospitalized COVID-19 patients during the first 12 months of the pandemic in the Central City of Iran

This study was designed and implemented to analyze and establish documents related to the above cases in the first to third COVID-19 epidemic waves for the use of researchers and doctors during and after the epidemic. The current case series study was conducted on 24,563 thousand hospitalized COVID-19 patients by examining their clinical characteristics within a one-year period from the beginning of the pandemic on 02.22.2020 to 02.14.2021, which included the first to the third waves, based on gender and severity of COVID-19. The mean age of the participants was 56 ± 20.71, and 51.8% were male. Out of a total of 24,563 thousand hospitalized COVID-19 patients until February 2021, there were 2185 mortalities (9.8%) and 2559 cases of severe COVID-19 (13.1%). The median length of hospitalization from the time of admission to discharge or death in the hospital (IQR: 13-41) was estimated to be 21 days. The rate of hospital mortality was higher in severe (37.8%) than in non-severe (4.8%) cases of COVID-19, While the risk of severe cases increased significantly in the third (HR = 1.65, 95% CI: 1.46-1.87, P < 0.001) and early fourth waves (HR = 2.145, 95% CI: 1.7-2.71, P < 0.001). Also, the risk of contracting severe COVID-19 increased significantly in patients aged ≥ 65 years old (HR = 2.1, 95% CI 1.1.93-2.72, P < 0.001). As shown by the results, the rates of hospital mortality (9.3% vs. 8.5%) and severe cases of COVID-19 (13.6% vs. 12.5%) were higher among men than women (P < 0.01). In our study, the mortality rate and severity of COVID-19 were within the scope of global studies. Men experienced higher severity and mortality than women. The was a significantly higher prevalence of old age and underlying diseases in individuals with severe COVID-19. Our data also showed that patients with a previous history of COVID-19 had a more severe experience of COVID-19, while most of these patients were also significantly older and had an underlying disease.

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.

Exploration of Sex and Age-Based Associations in Clinical Characteristics, Predictors of Severity, and Duration of Stay among COVID-19 Patients at the University Hospital of Saudi Arabia

COVID-19 infection has a spectrum of variable clinical severity between populations because of their characteristic demographic features, co-morbidities, and immune system reactions. This pandemic tested the healthcare system's preparedness, which depends on predictors of severity and factors related to the duration of hospital stays. Therefore, we carried out a single-center, retrospective cohort study in a tertiary academic hospital to investigate these clinical features and predictors of severe disease and study the different factors that affect hospital stay. We utilized medical records from March 2020 to July 2021, which included 443 confirmed (positive RT-PCR) cases. The data were explained using descriptive statistics and analyzed via multivariate models. Among the patients, 65.4% were female and 34.5% were male, with a mean age of 45.7 years (SD ± 17.2). We presented seven age groups with ranges of 10 years and noticed that patients aged 30-39 years old comprised 23.02% of the records, while patients aged 70 and above comprised 10%. Nearly 47% were diagnosed as having mild, 25% as moderate, 18% as asymptomatic, and 11% as having a severe case of COVID-19 disease. Diabetes was the most common co-morbidity factor in 27.6% of patients, followed by hypertension (26.4%). Our population's predictors of severity included pneumonia, identified on a chest X-ray, and co-morbid conditions such as cardiovascular disease, stroke, ICU stay, and mechanical ventilation. The median length of hospital stay was six days. It was significantly longer in patients with a severe disease and who were administered systemic intravenous steroids. An empirical assessment of various clinical parameters could assist in effectively measuring the disease progression and follow-up with patients.

Relation between Orotracheal Intubation, Inflammatory Markers, Breathing and Voice in Post-COVID-19

INTRODUCTION

COVID-19, an infectious disease with a wide spectrum of clinical manifestations and intensities in the human body, it can cause respiratory and vocal disorders, with fatigue.

OBJECTIVE

To verify the relation between biological Inflammatory markers D-dimers and C-Reactive Protein, Forced Vital Capacity, Maximum Phonation Time, vocal performance and fatigue, length of hospitalization period and gender of people affected by COVID-19 who were hospitalized, but did not use orotracheal intubation and compare with a group of post-COVID-19 patients with orotracheal intubation.

METHODS

Data on D-dimers and C-Reactive Protein, spirometry, Maximum Phonation Time, performance and vocal fatigue were collected. The study included 42 adult people affected by COVID-19 who were hospitalized, 22 (52.4%) female and 20 (47.6%) male; 23 (54.8%) critical cases composing the group with orotracheal intubation (average age 48.9 years old) and 19 (45.24%) severe cases in the group without orotracheal intubation (average age 49.9 years old).

RESULTS

hospital length of stay was significantly longer for the group with orotracheal intubation; D-dimers were significantly altered in all groups; correlations between maximum phonation times were positive and significant; correlations between maximum phonation times, vocal performance and fatigue were both negative and significant.

CONCLUSION

Patients with orotracheal intubation had longer hospital internment and increased D-dimers and were amazed that, whenever maximum phonation times decreased performance and vocal fatigue increased.

Parameters to Predict the Outcome of Severe and Critical COVID-19 Patients when Admitted to the Hospital

Manifestations of COVID-19 are diverse and range from asymptomatic to severe, critical illness and death. Cases requiring hospital care (in severe and critical illnesses) are associated with comorbidities and hyperactivation of the immune system. Therefore, in this exploratory observational study, we analyzed which parameters are associated with mortality. We evaluated: demographic characteristics (age, sex and comorbidities), laboratory data (albumin, leukocytes, lymphocytes, platelets, ferritin), days of hospital stay, interleukins (IL-2, IL-6, IL-7, IL-10, IL-17) and sP-selectin in 40 Mexican patients admitted to medical emergencies with a confirmed diagnosis of COVID-19, a complete clinical record, and who signed the informed consent. Twenty severe (they required intermediate care with non-invasive ventilation) and twenty critically ill patients (they required mechanical ventilation) were classified, and these were subsequently compared with healthy and recovered subjects. A significant difference was found between the hospitalized groups in the parameters of age, ferritin, days of hospital stay and death with p values = 0.0145, p = 0.0441, p = 0.0001 and p = 0.0001, respectively. In the determination of cytokines and P-selectin, a significant difference was found between the following groups: recovered patients and healthy volunteers compared with hospitalized patients in severe and critical condition. Importantly, IL-7 remained elevated one year later in recovered patients. Taken together, these values determined at the time of hospital admission could be useful to monitor patients closely and evaluate in-hospital progress, hospital discharge, and out-of-hospital progress.

Predictors of Basic Activity in Daily Living and Length of Hospitalization in Patients with COVID-19

Background: Patients recovered from COVID-19 often suffer from the sequelae of the disease, which can hinder the patients’ activity in daily living. Early recognition of the patients at risk of prolonged hospitalization and impaired physical functioning is crucial for early intervention. We aim to identify the predictors of prolonged hospitalization and impaired activity in daily living in this study. Methods: COVID-19 patients hospitalized in a medical center were divided into two groups according to the Barthel index three months after discharge and the median length of hospital stay, respectively. Chi-square test and Mann–Whitney U test were performed to check the differences between the two groups in patient characteristics as well as hematology tests at the emergency department, the intensive care unit mobility scale (ICUMS), and the medical research council sum score (MRCSS). Logistic regression and the receiver operating characteristic curve analysis were further performed for the factors with significant differences between the two groups. Results: Both ICUMS and MRCSS showed significant differences between the groups. The ICUMS had an odds ratio of 0.61 and the MRCSS of 0.93 in predicting a Barthel index score less than 100 three months after discharge. The MRCSS had an odds ratio of 0.82 in predicting a prolonged length of hospital stay. Conclusion: Both ICUMS and MRCSS upon admission are predictive of a Barthel index score of less than 100 three months after discharge. On the other hand, only MRCSS has predictive value of a prolonged hospitalization.

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

The COVID-19 pandemic severely affected healthcare systems and tested their preparedness. To date, the length of hospital stay (LoHS) and its factors among COVID-19 patients has not been thoroughly studied. Moreover, it is essential to identify the features of these patients. Adult COVID-19 patients in Saudi Arabia with complete electronic medical records and who were hospitalised for >1 day between 1 May 2020 and 30 July 2020 at one of two hospitals were considered for this retrospective cohort study. Descriptive statistics and multivariate generalized linear models were performed using the data. Of the patients, 34% were ≥50 years old and 80.14% were female. More than 70% had mild-to-moderate symptoms; 45% had either diabetes or hypertension. The median LoHS was 7.00 days (IQR: 3−11). Patients who were females, had either critical or severe disease, were on mechanical ventilation, had diabetes, and administered ceftriaxone had significantly longer LoHS (p < 0.05). Patients administered zinc sulphate had significantly shorter LoHS (p = 0.0008). During the first pandemic wave, COVID-19 patients were hospitalised for 7 days. Healthcare professionals should pay more attention to women, patients with diabetes, and those with severe or critical symptoms. Unnecessary use of ceftriaxone should be minimised, and zinc sulphate can be administered.

Fever, Tachypnea, and Monocyte Distribution Width Predicts Length of Stay for Patients with COVID-19: A Pioneer Study

(1) Background: Our study investigated whether monocyte distribution width (MDW) could be used in emergency department (ED) settings as a predictor of prolonged length of stay (LOS) for patients with COVID-19. (2) Methods: A retrospective cohort study was conducted; patients presenting to the ED of an academic hospital with confirmed COVID-19 were enrolled. Multivariable logistic regression models were used to obtain the odds ratios (ORs) for predictors of an LOS of >14 days. A validation study for the association between MDW and cycle of threshold (Ct) value was performed. (3) Results: Fever > 38 °C (OR: 2.82, 95% CI, 1.13−7.02, p = 0.0259), tachypnea (OR: 4.76, 95% CI, 1.67−13.55, p = 0.0034), and MDW ≥ 21 (OR: 5.67, 95% CI, 1.19−27.10, p = 0.0269) were robust significant predictors of an LOS of >14 days. We developed a new scoring system in which patients were assigned 1 point for fever > 38 °C, 2 points for tachypnea > 20 breath/min, and 3 points for MDW ≥ 21. The optimal cutoff was a score of ≥2. MDW was negatively associated with Ct value (β: −0.32 per day, standard error = 0.12, p = 0.0099). (4) Conclusions: Elevated MDW was associated with a prolonged LOS.