Association of Country Income Level With the Characteristics and Outcomes of Critically Ill Patients Hospitalized With Acute Kidney Injury and COVID-19.

Introduction: Acute kidney injury (AKI) has been identified as one of the most common and significant problems in hospitalized patients with COVID-19. However, studies examining the relationship between COVID-19 and AKI in low- and low-middle income countries (LLMIC) are lacking. Given that AKI is known to carry a higher mortality rate in these countries, it is important to understand differences in this population. Methods: This prospective, observational study examines the AKI incidence and characteristics of 32,210 patients with COVID-19 from 49 countries across all income levels who were admitted to an intensive care unit during their hospital stay. Results: Among patients with COVID-19 admitted to the intensive care unit, AKI incidence was highest in patients in LLMIC, followed by patients in upper-middle income countries (UMIC) and high-income countries (HIC) (53%, 38%, and 30%, respectively), whereas dialysis rates were lowest among patients with AKI from LLMIC and highest among those from HIC (27% vs. 45%). Patients with AKI in LLMIC had the largest proportion of community-acquired AKI (CA-AKI) and highest rate of in-hospital death (79% vs. 54% in HIC and 66% in UMIC). The association between AKI, being from LLMIC and in-hospital death persisted even after adjusting for disease severity. Conclusions: AKI is a particularly devastating complication of COVID-19 among patients from poorer nations where the gaps in accessibility and quality of healthcare delivery have a major impact on patient outcomes.

Artificial intelligence guided HRCT assessment predicts the severity of COVID-19 pneumonia based on clinical parameters.

BACKGROUND: The purpose of the study was to compare the results of AI (artificial intelligence) analysis of the extent of pulmonary lesions on HRCT (high resolution computed tomography) images in COVID-19 pneumonia, with clinical data including laboratory markers of inflammation, to verify whether AI HRCT assessment can predict the clinical severity of COVID-19 pneumonia. METHODS: The analyzed group consisted of 388 patients with COVID-19 pneumonia, with automatically analyzed HRCT parameters of volume: AIV (absolute inflammation), AGV (absolute ground glass), ACV (absolute consolidation), PIV (percentage inflammation), PGV (percentage ground glass), PCV (percentage consolidation). Clinical data included: age, sex, admission parameters: respiratory rate, oxygen saturation, CRP (C-reactive protein), IL6 (interleukin 6), IG - immature granulocytes, WBC (white blood count), neutrophil count, lymphocyte count, serum ferritin, LDH (lactate dehydrogenase), NIH (National Institute of Health) severity score; parameters of clinical course: in-hospital death, transfer to the ICU (intensive care unit), length of hospital stay. RESULTS: The highest correlation coefficients were found for PGV, PIV, with LDH (respectively 0.65, 0.64); PIV, PGV, with oxygen saturation (respectively - 0.53, -0.52); AIV, AGV, with CRP (respectively 0.48, 0.46); AGV, AIV, with ferritin (respectively 0.46, 0.45). Patients with critical pneumonia had significantly lower oxygen saturation, and higher levels of immune-inflammatory biomarkers on admission. The radiological parameters of lung involvement proved to be strong predictors of transfer to the ICU (in particular, PGV ≥ cut-off point 29% with Odds Ratio (OR): 7.53) and in-hospital death (in particular: AIV ≥ cut-off point 831 cm3 with OR: 4.31). CONCLUSIONS: Automatic analysis of HRCT images by AI may be a valuable method for predicting the severity of COVID-19 pneumonia. The radiological parameters of lung involvement correlate with laboratory markers of inflammation, and are strong predictors of transfer to the ICU and in-hospital death from COVID-19. TRIAL REGISTRATION: National Center for Research and Development CRACoV-HHS project, contract number SZPITALE-JEDNOIMIENNE/18/2020.

Effect of Methylprednisolone on Mortality and Clinical Courses in Patients with Severe COVID-19: A Propensity Score Matching Analysis.

Background: Whether methylprednisolone therapy can reduce the mortality rate of patients with severe coronavirus disease 2019 (COVID-19) remains controversial, and its effects on the length of hospital stay and virus shedding time are also unknown. This retrospective study investigates the previous issues to provide more evidence for methylprednisolone treatment in severe COVID-19. Methods: This retrospective study included 563 of 4827 patients with confirmed COVID-19 admitted to Wuhan Huoshenshan Hospital or Wuhan Guanggu Hospital between February 3, 2020 and March 30, 2020 who met the screening criteria. The participants' epidemiological and demographic data, comorbidities, laboratory test results, treatments, outcomes, and vital clinical time points were extracted from electronic medical records. The primary outcome was in-hospital death, and the secondary outcomes were 2 clinical courses: length from admission to viral clearance and discharge. Univariate and multivariate logistic or linear regression analyses were used to assess the role of methylprednisolone in different outcomes. Propensity score matching was performed to control for confounding factors. Results: Of the 563 patients who met the screening criteria and were included in the subsequent analysis, 138 were included in the methylprednisolone group and 425 in the nonmethylprednisolone group. The in-hospital death rate between the methylprednisolone and nonmethylprednisolone groups showed a significant difference (23.91% vs. 1.65%, P < 0.001), which was maintained after propensity score matching (13.98% vs. 5.38%, P = 0.048). However, univariate logistic analysis in the matched groups showed that methylprednisolone treatment (odds ratio [OR], 5.242; 95% confidence interval [CI], 0.802 to 34.246; P = 0.084) was not a risk factor for in-hospital death in severe patients. Further multivariate logistic regression analysis found comorbidities (OR, 3.327; 95% CI, 1.702 to 6.501; P < 0.001), lower lymphocyte count (OR, 0.076; 95% CI, 0.012 to 0.461; P = 0.005), higher lactate dehydrogenase (LDH) levels (OR, 1.008; 95% CI, 1.003 to 1.013; P = 0.002), and anticoagulation therapy (OR, 11.187; 95% CI, 2.459 to 50.900; P = 0.002) were associated with in-hospital mortality. Multivariate linear regression analysis in the matched groups showed that methylprednisolone treatment was not a risk factor for a prolonged duration from admission to viral clearance (ß Value 0.081; 95% CI, -1.012 to 3.657; P = 0.265) or discharge (ß Value 0.114; 95% CI, -0.723 to 6.408; P = 0.117). d-dimer (ß Value, 0.144; 95% CI, 0.012 to 0.817; P = 0.044), LDH (ß Value 0.260; 95% CI, 0.010 to 0.034; P < 0.001), and antiviral therapy (ß Value 0.220; 95% CI, 1.373 to 6.263; P = 0.002) were associated with a longer length from admission to viral clearance. The lymphocyte count (ß Value -0.206; 95% CI, -6.248 to -1.197; P = 0.004), LDH (ß Value 0.231; 95% CI, 0.012 to 0.048; P = 0.001), antiviral therapy (ß Value 0.143; 95% CI, 0.058 to 7.497; P = 0.047), and antibacterial therapy (ß Value 0.152; 95% CI, 0.133 to 8.154; P = 0.043) were associated with a longer hospitalization duration from admission to discharge. Further stratified analysis revealed that the low daily dose group (≤60 mg/d) and the low total dose group (≤200 mg) had shorter duration from admission to viral clearance (Z=-2.362, P = 0.018; Z=-2.010, P = 0.044) and a shorter hospital stay (Z=-2.735, P = 0.006; Z=-3.858, P < 0.001). Conclusions: In patients with severe COVID-19, methylprednisolone is safe and does not prolong the duration from admission to viral clearance or discharge. Low-dose, short-term methylprednisolone treatment may be more beneficial in shortening the disease course.

Chronic anti-coagulation therapy reduced mortality in patients with high cardiovascular risk early in COVID-19 pandemic.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with provoked thrombo-inflammatory responses. Early in the COVID-19 pandemic this was thought to contribute to hypercoagulability and multi-organ system complications in infected patients. Limited studies have evaluated the impact of therapeutic anti-coagulation therapy (AC) in alleviating these risks in COVID-19 positive patients. Our study aimed to investigate whether long-term therapeutic AC can decrease the risk of multi-organ system complications (MOSC) including stroke, limb ischemia, gastrointestinal (GI) bleeding, in-hospital and intensive care unit death in COVID-19 positive patients hospitalized during the early phase of the pandemic in the United States. METHODS: A retrospective analysis was conducted of all COVID-19 positive United States Veterans between March 2020 and October 2020. Patients receiving continuous outpatient therapeutic AC for a least 90 days prior to their initial COVID-19 positive test were assigned to the AC group. Patients who did not receive AC were included in a control group. We analyzed the primary study outcome of MOSC between the AC and control groups using binary logistic regression analysis (Odd-Ratio; OR). RESULTS: We identified 48,066 COVID-19 patients, of them 879 (1.8%) were receiving continuous therapeutic AC. The AC cohort had significantly worse comorbidities than the control group. On the adjusted binary logistic regression model, therapeutic AC significantly decreased in-hospital mortality rate (OR; 0.67, p = 0.04), despite a higher incidence of GI bleeding (OR; 4.00, p = 0.02). However, therapeutic AC did not significantly reduce other adverse events. CONCLUSION: AC therapy reduced in-hospital death early in the COVID-19 pandemic among patients who were hospitalized with the infection. However, it did not decrease the risk of MOSC. Additional trials are needed to determine the effectiveness of AC in preventing complications associated with ongoing emerging strains of the COVID-19 virus.

Effect of methylprednisolone therapy on hospital stay and viral clearance in patients with moderate COVID-19

Background The benefits and harms of methylprednisolone treatment in patients with moderate coronavirus disease 2019 (COVID-19) remain controversial. In this study, we investigated the effect of methylprednisolone on mortality rate, viral clearance, and hospitalization stay in patients with moderate COVID-19. Methods This retrospective study included 4827 patients admitted to Wuhan Huoshenshan and Wuhan Guanggu hospitals from February to March 2020 diagnosed with COVID-19 pneumonia. The participants’ epidemiological and demographic data, comorbidities, laboratory test results, treatments, outcomes, and vital clinical time points were extracted from electronic medical records. The primary outcome was in-hospital death;secondary outcomes were time from admission to viral clearance and hospital stay. Univariate and multivariate logistic or linear regression analysis were used to assess the roles of methylprednisolone in different outcomes. The propensity score matching (PSM) method was used to control for confounding factors. Results A total of 1320 patients were included in this study, of whom 100 received methylprednisolone. Overall in-hospital mortality was 0.91% (12/1320);the 12 patients who died were all in the methylprednisolone group, though multivariate logistic regression analysis showed methylprednisolone treatment was not a risk factor for in-hospital death in moderate patients before or after adjustment for confounders by PSM. Methylprednisolone treatment was correlated with longer length from admission to viral clearance time and hospital stay before and after adjustment for confounders. Conclusions Methylprednisolone therapy was not associated with increased in-hospital mortality but with delayed viral clearance and extended hospital stay in moderate COVID-19 patients.

Automatized lung disease quantification in patients with COVID-19 as a predictive tool to assess hospitalization severity.

The pandemic of COVID-19 led to a dramatic situation in hospitals, where staff had to deal with a huge number of patients in respiratory distress. To alleviate the workload of radiologists, we implemented an artificial intelligence (AI) - based analysis named CACOVID-CT, to automatically assess disease severity on chest CT scans obtained from those patients. We retrospectively studied CT scans obtained from 476 patients admitted at the University Hospital of Liege with a COVID-19 disease. We quantified the percentage of COVID-19 affected lung area (% AA) and the CT severity score (total CT-SS). These quantitative measurements were used to investigate the overall prognosis and patient outcome: hospital length of stay (LOS), ICU admission, ICU LOS, mechanical ventilation, and in-hospital death. Both CT-SS and % AA were highly correlated with the hospital LOS, the risk of ICU admission, the risk of mechanical ventilation and the risk of in-hospital death. Thus, CAD4COVID-CT analysis proved to be a useful tool in detecting patients with higher hospitalization severity risk. It will help for management of the patients flow. The software measured the extent of lung damage with great efficiency, thus relieving the workload of radiologists.

Age-specific associations between underlying health conditions and hospitalisation, death and in-hospital death among confirmed COVID-19 cases: a multi-country study based on surveillance data, June to December 2020.

BackgroundUnderlying conditions are risk factors for severe COVID-19 outcomes but evidence is limited about how risks differ with age.AimWe sought to estimate age-specific associations between underlying conditions and hospitalisation, death and in-hospital death among COVID-19 cases.MethodsWe analysed case-based COVID-19 data submitted to The European Surveillance System between 2 June and 13 December 2020 by nine European countries. Eleven underlying conditions among cases with only one condition and the number of underlying conditions among multimorbid cases were used as exposures. Adjusted odds ratios (aOR) were estimated using 39 different age-adjusted and age-interaction multivariable logistic regression models, with marginal means from the latter used to estimate probabilities of severe outcome for each condition-age group combination.ResultsCancer, cardiac disorder, diabetes, immunodeficiency, kidney, liver and lung disease, neurological disorders and obesity were associated with elevated risk (aOR: 1.5-5.6) of hospitalisation and death, after controlling for age, sex, reporting period and country. As age increased, age-specific aOR were lower and predicted probabilities higher. However, for some conditions, predicted probabilities were at least as high in younger individuals with the condition as in older cases without it. In multimorbid patients, the aOR for severe disease increased with number of conditions for all outcomes and in all age groups.ConclusionWhile supporting age-based vaccine roll-out, our findings could inform a more nuanced, age- and condition-specific approach to vaccine prioritisation. This is relevant as countries consider vaccination of younger people, boosters and dosing intervals in response to vaccine escape variants.

Real World Estimate of Vaccination Protection in Individuals Hospitalized for COVID-19.

Whether vaccination confers a protective effect against progression after hospital admission for COVID-19 remains to be elucidated. Observational study including all the patients admitted to San Paolo Hospital in Milan for COVID-19 in 2021. Previous vaccination was categorized as: none, one dose, full vaccination (two or three doses >14 days before symptoms onset). Data were collected at hospital admission, including demographic and clinical variables, age-unadjusted Charlson Comorbidity index (CCI). The highest intensity of ventilation during hospitalization was registered. The endpoints were in-hospital death (primary) and mechanical ventilation/death (secondary). Survival analysis was conducted by means of Kaplan-Meier curves and Cox regression models. Effect measure modification by age was formally tested. We included 956 patients: 151 (16%) fully vaccinated (18 also third dose), 62 (7%) one dose vaccinated, 743 (78%) unvaccinated. People fully vaccinated were older and suffering from more comorbidities than unvaccinated. By 28 days, the risk of death was of 35.9% (95%CI: 30.1-41.7) in unvaccinated, 41.5% (24.5-58.5) in one dose and 28.4% (18.2-38.5) in fully vaccinated (p = 0.63). After controlling for age, ethnicity, CCI and month of admission, fully vaccinated participants showed a risk reduction of 50% for both in-hospital death, AHR 0.50 (95%CI: 0.30-0.84) and for mechanical ventilation or death, AHR 0.49 (95%CI: 0.35-0.69) compared to unvaccinated, regardless of age (interaction p > 0.56). Fully vaccinated individuals in whom vaccine failed to keep them out of hospital, appeared to be protected against critical disease or death when compared to non-vaccinated. These data support universal COVID-19 vaccination.

Specific characteristics of STEMI in COVID-19 patients and their practical implications.

ST-elevation myocardial infarction (STEMI) is one of the cardiac emergencies whose management has been most challenged by the COVID-19 pandemic. Patients presenting with the "lethal combo" of STEMI and concomitant SARS-CoV-2 infection have faced dramatic issues related to the need for self-isolation, systemic inflammation with multi-organ disease and difficulties to obtain timely diagnosis and treatment. The interplay between these and other factors has partly neutralized the major advances in STEMI care achieved in the last decades, significantly impairing prognosis in these patients. In the present review article, we will provide an overview on mechanisms of myocardial injury, specific clinical and angiographic characteristics and contemporary management in different settings of STEMI patients with COVID-19, alongside the inherent implications in terms of in-hospital mortality and short-term clinical outcomes.

Optimal cut points of N-terminal of the prohormone brain natriuretic peptide (NT-proBNP) in patients with COVID-19.

BACKGROUND: COVID19 patients may suffer from multiple cardiovascular complications. Recently, N-terminal of the prohormone brain natriuretic peptide (NT-proBNP) was a potentially independent risk factor for COVID-19 in-hospital death. The present study aimed to find new optimal cut points for NT-proBNP across censored survival failure time outcomes in hospitalized COVID-19 patients. RESULTS: This cohort study was conducted on 272 patients with COVID-19 whose initial records were recorded from March 2020 to July 2020. Demographic characteristics, clinical examinations, and laboratory measurements were collected at the beginning of the admission registered in the patient record system located in the hospital. We used the maximally selected rank statistics to determine the optimal cut points for NT-proBNP (the most significant split based on the standardized log-rank test). Survival time was defined as the days from hospital admission to discharge day. In this cohort study, two optimal cut points for NT-proBNP were 331 (pg/mL) and 11,126 (pg/mL) based on a survival model. The adjusted HR of NT-proBNP for in-hospital death was 3.41 (95% CI: 1.22-9.51, P = 0.02) for medium against low category, and 3.84 (95% CI: 1.30-11.57, P = 0.01) for high in comparison with low group. CONCLUSIONS: We reported a dramatically increased concentration of NT-proBNP among COVID-19 patients without heart failure in both severe and non-severe cases. Moreover, our study showed that a high level of NT-proBNP was highly associated with the prolonged survival time of patients with COVID-19. NT-proBNP is a strong prognostic indicator of in-hospital death in the second week of admission.

Antiviral Abidol is Associated with the Reduction of In-Hospital Mortality in COVID-19 Patients.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) is a global public health crisis. There are no specific antiviral agents for the treatment of SARS-CoV-2. Information regarding the effect of Abidol on in-hospital mortality is scarce. The present study aimed to evaluate the treatment effect of Abidol for patients with COVID-19 before and after propensity score matching (PSM). METHODS: This retrospective cohort study analyzed 1019 patients with confirmed COVID-19 in China from December 22, 2019 to March 13, 2020. Patients were divided to Abidol (200 mg, tid, 5-7 days, n = 788, 77.3%) and No-Abidol (n = 231, 22.7%) groups. The primary outcome was the mortality during hospitalization. RESULTS: Among 1019 COVID-19 patients, the age was (60.4 ±â€Š14.5) years. Abidol-treated patients, compared with No-Abidol-treated patients, had a shorter duration from onset of symptoms to admission, less frequent renal dysfunction, lower white blood cell counts (lymphocytes <0.8) and erythrocyte sending rate, lower interleukin-6, higher platelet counts and plasma IgG and oxygen saturation, and less frequent myocardial injury. The mortality during hospitalization before PSM was 17.9% in Abidol group and 34.6% in No-Abidol (hazard ratio (HR) = 2.610, 95% confident interval (CI): 1.980-3.440), all seen in severe and critical patients. After PSM, the in-hospital death was 13.6% in Abidol and 28.6% in No-Abidol group (HR = 2.728, 95% CI: 1.598-4.659). CONCLUSIONS: Abidol-treatment results in less in-hospital death for severe and critical patients with COVID-19. Further randomized study is warranted to confirm the findings from this study.

Use of DPP4i reduced odds of clinical deterioration and hyperinflammatory syndrome in COVID-19 patients with type 2 diabetes: Propensity score analysis of a territory-wide cohort in Hong Kong.

BACKGROUND AND OBJECTIVES: Type 2 diabetes mellitus (T2DM) patients with Coronavirus Disease 2019 (COVID-19) have poorer prognosis. Inconclusive evidence suggested dipeptidyl peptidase-4 inhibitors (DPP4i) might reduce inflammation and prevent Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) entry, hence further evaluation on DPP4i is needed. METHODS: 1214 Patients with T2DM were admitted with COVID-19 between 21st January 2020 and 31st January 2021 in Hong Kong. Exposure was DPP4i use within the 90 days prior to admission for COVID-19. Assessed outcomes included clinical deterioration, clinical improvement, low viral load, positive Immunoglobulin G (IgG) antibody, hyperinflammatory syndrome, proportion of IgG antibody, clinical status and length of hospitalization. Multivariable logistic and linear regression models were performed to estimate odds ratios (OR) and their 95% confidence intervals (CI) of event outcomes and continuous outcomes, respectively. RESULTS: DPP4i users (N = 107) was associated with lower odds of clinical deterioration (OR=0.71, 95%CI 0.54 to 0.93, P = 0.013), hyperinflammatory syndrome (OR=0.56, 95%CI 0.45 to 0.69, P < 0.001), invasive mechanical ventilation (OR=0.30, 95%CI 0.21 to 0.42, P < 0.001), reduced length of hospitalization (-4.82 days, 95%CI -6.80 to -2.84, P < 0.001), proportion of positive IgG antibody on day-3 (13% vs 8%, p = 0.007) and day-7 (41% vs 26%, P < 0.001), despite lack of association between DPP4i use and in-hospital mortality. CONCLUSION: DPP4i use was associated with reduced odds of clinical deterioration and hyperinflammatory syndrome. Prospective studies are warranted to elucidate the role of DPP4i in T2DM and COVID-19.

Immune and cellular damage biomarkers to predict COVID-19 mortality in hospitalized patients.

Early prediction of COVID-19 in-hospital mortality relies usually on patients' preexisting comorbidities and is rarely reproducible in independent cohorts. We wanted to compare the role of routinely measured biomarkers of immunity, inflammation, and cellular damage with preexisting comorbidities in eight different machine-learning models to predict mortality, and evaluate their performance in an independent population. We recruited and followed-up consecutive adult patients with SARS-Cov-2 infection in two different Italian hospitals. We predicted 60-day mortality in one cohort (development dataset, n = 299 patients, of which 80% was allocated to the development dataset and 20% to the training set) and retested the models in the second cohort (external validation dataset, n = 402). Demographic, clinical, and laboratory features at admission, treatments and disease outcomes were significantly different between the two cohorts. Notably, significant differences were observed for %lymphocytes (p < 0.05), international-normalized-ratio (p < 0.01), platelets, alanine-aminotransferase, creatinine (all p < 0.001). The primary outcome (60-day mortality) was 29.10% (n = 87) in the development dataset, and 39.55% (n = 159) in the external validation dataset. The performance of the 8 tested models on the external validation dataset were similar to that of the holdout test dataset, indicating that the models capture the key predictors of mortality. The shap analysis in both datasets showed that age, immune features (%lymphocytes, platelets) and LDH substantially impacted on all models' predictions, while creatinine and CRP varied among the different models. The model with the better performance was model 8 (60-day mortality AUROC 0.83 ± 0.06 in holdout test set, 0.79 ± 0.02 in external validation dataset). The features that had the greatest impact on this model's prediction were age, LDH, platelets, and %lymphocytes, more than comorbidities or inflammation markers, and these findings were highly consistent in both datasets, likely reflecting the virus effect at the very beginning of the disease.

Acute kidney injury and mortality risk in older adults with COVID-19.

BACKGROUND: Research regarding COVID-19 and acute kidney injury (AKI) in older adults is scarce. We evaluated risk factors and outcomes of AKI in hospitalized older adults with and without COVID-19. METHODS: Observational study of patients admitted to two geriatric clinics in Stockholm from March 1st to June 15th, 2020. The difference in incidence, risk factors and adverse outcomes for AKI between patients with or without COVID-19 were examined. Odds ratios (OR) for the risk of AKI and in-hospital death were obtained from logistic regression. RESULTS: Three hundred-sixteen older patients were hospitalized for COVID-19 and 876 patients for non-COVID-19 diagnoses. AKI occurred in 92 (29%) patients with COVID-19 vs. 159 (18%) without COVID-19. The odds for developing AKI were higher in patients with COVID-19 (adjusted OR, 1.70; 95% confidence interval [CI] 1.04-2.76), low baseline kidney function as depicted by estimated glomerular filtration rate (eGFR) [4.19 (2.48-7.05), for eGFR 30 to < 60 mL/min, and 20.3 (9.95-41.3) for eGFR < 30 mL/min], and higher C reactive protein (CRP) (OR 1.81 (1.11-2.95) in patients with initial CRP > 10 mg/L). Compared to patients without COVID-19 and without AKI, the risk of in-hospital death was highest in patients with COVID-19 and AKI [OR 80.3, 95% CI (27.3-235.6)], followed by COVID-19 without AKI [16.3 (6.28-42.4)], and by patients without COVID-19 and with AKI [10.2 (3.66-28.2)]. CONCLUSIONS: Geriatric patients hospitalized with COVID-19 had a higher incidence of AKI compared to patients hospitalized for other diagnoses. COVID-19 and reduced baseline kidney function were risk factors for developing AKI. AKI and COVID-19 were associated with in-hospital death.

Clinical characteristics and predictive value of lower CD4+T cell level in patients with moderate and severe COVID-19: a multicenter retrospective study.

BACKGROUND: In December 2019, coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, Hubei, China. Moreover, it has become a global pandemic. This is of great value in describing the clinical symptoms of COVID-19 patients in detail and looking for markers which are significant to predict the prognosis of COVID-19 patients. METHODS: In this multicenter, retrospective study, 476 patients with COVID-19 were enrolled from a consecutive series. After screening, a total of 395 patients were included in this study. All-cause death was the primary endpoint. All patients were followed up from admission till discharge or death. RESULTS: The main symptoms observed in the study included fever on admission, cough, fatigue, and shortness of breath. The most common comorbidities were hypertension and diabetes mellitus. Patients with lower CD4+T cell level were older and more often male compared to those with higher CD4+T cell level. Reduced CD8+T cell level was an indicator of the severity of COVID-19. Both decreased CD4+T [HR:13.659; 95%CI: 3.235-57.671] and CD8+T [HR: 10.883; 95%CI: 3.277-36.145] cell levels were associated with in-hospital death in COVID-19 patients, but only the decrease of CD4+T cell level was an independent predictor of in-hospital death in COVID-19 patients. CONCLUSIONS: Reductions in lymphocytes and lymphocyte subsets were common in COVID-19 patients, especially in severe cases of COVID-19. It was the CD8+T cell level, not the CD4+T cell level, that reflected the severity of the patient's disease. Only reduced CD4+T cell level was independently associated with increased in-hospital death in COVID-19 patients. TRIAL REGISTRATION: Prognostic Factors of Patients With COVID-19, NCT04292964 . Registered 03 March 2020. Retrospectively registered.

Development of a Predictive Model for Mortality in Hospitalized Patients With COVID-19.

INTRODUCTION: Early identification of patients with novel corona virus disease 2019 (COVID-19) who may be at high mortality risk is of great importance. METHODS: In this retrospective study, we included all patients with COVID-19 at Huanggang Central Hospital from January 23 to March 5, 2020. Data on clinical characteristics and outcomes were compared between survivors and nonsurvivors. Univariable and multivariable logistic regression were used to explore risk factors associated with in-hospital death. A nomogram was established based on the risk factors selected by multivariable analysis. RESULTS: A total of 150 patients were enrolled, including 31 nonsurvivors and 119 survivors. The multivariable logistic analysis indicated that increasing the odds of in-hospital death associated with higher Sequential Organ Failure Assessment score (odds ratio [OR], 3.077; 95% confidence interval [CI]: 1.848-5.122; P < 0.001), diabetes (OR, 10.474; 95% CI: 1.554-70.617; P = 0.016), and lactate dehydrogenase greater than 245 U/L (OR, 13.169; 95% CI: 2.934-59.105; P = 0.001) on admission. A nomogram was established based on the results of the multivariable analysis. The AUC of the nomogram was 0.970 (95% CI: 0.947-0.992), showing good accuracy in predicting the risk of in-hospital death. CONCLUSIONS: This finding would facilitate the early identification of patients with COVID-19 who have a high-risk for fatal outcome.

Risk of in-hospital death associated with Covid-19 lung consolidations on chest computed tomography - A novel translational approach using a radiation oncology contour software.

PURPOSE: To determine whether the percentage of lung involvement at the initial chest computed tomography (CT) is related to the subsequent risk of in-hospital death in patients with coronavirus disease-2019 (Covid-19). MATERIALS AND METHODS: Using a cohort of 154 laboratory-confirmed Covid-19 pneumonia cases that underwent chest CT between February and April 2020, we performed a volumetric analysis of the lung opacities. The impact of relative lung involvement on outcomes was evaluated using multivariate logistic regression. The primary endpoint was the in-hospital mortality rate. The secondary endpoint was major adverse hospitalization events (intensive care unit admission, use of mechanical ventilation, or death). RESULTS: The median age of the patients was 65 years: 50.6 % were male, and 36.4 % had a history of smoking. The median relative lung involvement was 28.8 % (interquartile range 9.5-50.3). The overall in-hospital mortality rate was 16.2 %. Thirty-six (26.3 %) patients were intubated. After adjusting for significant clinical factors, there was a 3.6 % increase in the chance of in-hospital mortality (OR 1.036; 95 % confidence interval, 1.010-1.063; P = 0.007) and a 2.5 % increase in major adverse hospital events (OR 1.025; 95 % confidence interval, 1.009-1.042; P = 0.002) per percentage unit of lung involvement. Advanced age (P = 0.013), DNR/DNI status at admission (P < 0.001) and smoking (P = 0.008) also increased in-hospital mortality. Older (P = 0.032) and male patients (P = 0.026) had an increased probability of major adverse hospitalization events. CONCLUSIONS: Among patients hospitalized with Covid-19, more lung consolidation on chest CT increases the risk of in-hospital death, independently of confounding clinical factors.

Clinical predictors of COVID-19 disease progression and death: Analysis of 214 hospitalised patients from Wuhan, China.

INTRODUCTION: COVID-19 has spread rapidly worldwide and has been declared a pandemic. OBJECTIVES: To delineate clinical features of COVID-19 patients with different severities and prognoses and clarify the risk factors for disease progression and death at an early stage. METHODS: Medical history, laboratory findings, treatment and outcome data from 214 hospitalised patients with COVID-19 pneumonia admitted to Eastern Campus of Renmin Hospital, Wuhan University in China were collected from 30 January 2020 to 20 February 2020, and risk factors associated with clinical deterioration and death were analysed. The final date of follow-up was 21 March 2020. RESULTS: Age, comorbidities, higher neutrophil cell counts, lower lymphocyte counts and subsets, impairment of liver, renal, heart, coagulation systems, systematic inflammation and clinical scores at admission were significantly associated with disease severity. Ten (16.1%) moderate and 45 (47.9%) severe patients experienced deterioration after admission, and median time from illness onset to clinical deterioration was 14.7 (IQR 11.3-18.5) and 14.5 days (IQR 11.8-20.0), respectively. Multivariate analysis showed increased Hazards Ratio of disease progression associated with older age, lymphocyte count <1.1 × 109/L, blood urea nitrogen (BUN)> 9.5 mmol/L, lactate dehydrogenase >250 U/L and procalcitonin >0.1 ng/mL at admission. These factors were also associated with the risk of death except for BUN. Prediction models in terms of nomogram for clinical deterioration and death were established to illustrate the probability. CONCLUSIONS: These findings provide insights for early detection and management of patients at risk of disease progression or even death, especially older patients and those with comorbidities.