The study aimed to analyze in-hospital mortality (IHM) among all COVID-19 patients hospitalized in Spain between March 1, 2020, and December 31, 2021, and to compare two distinct periods: the prevaccination period (March 1, 2020, to January 31, 2021) and the vaccination period (February 1, 2021, to December 31, 2021). The objective was to assess the impact of vaccination on IHM and identify associated risk factors, using data from Spain's national hospitalization registry.

This retrospective analysis used data from the Spanish National Surveillance System for Hospital Data. The primary outcome was in-hospital mortality (IHM). Multivariate logistic regression identified risk factors across the overall study period, as well as during the prevaccination and vaccination periods. Risk factors included age (in 20-year intervals), sex, comorbidities (e.g., hypertension, diabetes, chronic kidney failure, obesity, neurodegenerative disorders, and others), and admission to the intensive care unit.

A total of 524,314 COVID-19 hospitalizations were recorded in Spain, with 329,690 during the prevaccination period and 194,624 during the vaccination period. Hospitalization rates dropped from 697/100,000 people to 411/100,000, and in-hospital mortality (IHM) decreased from 16.2 to 11.5% (adjusted odds ratio [AOR]: 0.71, 95% CI: 0.70-0.73, p < 0.001). IHM rose with age, from 0.8% in patients aged 18-39 to 31.7% in those ≥80 years (p < 0.001), but significant decreases were observed across all age groups after vaccination, especially in those ≥80 years (AOR: 0.76, 95% CI: 0.75-0.79, p < 0.001). Risk factors for IHM remained consistent, with leukemia, neoplasm, and lymphoma posing the highest risks, while female sex (AOR: 0.75, 95% CI: 0.74-0.77, p < 0.001) and dyslipidemia (AOR: 0.85, 95% CI: 0.32-0.86, p < 0.001) were protective factors.

During the vaccination period, the risk of in-hospital mortality (IHM) was 29% lower than in the prevaccination period, after adjusting for sex, age, and comorbidities. This reduced risk was observed across sexes, age groups, and comorbidities. The risk factors for IHM remained consistent between the two periods, with age as the main risk factor, while female sex and dyslipidemia were identified as protective factors.

Once the pandemic ended, SARS-CoV-2 became endemic, with flare-up phases. COVID-19 disease can still have a significant clinical impact, especially in older patients with multimorbidity and frailty.

This study aims at evaluating the main characteristics associated to in-hospital mortality among data routinely collected upon admission to identify older patients at higher risk of death.

The present study used data from Gerocovid-acute wards, an observational multicenter retrospective-prospective study conducted in geriatric and internal medicine wards in subjects ≥60 years old during the COVID-19 pandemic. Seventy-one routinely collected variables, including demographic data, living arrangements, smoking habits, pre-COVID-19 mobility, chronic diseases, and clinical and laboratory parameters were integrated into a web-based machine learning platform (Just Add Data Bio) to identify factors with the highest prognostic relevance. The use of artificial intelligence allowed us to avoid variable selection bias, to test a large number of models and to perform an internal validation.

The dataset was split into training and test sets, based on a 70:30 ratio and matching on age, sex, and proportion of events; 3,520 models were set out to train. The three predictive algorithms (optimized for performance, interpretability, or aggressive feature selection) converged on the same model, including 12 variables: pre-COVID-19 mobility, World Health Organization disease severity, age, heart rate, arterial blood gases bicarbonate and oxygen saturation, serum potassium, systolic blood pressure, blood glucose, aspartate aminotransferase, PaO2/FiO2 ratio and derived neutrophil-to-lymphocyte ratio.

Beyond variables reflecting the severity of COVID-19 disease failure, pre-morbid mobility level was the strongest factor associated with in-hospital mortality reflecting the importance of functional status as a synthetic measure of health in older adults, while the association between derived neutrophil-to-lymphocyte ratio and mortality, confirms the fundamental role played by neutrophils in SARS-CoV-2 disease.

Cardiovascular complications of COVID-19 are numerous and aspects of this phenomenon are not well known. The main objective of this manuscript is a systematic review of the acute and chronic cardiovascular complications secondary to COVID-19.

A systematic review of the literature through Medline via PubMed was conducted (2020-2024).

There is a plethora of effects of COVID-19 on the heart in the acute setting. Here we discuss pathophysiology, myocardial infarctions, heart failure, Takotsubo Cardiomyopathy, myocardial injury, myocarditis and arrhythmias that are caused by COVID-19. Additionally, these cardiovascular injuries can linger and may be an underlying cause of some Long COVID symptoms.

Cardiovascular complications of COVID-19 are numerous and life-threatening. Long COVID can affect cardiovascular health. Microclotting induced by SARS-CoV-2 infection could be a therapeutic target for some aspects of Long Covid.

This study aimed to describe the clinical presentation of COVID-19 in hospitalized patients aged 80 or above and to identify predictors for death and complications throughout the epidemic waves of the disease.

This was an observational, multicenter, ambispective study conducted between March 2020 and August 2021 using data collected in five centers from southern metropolitan area of Barcelona (COVID-MetroSud cohort). Patients were grouped based on the pandemic waves of inclusion in the registry. We conducted a descriptive analysis, followed by bivariate and multivariate analyses (binary logistic regression) to identify predictors of risk for death or complications.

A total of 1192 patients (mean [SD] age 85.7 [4.22] years and 46.8% female) were included. The most frequently reported symptoms in all waves were fever (63.1%), cough (56.5%), dyspnea (48.2%), and asthenia (27.5%). Laboratory and radiological findings consistently showed abnormal bilateral chest X-ray results (72.5% of patients) and elevated inflammatory markers such as lactate dehydrogenase (mean [SD] 335 [188] U/L), C-reactive protein (CRP) (mean [SD] 110 [88.4] U/L), and ferritin (mean [SD] 842 [1561] U/L). Acute respiratory distress syndrome (43.7%), renal failure (19.2%), and delirium (17.5%) were the most frequent complications. The overall mortality rate was 41.4% and declined across the epidemic waves. Age, diabetes mellitus, heart failure, dyspnea, and higher baseline levels of creatinine were identified as risk factors for complications, while a higher Barthel index and presence of cough were found to be protective. Age, dyspnea, abnormal bilateral chest X-ray, CRP, and sodium were identified as risk factors for death.

This study demonstrates the clinical presentation of COVID-19 (fever, cough, dyspnea, and asthenia) and the different risk factors for mortality and complications in octogenarian hospitalized patients throughout the pandemic. These findings could be highly valuable for managing future virus pandemics.

The goal of this study is to assess the clinical attributes exhibited by patients aged 85 years and older who present different manifestations of COVID-19, and to examine the factors influencing the classification of the disease severity.

This retrospective study was conducted at a single center, encompassing an analysis of clinical data obtained from patients with COVID-19 admitted to a general geriatric hospital in Hangzhou, Zhejiang, China, during the period from December 20, 2022, to February 1, 2023. The study focused on 91 eligible patients whose disease severity was compared based on the imaging findings.

A total of 91 patients aged 85 years and older, with a median age of 92, including 46 males, 10 exhibiting mild symptoms, 48 moderate cases, and 33 severe cases met the inclusion criteria. Notably, disease severity displayed a significant correlation with age (p < 0.011). All patients presented with complicated chronic underlying conditions and a history of prolonged medication use. Rheumatic immune diseases (p = 0.040) and endocrine medications, primarily hypoglycemic agents (p = 0.034), exhibited statistical significance. Additionally, markers such as lactate dehydrogenase (LDH) (p = 0.030), interleukin 6 (IL-6) (p = 0.014), and D-dimer (p = 0.007) revealed significant associations with disease severity. Chest computed tomography scans predominantly revealed inflammatory features (n = 81, 89.0%). Notably, patients classified as having mild or moderate conditions exhibited eventual improvement, while 13 patients (39.4%) among the severe cases succumbed to the disease.

The classification of disease among patients aged 85 years or older old is correlated with advanced age, concurrent rheumatic immune diseases, and prolonged administration of endocrine medications. Furthermore, patients with elevated levels of LDH, IL-6, and D-dimer demonstrated a higher propensity for developing severe diseases.

Despite the variability and complexity of geriatric conditions, few COVID-19 reports of clinical characteristic prognostication provide data specific to oldest-old adults (over age 85), and instead generally report broadly as 65 and older.

To examine metabolic syndrome criteria in adults across 25 hospitals with variation in chronological age.

This cohort study examined 39,564 hospitalizations of patients aged 18 or older with COVID-19 who received inpatient care between March 13, 2020, and February 28, 2022.

ICU admission and/or in-hospital mortality.

Metabolic syndrome criteria and patient demographics were examined as risk factors. The main outcomes were admission to ICU and hospital mortality.

Oldest old patients (≥ 85 years) hospitalized with COVID-19 accounted for 7.0% (2758/39,564) of all adult hospitalizations. They had shorter ICU length of stay, similar overall hospitalization duration, and higher rates of discharge destinations providing healthcare services (i.e., home health, skilled nursing facility) compared to independent care. Chronic conditions varied by age group, with lower proportions of diabetes and uncontrolled diabetes in the oldest-old cohort compared with young-old (65-74 years) and middle-old (75-84 years) groups. Evaluations of the effect of metabolic syndrome and patient demographics (i.e., age, sex, race) on ICU admission demonstrate minimal change in the magnitude of effect for metabolic syndrome on ICU admission across the different models.

Metabolic syndrome measures are important individual predictors of COVID-19 outcomes. Building on prior examinations that metabolic syndrome is associated with death and ARDS across all ages, this analysis supports that metabolic syndrome criteria may be more relevant than chronological age as risk factors for poor outcomes attributed to COVID-19.

Background: The prognostic nutritional index (PNI) and the systemic immune inflammation index (SII) have been used as simple risk-stratification predictors for COVID-19 severity and mortality in the general population. However, the associations between these indices and mortality might differ due to age-related changes such as inflammaging and several comorbid conditions in older patients. Therefore, we aimed to compare the predictivity of the PNI and SII for mortality among hospitalized older patients and patients under 65 years old. Methods: Patients hospitalized with COVID-19 from March 2020 to December 2020 were retrospectively included. The PNI and SII were calculated from hospital records within the first 48 h after admission. Data were evaluated in the whole group and according to age groups (≥65 < years). Receiver operating characteristic curves were drawn to evaluate the predictivity of the PNI and SII. Results: Out of 407 patients included in this study, 48.4% (n = 197) were older patients, and 51.6% (n = 210) were under 65 years old. For mortality, the area under the curve (AUC) of the PNI and SII in the adult group (<65 years) was 0.706 (95% CI 0.583-0.828) (p = 0.003) and 0.697 (95% CI 0.567-0.827) (p < 0.005), respectively. The AUC of the PNI and SII in the older group was 0.515 (95% CI 0.427-0.604) (p = 0.739) and 0.500 (95% CI 0.411-0.590) (p = 0.993). Conclusions: The accuracy of the PNI and SII in predicting mortality in adult COVID-19 patients seemed to be fair, but no association was found in geriatric patients in this study. The predictivity of the PNI and SII for mortality varies according to age groups.

The objective of the present study is to analyze the epidemiological, clinical and functional characteristics of patients admitted to the University Hospital of Navarra due to SARS-CoV-2 infection, as well as the predictors of mortality, during the first wave of the pandemic caused by this virus.

An observational, retrospective study was performed, including all hospitalized patients older than 75 years. Information has been obtained on multiple variables, among which it is worth mentioning previous geriatric syndromes or those that have appeared during hospitalization, or past medical history considered relevant in SARS-CoV-2 infection. A descriptive analysis of the data, comparisons according to various factors of interest and multivariate analysis to analyze factors associated with mortality were carried out.

Data have been obtained from a total of 426 patients with a mean age of 83.2 years (52.6% men). 34.7% died in hospital and 4.5% within 1 month after hospital discharge. The factors related to mortality were: worse baseline functional status, chronic kidney disease, and fever or dyspnea as forms of presentation. The most frequent typical symptoms were: fever, dyspnea, cough, asthenia and hyporexia. Up to 42.1% presented delirium as a symptom of atypical onset. We observed a functional deterioration that was not recover after a month of follow-up (baseline Barthel index 81.12; 70.08 at discharge; 75.55 after a month).

SARS-CoV-2 infection has caused high mortality rates in older adults. In this age group, the atypical presentation of this disease and functional deterioration during hospitalization are frequent. In the present study, a worse previous functional status has been identified as a predictor of mortality. More studies are needed to evaluate the impact that the disease and hospitalization have on the older patient, with the aim of implementing preventive, diagnostic and therapeutic measures that are necessary to avoid functional deterioration and adverse health events related to it.

We investigated predictors of mortality, including demographic, clinical, and laboratory parameters, in hospitalized patients with coronavirus disease 2019 (COVID-19) pneumonia.

This retrospective, single-center study included 651 consecutive Turkish adults who had been admitted to the emergency department with a diagnosis of COVID-19. We recorded the demographic, clinical, and laboratory parameters of the patients. The patients were divided into two groups: patients aged ≥65 years and patients aged <65 years. The predictors of mortality for hospitalized COVID-19 patients were evaluated.

The study included 651 patients (354 [54.4%] men and 297 [45.6%] women; mean age, 56.40 ± 15.70 years). The most common comorbidities were hypertension (37.6%), diabetes mellitus (28.9%), and coronary artery disease (CAD) (16.1%). The overall mortality rate was 10.6% (n = 69); the mortality rate was higher in men than in women. Advanced age; chronic renal failure (CRF); prolonged activated partial thromboplastin time; high serum neutrophil and platelet counts; high C-reactive protein to albumin (CRP/albumin) ratio; and high levels of albumin, lactate dehydrogenase (LDH), and high-sensitivity troponin I (TnI-hs) were independent predictors of mortality in all age groups.

Multivariate logistic regression analysis showed that chronic obstructive pulmonary disease (COPD), high serum platelet count, high CRP/albumin ratio, and high levels of albumin, TnI-hs, and D-dimer were independent predictors of mortality in patients aged <65 years. Conversely, advanced age, CAD, CRF, and high levels of serum CRP and LDH were independent predictors of mortality in patients aged ≥65 years.

Coronavirus disease 2019 (COVID-19) is characterized by poor outcomes and a high mortality rate, particularly among elderly patients. Since the beginning of the pandemic, an older age has been recognized as a critical risk factor for disease severity, with increasing mortality rates in each decade of life. This phenomenon may be a consequence of a poor previous health status, with a higher prevalence of pre-existing comorbidities and a higher degree of frailty. The majority of studies on the outcomes and risk factors of elderly patients refer to the first waves of the pandemic and the predictors of in-hospital mortality in these patients. The aim of the present study was to provide a detailed description of the clinical characteristics and management of a cohort of elderly patients (≥65 years of age) who were hospitalized with COVID-19-related pneumonia in all phases of the pandemic, presenting their outcomes, and investigating predictors of in-hospital and out-of-hospital mortality over a period of 1 year in this particularly vulnerable population. A total of 1,124 elderly patients (603 males, 53.7%) with a mean age of 78.51±7.42 years and a median Charlson comorbidity index (CCI) of 5 were included in the study. Of these patients, 104 (9.3%) were hospitalized during the period of prevalence of the original strain Wuhan, 385 (34.3%) were hospitalized during the period of prevalence of the Alpha variant, 221 (19.7%) were hospitalized during the period of prevalence of the Delta variant, and 414 (36.8%) were hospitalized during the period of prevalence of the Omicron variant. Overall, the in-hospital mortality rate was 33.4% (375 patients), and the 1-year mortality rate was 44.7% (502 patients). The majority of patients had not been vaccinated or had not completed full vaccination against severe acute respiratory syndrome coronavirus-2 (843 patients, 75%), given the period of infection. Age, immature granulocytes, lactate dehydrogenase (LDH) levels, ferritin levels, chest X-ray score, as well as the absence of full vaccination, cough and fatigue, were statistically significantly and independently associated with in-hospital mortality, while age, LDH levels, ferritin levels, alanine aminotransferase levels, CCI, chest X-ray score, the absence of cough and fatigue, and a history of dementia were statistically significantly and independently associated with 1-year mortality. On the whole, the present study demonstrates that both the in-hospital mortality and 1-year mortality rates of elderly patients hospitalized due to COVID-19-related pneumonia are high.

Coagulation disorders are frequently encountered among patients infected with coronavirus disease 2019 (COVID-19), especially among admitted patients with more severe symptoms. This study aims to determine the mortality rate and incidence and risk factors for venous thromboembolism (VTE) in hospitalized patients with COVID-19.

This retrospective observational cohort study was conducted from March to July 2020 using a hospital database. All adult patients (>18 years old) with laboratory-confirmed COVID-19 were included. Laboratory data and the real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) for SARS-CoV-2 were obtained from medical records. The mortality rate and the incidence of VTE were established as study results. A multivariate logistic regression analysis was performed to identify predictors of thrombotic events.

rA total of 1024 confirmed COVID-19 patients were treated, of whom 110 (10.7%) were deceased and 58 patients (5.7%) developed VTE. Death occurred more frequently in patients older than 50 years and those admitted to the intensive care unit (ICU, 95%) and who received mechanical ventilation (62.7%). Multivariate analysis revealed that cancer patients were two times more likely to have VTE (adjusted odds ratio = 2.614; 95% CI = (1.048-6.519); p = 0.039). Other chronic diseases, such as diabetes, hypertension, and chronic kidney disease, were not associated with an increased risk of VTE.

One-tenth of hospitalized COVID-19 patients were deceased, and VTE was prevalent among patients with chronic conditions, such as cancer, despite anticoagulation therapy. Healthcare professionals should closely monitor individuals with a high risk of developing VTE to prevent unwanted complications.

Background: Early detection of elderly patients with COVID-19 who are at high risk of mortality is vital for appropriate clinical decisions. We aimed to evaluate the risk factors associated with all-cause in-hospital mortality among elderly patients with COVID-19. Methods: In this retrospective study, the medical records of elderly patients aged over 60 who were hospitalized with COVID-19 at Thammasat University Hospital from 1 July to 30 September 2021 were reviewed. Multivariate logistic regression was used to identify independent predictors of mortality. The sum of weighted integers was used as a total risk score for each patient. Results: In total, 138 medical records of patients were reviewed. Four identified variables based on the odds ratio (age, respiratory rate, glomerular filtration rate and history of stroke) were assigned a weighted integer and were developed to predict mortality risk in hospitalized elderly patients. The AUROC of the scoring system were 0.9415 (95% confidence interval, 0.9033-0.9716). The optimized scoring system was developed and a risk score over 213 was considered a cut-off point for high mortality risk. Conclusions: A simple predictive risk score provides an initial assessment of mortality risk at the time of admission with a high degree of accuracy among hospitalized elderly patients with COVID-19.

In 2020, the COVID-19 pandemic followed a two-wave pattern in most countries. Hospital admission for COVID-19 in one wave or another could have affected mortality, especially among the older persons. The objective of this study was to evaluate whether the admission of older patients during the different waves, before SARS-CoV-2 vaccination was available, was associated with a different mortality. We compared the mortality rates of patients hospitalized during 2020 before (first wave) and after (second wave) July 7, 2020, included in the SEMI-COVID-19 Registry, a large, multicenter, retrospective cohort of patients admitted to 126 Spanish hospitals for COVID-19. A multivariate logistic regression analysis was performed to control for changes in either the patient or disease profile. As of December 26, 2022, 22,494 patients had been included (17,784 from the first wave and 4710 from the second one). Overall mortality was 20.4% in the first wave and 17.2% in the second wave (risk difference (RD) - 3.2%; 95% confidence interval (95% CI) - 4.4 to - 2.0). Only patients aged 70 and older (10,973 patients: 8571 in the first wave and 2386 in the second wave) had a significant reduction in mortality (RD - 7.6%; 95% CI - 9.7 to - 5.5) (unadjusted relative risk reduction: 21.6%). After adjusting for age, comorbidities, variables related to the severity of the disease, and treatment received, admission during the second wave remained a protective factor. In Spain, patients aged 70 years and older admitted during the second wave of the COVID-19 pandemic had a significantly lower risk of mortality, except in severely dependent persons in need of corticosteroid treatment. This effect is independent of patient characteristics, disease severity, or treatment received. This suggests a protective effect of a better standard of care, greater clinical expertise, or a lesser degree of healthcare system overload.

Predicting severe disease is important in provocative decision-making for the management of patients with the coronavirus disease 2019 (COVID-19); However, there are still some controversies about the COVID-19's severity predicting factors. This study aimed to investigate the relationships between clinical and laboratory findings regarding COVID-19's severity in patients admitted to a tertiary hospital in Mashhad, Iran.

A cross-sectional study was conducted on patients with documented COVID-19 infection based on the reverse transcription-polymerase chain reaction test. Clinical symptoms, vital signs, and medical history of the patients were recorded from their medical records. Laboratory findings and computed tomography (CT) study findings were documented. Disease severity was defined based on CT scan findings.

A total of 564 patients (58.8 ± 16.8 years old) were evaluated. The frequency of severe disease was 70.4%. There was a significant difference in heart rate (p = 0.0001), fever (p = 0.002), dyspnea (p = 0.0001), chest pain (p = 0.0001), diarrhea (p = 0.021), arthralgia (p = 0.0001), and chills (p = 0.044) as well as lymphopenia (p = 0.014), white blood cell count (p = 0.001), neutrophil count (p < 0.0001), lymphocyte count (p < 0.0001), and prothrombin time (p = 0.001) between disease severity groups. Predictors of severe COVID-19 were pulse rate (crude odds ratio [cOR] = 1.014, 95% confidence interval [CI] for cOR: 1.001, 1.027) and leukopenia (cOR = 3.910, 95% CI for cOR: 1.294, 11.809). Predictors for critical COVID-19 were pulse rate (cOR = 1.075, 95% CI for cOR: 1.046, 1.104), fever (cOR = 2.516, 95%CI for cOR: 1.020, 6.203), dyspnea (cOR = 4.190, 95% CI for cOR: 1.227, 14.306), and leukocytosis (cOR = 3.866, 95% CI for cOR: 1.815, 8.236).

Leukopenia and leukocytosis have the strongest correlation with the COVID-19 severity. These findings could be a valuable guild for clinicians in COVID-19 patient management in the inpatient setting.

Emerging evidence suggests that frailty may be a significant predictor of poor outcomes in older individuals hospitalized due to COVID-19. This study aims to determine the prognostic value of frailty on intrahospital patient survival.

This observational, multicenter, nationwide study included patients aged 70 years and older who were hospitalized due to COVID-19 in Spain between March 1 and December 31, 2020. Patient data were obtained from the SEMI-COVID-19 Registry of the Spanish Society of Internal Medicine. Frailty was assessed using the Clinical Frailty Scale. The primary outcome was hospital survival. Cox proportional hazards models were used to assess predictors of survival.

A total of 1,878 participants (52% men and 48% women) were included, with 1,351 (71.9%) survivors and 527 (28.1%) non-survivors. The non-survivor group had higher mean age (83.5 vs. 81 years), comorbidities (6.3 vs. 5.3 points on the Charlson index), degree of dependency (26.8% vs. 12.4% severely dependent patients), and frailty (34.5% vs. 14.7% severely frail patients) compared to survivors. However, there were no differences in terms of sex. Our results demonstrate that a moderate-severe degree of frailty is the primary factor independently associated with shorter survival [HR 2.344 (1.437-3.823; p<0.001) for CFS 5-6 and 3.694 (2.155-6.330; p<0.001) for CFS 7-9].

Frailty is the main predictor of adverse outcomes in older patients with COVID-19. The utilization of tools such as the Clinical Frailty Scale is crucial for early detection in this population.

Mortality is high in older patients hospitalized with COVID-19. Previous studies observed lower mortality during the Omicron wave, yet no data is available on older patients. The objective was to compare in-hospital mortality between the Omicron and previous waves in older patients hospitalized with COVID-19.

This retrospective observational multicenter cohort study used the Greater Paris University Hospitals Group's data warehouse (38 hospitals). Patients aged ≥ 75 years with a confirmed COVID-19 diagnosis and hospitalized from March 2020 to January 2022 were included. The study period was divided into five waves. The fifth wave (January 1st to 31st 2022) was considered as the Omicron wave as it was the predominant variant (≥ 50%), and was compared with waves 1 (March-July 2020), 2 (August-December 2020), 3 (January-June 2021) and 4 (July-December 2021). Primary outcome was in-hospital mortality. Secondary outcome was occurrence of ICU admission or in-hospital death. Multivariate logistic regression was performed, with a sensitivity analysis according to variant type.

Of the 195,084 patients hospitalized with COVID-19, 19,909 patients aged ≥ 75 years were included (median age 85 [IQR 79-90] years, 53% women). Overall in-hospital mortality was 4,337 (22%), reaching 345 (17%) during wave 5. Waves 1 and 3 were significantly associated with increased in-hospital mortality in comparison with wave 5 (adjusted Odds Ratios aOR 1.42 [95%CI 1.21-1.66] and 1.56 [95%CI 1.33-1.83] respectively). Waves 1 to 3 were associated with an increased risk of occurrence of ICU admission or in-hospital death in comparison with wave 5: aOR 1.29 [95% CI 1.12 to 1.49] for wave 1, aOR 1.25 [95% CI 1.08 to 1.45] for wave 2 and aOR 1.56 [95% CI 1.36 to 1.79] for wave 3. Sensitivity analysis found that Omicron variant was associated with decreased mortality, in comparison with previous variants.

Mortality was lower during the 5th Omicron wave in the older population, but remained high, implying that this variant could be considered as "milder" but not "mild". This persistently high mortality during the 5th Omicron wave highlights the importance of including older patients in clinical trials to confirm the benefit/risk balance of COVID-19 treatments in this fragile population.

Efforts to quantify the burden of SARS-CoV-2-associated sepsis have been limited by inconsistent definitions and underrecognition of viral sepsis.

To describe the incidence and outcomes of SARS-CoV-2-associated sepsis vs presumed bacterial sepsis using objective electronic clinical criteria.

This retrospective cohort study included adults hospitalized at 5 Massachusetts hospitals between March 2020 and November 2022.

SARS-CoV-2-associated sepsis was defined as a positive SARS-CoV-2 polymerase chain reaction test and concurrent organ dysfunction (ie, oxygen support above simple nasal cannula, vasopressors, elevated lactate level, rise in creatine or bilirubin level, and/or decline in platelets). Presumed bacterial sepsis was defined by modified US Centers for Disease Control and Prevention adult sepsis event criteria (ie, blood culture order, sustained treatment with antibiotics, and organ dysfunction using identical thresholds as for SARS-CoV-2-associated sepsis).

Trends in the quarterly incidence (ie, proportion of hospitalizations) and in-hospital mortality for SARS-CoV-2-associated and presumed bacterial sepsis were assessed using negative binomial and logistic regression models.

This study included 431 017 hospital encounters from 261 595 individuals (mean [SD] age 57.9 [19.8] years, 241 131 (55.9%) females, 286 397 [66.5%] from academic hospital site). Of these encounters, 23 276 (5.4%) were from SARS-CoV-2, 6558 (1.5%) had SARS-CoV-2-associated sepsis, and 30 604 patients (7.1%) had presumed bacterial sepsis without SARS-CoV-2 infection. Crude in-hospital mortality for SARS-CoV-2-associated sepsis declined from 490 of 1469 (33.4%) in the first quarter to 67 of 450 (14.9%) in the last (adjusted odds ratio [aOR], 0.88 [95% CI, 0.85-0.90] per quarter). Crude mortality for presumed bacterial sepsis was 4451 of 30 604 patients (14.5%) and stable across quarters (aOR, 1.00 [95% CI, 0.99-1.01]). Medical record reviews of 200 SARS-CoV-2-positive hospitalizations confirmed electronic health record (EHR)-based SARS-CoV-2-associated sepsis criteria performed well relative to sepsis-3 criteria (90.6% [95% CI, 80.7%-96.5%] sensitivity; 91.2% [95% CI, 85.1%-95.4%] specificity).

In this retrospective cohort study of hospitalized adults, SARS-CoV-2 accounted for approximately 1 in 6 cases of sepsis during the first 33 months of the COVID-19 pandemic. In-hospital mortality rates for SARS-CoV-2-associated sepsis were high but declined over time and ultimately were similar to presumed bacterial sepsis. These findings highlight the high burden of SARS-CoV-2-associated sepsis and demonstrate the utility of EHR-based algorithms to conduct surveillance for viral and bacterial sepsis.

COVID-19 is responsible for high mortality, but robust machine learning-based predictors of mortality are lacking. To generate a model for predicting mortality in patients hospitalized with COVID-19 using Gradient Boosting Decision Trees (GBDT). The Spanish SEMI-COVID-19 registry includes 24,514 pseudo-anonymized cases of patients hospitalized with COVID-19 from 1 February 2020 to 5 December 2021. This registry was used as a GBDT machine learning model, employing the CatBoost and BorutaShap classifier to select the most relevant indicators and generate a mortality prediction model by risk level, ranging from 0 to 1. The model was validated by separating patients according to admission date, using the period 1 February to 31 December 2020 (first and second waves, pre-vaccination period) for training, and 1 January to 30 November 2021 (vaccination period) for the test group. An ensemble of ten models with different random seeds was constructed, separating 80% of the patients for training and 20% from the end of the training period for cross-validation. The area under the receiver operating characteristics curve (AUC) was used as a performance metric. Clinical and laboratory data from 23,983 patients were analyzed. CatBoost mortality prediction models achieved an AUC performance of 84.76 (standard deviation 0.45) for patients in the test group (potentially vaccinated patients not included in model training) using 16 features. The performance of the 16-parameter GBDT model for predicting COVID-19 hospital mortality, although requiring a relatively large number of predictors, shows a high predictive capacity.

coronavirus disease 2019 (COVID-19) causes high mortality in elderly patients. Some studies have shown a benefit of statin treatment in the evolution of this disease. Since there are no similar publications in this population group, the aim of this study is to analyze in-hospital mortality in relation to preadmission treatment with statins in an exclusively elderly population of octogenarian patients.

A single-center retrospective cohort study was performed including a total of 258 patients ≥80 years with hospital admission for confirmed COVID-19 between March 1 and May 31, 2020. They were divided into two groups: taking statins prior to admission (n=129) or not (n=129).

In-hospital mortality due to COVID-19 in patients ≥80 years (86.13±4.40) during the first wave was 35.7% (95% CI: 30.1-41.7%). Mortality in patients previously taking statins was 25.6% while in those not taking statins was 45.7%. Female sex (RR 0.62 [0.44-0.89]; p=0.008), diabetes (RR 0.61 [0.41-0.92]; p=0.017) and pre-admission treatment with statins (RR 0.58 95% CI [0.41-0.83]; p=0.003) were associated with lower in-hospital mortality. Severe lung involvement was associated with increased in-hospital mortality (RR 1.45 95% CI [1.04-2.03]; p=0.028). Hypertension, obesity, age, cardiovascular disease and a higher Charlson index did not, however, show influence on in-hospital mortality.

In octogenarian patients treated with statins prior to admission for COVID-19 in the first wave, lower in-hospital mortality was observed.

To evaluate the relationship between high-sensitivity cardiac troponin T (hs-cTnT) and prognosis in elderly patients with coronavirus disease 2019 (COVID-19).

This study recruited 1399 COVID-19 patients aged 65 years or older admitted to Taizhou Hospital or Enze Hospital in Zhejiang Province from December 15, 2022, to January 4, 2023. The Cox regression model was used to evaluate the relationship between hs-cTnT level and in-hospital death. The logistic regression model was used to evaluate the relationship between hs-cTnT level and major adverse events. The restricted cubic spline (RCS) model is used for nonlinear analysis.

The increase of peak hs-cTnT was associated with the increased risk of death (HR 9.49; 95% CI 1.19-75.48; trend p = 0.012) and major adverse events (OR 20.59; 95% CI 10.41-40.71; trend p < 0.001). RCS model showed the association between peak hs-cTnT and death and major adverse events were nonlinear (P for non-line < 0.001). Starting from the limit of quantification of hs-cTnT (3ng/L), the risk of death and major adverse events had a steep S-shaped increase until hs-cTnT reached the 90th percentile point (hs-cTnT = 131 ng/L), followed by a plateau period. Peak hs-cTnT has good predictive value for death and major adverse events, with AUC of 0.834 and 0.804, respectively.

Peak hs-cTnT has high predictive value for the risk of in-hospital death and major adverse events in elderly patients with COVID-19. The association between peak hs-cTnT and death and major adverse events were nonlinear.

People with neurodegenerative disease and mild cognitive impairment (MCI) may have an elevated risk of acquiring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and may be disproportionally affected by coronavirus disease 2019 (COVID-19) once infected.

To review all eligible studies and quantify the strength of associations between various pre-existing neurodegenerative disorders and both SARS-CoV-2 susceptibility and COVID-19 illness course and outcome.

Pre-registered systematic review with frequentist and Bayesian meta-analyses. Systematic searches were executed in PubMed, Web of Science and preprint servers. The final search date was 9 January 2023. Odds ratios (ORs) were used as measures of effect.

In total, 136 primary studies (total sample size n = 97 643 494), reporting on 268 effect-size estimates, met the inclusion criteria. The odds for a positive SARS-CoV-2 test result were increased for people with pre-existing dementia (OR = 1.83, 95% CI 1.16-2.87), Alzheimer's disease (OR = 2.86, 95% CI 1.44-5.66) and Parkinson's disease (OR = 1.65, 95% CI 1.34-2.04). People with pre-existing dementia were more likely to experience a relatively severe COVID-19 course, once infected (OR = 1.43, 95% CI 1.00-2.03). People with pre-existing dementia or Alzheimer's disease were at increased risk for COVID-19-related hospital admission (pooled OR range: 1.60-3.72). Intensive care unit admission rates were relatively low for people with dementia (OR = 0.54, 95% CI 0.40-0.74). All neurodegenerative disorders, including MCI, were at higher risk for COVID-19-related mortality (pooled OR range: 1.56-2.27).

Our findings confirm that, in general, people with neurodegenerative disease and MCI are at a disproportionally high risk of contracting COVID-19 and have a poor outcome once infected.

Autoimmune/inflammatory rheumatic diseases (AIRDs) patients might be at-risk of severe COVID-19. However, whether this is linked to the disease or to its treatment is difficult to determine. This study aimed to identify factors associated with occurrence of severe COVID-19 in AIRD patients and to evaluate whether having an AIRD was associated with increased risk of severe COVID-19 or death.

Two databases were analyzed: the EDS (Entrepôt des Données de Santé, Clinical Data Warehouse), including all patients followed in Paris university hospitals and the French multi-center COVID-19 cohort [French rheumatic and musculoskeletal diseases (RMD)]. First, in a combined analysis we compared patients with severe and non-severe COVID-19 to identify factors associated with severity. Then, we performed a propensity matched score case-control study within the EDS database to compare AIRD cases and non-AIRD controls.

Among 1,213 patients, 195 (16.1%) experienced severe COVID-19. In multivariate analysis, older age, interstitial lung disease (ILD), arterial hypertension, obesity, sarcoidosis, vasculitis, auto-inflammatory diseases, and treatment with corticosteroids or rituximab were associated with increased risk of severe COVID-19. Among 35,741 COVID-19 patients in EDS, 316 having AIRDs were compared to 1,264 Propensity score-matched controls. AIRD patients had a higher risk of severe COVID-19 [aOR = 1.43 (1.08-1.87), p = 0.01] but analysis restricted to rheumatoid arthritis and spondyloarthritis found no increased risk of severe COVID-19 [aOR = 1.11 (0.68-1.81)].

In this multicenter study, we confirmed that AIRD patients treated with rituximab or corticosteroids and/or having vasculitis, auto-inflammatory disease, and sarcoidosis had increased risk of severe COVID-19. Also, AIRD patients had, overall, an increased risk of severe COVID-19 compares general population.

Wide arrays of laboratory parameters have been proposed by many studies for prognosis in COVID-19 patients. In this study, we wanted to determine if the International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score in addition to certain clinical and laboratory parameters would help in predicting mortality. We wanted to determine if a greater severity score on chest x-ray at presentation translated to poor patient outcomes using the COVID-19 chest radiography score.

This retrospective study was conducted at SDS TRC and Rajiv Gandhi Institute of chest diseases, Bangalore from March 2021 to June 2021. This study included 202 real-time-polymerase chain reaction-positive COVID-19 patients aged above 18 years admitted to the intensive care unit of our hospital. Demographic characteristics and baseline hematological and inflammatory markers (serum C-reactive protein, lactate dehydrogenase, troponin-I, ferritin, and d-dimer) were collected. Radiological severity on a chest x-ray was assessed using the validated COVID-19 chest radiography score. The International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score was assigned to each patient within 24 hours of intensive care unit admission. Outcome studied was in-hospital mortality.

The overall mortality was 54.9% (111 cases). Age more than 50 years, >4 days of symptoms, peripheral oxygen saturation/ fraction of inspired oxygen ratio less than 200, elevated serum lactate dehydrogenase >398.5 IU/L, and hypoalbuminemia (<2.95 g/dL) were detected as independent predictors of mortality. A significant correlation of risk stratification with mortality (P = .057) was seen with International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score. There was no significant correlation between the COVID-19 chest radiography score and mortality.

Age >50 years, peripheral oxygen saturation/fraction of inspired oxygen ratio <200, mean symptom duration of >4 days, elevated serum lactate dehydrogenase, and hypoalbuminemia are independent predictors of mortality in severe COVID-19 pneumonia. International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score was different in the survivors and deceased.

The significant impact of COVID-19 worldwide has made it necessary to develop tools to identify patients at high risk of severe disease and death. This work aims to validate the RIM Score-COVID in the SEMI-COVID-19 Registry. The RIM Score-COVID is a simple nomogram with high predictive capacity for in-hospital death due to COVID-19 designed using clinical and analytical parameters of patients diagnosed in the first wave of the pandemic. The nomogram uses five variables measured on arrival to the emergency department (ED): age, sex, oxygen saturation, C-reactive protein level, and neutrophil-to-platelet ratio. Validation was performed in the Spanish SEMI-COVID-19 Registry, which included consecutive patients hospitalized with confirmed COVID-19 in Spain. The cohort was divided into three time periods: T1 from February 1 to June 10, 2020 (first wave), T2 from June 11 to December 31, 2020 (second wave, pre-vaccination period), and T3 from January 1 to December 5, 2021 (vaccination period). The model's accuracy in predicting in-hospital COVID-19 mortality was assessed using the area under the receiver operating characteristics curve (AUROC). Clinical and laboratory data from 22,566 patients were analyzed: 15,976 (70.7%) from T1, 4,233 (18.7%) from T2, and 2,357 from T3 (10.4%). AUROC of the RIM Score-COVID in the entire SEMI-COVID-19 Registry was 0.823 (95%CI 0.819-0.827) and was 0.834 (95%CI 0.830-0.839) in T1, 0.792 (95%CI 0.781-0.803) in T2, and 0.799 (95%CI 0.785-0.813) in T3. The RIM Score-COVID is a simple, easy-to-use method for predicting in-hospital COVID-19 mortality that uses parameters measured in most EDs. This tool showed good predictive ability in successive disease waves.