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.

Development and validation of a prediction model based on comorbidities to estimate the risk of in-hospital death in patients with COVID-19.

Background: Most existing prognostic models of COVID-19 require imaging manifestations and laboratory results as predictors, which are only available in the post-hospitalization period. Therefore, we aimed to develop and validate a prognostic model to assess the in-hospital death risk in COVID-19 patients using routinely available predictors at hospital admission. Methods: We conducted a retrospective cohort study of patients with COVID-19 using the Healthcare Cost and Utilization Project State Inpatient Database in 2020. Patients hospitalized in Eastern United States (Florida, Michigan, Kentucky, and Maryland) were included in the training set, and those hospitalized in Western United States (Nevada) were included in the validation set. Discrimination, calibration, and clinical utility were evaluated to assess the model's performance. Results: A total of 17 954 in-hospital deaths occurred in the training set (n = 168 137), and 1,352 in-hospital deaths occurred in the validation set (n = 12 577). The final prediction model included 15 variables readily available at hospital admission, including age, sex, and 13 comorbidities. This prediction model showed moderate discrimination with an area under the curve (AUC) of 0.726 (95% confidence interval [CI]: 0.722-0.729) and good calibration (Brier score = 0.090, slope = 1, intercept = 0) in the training set; a similar predictive ability was observed in the validation set. Conclusion: An easy-to-use prognostic model based on predictors readily available at hospital admission was developed and validated for the early identification of COVID-19 patients with a high risk of in-hospital death. This model can be a clinical decision-support tool to triage patients and optimize resource allocation.

Pulmonary Rehabilitation Outcomes of Post-Acute COVID-19 Patients during Different Waves of the Pandemic.

(1) Background: Between the beginning of the coronavirus pandemic and summer 2022, we distinguished four pandemic waves, with different characteristics of the affected patients. This study investigated the impact of patient characteristics on the outcome of inpatient pulmonary rehabilitation (PR). (2) Methods: Using a prospective approach, the characteristics of post-acute COVID-19 patients of the different waves who participated in inpatient PR were compared based on their assessments and results collected as part of PR (Cumulative Illness Rating Scale (CIRS), six-minute walk test (6-MWT), Pulmonary Function Testing (PFT), and Functional Independent Measurement (FIM). (3) Results: A total of 483 patients were included in the analysis (Wave 1 n = 51, Wave 2 n = 202, Wave 3 n = 84, Wave 4 n = 146). Compared to Wave 3 + 4, patients of Wave 1 + 2 were older (69 vs. 63 years; p < 0.001), had a significantly lower CIRS (13.0 vs. 14.7 points; p = 0.004), had significant better PFT (FVC: 73 vs. 68%pred; p = 0.009; DLCOSB: 58 ± 18 vs. 50 ± 17%pred; p = 0.001), and showed significantly more comorbidities (2.0 vs. 1.6 n/pers.; p = 0.009). Wave 3 + 4 showed significantly greater improvements according to the 6-MWT (147 vs. 188 m; p < 0.001) and the FIM (5.6 vs. 21.1 points; p < 0.001). (4) Conclusions: Patients of the COVID-19 infection waves differed significantly according to their anthropometric data, incidence of comorbidities, and impact of the infection. All cohorts achieved clinically relevant and significant functional improvements during PR, with significant higher improvements in Wave 3 + 4.

Impact of comorbidities on the serological response to COVID-19 vaccination in a Taiwanese cohort.

BACKGROUND/AIMS: Vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is one of the best policies to control COVID-19 pandemic. The serological response to COVID-19 vaccination in Taiwanese patients with different comorbidities is elusive. METHODS: Uninfected subjects who received 3 doses of mRNA vaccines (BNT162b2 [Pfizer-BioNTech, BNT] and mRNA-1273 [Moderna]), viral vector-based vaccines (ChAdOx1-S (AZD1222, AZ) or protein subunit vaccines (Medigen COVID-19 vaccine) were prospectively enrolled. The SARS-CoV-2-IgG spike antibody level was determined within three months after the 3rd dose of vaccination. The Charlson Comorbidity Index (CCI) was applied to determine the association between vaccine titers and underlying comorbidities. RESULTS: A total of 824 subjects were enrolled in the current study. The proportions of CCI scores of 0-1, 2-3 and > 4 were 52.8% (n = 435), 31.3% (n = 258) and 15.9% (n = 131), respectively. The most commonly used vaccination combination was AZ-AZ-Moderna (39.2%), followed by Moderna-Moderna-Moderna (27.8%). The mean vaccination titer was 3.11 log BAU/mL after a median of 48 days after the 3rd dose. Factors associated with potentially effective neutralization capacity (IgG level ≥ 4160 AU/mL) included age ≥ 60 years (odds ratio [OR]/95% confidence interval [CI]: 0.50/0.34-0.72, P < 0.001), female sex (OR/CI: 1.85/1.30-2.63, P = 0.001), Moderna-Moderna-based vaccination (compared to AZ-AZ-based vaccination, OR/CI: 6.49/3.90-10.83, P < 0.001), BNT-BNT-based vaccination (compared to AZ-AZ-based vaccination, OR/CI: 7.91/1.82-34.3, P = 0.006) and a CCI score ≥ 4 (OR/CI: 0.53/0.34-0.82, P = 0.004). There was a decreasing trend in antibody titers with increasing CCI scores (trend P < 0.001). Linear regression analysis revealed that higher CCI scores (ß: - 0.083; 95% CI: - 0.094-0.011, P = 0.014) independently correlated with low IgG spike antibody levels. CONCLUSIONS: Subjects with more comorbidities had a poor serological response to 3 doses of COVID-19 vaccination.

Post COVID-19 condition diagnosis: A population-based cohort study of occurrence, associated factors, and healthcare use by severity of acute infection.

BACKGROUND: The occurrence and healthcare use trajectory of post COVID-19 condition (PCC) is poorly understood. Our aim was to investigate these aspects in SARS-CoV-2-positive individuals with and without a PCC diagnosis. METHODS: We conducted a population-based cohort study of adults in Stockholm, Sweden, with a verified infection from 1 March 2020 to 31 July 2021, stratified by the severity of the acute infection. The outcome was a PCC diagnosis registered any time 90-360 days after a positive test. We performed Cox regression models to assess baseline characteristics associated with the PCC diagnosis. Individuals diagnosed with PCC were then propensity-score matched to individuals without a diagnosis to assess healthcare use beyond the acute infection. RESULTS: Among 204,805 SARS-CoV-2-positive individuals, the proportion receiving a PCC diagnosis was 1% among individuals not hospitalized for their COVID-19 infection, 6% among hospitalized, and 32% among intensive care unit (ICU)-treated individuals. The most common new-onset symptom diagnosis codes among individuals with a PCC diagnosis were fatigue (29%) among nonhospitalized and dyspnea among both hospitalized (25%) and ICU-treated (41%) individuals. Female sex was associated with a PCC diagnosis among nonhospitalized and hospitalized individuals, with interactions between age and sex. Previous mental health disorders and asthma were associated with a PCC diagnosis among nonhospitalized and hospitalized individuals. Among individuals with a PCC diagnosis, the monthly proportion with outpatient care was substantially elevated up to 1 year after acute infection compared to before, with substantial proportions of this care attributed to PCC-related care. CONCLUSION: The differential association of age, sex, comorbidities, and healthcare use with the severity of the acute infection indicates different trajectories and phenotypes of PCC, with incomplete resolution 1 year after infection.

The Prevalence of Comorbidities and Substance Use Disorder.

Substance use disorders (SUDs) are complex illnesses and may occur in individuals with other physical and mental illnesses. Common comorbidities for SUDs include mental health illness and/or chronic pain. Nurses face additional risk factors for the development of SUD and comorbid illnesses. The relationships among these comorbidities and SUD are multifaceted, requiring understanding of the individual disease processes and how they may impact the manifestations of one another, as well as response to treatment considerations. Understanding the prevalence of these comorbidities and potential relationships is crucial to prevention, management, and treatment outcomes.

Associated effects of clinical characteristics, risk factors, and comorbidity on disease severity and mortality among patients with COVID-19 in Sulaimani City/ Kurdistan Region of Iraq.

The effects of clinical symptoms, laboratory indicators, and comorbidity status of SARS-CoV-2-infected patients on the severity of disease and the risk of death were investigated. Questionnaires and electronic medical records of 371 hospitalized COVID-19 patients were used for data collection (demographics, clinical manifestation, comorbidities, laboratory data). Association among categorical variables was determined using Kolmogorov-Smirnov test (P-value ≤0.05). Median age of study population (249 males, 122 females) was 65 years. Roc curves analysis found that age ≥64 years and age ≥67 years are significant cut-offs identifying patients with more severe disease and mortality at 30 days. CRP values at cut-off ≥80.7 and ≥95.8 significantly identify patients with more severe disease and mortality. Patients with more severe disease and risk of death were significantly identified with platelet value at the cut-off ≤160,000, hemoglobin value at the cut-off ≤11.7, D-Dimer values ≥1383 and ≥1270, and with values of neutrophil granulocytes (≥8.2 and ≤2) and lymphocytes (≤2 and ≤2.4). Detailed clinical investigation suggests granulocytes together with lymphopenia may be a potential indicator for diagnosis. Older age, several comorbidities (cancer, cardiovascular diseases, hypertension) and more laboratory abnormalities (CRP, D-Dimer, platelets, hemoglobin) were associated with development of more severity and mortality among COVID-19 patients.

Noncommunicable disease, clinical course and COVID-19 prognosis: results based on I-core registry

Background:There are no data on the association between clinical course and comorbidity in Iranian patients with COVID-19.Aims:To determine noncommunicable disease (NCD), clinical characteristics and prognosis of patients hospitalized with COVID-19 in Isfahan, Islamic Republic of Iran.Methods:This multicentric retrospective observational study was performed on all patients hospitalized with COVID-19 in Isfahan from 17 February to 6 April 2020. We recruited 5055 patients. Data on clinical course and comorbid NCDs such as hypertension, coronary heart disease (CHD), diabetes mellitus (DM), cancer, chronic kidney disease (CKD) and chronic respiratory disease (CRD) were collected. Statistical analyses were done by Mann–Whitney U, χ2 and logistic regression tests using Stata version 14.Results:DM and hypertension were the most prevalent comorbidities in patients with positive and negative reverse transcription polymerase chain reaction (RT-PCR). Odds ratio (95% confidence interval) of mortality-associated factors was significant for DM [1.35 (1.07–1.70)], CHD [1.58 (1.26–1.96)], CRD [2.18 (1.58–3.0)], and cancer [3.55 (2.42–5.21)]. These results remained significant for cancer after adjustment for age, sex and clinical factors. Among patients with positive RT-PCR, death was significantly associated with CRD and cancer, while this association disappeared after adjustment for all potential confounders. There was a significant association between NCDs and higher occurrence of low oxygen saturation, mechanical ventilation requirement and intensive care unit admission after adjustment for age and sex.Conclusion:The presence of NCDs alone did not increase mortality in patients with COVID-19, after adjustment for all potential confounders including clinical factors.

Clinical and epidemiological characteristics of the first 150 patients with COVID-19 in Lebanon: a prospective descriptive study

Background: Clinical features of confirmed COVID-19 cases cover a wide spectrum. Aims: To study the clinical, radiological and virological features of the first 150 patients with COVID-19 in Lebanon. Methods: Our university hospital was designated as the primary COVID-19 care centre in Lebanon. Between 21 February 2020, the date of the first confirmed case of COVID-19 in Lebanon, and 3 April 2020, our team treated 150 patients diagnosed with COVID-19. In this prospective descriptive study, we present our experience in treating these patients, specifically the diagnostic criteria, outcome, and demographic, clinical, radiological and biological characteristics. Results: Ninety-five (63.33%) of the patients were male and 55 (36.67%) were female. Most patients (58%) were aged > 50 years, and 8 (5.33%) were healthcare workers. Diagnosis was based on reverse transcription polymerase chain reaction, and patients were classified as mild, moderate or critical. Fifteen (10%) patients had a critical presentation and fever was the most prominent symptom at presentation. One hundred and thirty-eight (92%) patients underwent radiological evaluation. The most common laboratory findings were lymphocytopenia (34.38%), followed by neutropenia (28.13%), but leukocytosis was not prevalent (1.56%). Old age and comorbidity were significant indicators in patient risk stratification. Chest computed tomography was an invaluable method of diagnosis and management. Our radiological findings were consistent with the published literature. Conclusion: Our study underlines the variable presentation of COVID-19, the difference in severity, and the diverse methods of diagnosis. This suggests the need for a tailored approach, taking into consideration the wide spectrum of presentation.

Inflammatory endotypes of CRSwNP and responses to COVID-19.

PURPOSE OF REVIEW: Coronavirus disease 2019 (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has quickly become a great public health hazard globally. Nasal epithelial cells are an important site for SARS-CoV-2 infection and replication. The purpose of this review is to summarize recent findings on the endotypes of chronic rhinosinusitis with nasal polyps (CRSwNP) and the potential impact of SARS-CoV-2 infection. RECENT FINDINGS: Endotypes of CRSwNP are characterized by type 1, type 2 and type 3 inflammation according to patterns of inflammatory cells and the cytokines expressed in nasal tissue. Nasal epithelial cells show the highest expression of angiotensin-converting enzyme 2 (ACE2), the receptor for attachment and entry of SARS-CoV-2 into host cells, among all investigated cells in the respiratory tree. SARS-CoV-2 infection likely leads to increased activation of T-helper-1 (Th1) cell responses. Recent studies further suggest that ACE2 may be upregulated by type 1 and downregulated by type 2 inflammatory cytokines in nasal epithelial cells. SUMMARY: Expression of ACE2 in nasal epithelial cells is influenced by inflammatory endotypes of CRSwNP. Type 1 inflammation in nasal tissue may increase the risk of SARS-CoV-2 infection by upregulating ACE2 expression. However, clinical association between CRSwNP and COVID-19 is still unclear.

Comorbid asthma decreased the risk for COVID-19 mortality in the United Kingdom: Evidence based on a meta-analysis.

The study aimed to investigate the influence of comorbid asthma on the risk for mortality among patients with coronavirus disease 2019 (COVID-19) in the United Kingdom (UK) by utilizing a quantitative meta-analysis. The pooled odds ratio (OR) with 95% confidence interval (CI) was estimated by conducting a random-effects model. Sensitivity analysis, I2 statistic, meta-regression, subgroup analysis, Begg's analysis and Egger's analysis were all implemented. Our results presented that comorbid asthma was significantly related to a decreased risk for COVID-19 mortality in the UK based on 24 eligible studies with 1,209,675 COVID-19 patients (pooled OR = 0.81, 95% CI: 0.71-0.93; I2 = 89.2%, P < 0.01). Coming through further meta-regression to seek the possible cause of heterogeneity, none of elements might be responsible for heterogeneity. A sensitivity analysis proved the stability and reliability of the overall results. Both Begg's analysis (P = 1.000) and Egger's analysis (P = 0.271) manifested that publication bias did not exist. In conclusion, our data demonstrated that COVID-19 patients with comorbid asthma might bear a lower risk for mortality in the UK. Furthermore, routine intervention and treatment of asthma patients with severe acute respiratory syndrome coronavirus 2 infection should be continued in the UK.

COVID-19 in cancer patients.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has currently affected >220 million individuals worldwide. The complex interplay of immune dysfunction, active malignancy, the effect of cancer treatment on the immune system and additional comorbidities associated with cancer and COVID-19 all affect the outcomes of COVID-19 in patients with cancer. We have discussed the published findings (through the end of September 2021) on the effects of cancer on the morbidity and mortality of COVID-19, common factors between cancer and COVID-19, the interaction of cancer and COVID-19 treatments, the impact of COVID-19 on cancer clinical services, immune test findings in cancer patients with COVID-19 and the long-term effects of COVID-19 on cancer survivors.