Implications of Frailty in COVID-19.

As the global coronavirus disease-19 (COVID-19) pandemic caused by severe acute respiratory distress syndrome coronavirus 2 continues to cause higher mortality and hospitalization rates among older adults, strategies such as frailty screening have been suggested for resource allocation and clinical management. Frailty is a physiologic condition characterized by a decreased reserve to stressors and is associated with disability, hospitalization, and death. Measuring frailty can be a useful tool to determine the risk and prognosis of COVID-19 patients in the acute setting, and to provide higher quality of care for vulnerable individuals in the outpatient setting. A literature review was conducted to examine current research regarding frailty and COVID-19. Frailty can inform holistic care of COVID-19 patients, and further investigation is needed to elucidate how measuring frailty should guide treatment and prevention of COVID-19.

Patients without Increased Lymphocyte Counts and Decreased CT Scores During the Early 2nd Week of Illness Onset may Develop to Severe COVID-19.

BACKGROUND: Lymphopenia and high CT score is associated with COVID-19 severity. Herein we describe the change pattern in lymphocyte count and CT score during hospitalization and explore a possible association with the severity of COVID-19. METHODS: In this retrospective study, 13 non-severe COVID-19 patients diagnosed at admission were enrolled. One patient progressed to severe disease. Change patterns in lymphocyte counts and CT scores of all patients were analyzed. RESULTS: Lymphocyte count increased gradually from day 5 post-illness onset (day 5 vs. day 15, p = 0.001). Lymphocyte count of the severe patient fluctuated at low levels throughout the 15-day period. Chest CT scores of non-severe patients increased significantly during the first 5 days of illness onset, but decreased gradually beginning day 9 (illness onset vs. day 5, p = 0.002, day 9 vs. day 15, p = 0.015). In the severe patient, CT score continued to increase over the 11 days post-illness onset period. CONCLUSIONS: Non-severe COVID-19 patients had significantly increased lymphocyte counts and decreased CT scores beginning day 5 and day 9 of illness onset, respectively. The patients without increased lymphocyte counts and decreased CT scores during the early 2nd week of illness onset may develop to severe COVID-19.

Early COVID-19 Hospitalizations Among New York State Residents with a History of Invasive Cancer.

Background: Individuals with a history of cancer may be more susceptible to severe COVID-19 due to immunosuppression, comorbidities, or ongoing treatment. We linked inpatient claims data on COVID-19 hospitalizations to cancer diagnoses from the New York State Cancer Registry (NYSCR) to examine associations between prior cancer diagnoses and hospitalizations for COVID-19, and factors associated with death at discharge after COVID-19 hospitalization. Methods: New York State (NYS) residents diagnosed with invasive cancer before July 1, 2021, who were alive on January 1, 2020, were identified from NYSCR data. We obtained claims data for discharge year 2020 and the first half of 2021 from NYS's Statewide Planning and Research Cooperative System (SPARCS), and we linked inpatient records with COVID-19 as the primary diagnosis to cancer data from the NYSCR using deterministic matching methods. We calculated descriptive statistics and conducted multivariable-adjusted logistic regression analyses to examine associations of cancer case characteristics with COVID-19 hospitalization and with vital status at discharge among patients with a history of cancer. All analyses were conducted in SAS 9.4. Results: Our analysis included 1,257,377 individuals with a history of cancer, 10,210 of whom had a subsequent primary COVID-19 hospitalization. Individuals with a history of cancer were 16% more likely to be hospitalized with COVID-19, compared to the general population of NYS, after adjusting for age and sex (95% CI, 14%-19%). Factors independently associated with COVID-19 hospitalization among cancer patients included older age, male sex, non-Hispanic Black race or Hispanic ethnicity, diagnosis with late-stage cancer or with multiple tumors, more recent cancer diagnosis, and New York City (NYC) residency at the time of cancer diagnosis. Factors independently associated with death at discharge among individuals with COVID-19 hospitalization and a prior cancer diagnosis included older age, male sex, non-Hispanic Black or non-Hispanic Asian/Pacific Islander race or Hispanic ethnicity, residence in NYC at the time of COVID-19 hospitalization, and an active cancer diagnosis claim code at the time of COVID-19 hospitalization. Conclusion: This claims-based study identified higher risks of COVID-19 hospitalization and death at discharge among individuals with a history of cancer, and particularly those in certain demographic and diagnostic groups.

Mild Hyperglycaemia in Hospitalised Children with Moderate COVID-19 Infection.

Background and Objectives: COVID-19 infection may influence many physiological processes, including glucose metabolism. Acute hyperglycaemia has been related to a worse prognosis in patients with severe COVID-19 infection. The aim of our study was to find out if moderate COVID-19 infection is associated with hyperglycaemia. Materials and Methods: A total of 235 children were enrolled in the study between October 2021 and October 2022, 112 with confirmed COVID-19 infection and 123 with other RNA viral infection. In all patients, types of symptoms, glycaemia at the time of admission, and basic anthropometric and biochemical parameters were recorded. Results: Average glycaemia was significantly higher in COVID-19 patients compared to other viral infections (5.7 ± 1.12 vs. 5.31 ± 1.4 mmol/L, p = 0.011). This difference was more obvious in subgroups with gastrointestinal manifestations (5.6 ± 1.11 vs. 4.81 ± 1.38 mmol/L, p = 0.0006) and with fever (5.76±1.22 vs. 5.11±1.37 mmol/L, p = 0.002), while no significant difference was found in subgroups with mainly respiratory symptoms. The risk of hyperglycaemia (>5.6 mmol/L) was higher in COVID-19 patients compared to other viral infections (OR = 1.86, 95%CI = 1.10-3.14, p = 0.02). The risk of hyperglycaemia was significantly higher in COVID-19 compared to other viral infections in the subgroups of patients with fever (OR = 3.59, 95% CI 1.755-7.345, p = 0.0005) and with gastrointestinal manifestations (OR = 2.48, 95% CI 1.058-5.791, p = 0.036). Conclusion: According to our results, mild hyperglycaemia was significantly more common in children with moderate COVID-19 infection compared to other RNA virus respiratory and gastrointestinal infections, especially when accompanied by fever or gastrointestinal symptoms.

Evaluation of laboratory parameters in 1587 COVID-19 patients admitted to metropolitan hospital area of Bologna, Italy.

Since the outbreak of the 2019 pandemic coronavirus disease (COVID-19), great attention has been given to identifying the main clinical features of the disease. Identification of laboratory parameters able to classify patients based on their risk is mandatory to improve their clinical management. We retrospectively evaluated twenty-six laboratory tests measured in COVID-19 positive patients admitted to the hospital in March and April 2020 to find any correlation between their changes and the risk of death. We divided them into surviving and non-surviving patients. A total of 1587 patients were recruited, 854 males with median age of 71 (IQR 56-81) and 733 females with median age of 77 (IQR 61-87). On admission, death was found to be positively correlated with age (p=0.001), but not with sex (p=0.640) or with hospitalization in days (p=0.827). Brain natriuretic peptide (BNP), creatinine, C-reactive protein (CRP), INR, leukocyte count, lymphocyte count, neutrophil count, and procalcitonin (PCT) demonstrated a statistically significant difference between the two groups (p<0.001), suggesting their role as markers of disease severity; only lymphocyte count resulted as an independent risk factor for death.

Impact of corticosteroids use on midterm sequelae in survivors of COVID-19 admitted to hospital: A prospective cohort study.

An understanding of the midterm sequelae in COVID-19 and their association with corticosteroids use are needed. Between March and July 2020, we evaluated 1227 survivors of COVID-19, 3 months posthospitalization, of whom 213 had received corticosteroids within 7 days of admission. Main outcome was any midterm sequelae (oxygen therapy, shortness of breath, one major clinical sign, two minor clinical signs or three minor symptoms). Association between corticosteroids use and midterm sequelae was assessed using inverse propensity-score weighting models. Our sample included 753 (61%) male patients, and 512 (42%) were older than 65 years. We found a higher rate of sequelae among users than nonusers of corticosteroids (42% vs. 35%, odds ratio [OR] 1.40 [1.16-1.69]). Midterm sequelae were more frequent in users of low-dose corticosteroids than nonusers (64% vs. 51%, OR 1.60 [1.10-2.32]), whereas no association between higher doses (≥20 mg/day equivalent of dexamethasone) and sequelae was evidenced (OR 0.95 [0.56-1.61]). Higher risk of sequelae with corticosteroids use was observed among subjects with propensity score below the 90th percentile. Our study suggest that corticosteroids use during hospitalization for COVID-19 is associated with higher risk of midterm sequelae.

Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics.

In this work, we aim to accurately predict the number of hospitalizations during the COVID-19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising dynamics-based deep learning model for spatiotemporal COVID-19 hospitalization prediction. By drawing the analogy between locations and lattice sites in statistical mechanics, we use the Ising dynamics to guide the model to extract and utilize spatial relationships across locations and model the complex influence of granular information from real-world clinical evidence. By leveraging rich linked databases, including insurance claims, census information, and hospital resource usage data across the U.S., we evaluate the HOIST model on the large-scale spatiotemporal COVID-19 hospitalization prediction task for 2299 counties in the U.S. In the 4-week hospitalization prediction task, HOIST achieves 368.7 mean absolute error, 0.6 [Formula: see text] and 0.89 concordance correlation coefficient score on average. Our detailed number needed to treat (NNT) and cost analysis suggest that future COVID-19 vaccination efforts may be most impactful in rural areas. This model may serve as a resource for future county and state-level vaccination efforts.

Disproportionate impacts of COVID-19 in a large US city.

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5-24.8%) infection rate and 29.4% (95% CrI: 28.0-31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3-12.0%] vs 25.1% [95% CrI: 23.7-26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49-57%] vs 28% [95% CrI: 27-30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0-3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC's social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited.

Disparities in COVID-19 infection, hospitalisation and death in people with schizophrenia, bipolar disorder, and major depressive disorder: a cohort study of the UK Biobank.

People with severe mental illness (SMI; including schizophrenia/psychosis, bipolar disorder (BD), major depressive disorder (MDD)) experience large disparities in physical health. Emerging evidence suggests this group experiences higher risks of infection and death from COVID-19, although the full extent of these disparities are not yet established. We investigated COVID-19 related infection, hospitalisation and mortality among people with SMI in the UK Biobank (UKB) cohort study. Overall, 447,296 participants from UKB (schizophrenia/psychosis = 1925, BD = 1483 and MDD = 41,448, non-SMI = 402,440) were linked with healthcare and death records. Multivariable logistic regression analysis was used to examine differences in COVID-19 outcomes by diagnosis, controlling for sociodemographic factors and comorbidities. In unadjusted analyses, higher odds of COVID-19 mortality were seen among people with schizophrenia/psychosis (odds ratio [OR] 4.84, 95% confidence interval [CI] 3.00-7.34), BD (OR 3.76, 95% CI 2.00-6.35), and MDD (OR 1.99, 95% CI 1.69-2.33) compared to people with no SMI. Higher odds of infection and hospitalisation were also seen across all SMI groups, particularly among people with schizophrenia/psychosis (OR 1.61, 95% CI 1.32-1.96; OR 3.47, 95% CI 2.47-4.72) and BD (OR 1.48, 95% CI 1.16-1.85; OR 3.31, 95% CI 2.22-4.73). In fully adjusted models, mortality and hospitalisation odds remained significantly higher among all SMI groups, though infection odds remained significantly higher only for MDD. People with schizophrenia/psychosis, BD and MDD have higher risks of COVID-19 infection, hospitalisation and mortality. Only a proportion of these disparities were accounted for by pre-existing demographic characteristics or comorbidities. Vaccination and preventive measures should be prioritised in these particularly vulnerable groups.

Using Shapes of COVID-19 Positive Patient-Specific Trajectories for Mortality Prediction.

Machine learning can be used to identify relevant trajectory shape features for improved predictive risk modeling, which can help inform decisions for individualized patient management in intensive care during COVID-19 outbreaks. We present explainable random forests to dynamically predict next day mortality risk in COVID -19 positive and negative patients admitted to the Mount Sinai Health System between March 1st and June 8th, 2020 using patient time-series data of vitals, blood and other laboratory measurements from the previous 7 days. Three different models were assessed by using time series with: 1) most recent patient measurements, 2) summary statistics of trajectories (min/max/median/first/last/count), and 3) coefficients of fitted cubic splines to trajectories. AUROC and AUPRC with cross-validation were used to compare models. We found that the second and third models performed statistically significantly better than the first model. Model interpretations are provided at patient-specific level to inform resource allocation and patient care.

BEHRTDAY: Dynamic Mortality Risk Prediction using Time-Variant COVID-19 Patient Specific Trajectories.

Incorporating repeated measurements of vitals and laboratory measurements can improve mortality risk-prediction and identify key risk factors in individualized treatment of COVID-19 hospitalized patients. In this observational study, demographic and laboratory data of all admitted patients to 5 hospitals of Mount Sinai Health System, New York, with COVID-19 positive tests between March 1st and June 8th, 2020, were extracted from electronic medical records and compared between survivors and non-survivors. Next day mortality risk of patients was assessed using a transformer-based model BEHRTDAY fitted to patient time series data of vital signs, blood and other laboratory measurements given the entire patients' hospital stay. The study population includes 3699 COVID-19 positive (57% male, median age: 67) patients. This model had a very high average precision score (0.96) and area under receiver operator curve (0.92) for next-day mortality prediction given entire patients' trajectories, and through masking, it learnt each variable's context.

COVID-19 Outcomes by Cancer Status, Site, Treatment, and Vaccination.

BACKGROUND: Studies have shown an increased risk of severe SARS-CoV-2-related (COVID-19) disease outcome and mortality for patients with cancer, but it is not well understood whether associations vary by cancer site, cancer treatment, and vaccination status. METHODS: Using electronic health record data from an academic medical center, we identified a retrospective cohort of 260,757 individuals tested for or diagnosed with COVID-19 from March 10, 2020, to August 1, 2022. Of these, 52,019 tested positive for COVID-19 of whom 13,752 had a cancer diagnosis. We conducted Firth-corrected logistic regression to assess the association between cancer status, site, treatment, vaccination, and four COVID-19 outcomes: hospitalization, intensive care unit admission, mortality, and a composite "severe COVID" outcome. RESULTS: Cancer diagnosis was significantly associated with higher rates of severe COVID, hospitalization, and mortality. These associations were driven by patients whose most recent initial cancer diagnosis was within the past 3 years. Chemotherapy receipt, colorectal cancer, hematologic malignancies, kidney cancer, and lung cancer were significantly associated with higher rates of worse COVID-19 outcomes. Vaccinations were significantly associated with lower rates of worse COVID-19 outcomes regardless of cancer status. CONCLUSIONS: Patients with colorectal cancer, hematologic malignancies, kidney cancer, or lung cancer or who receive chemotherapy for treatment should be cautious because of their increased risk of worse COVID-19 outcomes, even after vaccination. IMPACT: Additional COVID-19 precautions are warranted for people with certain cancer types and treatments. Significant benefit from vaccination is noted for both cancer and cancer-free patients.

Quality of clinical evidence and political justifications of ivermectin mass distribution of COVID-19 kits in eight Latin American countries.

BACKGROUND: Several countries in Latin America conducted mass distribution of COVID-19 kits intended to treat mild COVID-19, thereby preventing excess hospitalisations. Many of the kits contained ivermectin, an antiparasitic medicine that was not approved at the time for the treatment of COVID-19. The study objective was to compare the timing of the publication of scientific evidence about the efficacy of ivermectin for COVID-19 with the timeline of distribution of COVID-19 kits in eight Latin American countries and to analyse whether evidence was used to justify ivermectin distribution. METHODS: We conducted a systematic review of randomised controlled trials (RCTs) published on the efficacy of ivermectin or ivermectin as adjuvant therapy on mortality from, or as prevention for, COVID-19. Each RCT was assessed using the Cochrane Grading of Recommendations, Assessment, Development and Evaluations (GRADE). Information on the timing and justification of government decisions was collected through a systematic search of leading newspapers and government press releases. RESULTS: After removing the duplicates and abstracts without full text, 33 RCTs met our inclusion criteria. According to GRADE, the majority had a substantial risk of bias. Many government officials made claims that ivermectin was effective and safe in the prevention or treatment of COVID-19, despite the lack of published evidence. CONCLUSION: All eight governments distributed COVID-19 kits to their populations despite the absence of high-quality evidence on the efficacy of ivermectin for prevention, hospitalisation and mortality in COVID-19 patients. Lessons learnt from this situation could be used to strengthen government institutions' capacities to implement evidence-informed public health policies.

COVID-19 Vaccination and Clinical Outcomes at a Secondary Referral Hospital During the Delta Variant-dominant Period in West Sumatra, Indonesia.

OBJECTIVES: The second wave of coronavirus disease 2019 (COVID-19) cases in Indonesia, during which the Delta variant predominated, took place after a vaccination program had been initiated in the country. This study was conducted to assess the impact of COVID-19 vaccination on unfavorable clinical outcomes including hospitalization, severe COVID-19, intensive care unit (ICU) admission, and death using a real-world model. METHODS: This single-center retrospective cohort study involved patients with COVID-19 aged ≥18 years who presented to the COVID-19 emergency room at a secondary referral teaching hospital between June 1, 2021 and August 31, 2021. We used a binary logistic regression model to assess the effect of COVID-19 vaccination on unfavorable clinical outcomes, with age, sex, and comorbidities as confounding variables. RESULTS: A total of 716 patients were included, 32.1% of whom were vaccinated. The elderly participants (≥65 years) had the lowest vaccine coverage among age groups. Vaccination had an effectiveness of 50% (95% confidence interval [CI], 25 to 66) for preventing hospitalization, 97% (95% CI, 77 to 99) for preventing severe COVID-19, 95% (95% CI, 56 to 99) for preventing ICU admission, and 90% (95% CI, 22 to 99) for preventing death. Interestingly, patients with type 2 diabetes had a 2-fold to 4-fold elevated risk of unfavorable outcomes. CONCLUSIONS: Among adults, COVID-19 vaccination has a moderate preventive impact on hospitalization but a high preventive impact on severe COVID-19, ICU admission, and death. The authors suggest that relevant parties increase COVID-19 vaccination coverage, especially in the elderly population.

[Clinical cases of complicated diverticulitis against the background of severe course COVID-19. Case report].

Two clinical cases of perforation of a previously undiagnosed colon diverticulum in patients with coronavirus infection caused by the SARS-CoV-2 virus treated at the Hospital №1 of Nalchik. Both patients were elderly, overweight, had a lot of chronic concomitant diseases. Patients received hormone therapy and were targeted: the first patient twice (tocilizumab on the first day of hospitalization and olokizumab on the 7th day of inpatient treatment). The second patient received levilimab on the 3rd day of his stay in the hospital. A short time after targeting, both patients developed acute diffuse abdominal pain, the patients were transferred to the surgical department and operated on. During the operation, both patients were found to have previously undiagnosed diverticular disease, complicated by diverticular perforation and peritonitis on the background of immunosuppression. Both patients died. Thus, when using targeted therapy for patients with COVID-19, it is necessary to take into account that they may have previously undiagnosed chronic diseases that can cause fatal complications against the background of immunosuppression.

Relationships between the COVID-19 lockdown, socioeconomic factors and acute coronary syndrome hospitalisations in France.

INTRODUCTION: Worldwide, the COVID-19 pandemic has been associated with an overall drop in acute coronary syndrome (ACS) hospitalizations. Additionally, there is a well-known association between ACS and socioeconomic status. This study aims to assess the COVID-19 effect on ACS admissions in France during the first national lockdown and investigate the factors associated with its spatial heterogeneity. MATERIALS AND METHODS: In this retrospective study, we used the French hospital discharge database (PMSI) to estimate ACS admission rates in all public and private hospitals in 2019 and 2020. A negative binomial regression explored the nationwide change in ACS admissions during lockdown compared with 2019. A multivariate analysis explored the factors associated with the ACS admission incidence rate ratio (IRR, 2020 incidence rate/2019 incidence rate) variation at the county level. RESULTS: We found a significant but geographically heterogeneous nationwide reduction in ACS admissions during lockdown (IRR 0·70 [0·64-0·76]). After adjustment for cumulative COVID-19 admissions and the ageing index, a higher share of people on short-term working arrangements during lockdown at the county level was associated with a lower IRR, while a higher share of individuals with a high school degree and a higher density of acute care beds were associated with a higher ratio. CONCLUSIONS: During the first national lockdown, there was an overall decrease in ACS admissions. Local provision of inpatient care and socioeconomic determinants linked to occupation were independently associated with the variation in hospitalizations.