Background The COVID-19 disease continues to cause severe mortality and morbidity. Biochemical parameters are being used to predict the severity of the infection. This study aims to predict disease severity and mortality to help reduce mortality through timely intervention in a cost-effective way. Methods A total of 324 COVID-19 cases admitted at our hospital (All India Institute of Medical Sciences, Patna, BR, India) between June 2020 to December 2020 (phase 1: 190 patients) and April 2021 to May 2021 (phase 2: 134 patients) were recruited for this study. Statistical analysis was done using SPSS Statistics version 23 (IBM Corp., Armonk, NY, USA) and model prediction using Python (The Python Software Foundation, Wilmington, DE, USA). Results There were significant differences in biochemical parameters at the time of admission among COVID-19 patients between phases 1 and 2, ICU and non-ICU admissions, and expired and discharged patients. The receiver operating characteristic (ROC) curves predicted mortality solely based on biochemical parameters. Using multiple logistic regression in Python, a total of four models (two each) were developed to predict ICU admission and mortality. A total of 92 out of 96 patients were placed into the correct management category by our model. This model would have allowed us to preserve 17 of the 21 patients we lost. Conclusions We developed predictive models for admission (ICU or non-ICU) and mortality based on biochemical parameters at the time of admission. A predictive model with a significant predictive capability for IL-6 and procalcitonin values using normal biochemical parameters was proposed. Both can be used as machine learning tools to prognosticate the severity of COVID-19 infections. This study is probably the first of its kind to propose triage for admission in the ICU or non-ICU at the medical emergency department during the first presentation for the necessary optimal treatment of COVID-19 based on a predictive model.

Information on antibody responses following SARS-CoV-2 infection, including the magnitude and duration of responses, is limited. In this analysis, we aimed to identify clinical biomarkers that can predict long-term antibody responses following natural SARS-CoV-2 infection.

In this prospective study, we enrolled 100 COVID-19 patients between November 2020 and February 2021 and followed them for 6 months. The association of clinical laboratory parameters on enrollment, including lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), ferritin, procalcitonin (PCT), and D-dimer, with predicting the geometric mean (GM) concentration of SARS-CoV-2 receptor-binding domain (RBD)-specific IgG antibody at 3 and 6 months post-infection was assessed in multivariable linear regression models.

The mean ± SD age of patients in the cohort was 46.8 ± 14 years, and 58.8% were male. Data from 68 patients at 3 months follow-up and 55 patients at 6 months follow-up were analyzed. Over 90% of patients were seropositive against RBD-specific IgG till 6 months post-infection. At 3 months, for any 10% increase in absolute lymphocyte count and NLR, there was a 6.28% (95% CI: 9.68, -2.77) decrease and 4.93% (95% CI: 2.43, 7.50) increase, respectively, in GM of IgG concentration, while any 10% increase for LDH, CRP, ferritin, and procalcitonin was associated with a 10.63, 2.87, 2.54, and 3.11% increase in the GM of IgG concentration, respectively. Any 10% increase in LDH, CRP, and ferritin was similarly associated with an 11.28, 2.48, and 3.0% increase in GM of IgG concentration at 6 months post-infection.

Several clinical biomarkers in the acute phase of SARS-CoV-2 infection are associated with enhanced IgG antibody response detected after 6 months of disease onset. The measurement of SARS-CoV-2 specific antibody responses requires improved techniques and is not feasible in all settings. Baseline clinical biomarkers can be a useful alternative as they can predict antibody response during the convalescence period. Individuals with an increased level of NLR, CRP, LDH, ferritin, and procalcitonin may benefit from the boosting effect of vaccines. Further analyses will determine whether biochemical parameters can predict RBD-specific IgG antibody responses at later time points and the association of neutralizing antibody responses.

The reasons of variability of clinical presentation of coronavirus disease-19 (COVID-19) across different pandemic waves are not fully understood, and may include individual risk profile, SARS-CoV-2 lineage and seasonal variations of viral spread. The objective of this retrospective study was to compare the characteristics and outcomes of patients admitted with confirmed coronavirus disease-19 (COVID-19) in the same season during the first (March 2020) and the third pandemic wave (March 2021, dominance of SARS-CoV-2 B.1.1.7 lineage) in an internal medicine ward of a large teaching hospital in Italy.

Data of 769 unvaccinated patients (399 from the first and 370 from the third wave) were collected from clinical records, including symptom type and duration, extension of lung abnormalities on chest computed tomography (CT) and PaO₂/FiO₂ ratio on admission arterial blood gas analysis.

Third wave patients were in average younger (median 65, interquartile range [IQR] 55-75, vs. 72, IQR 61-81 years old, p < 0.001), with less comorbidities and better pulmonary (CT visual score median 25, IQR 15-40, vs. 30, IQR 15-50, age- and sex-adjusted p = 0.017) and respiratory involvement (PaO₂/FiO₂ median 288, IQR 237-338, vs. 233, IQR 121-326 mmHg, age- and sex-adjusted p < 0.001) than first wave patients. Hospital mortality was lower (19% vs. 36%, p < 0.001), but not for subjects over 75 years old (46 vs. 49%). Age, number of chronic illnesses, PCT levels, CT visual score [Odds Ratio (OR) 1.022, 95% confidence interval (CI) 1.009-1.036, p < 0.001] and PaO₂/FiO₂ (OR 0.991, 95% CI 0.988-0.994, p < 0.001), but not the pandemic wave, were associated with mortality on stepwise multivariate logistic regression analysis.

Despite the higher virulence of B.1.1.7 lineage, we detected milder clinical presentation and improved mortality in patients hospitalized during the third COVID-19 wave, with involvement of younger subjects. The reasons of this discrepancy are unclear, but could involve the population effect of vaccination campaigns, that were being conducted primarily in older frail subjects during the third wave.

COVID-19 can trigger an intense systemic inflammation and prothrombotic state, leading to a rapid and disproportionate deterioration of lung function. An effective screening tool is essential to identify the patients at risk for severe disease. This observational study was conducted on hospitalized patients with moderate and severe COVID-19 pneumonia in a general hospital in Mexico City between 1 March 2021 and 15 March 2021. Serum samples were analyzed to explore the role of biomarkers of inflammation, coagulation, oxidative stress, and endothelial damage with the severity of the disease. Our results demonstrated that Syndecan-1 and nitrites/nitrates showed a high correlation in severely ill patients. In conclusion, COVID-19 patients with elevated levels of SDC-1 were associated with severe disease. This molecule can potentially be used as a marker for the progression or severity of COVID-19. Preservation of glycocalyx integrity may be a potential treatment for COVID-19.

The aims of this study were to describe the characteristics of patients hospitalized with delta SARS-CoV-2 breakthrough infection, and to identify factors associated with pneumonia on chest Computed Tomography (CT) and mortality. The clinical records of 229 patients (105 F), with a median age of 81 (interquartile range, IQR, 73−88) years old, hospitalized between June and December 2021 after completion of the primary vaccination cycle, were retrospectively analyzed, retrieving data on comorbidities, Clinical Frailty Scale (CFS), clinical presentation and outcomes. Multimorbidity (91.7% with ≥2 chronic illnesses) and frailty (61.6% with CFS ≥ 5) were highly prevalent. CFS (OR 0.678, 95% CI 0.573−0.803, p < 0.001) and hypertension were independently associated with interstitial pneumonia. Mortality was 25.1% and unrelated with age. PaO2/FiO2 on blood gas analysis performed upon admission (OR 0.986, 95% CI 0.977−0.996, p = 0.005), and CFS (OR 1.723, 95% CI 1.152−2.576, p = 0.008) were independently associated with mortality only in subjects < 85 years old. Conversely, serum PCT levels were associated with mortality in subjects ≥ 85 years old (OR 3.088, 95% CI 1.389−6.8628, p = 0.006). In conclusion, hospitalization for COVID-19 breakthrough infection mainly involved geriatric patients, with those aged ≥ 85 more characterized by decompensation of baseline comorbidities rather than typical COVID-19 respiratory symptoms.

The benefits of remdesivir treatment, with or without co-administration of antibiotics such as azithromycin, are uncertain in COVID-19 pneumonia. The aim of this retrospective single-center study was to assess the effects of remdesivir, with or without azithromycin, on hospital mortality, intensive care unit (ICU) admission, and need of non-invasive ventilation. The clinical records of the COVID-19 patients hospitalized in an Italian ward in March 2021 were analyzed, and data on comorbidities and clinical, radiological, and laboratory presentation of the disease were collected. Among 394 participants (234 M), 173 received remdesivir (43.9%), including 81 with azithromycin (20.5%). Remdesivir recipients were younger, with less comorbidities, and had better PaO₂/FiO₂ and clinical outcomes, including reduced mortality, but the differences were not independent of covariates. Rates of ICU transferal were 17%, 9%, and 1% in the no remdesivir, remdesivir without azithromycin, and remdesivir/azithromycin groups, respectively. In a stepwise multivariate logistic regression model, remdesivir/azithromycin co-treatment was independently associated with reduced ICU admission (vs remdesivir alone, OR 0.081, 95% CI 0.008-0.789, p = 0.031; vs no remdesivir, OR 0.060, 95% CI 0.007-0.508, p = 0.010). These data suggest that the therapeutical effect of remdesivir in COVID-19 pneumonia may be potentiated by azithromycin. The association between the two drugs should be further investigated.

To reduce the mortality of COVID-19 older patients, clear criteria to predict in-hospital mortality are urgently needed. Here, we aimed to evaluate the performance of selected routine laboratory biomarkers in improving the prediction of in-hospital mortality in 641 consecutive COVID-19 geriatric patients (mean age 86.6 ± 6.8) who were hospitalized at the INRCA hospital (Ancona, Italy). Thirty-four percent of the enrolled patients were deceased during the in-hospital stay. The percentage of severely frail patients, assessed with the Clinical Frailty Scale, was significantly increased in deceased patients compared to the survived ones. The age-adjusted Charlson comorbidity index (CCI) score was not significantly associated with an increased risk of death. Among the routine parameters, neutrophilia, eosinopenia, lymphopenia, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein, procalcitonin, IL-6, and NT-proBNP showed the highest predictive values. The fully adjusted Cox regressions models confirmed that high neutrophil %, NLR, derived NLR (dNLR), platelet-to-lymphocyte ratio (PLR), and low lymphocyte count, eosinophil %, and lymphocyte-to-monocyte ratio (LMR) were the best predictors of in-hospital mortality, independently from age, gender, and other potential confounders. Overall, our results strongly support the use of routine parameters, including complete blood count, in geriatric patients to predict COVID-19 in-hospital mortality, independent from baseline comorbidities and frailty.

Elderly patients represent a high-risk group with increased risk of death from COVID-19. Despite the number of published studies, several unmet needs in care for older adults exist.

To discuss unmet needs of COVID-19 in this special population.

A literature review for studies on COVID-19 in elderly patients published between December 2019 and November 2021 was performed. Clinical questions were formulated to guide the literature search. The search was conducted in the MEDLINE database, combining specific search terms. Two reviewers independently conducted the search and selected the studies according to the prespecified clinical questions.

Elderly patients with COVID-19 have peculiar characteristics. They may have atypical clinical presentation, with no fever and with delirium or neurological manifestations as the most common signs, with potential delayed diagnosis and increased risk of death. The reported fatality rates among elderly patients with COVID-19 are extremely high. Several factors, including comorbidities, atypical presentation, and exclusion from intensive care unit care, contribute to this excess of mortality. Age alone is frequently used as a key factor to exclude the elderly from intensive care, but there is evidence that frailty rather than age better predicts the risk of poor outcome in this category. Durability of vaccine efficacy in the elderly remains debated, and the need for a third booster dose is becoming increasingly evident. Finally, efforts to care for elderly patients who have survived after acute COVID-19 should be implemented, considering the high rates of long COVID sequelae and the risk of longitudinal functional and cognitive decline.

We highlight peculiar aspects of COVID-19 in elderly patients and factors contributing to high risk of poor outcome in this category. We also illuminated gaps in current evidence, suggesting future research directions and underlining the need for further studies on the optimal management of elderly patients with COVID-19.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) causes a wide spectrum of clinical manifestations, with progression to multiorgan failure in the most severe cases. Several biomarkers can be altered in coronavirus disease 2019 (COVID-19), and they can be associated with diagnosis, prognosis, and outcomes. The most used biomarkers in COVID-19 include several proinflammatory cytokines, neuron-specific enolase (NSE), lactate dehydrogenase (LDH), aspartate transaminase (AST), neutrophil count, neutrophils-to-lymphocytes ratio, troponins, creatine kinase (MB), myoglobin, D-dimer, brain natriuretic peptide (BNP), and its N-terminal pro-hormone (NT-proBNP). Some of these biomarkers can be readily used to predict disease severity, hospitalization, intensive care unit (ICU) admission, and mortality, while others, such as metabolomic and proteomic analysis, have not yet translated to clinical practice. This narrative review aims to identify laboratory biomarkers that have shown significant diagnostic and prognostic value for risk stratification in COVID-19 and discuss the possible clinical application of novel analytic strategies, like metabolomics and proteomics. Future research should focus on identifying a limited but essential number of laboratory biomarkers to easily predict prognosis and outcome in severe COVID-19.