COVID-19 Mortality Research Articles

Prevalence and Correlates of SARS-CoV-2 Vaccine Uptake and Hesitancy Among People With HIV Across the United States.

People with HIV (PWH) have higher risk of COVID-19 mortality. SARS-CoV-2 vaccination is highly effective among PWH, although vaccine hesitancy could limit the population-level impact. From February 2021 to April 2022, PWH from 8 sites in the Centers for AIDS Research Network of Integrated Clinical Systems completed a vaccine hesitancy instrument as part of routine care. Participants were defined as vaccine hesitant if they had not received the SARS-CoV-2 vaccine and would probably/definitely not receive it. We assessed factors associated with SARS-CoV-2 vaccine hesitancy using logistic regression adjusted for demographics, unsuppressed viral load (VL > 200 copies/mL), month, and time on ART; using inverse probability weighting for survey nonresponse. Overall, 3288 PWH with a median age of 55 were included; 18% were female and 94% were virally suppressed. At the time of survey, 27% reported they had not received the SARS-CoV-2 vaccine, and 9% (n = 279) reported vaccine hesitancy. Factors associated with vaccine hesitancy included female sex (adjusted odds ratio [AOR] = 2.3; 95% confidence interval (CI): 1.6-3.2), Black vs. White race (AOR 1.7; 95% CI: 1.2 to 2.4), younger age (AOR 1.4; 95% CI: 1.2 to 1.5), and unsuppressed VL (AOR 1.9; 95% CI: 1.3 to 3.0). Overall, over one-quarter of PWH in this multisite cohort were unvaccinated for SARS-CoV-2 when interviewed February 21-April 22. Vaccine hesitancy was reported by approximately 9% of PWH and was higher among women, Black PWH, younger PWH, PWH with unsuppressed VL, and those in the South/Midwest. Renewed efforts are needed to address concerns of PWH about vaccinations against COVID-19 as the pandemic evolves, and vaccines in general, given the potential for future pandemics.

U.S. County-level Food Insecurity and COVID-19 Mortality: A Bayesian Spatial Analysis with R-INLA.

U.S. County-level Food Insecurity and COVID-19 Mortality: A Bayesian Spatial Analysis with R-INLA.

Impact of short-term exposure to ambient air pollutants and meteorological factors on COVID-19 incidence and mortality: A retrospective study from Dammam, Saudi Arabia

The symptoms of COVID-19 included fever with or without respiratory syndrome, but patients subsequently developed pulmonary abnormalities. Exposure to air pollution, meanwhile, is associated with complications such as acute respiratory inflammations, asthma attack, and deaths from cardiorespiratory disease. To analyze the association of the air quality index (AQI), ambient air pollutants (PM10, SO2 and O3) and meteorological parameters (temperature and relative humidity [RH]) with COVID-19 incidence and mortality, a retrospective study was conducted to examine COVID-19 infection, meteorological parameters, ambient air quality and ambient air pollutants in Dammam from 1 January to 30 April 2021. Data of COVID-19 incidence and mortality for Dammam were retrieved from Saudi Arabia Ministry of Health's publicly accessible database. Meteorological data, AQI and average PM10, SO2 and O3 values were extracted from the publicly available website of Ministry of Environment, Water and Agriculture. The correlation of COVID-19 incidence and mortality with the independent variables was analysed by Pearson's correlation test or Spearman's rho test as applicable, and a p-value less than 0.05 was considered significant. COVID-19 incidence exhibited a positive correlation with temperature (r = 0.537, p = .0001) and a negative correlation with RH (r=−0.487, p=.0001). No correlation was observed between the meteorological variables and COVID-19 mortality. COVID-19 incidence showed a positive correlation with AQI (r=0.269, p=.015) and with the ambient air pollutants SO2 and O3 (r=0.258, p=.018), and COVID-19 mortality showed a positive correlation with PM10 (rs = 0.344, p=.002). Short-term exposure to O3, SO2 and higher temperature had direct relationship with COVID-19 incidence, while RH had inverse relationship. PM10 is positively associated with COVID-19 mortality.

Spatial nonstationarity and the role of environmental metal exposures on COVID-19 mortality in New Mexico

Worldwide, the COVID-19 pandemic has been influenced by a combination of environmental and sociodemographic drivers. To date, population studies have overwhelmingly focused on the impact of societal factors. In New Mexico, the rate of COVID-19 infection and mortality varied significantly among the state's geographically dispersed, and racially and ethnically diverse populations who are exposed to unique environmental contaminants related to resource extraction industries (e.g. fracking, mining, oil and gas exploration). By looking at local patterns of COVID-19 disease severity, we sought to uncover the spatially varying factors underlying the pandemic. We further explored the compounding role of potential long-term exposures to various environmental contaminants on COVID-19 mortality prior to widespread applications of vaccinations. To illustrate the spatial heterogeneity of these complex associations, we leveraged multiple modeling approaches to account for spatial non-stationarity in model terms. Multiscale geographically weighted regression (MGWR) results indicate that increased potential exposure to fugitive mine waste is significantly associated with COVID-19 mortality in areas of the state where socioeconomically disadvantaged populations were among the hardest hit in the early months of the pandemic. This relationship is paradoxically reversed in global models, which fail to account for spatial relationships between variables. This work contributes both to environmental health sciences and the growing body of literature exploring the implications of spatial nonstationarity in health research.

Preparedness of Nursing Homes: A Typology and Analysis of Responses to the COVID-19 Crisis in a French Network.

Preparing healthcare systems for emergencies is crucial to maintaining healthcare quality. Nursing homes (NHs) require tailored emergency plans. This article aims to develop a typology of French private NHs and study their early COVID-19 responses and mortality outcomes. We conducted a cross-sectional survey among NHs of a French network consisting of 290 facilities during the first wave of the COVID-19 pandemic. A Hierarchical Clustering on Principal Components (HCPC) was conducted to develop the typology of the NHs. Association tests were used to analyze the relationships between the typology, prevention and control measures, COVID-19 mortality, and the satisfaction of hospitalization requests. The 290 NHs vary in size, services, and location characteristics. The HCPC identified three clusters: large urban NHs with low levels of primary care (Cluster 1), small rural NHs (Cluster 2), and medium urban NHs with high levels of primary care (Cluster 3). The COVID-19 outcomes and response measures differed by cluster, with Clusters 1 and 2 experiencing higher mortality rates. Nearly all the NHs implemented preventive measures, but the timing and extent varied. This typology could help in better preparing NHs for future health emergencies, allowing for targeted resource allocation and tailored adaptations. It underscores the importance of primary care territorial structuring in managing health crises.

Does timing of tocilizumab administration affect mortality in COVID-19? A Scottish multicentre retrospective cohort study

BackgroundThe optimal timing of tocilizumab treatment during the disease course of COVID-19 has yet to be adequately defined in the context of randomised controlled trials and the effect of tocilizumab...

Clinical, Laboratory, and Demographic Factors Contributing to COVID-19 Mortality Risk

Background: This study aimed to identify COVID-19 mortality risk factors and relevant laboratory markers to inform prevention and treatment strategies. Methods: This cross-sectional study was conducted in Iran, involving 1561 patients diagnosed with COVID-19 between 2019 and 2021. Demographic, clinical, and laboratory data were collected from hospital records and analyzed using a logistic regression model. Results: The mortality rate attributed to COVID-19 in this study was 24.7%. Identified risk factors for mortality included intubation, multiple chronic conditions, liver or kidney disease or cancer, low RBC levels, and abnormal creatinine (Cr) levels. The adjusted odds ratios (adjOR) and 95% confidence intervals (CIs) for these factors were as follows: intubation (70.75, 14.07:355.84), concurrent chronic diseases (24.29, 3.25:181.24), liver or kidney disease or cancer (5.13, 1.21:21.81), low RBC levels (5.21, 1.24:21.79), and abnormal Cr levels (5.09, 1.21:21.43). Conclusion: The findings from this study indicated that several factors, including intubation, multiple chronic conditions, liver or kidney disease or cancer, and low Cr levels were associated with a higher risk of death from COVID-19. These results highlight the significance of continuous monitoring and specialized care for patients exhibiting these risk factors to reduce the risk of COVID-19 mortality.

Comparative analysis of COVID-19 diagnoses and mortality among hospitalized indigenous and non-indigenous populations in Chile: 2020–2021

BackgroundCurrent literature presents mixed effects of the COVID-19 pandemic on Indigenous communities. We aim to highlight potential disparities and temporal shifts in both the impact of COVID-19 and vaccine uptake among hospitalized Indigenous populations in Chile.MethodsWe conducted an observational analysis utilizing 1,598,492 hospitalization records from 2020 to 2021 based on publicly accessible hospital discharge data spanning 65 healthcare facilities of medium and high complexity funded through the Diagnosis-Related Groups (DRG) mechanism in Chile, representing roughly 70% of the country’s total hospitalizations. This was supplemented with publicly available municipal data on COVID-19 vaccinations and socio-demographic variables. We performed logistic regression analysis at 0.05 level of significance to assess the bivariate and multivariable association of Indigenous status with COVID-19 diagnosis and COVID-19 deaths among hospitalized populations. We also performed univariate and multiple linear regression to assess the association of COVID-19 vaccination rate and Indigenous status at the municipality level. In addition, we report the distribution of top 10 secondary diagnoses among hospitalized COVID-19 cases and deaths separately for Indigenous and non-Indigenous populations.ResultsIndigenous populations displayed lower adjusted odds for both COVID-19 diagnosis (OR: 0.76, 95% CI: 0.74, 0.77) and death (OR: 0.91, 95% CI: 0.85, 0.97) when compared to non-Indigenous groups. Notably, the adjusted odds ratio for COVID-19 diagnosis in Indigenous populations rose from 0.59 (95% CI: 0.57, 0.61) in 2020 to 1.17 (95% CI: 1.13, 1.21) in 2021. Factors such as the significantly higher median age and greater number of comorbidities in the non-Indigenous hospitalized groups could account for their increased odds of COVID-19 diagnosis and mortality. Additionally, our data indicates a significantly negative adjusted association between COVID-19 vaccination rates and the proportion of Indigenous individuals.ConclusionAlthough Indigenous populations initially showed lower odds of COVID-19 diagnosis and mortality, a marked rise in diagnosis odds among these groups in 2021 underscores the urgency of targeted interventions. The observed negative association between the proportion of Indigenous populations and vaccination rates further underscores the necessity to tackle vaccine access barriers and work towards equitable distribution.

Insulin Resistance in Long COVID-19 Syndrome.

Background: The COVID-19 pandemic has caused severe health issues worldwide and contributed to huge financial losses. Key comorbidities linked to an increased risk of severe COVID-19 and higher mortality rates include cardio-metabolic disorders such as type 1 and type 2 diabetes mellitus (T1DM and T2DM), atherosclerotic cardiovascular disease, chronic kidney disease, hypertension, heart failure, and obesity. The persistence of symptoms even after the acute phase is over is termed long COVID-19 syndrome. This study aimed to evaluate the relationship between long COVID-19 syndrome and the development of insulin resistance in previously non-diabetic patients. Methods: A prospective observational study was performed on 143 non-diabetic patients who had tested positive for SARS-CoV-2 infection by a PCR test and were hospitalized in our hospital between January 2020 and December 2022. The clinical and para-clinical data at 0, 4, and 12 months of hospital admission for post-COVID-19 infection follow-up was collected and labeled as t0, t4, and t12. Blood glucose, insulin, and C-peptide levels were measured at the beginning and further at 2, 5, 10, and 30 min after the intravenous arginine stimulation test. Similarly, BMI was calculated, and hs-CRP and ESR levels were noted. The results obtained were statistically analyzed. Results: More than one-third (30.7%) of the included patients developed long COVID-19 syndrome. It was found that 75% of patients with long COVID-19 hospitalized in our clinic developed diabetes within a year of acute infection with COVID-19; therefore, it can be said that the presence of long COVID-19 is a major risk for an altered metabolic status, which can cause diabetes. When comparing the glycemia levels (106 mg/dL) with the BMI at t0, t4, and t12 time intervals, the p-values were found to be 0.214, 0.042, and 0.058, respectively. Almost 62% of the patients having BMI > 30 kg/m2 were found to have an increase in blood glucose levels at 1 year. Similarly, insulin resistance was noted during this interval. A negative correlation of 0.40 for hsCRP and 0.38 for ESR was noted when compared with acute infection with COVID-19. Conclusions: The association between long COVID-19 and insulin resistance highlights the varied and widespread impacts of SARS-CoV-2 infection. Addressing the complexities of long COVID-19 requires a holistic strategy that encompasses both respiratory and metabolic considerations, which is crucial for enhancing the well-being of those enduring this persistent condition.

Evaluation of Rhode Island's Early Geographic COVID-19 Vaccine Prioritization Policy.

Objectives. To determine whether geographic prioritization of limited COVID-19 vaccine supply was effective for reducing geographic disparities in case rates. Methods. Rhode Island allocated a portion of the initial COVID-19 vaccine supply to residents of Central Falls, a community already affected by structural policies and inadequate systems that perpetuate health inequities and experiencing disproportionately high COVID-19 morbidity and mortality. The policy was implemented with a culturally and linguistically appropriate community engagement plan and was intended to reduce observed disparities. Using a Bayesian causal analysis with population surveillance data, we evaluated the impact of this prioritization policy on recorded cases over the subsequent 16 weeks. Results. Early geographic prioritization of Central Falls accelerated vaccine uptake, averting an estimated 520 cases (95% confidence interval = 22, 1418) over 16 weeks and reducing cases by approximately 34% during this period (520 averted vs 1519 expected without early prioritization). Conclusions. Early geographic prioritization increased vaccine uptake and reduced cases in Central Falls, thereby reducing geographic disparities. Public Health Implications. Public health institutions should consider geographic prioritization of limited vaccine supply to reduce geographic disparities in case rates. (Am J Public Health. 2024;114(S7):S580-S589. https://doi.org/10.2105/AJPH.2024.307741).

To investigate the relation between LDH and CRP levels and mortality of COVID-19 (SARS-CoV-2) patients admitted in SURGERY ICU at Shariati Hospital from 19 February 2020 till 19 February 2021

Background: The aim of this study is to investigate the relationship between LDH and CRP levels and the mortality of COVID-19 (SARS-CoV-2) patients admitted to the SURGERY ICU at Shariati Hospital from 19 February 2020 to 19 February 2021. Methods: Our study is a cross-sectional descriptive study. A total of 81 patients were enrolled in this study. We examined lab reports, including CBC parameters (WBC, neutrophil count, neutrophil percentage), other inflammatory markers ESR and CRP, and also studied the medical records of cases to collect details about age, gender, length of stay in SURGERY ICU, BMI, fever, survival status, comorbidity, intubation, and NIV. These patients were referred to Shariati Hospital, Tehran, from 19 February 2020 to 19 February 2021, regarding the relationship between LDH and CRP levels and the mortality of COVID-19 (SARS-CoV-2) patients admitted to the SURGERY ICU. The data obtained was analyzed using SPSS software, and a significance value of < 0.05 was considered. Results: In this study, the data of 81 patients were considered. The data were obtained from the medical records of Shariati Hospital, Tehran. We used 11 variables to compare data from different patients that were recorded in the registry and system of medical records in Shariati Hospital, Tehran. The study population included 81 patients, of which 41 were female and 40 were male, ranging in age from 25 to 89. In total, 80.2% had mild disease versus 18.5% who had severe disease. Sixty-five patients survived, and 16 were admitted to the SURGERY ICU with endotracheal intubation and later died. The length of stay in the SURGERY ICU ranged from 1 to 25 days. Out of 81 patients, 52 (62.4%) had comorbidity, and 29 (35.8%) did not have comorbidity. Twelve patients (14.8%) received NIV, and 69 patients (85.2%) did not receive NIV. Most of the patients did not have a fever. The minimum and maximum levels of CRP were 4 and 416, respectively, and for LDH were 9 and 2401, respectively. The prognostic factors for the severity of COVID-19 infection identified in this study (CRP and LDH) help predict the course of the disease at an early stage. Elevated concentrations of CRP and LDH at admission were found to be associated with a higher risk for COVID-19 severity as they were significant (p-value =0.049 and 0.048, respectively). Conclusions: This study’s laboratory investigation showed that the SURGERY ICU patients had significantly higher values of inflammatory markers CRP and LDH than the non-SURGERY ICU patients. LDH and CRP were superior and effective biomarkers in predicting the severity of COVID-19.

Social trust and COVID-19 mortality in the United States: lessons in planning for future pandemics using data from the general social survey

BackgroundThe United States has lost many lives to COVID-19. The role of social capital and collective action has been previously explored in the context of COVID-19. The current study specifically investigates the role of social trust at the county level and COVID-19 mortality in the US, hypothesizing that counties with higher social trust will have lower COVID-19 mortality rates.MethodsWe used cross-sectional data from the General Social Survey (GSS). We collected COVID-19 mortality data from the COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University until October 31, 2021. We obtained county characteristics from the 2019 American Community Survey and supplemented this data source with additional publicly available county-level data, such as measures of income inequality and political leanings. We measured social trust as a single item from the GSS and calculated mean social trust in a county by pooling responses from 2002 to 2018. We then modeled the relationship between mean social trust and COVID-19 mortality.ResultsResults indicate that counties with higher social trust have lower COVID-19 mortality rates. Higher values of mean social trust at the county level are associated with a decrease in COVID-19 mortality (b= -0.25, p-value < 0.001), after adjustment for confounding. The direction of association is consistent in a sensitivity analysis.ConclusionsOur findings underscore the importance of investment in social capital and social trust. We believe these findings can be applied beyond the COVID-19 pandemic, as they demonstrate the potential for social trust as a method for emergency preparedness.

Assessing educational disparities in COVID-19 related excess mortality in Spain: a population register-linked mortality study.

Data on the increase in mortality during the COVID-19 pandemic based on individuals' socioeconomic positions are limited. This study examines this increase in mortality in Spain during the epidemic waves of 2020 and 2021. We calculated the overall and cause-specific mortality rates during the 2017-2019 pre-pandemic period and four epidemic periods in 2020 and 2021 (first, second, third-fourth, and fifth-sixth waves). Mortality rates were analyzed based on educational levels (low, medium, and high) and across various age groups (25-64, 65-74, and 75+). The increase in mortality during each epidemic period compared to the pre-pandemic period was estimated using mortality rate ratios (MRR) derived from Poisson regression models. An inverse educational gradient in overall mortality was observed across all periods; however, this pattern was not consistent for COVID-19 mortality in some age groups. Among those aged 75 years and older, highly educated individuals showed higher COVID-19 mortality during the first wave. In the 25-64 age group, individuals with low education experienced the highest overall mortality increase, while those with high education had the lowest increase. The MRRs were 1.21 and 1.06 during the first wave and 1.12 and 0.97 during the last epidemic period. In the 65-74 age group, highly educated individuals showed the highest overall mortality increase during the first wave, whereas medium-educated individuals had the highest increase during the subsequent epidemic periods. Among those aged 75 and older, highly educated individuals exhibited the highest overall mortality increase while the individuals with low education showed the lowest overall mortality increment, except during the last epidemic period. The varying educational patterns of COVID-19 mortality across different age groups contributed to the disparities of findings in increased overall mortality by education levels during the COVID-19 pandemic.

Health insurance system fragmentation and COVID-19 mortality: Evidence from Peru.

Peru has a fragmented health insurance system in which most insureds can only access the providers in their insurer's network. The two largest sub-systems covered about 53% and 30% of the population at the start of the pandemic; however, some individuals have dual insurance and can thereby access both sets of providers. We use data on 24.7 million individuals who belonged to one or both sub-systems to investigate the effect of dual insurance on COVID-19 mortality. We estimate recursive bivariate probit models using the difference in the distance to the nearest hospital in the two insurance sub-systems as Instrumental Variable. The effect of dual insurance was to reduce COVID-19 mortality risk by 0.23% compared with the sample mean risk of 0.54%. This implies that the 133,128 COVID-19 deaths in the sample would have been reduced by 56,418 (95%CI: 34,894, 78,069) if all individuals in the sample had dual insurance.

Epidemiological Pattern and Case Fatality Rate among COVID-19 Patients during First and Second Wave of Pandemic in Madhesh Province, Nepal

Background &amp; Objectives: Evaluating multi-wave patterns of COVID-19 infection, hospitalization, and mortality across spatial scales can inform public health strategies for future pandemics. However, there is limited understanding of these patterns in developing countries, including Nepal. This study aimed to analyze epidemiological patterns, fatality rates, and factors associated with severe outcomes during the first and second waves of COVID-19 in Madhesh Province, Nepal. Materials and Methods: This retrospective cross-sectional study used provincial health records from Madhesh Province, covering April 9, 2020, to December 15, 2021, to analyze 37,551 positive COVID-19 cases and 1,037 deaths across two waves. The study examined changes in COVID-19-related deaths, with data on demographics, residence, isolation sites, treatment hospitals, care levels, and testing laboratories. The frequency and percentage of the variables were presented. The case fatality rate (CFR) for different categories were calculated. Additionally, the case fatality rate ratio (CFRR) for the first wave against second wave was obtained. Finally, case fatality risk ratios with 95% confidence intervals were presented. A p-value of &lt;0.05 was set as statistically significant. Results: The case fatality rate (CFR) for COVID-19 was significantly higher in the second wave, especially among the elderly (≥47 years), and in institutional isolation (7.82%). Tertiary level care and private hospitals consistently showed higher CFRs. Furthermore, the multivariable analysis of risk ratios (RR) for COVID-19 case fatality in revealed that the 25–34 year age group had the highest RR (2.39). Similarly, males had a higher RR (1.42) than females, and institutional isolation had a substantially higher RR (93.33) compared to home isolation. Primary level care RR (28.11) and government hospitals RR (4.32) showed a higher risk. Place of residence also impacted the RR, with Sarlahi (7.68) having higher. Conclusion: A significant increase in COVID-19 case fatality rates during the second wave, particularly among the elderly and those in institutional isolation. Higher mortality was observed in tertiary hospitals and among those who were tested in private laboratories, with substantial variations in risk ratios based on age, isolation type, and place of residence.

Disentangling the relationship between cancer mortality and COVID-19 in the US

Cancer is considered a risk factor for COVID-19 mortality, yet several countries have reported that deaths with a primary code of cancer remained within historic levels during the COVID-19 pandemic. Here, we further elucidate the relationship between cancer mortality and COVID-19 on a population level in the US. We compared pandemic-related mortality patterns from underlying and multiple cause (MC) death data for six types of cancer, diabetes, and Alzheimer’s. Any pandemic-related changes in coding practices should be eliminated by study of MC data. Nationally in 2020, MC cancer mortality rose by only 3% over a pre-pandemic baseline, corresponding to ~13,600 excess deaths. Mortality elevation was measurably higher for less deadly cancers (breast, colorectal, and hematological, 2–7%) than cancers with a poor survival rate (lung and pancreatic, 0–1%). In comparison, there was substantial elevation in MC deaths from diabetes (37%) and Alzheimer’s (19%). To understand these differences, we simulated the expected excess mortality for each condition using COVID-19 attack rates, life expectancy, population size, and mean age of individuals living with each condition. We find that the observed mortality differences are primarily explained by differences in life expectancy, with the risk of death from deadly cancers outcompeting the risk of death from COVID-19.

Disentangling the relationship between cancer mortality and COVID-19 in the US.

Cancer is considered a risk factor for COVID-19 mortality, yet several countries have reported that deaths with a primary code of cancer remained within historic levels during the COVID-19 pandemic. Here, we further elucidate the relationship between cancer mortality and COVID-19 on a population level in the US. We compared pandemic-related mortality patterns from underlying and multiple cause (MC) death data for six types of cancer, diabetes, and Alzheimer's. Any pandemic-related changes in coding practices should be eliminated by study of MC data. Nationally in 2020, MC cancer mortality rose by only 3% over a pre-pandemic baseline, corresponding to ~13,600 excess deaths. Mortality elevation was measurably higher for less deadly cancers (breast, colorectal, and hematological, 2-7%) than cancers with a poor survival rate (lung and pancreatic, 0-1%). In comparison, there was substantial elevation in MC deaths from diabetes (37%) and Alzheimer's (19%). To understand these differences, we simulated the expected excess mortality for each condition using COVID-19 attack rates, life expectancy, population size, and mean age of individuals living with each condition. We find that the observed mortality differences are primarily explained by differences in life expectancy, with the risk of death from deadly cancers outcompeting the risk of death from COVID-19.

Handgrip strength as a prognostic factor for COVID-19 mortality among older adult patients admitted to the intensive care unit (ICU): a comparison Alpha (B.1.1.7) and Delta (B.1.617.2) variants

Handgrip strength (HGS) is a non-invasive and reliable biomarker of overall health, physical function, mobility, and mortality. This study aimed to investigate the possible relationship between HGS and mortality in older adult patients hospitalized with COVID-19 in the intensive care unit (ICU) by Alpha (B.1.1.7) and Delta (B.1.617.2) variants. This retrospective cohort study was conducted on 472 COVID-19 patients (222 female and 250 male) aged 60–85 years admitted to the ICU. Demographic data, underlying comorbidities, COVID-19-related symptoms, as well as laboratory and computed tomography (CT) findings were obtained from the patient’s medical records. Using a JAMAR® hydraulic dynamometer, the average grip strength value (kg) after three measurements on the dominant side was recorded for subsequent analysis. Low grip strength (LGS) was defined as an arbitrary cut-off of two standard deviations below the gender-specific peak mean value of normative HGS in Iranian healthy population, i.e. < 26 kg in males and < 14 kg in females. The findings showed lower mean grip strength and high frequency of LGS in the non-survivors patients versus survivors group and in the Delta (B.1.617.2) variant vs. Alpha (B.1.1.7) variant, respectively (both p < 0.01). The binary logistic regression analysis showed that chronic obstructive pulmonary disease (COPD) (adjusted odds ratio [OR] 5.125, 95% CI 1.425–25.330), LGS (OR 4.805, 95% CI 1.624–10.776), SaO2 (OR − 3.501, 95% CI 2.452–1.268), C-reactive protein (CRP) level (OR 2.625, 95% CI 1.256–7.356), and age (OR 1.118, 95% CI 1.045–1.092) were found to be independent predictors for mortality of patients with Alpha (B.1.1.7) variant (all p < 0.05). However, only four independent predictors including COPD (OR 6.728, 95% CI 1.683–28.635), LGS (OR 5.405, 95% CI 1.461–11.768), SaO2 (OR − 4.120, 95% CI 2.924–1.428), and CRP level (OR 1.893, 95% CI 1.127–8.692) can be predicted the mortality of patients with Delta (B.1.617.2) variant (p < 0.05). Along with the well-known and common risk factors (i.e. COPD, CRP, and SaO2), handgrip strength can be a quick and low-cost prognostic tool in predicting chances of mortality in older adults who are afflicted with COVID-19 variants.

Prediction of COVID-19 Hospitalization and Mortality Using Artificial Intelligence.

COVID-19 has had a substantial influence on healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. AI systems have been used by healthcare practitioners for investigating, anticipating, and predicting diseases, through means including medication development, clinical trial analysis, and pandemic forecasting. This study proposes the use of AI to predict disease severity in terms of hospital mortality among COVID-19 patients. A cross-sectional study was conducted at King Abdulaziz University, Saudi Arabia. Data were cleaned by encoding categorical variables and replacing missing quantitative values with their mean. The outcome variable, hospital mortality, was labeled as death = 0 or survival = 1, with all baseline investigations, clinical symptoms, and laboratory findings used as predictors. Decision trees, SVM, and random forest algorithms were employed. The training process included splitting the data set into training and testing sets, performing 5-fold cross-validation to tune hyperparameters, and evaluating performance on the test set using accuracy. The study assessed the predictive accuracy of outcomes and mortality for COVID-19 patients based on factors such as CRP, LDH, Ferritin, ALP, Bilirubin, D-Dimers, and hospital stay (p-value ≤ 0.05). The analysis revealed that hospital stay, D-Dimers, ALP, Bilirubin, LDH, CRP, and Ferritin significantly influenced hospital mortality (p ≤ 0.0001). The results demonstrated high predictive accuracy, with decision trees achieving 76%, random forest 80%, and support vector machines (SVMs) 82%. Artificial intelligence is a tool crucial for identifying early coronavirus infections and monitoring patient conditions. It improves treatment consistency and decision-making via the development of algorithms.

Comorbid burden at ICU admission in COVID-19 compared to sepsis and acute respiratory distress syndrome.

Comorbidities are similarly associated with short-term mortality for COVID-19, acute respiratory distress syndrome (ARDS) and sepsis in intensive care unit (ICU) patients, but their adjusted frequencies at admission are unknown. Thus, we aimed to evaluate the adjusted distribution, reported as odds ratios, of known risk factors (i.e., age, sex and comorbidities) for ICU admission between COVID-19, sepsis and ARDS patients in this nationwide registry-based study. In this cohort study, we included adult patients admitted to Swedish ICUs with COVID-19 (n = 7382) during the pandemic and compared them to patients admitted to ICU with sepsis (n = 22,354) or ARDS (n = 2776) during a pre-COVID-19 period. The main outcomes were the adjusted odds for comorbidities, sex, and age in multivariable logistic regression on diagnostic categories in patients admitted to ICU, COVID-19 or sepsis and COVID-19 or ARDS. We found that most comorbidities, as well as age, had a stronger association with sepsis admission than COVID-19 admission with the exception of male sex, type 2 diabetes mellitus, and asthma that were more strongly associated with COVID-19 admission, while no difference was seen for chronic renal failure and obesity. For COVID-19 and ARDS admission most risk factors were more strongly associated with ARDS admission except for male sex, type 2 diabetes mellitus, chronic renal failure, and obesity which were more strongly associated with COVID-19 admission, whereas hypertension, chronic obstructive pulmonary disease and asthma were not different. Patients admitted to ICU with sepsis or ARDS carry a heavier burden of comorbidity and high age than patients admitted with COVID-19. This is likely caused by a combination of: (1) respiratory failure in COVID-19 being less dependent on comorbidities than in other forms of ARDS, and the cause of critical illness in other infections causing sepsis and (2) COVID-19 patients being deferred admission in situations where patients with the other syndromes were admitted.