Seeking bridge symptoms of anxiety, depression, and sleep disturbance among the elderly during the lockdown of the COVID-19 pandemic-A network approach.

Background: Besides physical changes, elderly adults are prone to have mental disorders such as anxiety, depression, and sleep disturbance, and the pandemic of COVID-19 worsened the situation. However, internal relationships and co-occurrence of psychopathologies were scarcely examined. Therefore, in the current study, through network analysis, we inspected relationships among symptoms of depression, anxiety, and sleep disturbance and identified key symptoms that espoused the disease. Methods: We asked 1,302 elderly adults to fill in Patient Health Questionnaire-2 (depressive symptoms), the Generalized Anxiety Disorder-2 (anxiety symptoms), and the Youth Self-rating Insomnia Scale (sleep disturbance) and then constructed three networks for elderly adults, male elderly, and female elderly. Via network analysis, we accomplished four goals. First, we identified symptom with the highest centrality (i.e., strength) index for each network; then, we found the strongest correlation (i.e., edges) in each network; thirdly, we confirmed specific nodes that could bridge anxiety, depression, and sleep disturbance; the last was to compare networks based on genders. Network stability and accuracy tests were performed. Results: Networks of elderly adults, male elderly, and female elderly were stable, accurate, and intelligible. Among all networks, "Nervousness"- "Excessive worry" (GAD-1- GAD-2) had the strongest correlation, and "Nervousness" (GAD-1) had the highest strength and bridge strength value. When we made a comparison between female elderly's and male elderly's networks, except for the significant difference in the mean value of "Difficulty initiating sleep" (YSIS-3), the findings showed that the two networks were similar. Network stability and accuracy proved to be reliable. Conclusions: In networks of anxiety, depression, and sleep disturbance, anxiety played a conspicuous role in comorbidity, which could be a target for practical intervention and prevention.

COVID-19 in China and the US: Differences in Hospital Admission Co-Variates and Outcomes.

(1) Background: Although there are extensive data on admission co-variates and outcomes of persons with coronavirus infectious disease-2019 (COVID-19) at diverse geographic sites, there are few, if any, subject-level comparisons between sites in regions and countries. We investigated differences in hospital admission co-variates and outcomes of hospitalized people with COVID-19 between Wuhan City, China and the New York City region, USA. (2) Methods: We retrospectively analyzed clinical data on 1859 hospitalized subjects with COVID-19 in Wuhan City, China, from 20 January to 4 April 2020. Data on 5700 hospitalized subjects with COVID-19 in the New York City region, USA, from 1 March to 4 April 2020 were extracted from an article by Richardson et al. Hospital admission co-variates (epidemiological, demographic, and laboratory co-variates) and outcomes (rate of intensive care unit [ICU] admission, invasive mechanical ventilation [IMV], major organ failure and death, and length of hospital stay) were compared between the cohorts. (3) Results: Wuhan subjects were younger, more likely female, less likely to have co-morbidities and fever, more likely to have a blood lymphocyte concentration > 1 × 109/L, and less likely to have abnormal liver and cardiac function tests compared with New York subjects. There were outcomes data on all Wuhan subjects and 2634 New York subjects. Wuhan subjects had higher blood nadir median lymphocyte concentrations and longer hospitalizations, and were less likely to receive IMV, ICU hospitalization, and interventions for kidney failure. Amongst subjects not receiving IMV, those in Wuhan were less likely to die compared with New York subjects. In contrast, risk of death was similar in subjects receiving IMV at both sites. (4) Conclusions: We found different hospital admission co-variates and outcomes between hospitalized persons with COVID-19 between Wuhan City and the New York region, which should be useful developing a comprehensive global understanding of the SARS-CoV-2 pandemic and COVID-19.

Demographic, signs and symptoms, imaging characteristics of 2126 patients with COVID-19 pneumonia in the whole quarantine of Wuhan, China.

OBJECTIVE: The accurate knowledge of demographic, signs and symptoms, imaging characteristics of coronavirus disease 2019 (COVID-19) is essential for the accurate management of these patients. However, the claims between the previous papers are not always consistent and may even contradict each other, for example, some claims the virus infects more men than women in Wuhan. In this large-scale cohort study, we aimed to update the demographic, signs and symptoms, imaging characteristics of patients with COVID-19 in the whole quarantine of Wuhan, China. METHODS: A cohort of 2126 patients with a diagnosis of COVID-19 pneumonia (confirmed by real-time reverse transcriptase-polymerase chain reaction, RT-PCR) who were admitted to one hospital in Wuhan were retrospectively enrolled. Data were collected between January 13, 2020, and April 8, 2020, the end of Wuhan quarantine. Demographic, signs and symptoms, imaging characteristics were analyzed. CT imaging characteristics associated with respiratory failure or death were identified. RESULTS: Of the 2126 patients with COVID-19, 1051 (49.44%) were men and 1075 (50.56%) were women, 1933 (90.92%) have fever and 1328 (62.46%) have dry cough. The mean age was 57.43 years of age (range 1-95). The CT imaging findings were bilateral pneumonia (1883[88.57%]), unilateral pneumonia (243[11.43%]), ground-glass opacity (GGO) or consolidation (1175[55.27%]), pleural effusion (69[3.25%]). Patients with respiratory failure or death were more likely to have pleural effusion on CT than patients without respiratory failure or death (p < 0.05). CONCLUSION: Men and women have been infected by SARS-CoV-2 in roughly equal numbers. Fever and cough are the most prevalent symptoms at disease onset in patients. Other prevalent symptoms include fatigue, and sputum production. COVID-19 patients with bilateral pneumonia and pleural effusion are more likely to develop respiratory failure or death.

Cancer increases risk of in-hospital death from COVID-19 in persons <65 years and those not in complete remission.

The impact of cancer on outcome of persons with coronavirus disease 2019 (COVID-19) after infection with acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is controversial. We studied 1859 subjects with COVID-19 from seven centers in Wuhan, China, 65 of whom had cancer. We found having cancer was an independent risk factor for in-hospital death from COVID-19 in persons <65 years (hazard ratio [HR] = 2.45, 95% confidence interval [CI], 1.04, 5.76; P = 0.041) but not in those ≥65 years (HR = 1.12 [0.56, 2.24]; P = 0.740). It was also more common in those not in complete remission. Risks of in-hospital death were similar in subjects with solid cancers and those with hematological cancers. These data may help predict outcomes of persons with cancer and COVID-19.

Risk factors for death in 1859 subjects with COVID-19.

We studied 1859 subjects with confirmed COVID-19 from seven centers in Wuhan 1651 of whom recovered and 208 died. We interrogated diverse covariates for correlations with risk of death from COVID-19. In multi-variable Cox regression analyses increased hazards of in-hospital death were associated with several admission covariates: (1) older age (HR = 1.04; 95% Confidence Interval [CI], 1.03, 1.06 per year increase; P < 0.001); (2) smoking (HR = 1.84 [1.17, 2.92]; P = 0.009); (3) admission temperature per °C increase (HR = 1.32 [1.07, 1.64]; P = 0.009); (4) Log10 neutrophil-to-lymphocyte ratio (NLR; HR = 3.30 [2.10, 5.19]; P < 0.001); (5) platelets per 10 E + 9/L decrease (HR = 0.996 [0.994, 0.998]; P = 0.001); (6) activated partial thromboplastin (aPTT) per second increase (HR = 1.04 [1.02, 1.05]; P < 0.001); (7) Log10 D-dimer per mg/l increase (HR = 3.00 [2.17, 4.16]; P < 0.001); and (8) Log10 serum creatinine per µmol/L increase (HR = 4.55 [2.72, 7.62]; P < 0.001). In piecewise linear regression analyses Log10NLR the interval from ≥0.4 to ≤1.0 was significantly associated with an increased risk of death. Our data identify covariates associated with risk of in hospital death in persons with COVID-19.

Hematological features of persons with COVID-19.

We studied admission and dynamic demographic, hematological and biochemical co-variates in 1449 hospitalized subjects with coronavirus infectious disease-2019 (COVID-19) in five hospitals in Wuhan, Hubei province, China. We identified two admission co-variates: age (Odds Ratio [OR] = 1.18, 95% Confidence Interval [CI] [1.02, 1.36]; P = 0.026) and baseline D-dimer (OR = 3.18 [1.48, 6.82]; P = 0.003) correlated with an increased risk of death in persons with COVID-19. We also found dynamic changes in four co-variates, Δ fibrinogen (OR = 6.45 [1.31, 31.69]; P = 0.022), Δ platelets (OR = 0.95 [0.90-0.99]; P = 0.029), Δ C-reactive protein (CRP) (OR = 1.09 [1.01, 1.18]; P = 0.037), and Δ lactate dehydrogenase (LDH) (OR = 1.03 [1.01, 1.06]; P = 0.007) correlated with an increased risk of death. The potential risk factors of old age, high baseline D-dimer, and dynamic co-variates of fibrinogen, platelets, CRP, and LDH could help clinicians to identify and treat subjects with poor prognosis.

COVID-19 in persons with haematological cancers.

Infection with SARS-CoV-2, the cause of coronavirus infectious disease-19 (COVID-19), has caused a pandemic with >850,000 cases worldwide and increasing. Several studies report outcomes of COVID-19 in predominately well persons. There are also some data on COVID-19 in persons with predominately solid cancer but controversy whether these persons have the same outcomes. We conducted a cohort study at two centres in Wuhan, China, of 128 hospitalised subjects with haematological cancers, 13 (10%) of whom developed COVID-19. We also studied 226 health care providers, 16 of whom developed COVID-19 and 11 of whom were hospitalised. Co-variates were compared with the 115 subjects with haematological cancers without COVID-19 and with 11 hospitalised health care providers with COVID-19. There were no significant differences in baseline co-variates between subjects with haematological cancers developing or not developing COVID-19. Case rates for COVID-19 in hospitalised subjects with haematological cancers was 10% (95% Confidence Interval [CI], 6, 17%) compared with 7% (4, 12%; P = 0.322) in health care providers. However, the 13 subjects with haematological cancers had more severe COVID-19 and more deaths compared with hospitalised health care providers with COVID-19. Case fatality rates were 62% (32, 85%) and 0 (0, 32%; P = 0.002). Hospitalised persons with haematological cancers have a similar case rate of COVID-19 compared with normal health care providers but have more severe disease and a higher case fatality rate. Because we were unable to identify specific risk factors for COVID-19 in hospitalised persons with haematological cancers, we suggest increased surveillance and possible protective isolation.