Among COVID-19 patients in the United States from February 12 to March 16, 2020, estimated case-fatality rates were highest for adults aged 85 years and older. Younger people appeared to have milder symptoms, and there were no deaths reported among persons aged 19 years and under.

Tracking the virus in the United States The outbreak of a previously unknown viral pneumonia was first reported in China toward the end of December 2019. The first U.S. case of COVID-19 was recorded in mid-January 2020, confirmed in a patient who had returned to the United States from China. The virus quickly started to spread, and the first community-acquired case was confirmed one month later in California. Overall, there had been approximately 4.5 million coronavirus cases in the country by the start of August 2020.

U.S. health care system stretched California, Florida, and Texas are among the states with the most coronavirus cases. Even the best-resourced hospitals in the United States have struggled to cope with the crisis, and certain areas of the country were dealt further blows by new waves of infections in July 2020. Attention is rightly focused on fighting the pandemic, but as health workers are redirected to care for COVID-19 patients, the United States must not lose sight of other important health care issues.

Between the beginning of January 2020 and June 14, 2023, of the 1,134,641 deaths caused by COVID-19 in the United States, around 307,169 had occurred among those aged 85 years and older. This statistic shows the number of coronavirus disease 2019 (COVID-19) deaths in the U.S. from January 2020 to June 2023, by age.

Objective: Coronavirus disease 2019 (COVID-19) is a disease with a high rate of progression to critical illness. However, the stratification of patients at risk of mortality is not well defined. In this study, we aimed to define a mortality risk index to allocate patients to the appropriate intensity of care.Methods: This is a 12 months observational longitudinal study designed to develop and validate a pragmatic mortality risk score to stratify COVID-19 patients aged ≥18 years and admitted to hospital between March 2020 and March 2021. Main outcome was in-hospital mortality.Results: 244 patients were included in the study (mortality rate 29.9%). The Covid-19 Assessment for Survival at Admission (CASA) index included seven variables readily available at admission: respiratory rate, troponin, albumin, CKD-EPI, white blood cell count, D-dimer, Pa02/Fi02. The CASA index showed high discrimination for mortality with an AUC of 0.91 (sensitivity 98.6%; specificity 69%) and a better performance compared to SOFA (AUC = 0.76), age (AUC = 0.76) and 4C mortality (AUC = 0.82). The cut-off identified (11.994) for CASA index showed a negative predictive value of 99.16% and a positive predictive value of 57.58%.Conclusions: A quick and readily available index has been identified to help clinicians stratify COVID-19 patients according to the appropriate intensity of care and minimize hospital admission to patients at high risk of mortality.

Univariable and multivariable cause-specific hazard ratios (HRCS) including 95% confidence intervals for death and recovery.

Background: The older population is often disproportionately and adversely affected during humanitarian emergencies, as has also been seen during the COVID-19 pandemic. Data regarding COVID-19 in older adults is usually over-generalised and does not delve into details of the clinical characteristics in them. This study was conducted to analyse clinical and laboratory characteristics, risk factors, and complications of COVID-19 between older adults who survived and those who did not. Methods: We conducted a case-control study among older adults(age > 60 years) admitted to the Intensive Care Unit(ICU) during the COVID-19 pandemic. The non-survivors (cases) were matched with age and sex-matched survivors (control) in a ratio of 1: 3. The data regarding socio-demographics, clinical characteristics, complications, treatment, laboratory data, and outcomes were analysed. Results: The most common signs and symptoms observed were fever (cases vs controls) (68.92 vs. 68.8%), followed by shortness of breath (62.2% Vs. 52.2%), and cough (47.3% Vs. 60.2%). Our analysis found no association between the presence of any of the comorbidities and mortality. At admission, laboratory markers such as LDH(Lactate Dehydrogenase), WBC(White Blood Count), creatinine, CRP(C-Reactive Protein), D-dimer, ferritin, and IL-6(Interleukin-6) were found to be significantly higher among the cases than among the controls. Complications such as development of seizure, bacteremia, acute renal injury, respiratory failure, and septic shock were seen to have a significant association with non-survivors. Conclusions: Hypoxia, tachycardia, and tachypnoea at presentation were associated with higher mortality. The older adults in this study mostly presented with the typical clinical features of COVID-19 pneumonia. The presence of comorbid illnesses among them did not affect mortality. Higher death was seen among those with higher levels of CRP, LDH, D-dimer, and ferritin; and with lower lymphocyte counts. Methods A hospital-based case-control study was undertaken. Data was collected from the Intensive Care Unit(ICU) from December 2020 to September 2022. The sample size was calculated with a two-sided confidence level(1-α) of 95, 80% power, and with a ratio of controls to cases at 3:1. A sample size of 260 was calculated consisting of 195 controls and 65 cases. A Case was defined as a COVID-19-positive individual older than 60 years who, after being admitted or transferred to the ICU, did not survive, i.e., non-survivor. A Control was defined as a COVID-19-positive individual with age greater than 60 years who was admitted or transferred to the ICU, following which the patient recovered(survived) and was discharged alive from the hospital, i.e., survivor. Those patients who were admitted for post-COVID-19 complications or for COVID-19 unrelated medical conditions following discharge after initial treatment for COVID-19 pneumonia were excluded. The cases (non-survivors) were recruited according to the inclusion and exclusion criteria mentioned above and were then matched with an age and sex-matched control (survivor) in a ratio of 1: 3, respectively. The data regarding socio-demographics, clinical characteristics, complications, treatment, laboratory data, and outcomes were collected using a modified ISARIC form. The patient's identity was anonymized by assigning a code. The comorbidities and risk factors recorded in the study were chronic cardiac disease(including hypertension), chronic pulmonary disease(including asthma), chronic kidney disease, obesity, liver disease, asplenia, chronic neurological disorder, malignant neoplasm, chronic hematological disease, AIDS/HIV, diabetes mellitus, rheumatological disorder, dementia, tuberculosis, malnutrition, and smoking. Before the study's launch and data collection, approval was acquired from the Institutional Ethics Committee and the medical directors of the participating institutions. Data collection was done using Microsoft Excel. Data were analysed using the IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp. The data were expressed as mean and SD for continuous variables. Based on the type of distribution of data, a t-test or Mann-Whitney U test was applied. The categorical variables were analysed using Pearson's chi-square or Fisher's exact test based on the data distribution.

This study aimed to analyze the effect of sociodemographic characteristics on COVID-19 in-hospital mortality in Ecuador from March 1 to December 31, 2020. This retrospective longitudinal study was performed with data from publicly accessible registries of the Ecuadorian National Institute of Statistics and Censuses (INEC). Data underwent a competing risk analysis with estimates of the cumulative incidence function (CIF). The effect of covariates on CIFs was estimated using the Fine-Gray model and results were expressed as adjusted subdistribution hazard ratios (SHR). The analysis included 30,991 confirmed COVID-19 patients with a mean age of 56.57±18.53 years; 60.7% (n = 18,816) were men and 39.3% (n = 12,175) were women. Being of advanced age, especially older than or equal to 75 years (SHR = 17.97; 95%CI: 13.08-24.69), being a man (SHR = 1.29; 95%CI: 1.22-1.36), living in rural areas (SHR = 1.18; 95%CI: 1.10-1.26), and receiving care in a public health center (SHR = 1.64; 95%CI: 1.51-1.78) were factors that increased the incidence of death from COVID-19, while living at an elevation higher than 2,500 meters above sea level (SHR = 0.69; 95%CI: 0.66-0.73) decreased this incidence. Since the incidence of death for individuals living in rural areas and who received medical care from the public sector was higher, income and poverty are important factors in the final outcome of this disease.

Background and aims There is poor knowledge on characteristics, comorbidities and laboratory measures associated with risk for adverse outcomes and in-hospital mortality in European Countries. We aimed at identifying baseline characteristics predisposing COVID-19 patients to in-hospital death. Methods and results Retrospective observational study on 3894 patients with SARS-CoV-2 infection hospitalized from February 19th to May 23rd, 2020 and recruited in 30 clinical centres distributed throughout Italy. Machine learning (random forest)-based and Cox survival analysis. 61.7% of participants were men (median age 67 years), followed up for a median of 13 days. In-hospital mortality exhibited a geographical gradient, Northern Italian regions featuring more than twofold higher death rates as compared to Central/Southern areas (15.6% vs 6.4%, respectively). Machine learning analysis revealed that the most important features in death classification were impaired renal function, elevated C reactive protein and advanced age. These findings were confirmed by multivariable Cox survival analysis (hazard ratio (HR): 8.2; 95% confidence interval (CI) 4.6-14.7 for age ?85 vs 18-44 y); HR = 4.7; 2.9-7.7 for estimated glomerular filtration rate levels <15 vs ? 90 mL/min/1.73 m2; HR = 2.3; 1.5-3.6 for C-reactive protein levels ?10 vs ? 3 mg/L). No relation was found with obesity, tobacco use, cardiovascular disease and related-comorbidities. The associations between these variables and mortality were substantially homogenous across all sub-groups analyses. Conclusions Impaired renal function, elevated C-reactive protein and advanced age were major predictors of in-hospital death in a large cohort of unselected patients with COVID-19, admitted to 30 different clinical centres all over Italy.

ObjectiveTo analyse if the health progression of geriatric Covid-19 survivors three months after an acute Covid-19 infection was worse than in other geriatric patients. Specifically, we wanted to see if we could see distinct health profiles in the flow of re-admitted Covid-19 patients compared to re-admitted non-Covid-19 controls.DesignMatched cohort study.Setting and participantsElectronic medical records of geriatric patients hospitalised in geriatric clinics in Stockholm, Sweden, between March 2020 and January 2022. Patients readmitted three months after initial admission were selected for the analysis and Covid-19 survivors (n = 895) were compared to age-sex-Charlson comorbidity index (CCI)-matched non-Covid-19 controls (n = 2685).MethodsWe assessed using binary logistic and Cox regression if a previous Covid-19 infection could be a risk factor for worse health progression indicated by the CCI, hospital frailty risk score (HFRS), mortality and specific comorbidities.ResultsThe patients were mostly older than 75 years and, already at baseline, had typically multiple comorbidities. The Covid-19 patients with readmission had mostly had their acute-phase infection in the 1st or 2nd pandemic waves before the vaccinations. The Covid-19 patients did not have worse health after three months compared to the matched controls according to the CCI (odds ratio, OR[95% confidence interval, CI] = 1.12[0.94–1.34]), HFRS (OR[95%CI] = 1.05[0.87–1.26]), 6-months (hazard ratio, HR[95%CI] = 1.04[0.70–1.52]) and 1-year-mortality risk (HR[95%CI] = 0.89[0.71–1.10]), adjusted for age, sex and health at baseline (the CCI and HFRS).Conclusions and implicationsThe overall health progression of re-hospitalized geriatric Covid-19 survivors did not differ dramatically from other re-hospitalized geriatric patients with similar age, sex and health at baseline. Our results emphasize that Covid-19 was especially detrimental for geriatric patients in the acute-phase, but not in the later phase. Further studies including post-vaccination samples are needed.

BackgroundThis study aims to develop a machine learning model to predict the 30-day mortality risk of hospitalized COVID-19 patients while leveraging federated learning to enhance data privacy and expand the model's applicability. Additionally, SHapley Additive exPlanations (SHAP) values were utilized to assess the impact of comorbidities on mortality.MethodsA retrospective analysis was conducted on 6,321 clinical records of hospitalized COVID-19 patients between January 2021 and October 2022. After excluding cases involving patients under 18 years of age and non-Omicron infections, a total of 4,081 records were analyzed. Key features included three demographic data, six vital signs at admission, and 79 underlying comorbidities. Four machine learning models were compared, including Lasso, Random Forest, XGBoost, and TabNet, with XGBoost demonstrating superior performance. Federated learning was implemented to enable collaborative model training across multiple medical institutions while maintaining data security. SHAP values were applied to interpret the contribution of each comorbidity to the model's predictions.ResultsA subset of 2,156 records from the Taipei branch was used to evaluate model performance. XGBoost achieved the highest AUC of 0.96 and a sensitivity of 0.94. Two versions of the XGBoost model were trained: one incorporating vital signs, suitable for emergency room applications where patients come in with unstable vital signs, and another excluding vital signs, optimized for outpatient settings where we encounter patients with multiple comorbidities. After implementing federated learning, the AUC of the Taipei cohort decreased to 0.90, while the performance of other cohorts improved to meet the required standards. SHAP analysis identified comorbidities including diabetes mellitus, cerebrovascular disease, and chronic lung disease to have a neutral or even protective association with 30-day mortality.ConclusionXGBoost outperformed other models making it a viable tool for both emergency and outpatient settings. The study underscores the importance of chronic disease assessment in predicting COVID-19 mortality, revealing some comorbidities such as diabetes mellitus, cerebrovascular disease and chronic lung disease to have protective association with 30-day mortality. These findings suggest potential refinements in current treatment guidelines, particularly concerning high-risk conditions. The integration of federated learning further enhances the model's clinical applicability while preserving patient privacy.

Despite the social distancing and mobility restriction measures implemented for susceptible people around the world, infections and deaths due to COVID-19 continued to increase, even more so in the first months of 2021 in Mexico. Thus, it is necessary to find risk groups that can benefit from more aggressive preventive measures in a high-density population. This is a case-control study of suspected COVID-19 patients from Nuevo León, Mexico. Cases were: (1) COVID-19-positive patients and COVID-19-positive patients who (2) developed pneumonia, (3) were intubated and (4) died. Controls were: (1) COVID-19-negative patients, (2) COVID-19-positive patients without pneumonia, (3) non-intubated COVID-19-positive patients and (4) surviving COVID-19-positive patients. ≥ 18 years of age, not pregnant, were included. The pre-existing conditions analysed as risk factors were age (years), sex (male), diabetes mellitus, hypertension, chronic obstructive pulmonary disease, asthma, immunosuppression, obesity, cardiovascular disease, chronic kidney disease and smoking. The Mann-Whitney U tests, Chi square and binary logistic regression were used. A total of 56,715 suspected patients were analysed in Nuevo León, México, with 62.6% being positive for COVID-19 and, of those infected, 14% developed pneumonia, 2.9% were intubated and 8.1% died. The mean age of those infected was 44.7 years, while of those complicated it was around 60 years. Older age, male sex, diabetes, hypertension, and obesity were risk factors for infection, complications, and death from COVID-19. This study highlights the importance of timely recognition of the population exposed to pre-existing conditions to prioritise preventive measures against the virus.

IntroductionCertain trace elements are essential for life and affect immune system function, and their intake varies by region and population. Alterations in serum Se, Zn and Cu have been associated with COVID-19 mortality risk. We tested the hypothesis that a disease-specific decline occurs and correlates with mortality risk in different countries in Europe.MethodsSerum samples from 551 COVID-19 patients (including 87 non-survivors) who had participated in observational studies in Europe (Belgium, France, Germany, Ireland, Italy, and Poland) were analyzed for trace elements by total reflection X-ray fluorescence. A subset (n=2069) of the European EPIC study served as reference. Analyses were performed blinded to clinical data in one analytical laboratory.ResultsMedian levels of Se and Zn were lower than in EPIC, except for Zn in Italy. Non-survivors consistently had lower Se and Zn concentrations than survivors and displayed an elevated Cu/Zn ratio. Restricted cubic spline regression models revealed an inverse nonlinear association between Se or Zn and death, and a positive association between Cu/Zn ratio and death. With respect to patient age and sex, Se showed the highest predictive value for death (AUC=0.816), compared with Zn (0.782) or Cu (0.769).DiscussionThe data support the potential relevance of a decrease in serum Se and Zn for survival in COVID-19 across Europe. The observational study design cannot account for residual confounding and reverse causation, but supports the need for intervention trials in COVID-19 patients with severe Se and Zn deficiency to test the potential benefit of correcting their deficits for survival and convalescence.

Background. The outbreak of COVID-19 has attracted the attention of the whole world. Our study aimed to describe illness progression and risk profiles for mortality in non-survivors.Methods. We retrospectively analyzed 155 patients with COVID-19 in Wuhan and focused on 18 non-survivors among them. Briefly, we compared the dynamic profile of biochemical and immune parameters and drew an epidemiological and clinical picture of disease progression from disease onset to death in non-survivors. The survival status of the cohort was indicated by a Kaplan–Meier curve.Results. Of the non-survivors, the median age was 73.5 years, and the proportion of males was 72.2%. Five and 13 patients were hospital-acquired and community-acquired infection of SARS-CoV-2, respectively. The interval between disease onset and diagnosis was 8.5 days (IQR, [4–11]). With the deterioration of disease, most patients experienced consecutive changes in biochemical parameters, including lymphopenia, leukocytosis, thrombocytopenia, hypoproteinemia, as well as elevated D-dimer and procalcitonin. Regarding the immune dysregulation, patients exhibited significantly decreased T lymphocytes in the peripheral blood, including CD3+T, CD3+CD4+Th, and CD3+CD8+Tc cells. By the end of the disease, most patients suffered from severe complications, including ARDS (17/18; 94.4%), acute cardiac injury (10/18; 55.6%), acute kidney injury (7/18; 38.9%), shock (6/18; 33.3%), gastrointestinal bleeding (1/18; 5.6%), as well as perforation of intestine (1/18; 5.6%). All patients died within 45 days after the initial hospital admission with a median survivor time of 13.5 days (IQR, 8–17).Conclusions. Our data show that patients experienced consecutive changes in biochemical and immune parameters with the deterioration of the disease, indicating the necessity of early intervention.

Demographic and clinical characteristics of study participants (I) and prevalence of outcomes (II).

Background: This study was carried out to compare characteristics and outcomes in patients with acute respiratory failure related to COVID-19 during first, second, and third waves. Methods: We included consecutive adults admitted to the intensive care unit between March 2020 and July 2021. We compared three groups defined by the epidemic intake phase: waves 1 (W1), 2 (W2), and 3 (W3). Results: We included 289 patients. Two hundred and eight (72%) patients were men with a median age of 63 years (IQR: 54–72), of whom 68 (23.6%) died in hospital. High-flow nasal oxygen (HFNO) was inversely associated with the need for invasive mechanical ventilation (MV) in multivariate analysis (p = 0.003) but not dexamethasone (p = 0.25). The day-90 mortality rate did not vary from W1 (27.4%) to W2 (23.9%) and W3 (22%), p = 0.67. By multivariate analysis, older age (odds ratio [OR]: 0.94/year, p < 0.001), immunodeficiency (OR: 0.33, p = 0.04), acute kidney injury (OR: 0.26, p < 0.001), and invasive MV (OR: 0.13, p < 0.001) were inversely associated with higher day-90 survival as opposed to the use of intermediate heparin thromboprophylaxis dose (OR: 3.21, p = 0.006). HFNO use and dexamethasone were not associated with higher day-90 survival (p = 0.24 and p = 0.56, respectively). Conclusions: In patients with acute respiratory failure due to COVID-19, survival did not change between first, second, and third waves while the use of invasive MV decreased. HFNO or intravenous steroids were not associated with better outcomes, whereas the use of intermediate dose of heparin for thromboprophylaxis was associated with higher day-90 survival. Larger multicentric studies are needed to confirm our findings.

Background: Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are two major infectious diseases posing significant public health threats, and their coinfection (aptly abbreviated COVID-TB) makes the situation worse. This study aimed to investigate the clinical features and prognosis of COVID-TB cases.Methods: The PubMed, Embase, Cochrane, CNKI, and Wanfang databases were searched for relevant studies published through December 18, 2020. An overview of COVID-TB case reports/case series was prepared that described their clinical characteristics and differences between survivors and deceased patients. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) for death or severe COVID-19 were calculated. The quality of outcomes was assessed using GRADEpro.Results: Thirty-six studies were included. Of 89 COVID-TB patients, 19 (23.46%) died, and 72 (80.90%) were male. The median age of non-survivors (53.95 ± 19.78 years) was greater than that of survivors (37.76 ± 15.54 years) (p < 0.001). Non-survivors were more likely to have hypertension (47.06 vs. 17.95%) or symptoms of dyspnea (72.73% vs. 30%) or bilateral lesions (73.68 vs. 47.14%), infiltrates (57.89 vs. 24.29%), tree in bud (10.53% vs. 0%), or a higher leucocyte count (12.9 [10.5–16.73] vs. 8.015 [4.8–8.97] × 109/L) than survivors (p < 0.05). In terms of treatment, 88.52% received anti-TB therapy, 50.82% received antibiotics, 22.95% received antiviral therapy, 26.23% received hydroxychloroquine, and 11.48% received corticosteroids. The pooled ORs of death or severe disease in the COVID-TB group and the non-TB group were 2.21 (95% CI: 1.80, 2.70) and 2.77 (95% CI: 1.33, 5.74) (P < 0.01), respectively.Conclusion: In summary, there appear to be some predictors of worse prognosis among COVID-TB cases. A moderate level of evidence suggests that COVID-TB patients are more likely to suffer severe disease or death than COVID-19 patients. Finally, routine screening for TB may be recommended among suspected or confirmed cases of COVID-19 in countries with high TB burden.

Results from zero-inflated Poisson regression of number of IADL limitations on potential risk factors (n = 431).

Background: Elderly patients infected with COVID-19 are reported to be facing a substantially increased risk of mortality. Clinical characteristics, treatment options, and potential survival factors remain under investigation. This study aimed to fill this gap and provide clinically relevant factors associated with survival of elderly patients with COVID-19.Methods: In this multi-center study, elderly patients (age ≥65 years old) with laboratory-confirmed COVID-19 from 4 Wuhan hospitals were included. The clinical end point was hospital discharge or deceased with last date of follow-up on Jul. 08, 2020. Clinical, demographic, and laboratory data were collected. Univariate and multivariate analysis were performed to analyze survival and risk factors. A metabolic flux analysis using a large-scale molecular model was applied to investigate the pathogenesis of SARS-CoV-2 with regard to metabolism pathways.Results: A total of 223 elderly patients infected with COVID-19 were included, 91 (40.8%) were discharged and 132 (59.2%) deceased. Acute respiratory distress syndrome (ARDS) developed in 140 (62.8%) patients, 23 (25.3%) of these patients survived. Multivariate analysis showed that potential risk factors for mortality were elevated D-Dimer (odds ratio: 1.13 [95% CI 1.04 - 1.22], p = 0.005), high immune-related metabolic index (6.42 [95% CI 2.66–15.48], p < 0.001), and increased neutrophil-to-lymphocyte ratio (1.08 [95% 1.03–1.13], p < 0.001). Elderly patients receiving interferon atmotherapy showed an increased probability of survival (0.29 [95% CI 0.17–0.51], p < 0.001). Based on these factors, an algorithm (AlgSurv) was developed to predict survival for elderly patients. The metabolic flux analysis showed that 12 metabolic pathways including phenylalanine (odds ratio: 28.27 [95% CI 10.56–75.72], p < 0.001), fatty acid (15.61 [95% CI 6.66–36.6], p < 0.001), and pyruvate (12.86 [95% CI 5.85–28.28], p < 0.001) showed a consistently lower flux in the survivors vs. the deceased subgroup. This may reflect a key pathogenic mechanism of COVID-19 infection.Conclusion: Several factors such as interferon atmotherapy and recreased activity of specific metabolic pathways were found to be associated with survival of elderly patients. Based on these findings, a survival algorithm (AlgSurv) was developed to assist the clinical stratification for elderly patients. Dysregulation of the metabolic pathways revealed in this study may aid in the drug and vaccine development against COVID-19.

Introduction: While acute Coronavirus disease 2019 (COVID-19) affects the cardiovascular (CV) system according to recent data, an increased CV risk has been reported also during long-term follow-up (FU). In addition to other CV pathologies in COVID-19 survivors, an enhanced risk for arrhythmic events and sudden cardiac death (SCD) has been observed. While recommendations on post-discharge thromboprophylaxis are conflicting in this population, prophylactic short-term rivaroxaban therapy after hospital discharge showed promising results. However, the impact of this regimen on the incidence of cardiac arrhythmias has not been evaluated to date.Methods: To investigate the efficacy of this therapy, we conducted a single center, retrospective analysis of 1804 consecutive, hospitalized COVID-19 survivors between April and December 2020. Patients received either a 30-day post-discharge thromboprophylaxis treatment regimen using rivaroxaban 10 mg every day (QD) (Rivaroxaban group (Riva); n = 996) or no thromboprophylaxis (Control group (Ctrl); n = 808). Hospitalization for new atrial fibrillation (AF), new higher-degree Atrioventricular-block (AVB) as well as incidence of SCD were investigated in 12-month FU [FU: 347 (310/449) days].Results: No differences in baseline characteristics (Ctrl vs Riva: age: 59.0 (48.9/66.8) vs 57 (46.5/64.9) years, p = n.s.; male: 41.5% vs 43.7%, p = n.s.) and in the history of relevant CV-disease were observed between the two groups. While hospitalizations for AVB were not reported in either group, relevant rates of hospitalizations for new AF (0.99%, n = 8/808) as well as a high rate of SCD events (2.35%, n = 19/808) were seen in the Ctrl. These cardiac events were attenuated by early post-discharge prophylactic rivaroxaban therapy (AF: n = 2/996, 0.20%, p = 0.026 and SCD: n = 3/996, 0.30%, p < 0.001) which was also observed after applying a logistic regression model for propensity score matching (AF: χ2-statistics = 6.45, p = 0.013 and SCD: χ2-statistics = 9.33, p = 0.002). Of note, no major bleeding complications were observed in either group.Conclusion: Atrial arrhythmic and SCD events are present during the first 12 months after hospitalization for COVID-19. Extended prophylactic Rivaroxaban therapy after hospital discharge could reduce new onset of AF and SCD in hospitalized COVID-19 survivors.

ObjectiveThere is a lack of estimates regarding the at-risk population associated with long COVID in Pakistan due to the absence of prospective longitudinal studies. This study aimed to determine the prevalence of long COVID and its association with disease severity and vaccination status of the patient.Design and data sourcesThis prospective cohort study was conducted at the Aga Khan University Hospital and recruited patients aged > 18 years who were admitted between February 1 and June 7, 2021. During this time, 901 individuals were admitted, after excluding patients with missing data, a total of 481 confirmed cases were enrolled.ResultsThe mean age of the study population was 56.9±14.3 years. Among patients with known vaccination status (n = 474), 19%(n = 90) and 19.2%(n = 91) were fully and partially vaccinated, respectively. Severe/critical disease was present in 64%(n = 312). The mortality rate following discharge was 4.58%(n = 22). Around 18.9%(n = 91) of the population required readmission to the hospital, with respiratory failure (31.8%, n = 29) as the leading cause. Long COVID symptoms were present in 29.9%(n = 144), and these symptoms were more prevalent in the severe/critical (35.5%, n = 111) and unvaccinated (37.9%, n = 105) cohort. The most prominent symptoms were fatigue (26.2%, n = 126) and shortness of breath (24.1%, n = 116), followed by cough (15.2%, n = 73). Vaccinated as compared to unvaccinated patients had lower readmissions (13.8% vs. 21.51%) and post-COVID pulmonary complications (15.4% vs. 24.2%). On multivariable analysis, after adjusting for age, gender, co-morbidity, and disease severity, lack of vaccination was found to be an independent predictor of long COVID with an Odds ratio of 2.42(95% CI 1.52–3.84). Fully and partially vaccinated patients had 62% and 56% reduced risk of developing long COVID respectively.ConclusionsThis study reports that the patients continued to have debilitating symptoms related to long COVID, one year after discharge, and most of its effects were observed in patients with severe/critical disease and unvaccinated patients.

PurposeThe effect of renin-angiotensin-aldosterone system (RAAS) inhibitors in combination with COVID-19 and diabetes mellitus (DM) remains unknown. We assessed the risk of death in COVID-19 inpatients based on the presence or absence of DM, arterial hypertension (AH) and the use of RAAS inhibitors or other antihypertensives.MethodsThe results of treatment of all adult PCR-confirmed COVID-19 inpatients (n = 1097, women 63.9%) from 02/12/2020 to 07/01/2022 are presented. The presence of DM at the time of admission and the category of antihypertensive drugs during hospital stay were noted. Leaving the hospital due to recovery or death was considered as a treatment outcome. Multivariable logistic regression analysis was used to assess the risk of death. Patients with COVID-19 without AH were considered the reference group.ResultsDM was known in 150 of 1,097 patients with COVID-19 (13.7%). Mortality among DM inpatients was higher: 20.0% vs. 12.4% respectively (p=0.014). Male gender, age, fasting plasma glucose (FPG) and antihypertensives were independently associated with the risk of dying in patients without DM. In DM group such independent association was confirmed for FPG and treatment of AH. We found a reduction in the risk of death for COVID-19 inpatients without DM, who received RAAS inhibitors compared with the corresponding risk of normotensive inpatients, who did not receive antihypertensives: OR 0.22 (95% CI 0.07–0.72) adjusted for age, gender and FPG.ConclusionThis result raises a question about the study of RAAS inhibitors effect in patients with Covid-19 without AH.