Risk factors associated with in-hospital mortality patients with COVID-19 in Saudi Arabia

Interplay of Demographic Influences, Clinical Manifestations, and Longitudinal Profile of Laboratory Parameters in the Progression of SARS-CoV-2 Infection: Insights from the Saudi Population

Understanding the factors driving SARS-CoV-2 infection progression and severity is complex due to the dynamic nature of human physiology. Therefore, we aimed to explore the severity risk indicators of SARS-CoV-2 through demographic data, clinical manifestations, and the profile of laboratory parameters. The study included 175 patients either hospitalized at King Abdulaziz Medical City-Riyadh or placed in quarantine at designated hotels in Riyadh, Saudi Arabia, from June 2020 to April 2021. Hospitalized patients were followed up through the first week of admission. Demographic data, clinical presentations, and laboratory results were retrieved from electronic patient records. Our results revealed that older age (OR: 1.1, CI: [1.1-1.12]; p < 0.0001), male gender (OR: 2.26, CI: [1.0-5.1]; p = 0.047), and blood urea nitrogen level (OR: 2.56, CI: [1.07-6.12]; p = 0.034) were potential predictors of severity level. In conclusion, the study showed that apart from laboratory parameters, age and gender could potentially predict the severity of SARS-CoV-2 infection in the early stages. To our knowledge, this study is the first in Saudi Arabia to explore the longitudinal profile of laboratory parameters among risk factors, shedding light on SARS-CoV-2 infection progression parameters.

Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34

A lot of underdeveloped nations particularly in Africa struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (ResNet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. 1, 200 datasets of breast x-ray mammography images from the National Radiological Society's (NRS) archives were employed in the study. The dataset was categorised as implant cancer negative, implant cancer positive, cancer negative and cancer positive in order to increase the consistency of x-ray mammography images classification and produce better features. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.

The Relationship of ABO and Rh Blood Group Types With Severe COVID-19 Disease Mortality in ICU Patients: Insights From a Single-Center Experience in Southern Saudi Arabia

Introduction The global COVID-19 pandemic has triggered an unprecedented public health crisis, emphasizing the need to understand factors influencing disease outcomes. This study explores the role of genetic variations in blood group antigens, particularly ABO and RhD, in shaping mortality rates among critically ill COVID-19 patients in the southern region of Saudi Arabia. Methods Utilizing a retrospective, noninterventional approach, we analyzed medical records of 594 COVID-19 patients admitted to the intensive care unit (ICU) at Aseer Central Hospital from August 2020 to April 2021. The cohort, with a mean age of 60.5 years, consisted of a predominantly male population. Results The study encompassed a diverse age range of 18 to 103 years, with a mean age of 60.5 ± 17.3 years. Of the 594 patients, 398 (67%) were male, and only 5 (0.8%) had a history of smoking. Blood group distribution revealed 275 (48.4%) with O-, 189 (33.3%) with A+, and 51 (9%) with AB- types. Predominant chronic conditions included diabetes mellitus (35.5%). Tragically, 320 patients (54.6%) experienced mortality, with a 100% mortality rate for the B+ blood group and 92.9% for O- blood group. Conclusion This analysis establishes significant statistical links, underscoring the pivotal role of blood type, particularly the Rh factor, in influencing mortality risk among critically ill COVID-19 patients. These findings contribute valuable insights into risk stratification and personalized care for severe cases, emphasizing the importance of genetic considerations in understanding disease outcomes.

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.

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.

COVID-19 Mortality in the Colorado Center for Personalized Medicine Biobank

Over 6.37 million people have died from COVID-19 worldwide, but factors influencing COVID-19-related mortality remain understudied. We aimed to describe and identify risk factors for COVID-19 mortality in the Colorado Center for Personalized Medicine (CCPM) Biobank using integrated data sources, including Electronic Health Records (EHRs). We calculated cause-specific mortality and case-fatality rates for COVID-19 and common pre-existing health conditions defined by diagnostic phecodes and encounters in EHRs. We performed multivariable logistic regression analyses of the association between each pre-existing condition and COVID-19 mortality. Of the 155,859 Biobank participants enrolled as of July 2022, 20,797 had been diagnosed with COVID-19. Of 5334 Biobank participants who had died, 190 were attributed to COVID-19. The case-fatality rate was 0.91% and the COVID-19 mortality rate was 122 per 100,000 persons. The odds of dying from COVID-19 were significantly increased among older men, and those with 14 of the 61 pre-existing conditions tested, including hypertensive chronic kidney disease (OR: 10.14, 95% CI: 5.48, 19.16) and type 2 diabetes with renal manifestations (OR: 5.59, 95% CI: 3.42, 8.97). Male patients who are older and have pre-existing kidney diseases may be at higher risk for death from COVID-19 and may require special care.

Profiles of Independent-Comorbidity Groups in Senior COVID-19 Patients Reveal Low Fatality Associated with Standard Care and Low-Dose Hydroxychloroquine over Antivirals

Introduction

The lack of feasible therapies and comorbidities aggravate the COVID-19 case-fatality rate (CFR). However, reports examining CFR associations with diabetes, concomitant cardiovascular diseases, chronic kidney disease, and chronic liver disease (CLD) are limited. More studies assessing hydroxychloroquine (Hcq) and antivirals are needed.

Purpose

To examine associations of COVID-19 CFR in comorbid patient groups each with single comorbidities and after treatment with Hcq, favipiravir, and dexamethasone (Dex), either alone or in combination versus standard care.

Methods

Using statistical analysis, we descriptively determined these associations among 750 COVID-19 patient groups during the last quarter of 2021.

Results

A diabetes comorbidity (40%, n=299) showed twice the fatality (CFR 14%) of the others (CFR 7%; P=0.001). Hypertension (Htn) was the second-commonest comorbidity (29.5%, n=221), with similar CFR to diabetes (15% and 7% for Htn and non-Htn, respectively), but with higher significance (P=0.0006167). Although only 4% (n=30) heart failure (HF) was reported, the CFR (40%) was much higher than in those without it (8%). A similar rate (4%) for chronic kidney disease was reported, with CFRs of 33% and 9% among those with and without it, respectively (P=0.00048). Ischemic heart disease was 11% (n=74), followed by chronic liver disease (0.4%) and history of smoking (1%); however, these were not significant due to the sample sizes. Treatment indicated standard care and Hcq alone or in combination were superior (CFR of 4% and 0.5%, respectively) compared to favipiravir (25%) or Dex (38.5%) independently or in combination (35.4%). Furthermore, Hcq performed well (CFR 9%) when combined with Dex (9%; P=4.28-²⁶).

Conclusion

The dominance of diabetes and other comorbidities with significant association with CFR implied existence of a common virulence mechanism. The superiority of low-dose Hcq and standard care over antivirals warrants further studies.

Comparison Between Physiological Scores SIPF, CURB-65, and APACHE II as Predictors of Prognosis and Mortality in Hospitalized Patients with COVID-19 Pneumonia: A Multicenter Study, Saudi Arabia

Background

A coronavirus pandemic (COVID-19) is associated with catastrophic effects on the world with high morbidity and mortality. We aimed to evaluate the accuracy of physiological shock index (SIPF) (shock index and hypoxemia), CURB -65, acute physiology, and chronic health assessment II (APACHE II) as predictors of prognosis and in-hospital mortality in patients with COVID-19 pneumonia.

Methods

In Saudi Arabia, a multicenter retrospective study was conducted on hospitalized adult patients confirmed to have COVID-19 pneumonia. Information needed to calculate SIPF, CURB-65, and APACHE II scores were obtained from medical records within 24 hours of admission.

Results

The study included 1131 COVID-19 patients who met the inclusion criteria. They were divided into two groups: (A) the ICU group (n=340; 30.1%) and (B) the ward group (n=791; 69.9%). The most common concomitant diseases of patients at initial ICU admission were hypertension (71.5%) and diabetes (62.4%), and most of them were men (63.8%). The overall mortality was 18.7%, and the mortality rate was higher in the ICU group than in the ward group (39.4% vs 9.6%; p < 0.001). The SIPF score showed a significantly higher ability to predict both ICU admission and mortality in patients with COVID-19 pneumonia compared with APACHE II and CURB -65; (AUC 0.89 vs 0.87; p < 0.001) and (AUC 0.89 vs 0.84; p < 0.001) for ICU admission and (AUC 0.90 vs 0.65; p < 0.001) and (AUC 0.90 vs 0.80; p < 0.001) for mortality, respectively.

Conclusion

The ability of the SIPF score to predict ICU admission and mortality in COVID-19 pneumonia is higher than that of APACHE II and CURB-65. The overall mortality was 18.7%, and the mortality rate was higher in the ICU group than in the ward group (39.4% vs 9.6%; p < 0.001).