Research progress on factors, risk assessment, and prevention strategies associated with SARS-CoV-2 survival in food and media.

This paper presented a brief introduction to the outbreak process and symptoms of coronavirus disease 2019 (COVID-19), elucidated the detection methods and transmission modes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the disease, and summarized the survival time of SARS-COV-2 in different media and different physical and chemical conditions and factors that could affect the long-term survival of the virus. This paper also summed up current risk assessments of SARS-COV-2 in food safety conducted in various countries, and concluded that the risk of SARS-COV-2 to food safety is very low, but preventive measures are still in need after referring to latest research. Finally, some methods to prevent SARS-COV-2 contamination in food were introduced, aiming to provide a basis for the formulation of policy measures in the future.Copyright © 2021, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.

Asthma in the elderly and COVID-19: results of the 90-day post-hospital follow-up.

Background. After the first wave of the new SARS-CoV-2 coronavirus infection, the researchers focused on identifying potential short-and long-term complications of COVID-19, especially in high-risk patients, after prolonged hospitalization and intensive care. Objective. To study the outcomes, adverse effects of severe COVID-19 and their predictors 90 days after hospital discharge in elderly patients with asthma. Material and methods. The study included elderly patients (101 subjects, 42 males and 59 females;median age 74 (67;79) years) with asthma, discharged from the hospital after treatment of severe COVID-19. They were followed up for 90 days after discharge. In the hospital, COVID-19 was confirmed by laboratory tests (polymerase chain reaction method) and/or clinically and radiologically. All patients had a documented history of asthma according to GINA 2020 criteria. Results and discussion. During the 90-day post-hospital follow-up, 86 (85%) patients survived, and 15 (15%) died after discharge. Deaths were reported within 1 to 4 weeks after discharge: 6 subjects died during re-hospitalization, 6 at home, and 3 in a rehabilitation center. The multivariate regression analysis model, adjusted for all statistically significant indicators, and the ROC analysis showed the most significant predictors of 90-day post-hospital mortality and their threshold values. They include the Charlson comorbidity index >=4 points, lung damage according to computed tomography >=30%, the absolute number of eosinophils <=100 cells/muL, and concomitant diabetes mellitus. The analysis showed that 90-day post-hospital mortality depends on combinations of identified risk factors;a combination of two, three, and especially four risk factors statistically significantly is associated with patients' lower average survival time. Conclusion. The key risk factors for 90-day post-hospital mortality in elderly patients with asthma after severe COVID-19 include the Charlson comorbidity index, lung damage >=30% according to computed tomography, the absolute number of eosinophils <=100 cells/muL, and concomitant diabetes mellitus. The 90-day post-hospital survival rate is correlated with the number of risk factors identified in patients. The effect of asthma severity on 90-day post-hospital mortality in elderly patients was not observed.Copyright © 2023, Media Sphera Publishing Group. All rights reserved.

Does tracheostomy predict outcome in severe COVID-19?

Introduction: The association of tracheostomy timing and clinical outcomes in ventilated COVID-19 patients remains controversial. Data from the pre-pandemic era has demonstrated the use of tracheostomy for ventilator weaning [1]. However, the use of tracheostomy in COVID- 19 patients was a subject of discussion [2]. Nevertheless, evidence of the impact of tracheostomy on the outcome in critically ill COVID patients is still lacking. This study aims to evaluate the impact on Intensive Care Unit (ICU) outcome (survival) of tracheostomy in COVID- 19 ventilated patients. Method(s): Monocentric descriptive observational study. Demographic and clinical data, timing of tracheostomy and outcome (ICU mortality) from 1st January to 31st December 2021 were registered. Analysis of descriptive statistics for continuous variables and survival analysis (log rank test). Result(s): 115 patients were included (72% males), all mechanically ventilated, 7 (6%) were subjected to tracheostomy. The mean age was 67.2 years (range 36-84 years). The ICU mortality was 62% (71). The group of patients not submitted to tracheostomy had a mean survival time of 24.4 days (SD +/- 1.5) and median survival time of 22 days (SD +/- 1.7). The group of patients that were subjected to tracheostomy, the mean survival time was 68.5 days (SD +/- 12.2) and median survival time was 50 days (SD +/- 2). This comparison is significative (Log Rank test, p = 0.0001). Conclusion(s): The present study demonstrates a better survival likelihood of the tracheostomized subpopulation. Tracheostomy was only done in 6% of patients, which elucidates a need to further prospective, randomized studies to assess the impact on the outcome of tracheostomy in ventilated COVID19 patients.

Characterization of the Immune Reaction to Covid-19 Treatment with Dexamethasone: A Narrative Review

Purpose of Study: The spread of SARS-CoV-2 and the resulting Coronavirus Disease 2019 (COVID-19) continues to manifest in individuals in varying severity with limited treatment options available. Despite research efforts put forth in developing therapeutic options for treatment of COVID-19 disease, effective and well understood mechanisms remain limited. Corticosteroid treatment with dexamethasone was shown to be beneficial for those with severe illness early in the pandemic with little understanding of its beneficial mechanism. This narrative review describes the current findings regarding the mechanism of action of dexamethasone treatment in the setting of SARS-CoV-2 infection. Methods Used: A comprehensive search of Embase and PubMed was conducted in consultation with a health sciences librarian. Search terms included (1) COVID-19 (2) dexamethasone (3) animal model and (4) immune response. No limits were used on the search and other reviews were excluded. Search results were screened based on titles and s before being selected for full text review. Outcomes recorded included characterization of the microenvironment of lung tissue following SARS-CoV-2 through cytokine measurement, histopathological staining and analysis of lung tissue, and clinical outcomes such as survival time. Summary of Results: The search resulted in 100 articles. Of these, 8 articles were identified that met the inclusion criteria. Three conducted experiments with Syrian hamsters, two with mice, two with alveolar macrophages, and one study was conducted with human subjects. Dexamethasone treatment was found to diminish inflammatory cytokine levels and preserve the integrity of lung tissue in several animal models and in vitro experiments in the setting of SARS-CoV-2 infection. Dexamethasone treatment was also found to reduce inflammatory cell infiltration of lung tissue infected with SARS-CoV-2. In humans, combination therapy of low dose dexamethasone with spironolactone proved more effective at lowering inflammatory markers than high dose dexamethasone alone. Conclusion(s): Collectively, the articles included in this review support the use of dexamethasone treatment in SARS-CoV-2 infection. Protective effects exhibited with dexamethasone treatment suggest that its action may be linked to the inflammatory nature of COVID-19 disease. Macrophage regulation and diminished inflammatory cytokine levels were hypothesized as possible mechanistic features of dexamethasone action but lacked exact characterization. Further exploration of combination treatment with dexamethasone and its mechanism of action is needed to identify specific and effective therapeutic strategies in the future.

Asthma in the elderly and COVID-19: results of the 90-day post-hospital follow-up.

Background. After the first wave of the new SARS-CoV-2 coronavirus infection, the researchers focused on identifying potential short-and long-term complications of COVID-19, especially in high-risk patients, after prolonged hospitalization and intensive care. Objective. To study the outcomes, adverse effects of severe COVID-19 and their predictors 90 days after hospital discharge in elderly patients with asthma. Material and methods. The study included elderly patients (101 subjects, 42 males and 59 females;median age 74 (67;79) years) with asthma, discharged from the hospital after treatment of severe COVID-19. They were followed up for 90 days after discharge. In the hospital, COVID-19 was confirmed by laboratory tests (polymerase chain reaction method) and/or clinically and radiologically. All patients had a documented history of asthma according to GINA 2020 criteria. Results and discussion. During the 90-day post-hospital follow-up, 86 (85%) patients survived, and 15 (15%) died after discharge. Deaths were reported within 1 to 4 weeks after discharge: 6 subjects died during re-hospitalization, 6 at home, and 3 in a rehabilitation center. The multivariate regression analysis model, adjusted for all statistically significant indicators, and the ROC analysis showed the most significant predictors of 90-day post-hospital mortality and their threshold values. They include the Charlson comorbidity index >=4 points, lung damage according to computed tomography >=30%, the absolute number of eosinophils <=100 cells/muL, and concomitant diabetes mellitus. The analysis showed that 90-day post-hospital mortality depends on combinations of identified risk factors;a combination of two, three, and especially four risk factors statistically significantly is associated with patients' lower average survival time. Conclusion. The key risk factors for 90-day post-hospital mortality in elderly patients with asthma after severe COVID-19 include the Charlson comorbidity index, lung damage >=30% according to computed tomography, the absolute number of eosinophils <=100 cells/muL, and concomitant diabetes mellitus. The 90-day post-hospital survival rate is correlated with the number of risk factors identified in patients. The effect of asthma severity on 90-day post-hospital mortality in elderly patients was not observed.Copyright © 2023, Media Sphera Publishing Group. All rights reserved.

Efficacy evaluation of an air-assisted electrostatic disinfection device for the effective disinfection and sanitization against the spread of pathogenic infections

This research aims to check the chargeability of sodium hypochlorite and the efficacy evaluation of an air-assisted electrostatic disinfection device. Five different inanimate surfaces i.e., wood, glass, stainless steel, plastic and fabric were considered to examine the performance in terms of efficacy, survival time, off-target losses, spray coverage and the volume of disinfectant consumed. A significant charge-to-mass level of 2.43 mC/kg was achieved for sodium hypochlorite at an applied voltage of 2.0 kV, a liquid flowrate of 253 ml/min and applied air pressure of 4.0 bar. The experimental results found that 1000 mg/L of sodium hypochlorite concentration effectively eliminated Pseudomonas aeruginosa, Clostridium perfringens and Bacteriophage MS2 colonies. © 2023 Elsevier B.V.

Neutrophil-to-lymphocyte ratio (NLR) as a predictor of survival in COVID-19-associated pneumonia patients (pts)

Prior studies have shown that increased NLR can be easily used as a rapid and available marker for COVID-19- associated pneumonia pts severity and survival. Aim(s): to determine the role in predicting the surviving in COVID-19-associated pneumonia pts. Material(s) and Method(s): we studied 105 hospitalized pts ((age - 58,4 +/- 1,4 yrs, male - 62 (59 %)) with severe, moderate and critical Covid-19 confirmed by RNA detection of the 2019-nCoV. Measurements included medical history, clinical status, hematological parameters with calculating of NLR. Patients were divided into three groups according to disease severity (1-Group - 37 critical pts (male - 21 (56,7 %)), 2-Group-44 severe (male-25 (26,8 %)), 3-Group-24 moderate (male-11 (45,8 %)). Result(s): research has shown that NLR level was the highest in critical pts 8,5 (6,2;10,1) (p=0,001). According to ROC curve analysis the cut off points of the NLR level was 5,65, and patients were divided into two groups according to NLR level-NLR>=5,65 (57 (53,7 % pts) and NLR<=5,65 (49 (46,3 % pts). We estimated the mean survival time according to NLR level. Estimated mean time until death was 9,7 days for NLR>=5.6 and 7,7 days for NLR<=5.6 (p<0.01) (figure 1). Conclusion(s): high NLR levels on admission were associated with severity of COVID-19. COVID-19-associated pneumonia pts with NLR>=5,6, were less likely to survive during observation period.

Risk of in-Hospital Mortality Due to Covid19 in Patients with Bronchial Asthma: Application of 4c Mortality Scale

Role of asthma as a risk factor in severity and mortality in COVID-19 varies in literature. In 2020, 4CMortality score was published, which through 8 parameters in the initial evaluation (age, sex, comorbidity index Charlson, respiratory rate, peripheral oxygen saturation, renal function, Glasgow scale and C-reactive protein) stratified risk of in-hospital mortality from COVID-19 into low(0-3 points), intermediate(4-8), high(9-14) and very high(from 15). Our objective is to assess usefulness of 4CMortality in asthmatic patients admitted for COVID-19 and to verify the degree of correlation between the score and the mortality data and hospital stay. Observational retrospective study of asthmatic patients admitted for COVID-19 between March 2020 and March 2021. Statistical analysis is performed using Fisher's exact test(risk scale-death), ANOVA(risk scale-days hospitalization), and Kaplan Meier curve, considering statistically significant those results with a p<0.05. Sample of 99 patients, 18 in low risk group, 35 intermediate risk, 44 high risk and 2 very high risk. In terms of mortality, 7 deaths(high risk, 15.9%) and 2(very high risk, 100%), statistically significant (Fisher 17.07, p<0.0001). In terms of hospitalization days, median 7 days(low risk), 10(intermediate risk), 17 (high risk) and 5 (very high risk);statistically significant(F 6.37, p 0.001). In the survival analysis, median survival of 7 days(low-risk), 10(intermediate risk) and 19(high risk)(Log Rank 32.887, p<0.0001)(Fig 1). In conclusion, 4CMortality score is a good tool to establish the probability of poor evolution in asthmatic patients admitted for COVID-19 due to increased mortality and hospital stay.

Conformalized survival analysis

In this paper, we develop an inferential method based on conformal prediction, which can wrap around any survival prediction algorithm to produce calibrated, covariate-dependent lower predictive bounds on survival times. In the Type I right-censoring setting, when the censoring times are completely exogenous, the lower predictive bounds have guaranteed coverage in finite samples without any assumptions other than that of operating on independent and identically distributed data points. Under a more general conditionally independent censoring assumption, the bounds satisfy a doubly robust property which states the following: marginal coverage is approximately guaranteed if either the censoring mechanism or the conditional survival function is estimated well. The validity and efficiency of our procedure are demonstrated on synthetic data and real COVID-19 data from the UK Biobank. © 2023 Blackwell Publishing Ltd. All rights reserved.

Original Article Evaluation of clinical outcomes, laboratory and imaging data of patients with solid tumor infected with COVID-19 infection

Background: COVID-19 is associated with higher mortality rates in patients with cancer. In this study, we aimed to evaluate the clinical outcomes, and laboratory and imaging data of patients with solid tumor infected with COVID-19 infection. Method(s): This is a cross-sectional retrospective study performed in 2020-2022 on 85 patients with a previous diagnosis of solid tumors infected with COVID-19. We included all patients with tumors of solid organs that were diagnosed with COVID-19 infection and required hospitalization those patients previously hospitalized for treatments and were infected with COVID-19 during hospitalization. Demographic data of patients were collected using a checklist. We collected data regarding clinical outcome (discharge, hospitalization or death), duration of hospitalization, requiring ICU admission, duration of hospitalization divided by received drugs and type of tumor and mean survival time. Furthermore, we collected laboratory data from all patients. The radiologic characteristics of patients were also extracted from their data. Result(s): Breast cancer was the most common solid tumor (34.9%), followed by lung cancer (19.3%). The mortality rate was 24.1% (20 patients). The highest mortality rate in this study was for metastatic intestinal cancer to the lung (100%, one patient), followed by metastatic prostatic cancer to lung (50%, three patients). The highest hospitalization duration was for patients with glioblastoma multiform (GBM) (30 days). The mean survival time among patients with mortality was 19.15+/-1.80 days. The mean CT severity score of all patients was 27.53+/-22.90. Patient's most common radiologic sign was air space consolidation (89.1%). The highest CT severity score was found in patients with stomach cancer (46.67+/-5.77). Conclusion(s): The mortality rate in this study was 24.1%. Based on the results of our study and previous research, special care should be provided to patients with solid tumors during the COVID-19 pandemic and in infected cases.Copyright © 2022, E-Century Publishing Corporation. All rights reserved.

Modeling and analysis of recovery time for the COVID-19 patients: a Bayesian approach

The ongoing pandemic of COVID-19 has changed every aspect of life. Most of the people who become a victim of COVID-19 experience mild to moderate symptoms, but some people may become seriously ill. This illness, sometimes, may lead to a very painful death. The Fréchet distribution is one of the flexible distribution for survival time. Hence, in this article, the recovery time of COVID-19 patients is modeled by a new Fréchet-exponential (FE) distribution, and the parameters of the distribution are estimated in the classical and Bayesian paradigms. Since the Bayes estimators using informative priors are not in the closed form, the Lindley and Tierney–Kadane approximation methods are used for their evaluation. The results obtained through simulation studies and the COVID-19 data set assess the superiority of the Bayes estimators over the classical estimators in terms of minimum risks. Mathematically and graphically, it is shown that our proposed model appropriately fits the data set. The minimum values of Akaike information criterion, Bayesian information criterion, corrected Akaike information criterion, and Hannan-Quinn information criterion proves that the FE distribution better fit than the competitors' distribution for the data set about the recovery time of COVID-19 patients. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the University of Bahrain.

Comparison of clinical characteristics between COVID-19 and H7N9 fatal cases: An observational study.

Objective: The outbreak of COVID-19 in 2020 is reminiscent of the H7N9 outbreak in 2013, which poses a huge threat to human health. We aim to compare clinical features and survival factors in fatal cases of COVID-19 and H7N9. Methods: Data on confirmed COVID-19 and H7N9 fatal cases identified in mainland China were analyzed to compare demographic characteristics and clinical severity. Survival curves were estimated by the Kaplan-Meier method and compared using log-rank tests and a restricted mean survival time model. A Cox regression model was used to identify survival factors in fatal cases of COVID-19 and H7N9. Results: Similar demographic characteristics were observed in fatal cases of COVID-19 and H7N9. The proportion of fatal cases of H7N9 receiving antibiotics, antiviral drugs, and oxygen treatment was higher than that of COVID-19. The potential protective factors for fatal COVID-19 cases were receiving antibiotics (HR: 0.37, 95% CI: 0.22-0.61), oxygen treatment (HR: 0.66, 95% CI: 0.44-0.99), and corticosteroids (HR: 0.46, 95% CI: 0.35-0.62). In contrast, antiviral drugs (HR: 0.21, 95% CI: 0.08-0.56) and corticosteroids (HR: 0.45, 95% CI: 0.29-0.69) were the protective factors for H7N9 fatal cases. Conclusion: The proportion of males, those having one or more underlying medical condition, and older age was high in COVID-19 and H7N9 fatal cases. Offering antibiotics, oxygen treatment, and corticosteroids to COVID-19 cases extended the survival time. Continued global surveillance remains an essential component of pandemic preparedness.

Critical Progression, Risk Factors and Survival in Mis-C: Amazon Experience

BACKGROUND AND AIM: Highlighting the importance of risk factors for mortality in critical Multisystem Inflammatory Syndrome in children (MIS-C). To identify risk factors and survival time in children with critical MIS-C. METHOD(S): A multicenter prospective cohort in metropolitan Belem city, involving 65 children with critical MIS-C. We determined short-term (all-cause) mortality in MIS-C group compared with a cohort of 326 critical ill subjects followed up for a median of 5.4 months. The study outcome included the follows: death, need of invasive ventilation or more than 3 organs dysfunctions. Risk factors were tested using univariate regression models, followed by multivariable Cox regression models. RESULT(S): The main featuring were lymphopenia (day one median 1249, IQR: 960-1773 vs. 2393, IQR: 1108-4280.75, p=0.033, HR:1.0, CI: 0.99-1.1), hyperlactatemia (day three median 1.93, IQR:1.2-4.0 vs.1.6, IQR:1.0-2.5, p=0,020, HR: 1.17, CI:1.1-1.23), CK-MB (day one median 28.1, IQR: 24-84.1 vs. 13.3, HR:8.1-26.4, p=0.01, HR: 1.2, CI:1.0- 1.3), high troponin I (day one 0.28, IQR: 0.02-1.8 vs. 0.11, IQR:0.04-0.18, p<0.0001, HR:1.1, CI:1.0-1.2 and day three 0.2, IQR:0.012-13.4 vs. 0.06, IQR:0.02-0.10, p=0.002, HR: 1.09, CI: 1.07-1.2) and shock [26 (51%) vs.34 (22.4%), p<0.0001, HR: 6.7, CI: 3.9-11.7]. Comorbidities (HR:8.8, CI: 1.65-47.14, p=0.011) and high drive pressure (HR:5.9, CI:2.23-15.51, p<0.0001) were associated with mortality. Survival time in patients with MIS-C was shorter (mean 89.5;SD 8.4 vs. 134.8;SD 4.7 days, log-rank 29.7, p<0.0001). CONCLUSION(S): The main factors associated with critical MIS-C were comorbidities, high drive pressure and pneumonia at admission, with shorter survival times. (Figure Presented).

The impact of the SARS-CoV-2 pandemic upon pancreatic cancer treatment (CONTACT Study): A UK national observational cohort study

Background: The SARS-CoV-2 pandemic has had an unprecedented impact on healthcare systems, and cancer patients were amongst the most vulnerable. CONTACT is a national multidisciplinary study assessing the impact of the SARS-CoV-2 pandemic upon diagnostic and treatment pathways among patients with pancreatic ductal adenocarcinoma (PDAC). Method(s): A novel, mixed prospective and retrospective design, with retrospective case identification of both cohorts, and trainee-performed data collection. The treatment of consecutive patients with newly diagnosed PDAC from a pre-COVID cohort (07/01/ 2019-03/03/2019) were compared to a cohort diagnosed during the first wave of SARS-CoV-2 in the UK ('COVID' cohort, 16/03/2020-10/05/ 2020), with 12-month follow-up. Result(s): Among 984 patients (pre-COVID: N=483, COVID: N=501), across 96 hospitals, the COVID cohort were less likely to receive staging investigations other than CT scan (148/501, 29.5% vs 180/486, 37.2%;p=0.01). Among patients treated with curative intent, there was a reduction in the proportion of patients recommended surgery (42/77, 54.5% vs 72/94, 76.6%, p=0.001) and increase in the proportion recommended neoadjuvant therapy (35/77, 45.5% vs 22/94, 23.4%, p=0.002). Among patients within a non-curative pathway, fewer patients were recommended (201/424, 47.4% vs 223/389, 57.3%, p=0.004) or received palliative therapy (87/424, 20.5% vs 103/389, 26.5%, p=0.045). Ultimately, fewer patients in the COVID cohort underwent resection surgery (29/501, 6.4% vs 45/483, 9.3%, OR 0.64, 95%CI: 0.37- 0.97, p=0.036), whilst more patients received no treatment whatsoever (347/ 501, 69.3% vs 286/483, 59.2% p=0.009). There was no difference in median survival between the COVID and pre-COVID cohorts, (105 days, IQR: 86-124 vs 130 days, IQR: 108-157, p=0.093). Conclusion(s): The CONTACT study confirms alarming reduction in the staging and treatment provided to patients with PDAC diagnosed during the SARS-CoV-2 pandemic. Restoration of cancer services to pre-pandemic standards must be urgently addressed.

Asthma and COVID-19 in the elderly: course, survival, predictors of mortality

Bronchial asthma occurs in 0.9 - 17% of patients hospitalized with COVID-19. However, it is not clear whether asthma is a risk factor for the development and severity of COVID-19. Studies have shown that patients with asthma appear to be more susceptible to COVID-19 infection, but severe disease progression is not related to medication use, including asthma biologics, but rather to older age and comorbidities. Aim. To evaluate the clinical course of SARS-CoV-2 infection in elderly patients with asthma, to examine the effect of asthma and comorbidities on COVID-19-related outcomes, and to determine predictors of mortality. Methods. Elderly patients [WHO, 2020] (> 60 years, n = 131, median age 74 (67;80) years;59 men, 72 women) with asthma hospitalized for COVID-19 were included in the study. COVID-19 was confirmed by laboratory tests (PCR smear) and/or clinical and radiological examinations. All patients had a history of a documented diagnosis of asthma (GINA, 2020). Results. Out of 131 patients, 30 (22.9%) died in the hospital, and 15 (14.9%) died after discharge from the hospital (within 90 days). The group of patients with lethal outcome showed the following differences from those who recovered: values of Charlson index, respiration rate, degree of lung damage on CT scan, absolute number of leukocytes, neutrophils and neutrophils-to-lymphocytes ratio, C-reactive protein on the 5th day of hospitalization, and LDH were statistically significantly higher, while absolute number of eosinophils, total protein content, SpO2 and SpO2/FiO2 levels were lower;steroid intake during the year and non-atopic asthma were more common. Multivariate and ROC analysis revealed the most significant predictors of hospital mortality and their thresholds: Charlson comorbidity index ≥ 6 points, neutrophil/lymphocyte ratio ≥ 4.5, total protein ≤ 60 g/l, eosinophil level ≤ 100 cells/μL. Conclusion. The most significant predictors of hospital mortality in elderly patients with severe COVID-19 against asthma are Charlson comorbidity, neutrophil/lymphocyte ratio;lower eosinophil and total protein levels. Survival time of patients has an inverse correlation with the number of mortality risk factors present.

One-Year Outcomes in Kidney Transplant Recipients with Acute Kidney Injury and COVID-19

Purpose: Acute kidney injury (AKI) occurs in more than half of kidney transplant recipients (KTRs) with COVID-19. The longitudinal trajectory of kidney function is unclear. To study this, we compare mortality outcomes and long-term allograft function as measured by the change in serum creatinine (SCR) after hospital discharge between kidney transplant patients with SARS CoV-2 who experienced in-hospital AKI with who did not have AKI. Method(s): In this retrospective, multi-center study, we identified 149 KTRs who tested positive for SARS-CoV-2 between March 1st, 2020 and March 31st, 2021. Data from electronic medical records were retrieved and compared between KTRs without AKI and KTRs with AKI who were hospitalized with COVID-19. Creatinine was trended at 0,1,3,6, and 12 months. Result(s): A total of 149 COVID-19 infected KTRs were hospitalized. Of them, 102 (69%) had AKI with 45 (44.1%) in Stage 1, 9 (8.8%) in Stage 2 and 41 (40.2%) in Stage 3. Thirty-three patients died and 97% of them was in AKI group. Patients in AKI group had median survival time of 1.18 months, compared to more than 8 months in non-AKI group (p=0.002), Figure 1. Regression analysis for Intercept and slope were estimated by AKI groups, showing mild improvement in mean SCR over the 1 year at 2.2 mg/dL from peak SCR of 3.6 mg/dL, lowest eGFR 23.9 (SD 14.39) but didn't reach pre-AKI baseline in patients with AKI (1.8mg/dL), Figure 2. Conclusion(s): Patients in AKI group had higher mortality most of which was in the early period. There was mild improvement in creatinine over the following 12 months in AKI group but SCR didn't return to baseline. There with no significant change in slope of creatinine for non-AKI COVID patients.

Fibrinolysis Shutdown Subtype of Early Allograft Dysfunction Represents the Predominant Cohort of Patients with Graft Failure Following Liver Transplantation

Purpose: Early allograft dysfunction (EAD) is associated with an increased rate of graft failure in liver transplantation (LT), but a mechanism remains unclear. Recent experience with COVID-19 has identified that microvascular clots related to fibrinolysis shutdown [ SD (lack of ability to break down clot)] drives organ failure, which can be treated with tissue plasminogen activator (tPA). It remains unclear how SD interacts with EAD, and if it may be associated with a worse outcome in LT. We hypothesize that fibrinolysis shutdown with EAD would be an unfavorable combination with an increased risk of graft failure compared to patients with EAD without SD. Method(s): Adult liver transplant recipients underwent thrombelastography (TEG) on post-operative day-1 to assess for fibrinolysis shutdown. Fibrinolysis activity was quantified by the amount of clot strength lost from peak clot strength in 30-minutes (LY30) in the presence of tPA (75ng/ml). SD was defined as an LY30 of 0%, indicative or no fibrinolytic activity in the presence of a fibrinolytic activator (median LY30 = 8% in healthy volunteers). EAD was defined on laboratory measurements defined and validated by Olthoff et al. Graft survival analysis was conducted with logistic regression analysis when controlling for recipient severity of liver disease (MELD) and graft quality (donor risk index). Survival time was assessed with Kaplan Meier curve. Result(s): 230 liver transplant patient recipients with a median laboratory MELD of 23 were included in the analysis. Graft failure rate was 13% with a median follow up time of 345 days. EAD occurred in 31%, and SD was present in 45%. The combination of EAD and SD was associated with a 46% graft loss rate which was significantly higher than EAD without SD (8% p<0.001). When controlling for MELD, and donor risk index, EAD (OR 3.3 95%CI 1.3-8.4 p=0.014) and fibrinolysis shutdown (OR 2.9 95%CI 1.1-7.9 p=0.034) were independent predictors of graft failure. Graft survival times were significantly different when patients were stratified by EAD and SD (figure p<0.001). Conclusion(s): The combination of EAD and SD bodes poor prognosis following liver transplantation. The stark difference in survival warrants further investigation if activation of the fibrinolytic system can safely improve graft survival time in LT recipients with EAD without the risk of excessive bleeding. (Figure Presented).

Comparison of abiraterone acetate and prednisolone (AAP) or combination enzalutamide (ENZ) + AAP for metastatic hormone sensitive prostate cancer (mHSPC) starting androgen deprivation therapy (ADT): Overall survival (OS) results of 2 randomised phase III trials from the STAMPEDE protocol

Background: AAP or ENZ added to ADT improves outcomes for mHSPC. Any benefit of combining ENZ & AAP in this disease setting is uncertain. Methods: STAMPEDE is a multi-arm, multi-stage (MAMS), platform protocol conducted at 117 sites in the UK & Switzerland. 2 trials with no overlapping controls randomised mHSPC patients (pts) 1:1 to ADT +/- AAP (1000mg od AA + 5mg od P) or AAP + ENZ (160mg od). Treatment was continued to progression. From Jan 2016 docetaxel 75mg/m2 3-weekly with P 10mg od was permitted + ADT. Using meta-analysis methods, we tested for evidence of a difference in OS and secondary outcomes (as described previously: failure-free, metastatic progression-free, progression-free & prostate cancer specific survival) across the 2 trials using data frozen 3 Jul 2022. All confidence intervals (CI) 95%. Restricted mean survival times (RMST) restricted to 84 months (m). Results: Between Nov 2011 & Jan 2014, 1003 pts were randomised ADT +/- AAP & between Jul 2014 & Mar 2016, 916 pts were randomised ADT +/- AAP + ENZ. Randomised groups were well balanced across both trials. Pt cohort: age, median 68 years (yr), IQR 63, 72;PSA prior to ADT, median 95.7 ng/ml, IQR 26.5, 346;de novo 94%, relapsed after radical treatment, 6%. In AAP + ENZ trial, 9% had docetaxel + ADT. OS benefit in AAP + ENZ trial, HR 0.65 (CI 0.55‒0.77) p = 1.4×10-6;in AAP trial, HR 0.62 (0.53, 0.73) p = 1.6×10-9. No evidence of a difference in treatment effect (interaction HR 1.05 CI 0.83‒1.32, p = 0.71) or between-trial heterogeneity (I2 p = 0.70). Same for secondary end-points. % (CI) of pts reporting grade 3-5 toxicity in 1st 5 yr: AAP trial, ADT: 38.5 (34.2-42.8), + AAP: 54.4 (50.0-58.8);AAP + ENZ trial, ADT: 45.2 (40.6 – 49.8), + AAP + ENZ: 67.9 (63.5 – 72.2);most frequently increased with AAP or AAP + ENZ = liver derangement, hypertension. At 7 yr in AAP trial (median follow-up: 95.8m), % (CI) pts alive with ADT: 30 (26, 34) versus with ADT + AAP: 48 (43, 52);RMST: ADT: 50.4m, ADT + AAP: 60.6m, p = 6.6 x 10-9. Conclusions: ENZ + AAP need not be combined for mHSPC. Clinically important improvements in OS when adding AAP to ADT are maintained at 7 yr. Clinical trial identification: NCT00268476. Legal entity responsible for the study: Medical Research Council Clinical Trials Unit at University College London. Funding: Cancer Research UK, Medical Research Council, Janssen, Astellas. Disclosure: G. Attard: Financial Interests, Personal, Invited Speaker: Janssen, Astellas, AstraZeneca;Financial Interests, Personal, Advisory Board: Janssen, Astellas, Novartis, Bayer, AstraZeneca, Pfizer, Sanofi, Sapience, Orion;Financial Interests, Personal, Royalties, Included in list of rewards to discoverers of abiraterone: Institute of Cancer Research;Financial Interests, Institutional, Research Grant: Janssen, Astellas;Non-Financial Interests, Principal Investigator: Janssen, Astellas;Non-Financial Interests, Advisory Role: Janssen, AstraZeneca. W.R. Cross: Financial Interests, Personal, Invited Speaker, Speaker fee: Myriad Genetics, Janssen, Astellas;Financial Interests, Personal, Advisory Board, Advisory Board fee: Bayer;Financial Interests, Institutional, Research Grant, Research grant: Myriad Genetics. S. Gillessen: Financial Interests, Personal, Advisory Board, 2018: Sanofi, Roche;Financial Interests, Personal, Advisory Board, 2018, 2019: Orion;Financial Interests, Personal, Invited Speaker, 2019 Speaker's Bureau: Janssen Cilag;Financial Interests, Personal, Advisory Board, 2020: Amgen;Financial Interests, Personal, Invited Speaker, 2020: ESMO;Financial Interests, Personal, Other, Travel Grant 2020: ProteoMEdiX;Financial Interests, Institutional, Advisory Board, 2018, 2019, 2022: Bayer;Financial Interests, Institutional, Advisory Board, 2020: Janssen Cilag, Roche, MSD Merck Sharp & Dohme, Pfizer;Financial Interests, Institutional, Advisory Board, 2018: AAA International, Menarini Silicon Biosystems;Financial Interests, Institutional, Advisory Board, 2019, 2020: Astellas Pharma;Financial Interests, Institutional, Advisory B ard, 2019: Tolero Pharmaceuticals;Financial Interests, Personal, Invited Speaker, 2021, 2022: SAKK, DESO;Financial Interests, Institutional, Advisory Board, 2021: Telixpharma, BMS, AAA International, Novartis, Modra Pharmaceuticas Holding B.V.;Financial Interests, Institutional, Other, Steering Committee 2021: Amgen;Financial Interests, Institutional, Advisory Board, 2021, 2022: Orion, Bayer;Financial Interests, Personal, Invited Speaker, 2021: SAKK, SAKK, SAMO - IBCSG (Swiss Academy of Multidisciplinary oncology);Financial Interests, Personal, Advisory Board, 2021: MSD Merck Sharp & Dhome;Financial Interests, Personal, 2021: RSI (Televisione Svizzera Italiana);Financial Interests, Institutional, Invited Speaker, 2021: Silvio Grasso Consulting;Financial Interests, Institutional, Other, Faculty activity 2022: WebMD-Medscape;Financial Interests, Institutional, Advisory Board, 2022: Myriad genetics, AstraZeneca;Financial Interests, Institutional, Invited Speaker, 2022: TOLREMO;Financial Interests, Personal, Other, Travel support 2022: AstraZeneca;Financial Interests, Institutional, Funding, 2021, Unrestricted grant for a Covid related study as co-investigator: Astellas;Non-Financial Interests, Advisory Role, 2019: Menarini Silicon Biosystems, Aranda;Non-Financial Interests, Advisory Role, Continuing: ProteoMediX. C. Pezaro: Financial Interests, Personal, Advisory Board, Ad board Dec 2020: Advanced Accelerator Applications;Financial Interests, Personal, Advisory Board, Aug 2021: Astellas;Financial Interests, Personal, Advisory Board, Oct 2021: Bayer;Financial Interests, Personal, Invited Speaker, Sept-Oct 2020: AstraZeneca;Financial Interests, Personal, Invited Speaker, Oct 2020: Janssen;Financial Interests, Personal, Advisory Board, July-Sept 2022: Pfizer. Z. Malik: Financial Interests, Personal, Advisory Board, advisry board for new hormonal therapy for breast cancer: sanofi;Financial Interests, Institutional, Invited Speaker, research grant for CHROME study: sanofi;Other, Other, support to attend meetings or advisory boards in the past: Astellas,Jaansen,Bayer;Other, Other, Sponsorship to attend ASCO meeting 2022: Bayer. M.R. Sydes: Financial Interests, Personal, Invited Speaker, Speaker fees at clinical trial statistics training sessions for clinicians (no discussion of particular drugs): Janssen;Financial Interests, Personal, Invited Speaker, Speaker fees at clinical trial statistics training session for clinicians (no discussion of particular drugs): Eli Lilly;Financial Interests, Institutional, Research Grant, Educational grant and drug for STAMPEDE trial: Astellas, Janssen, Novartis, Pfizer, Sanofi;Financial Interests, Institutional, Research Grant, Educational grant and biomarker costs for STAMPEDE trial: Clovis Oncology. L.C. brown: Financial Interests, Institutional, Research Grant, £170k educational grant for the FOCUS4-C Trial from June 2017 to Dec 2021: AstraZeneca;Financial Interests, Institutional, Funding, Various grants awarded to my institution for work undertaken as part of the STAMPEDE Trial: janssen pharmaceuticals;Non-Financial Interests, Other, I am a member of the CRUK CERP funding advisory panel and my Institution also receive grant funding from CRUK for the STAMPEDE and FOCUS4 trials: Cancer Research UK. M.K. Parmar: Financial Interests, Institutional, Full or part-time Employment, Director at MRC Clinical Trials Unit at UCL: Medical Research Council Clinical Trials Unit at UCL;Financial Interests, Institutional, Research Grant: AstraZeneca, Astellas, Janssen, Clovis;Non-Financial Interests, Advisory Role, Euro Ewing Consortium: University College London;Non-Financial Interests, Advisory Role, rEECur: University of Birmingham;Non-Financial Interests, Advisory Role, CompARE Trial: University of Birmingham. N.D. James: Financial Interests, Personal, Advisory Board, Advice around PARP inhibitors: AstraZeneca;Financial Interests, Personal, Advisory Board, Prostate cancer therapies: Janssen, Clovis, Novartis;Financial Interests, Institutional, Expert Testimony, Assisted with submissions regarding licencing for abiraterone: Janssen;Financial Interests, Personal, Advisory Board, Docetaxel: Sanofi;Financial Interests, Institutional, Expert Testimony, Providing STAMPEDE trial data to facilitate licence extensions internationally for docetaxel: Sanofi;Financial Interests, Personal, Advisory Board, Bladder cancer therapy: Merck;Financial Interests, Personal, Advisory Board, Advice around novel hormone therapies for prostate cancer: Bayer;Financial Interests, Personal, Invited Speaker, Lecture tour in Brazil August 2022 - speaking on therapy for advanced prostate cancer: Merck Sharp & Dohme (UK) Limited;Financial Interests, Institutional, Invited Speaker, Funding for STAMPEDE trial: Janssen, Astellas;Financial Interests, Institutional, Invited Speaker, Funding for RADIO trial bladder cancer: AstraZeneca. All other authors have declared no conflicts of interest.

PROGNOSTIC FACTORS IN PATIENTS IN THE TERMINAL PHASE OF HEMATOLOGICAL MALIGNANCIES WHO RECEIVED HOME MEDICAL CARE

Background: In the current global COVID-19 pandemic, terminal care in a patient's home has been expanded as a positive choice even for patients suffering from hematological malignancy (HM). Although there are many tools for predicting the prognosis of patients in the terminal phase of solid tumors, there is little information about prognostic factors in patients in the terminal phase of HM, especially patients receiving home medical care (HMC). In comparison to patients with solid tumors, those with HM are more likely to have acute diseases such as acute bleeding and acute infection leading to death. A previous report revealed that HM was a factor associated with aggressive end-of-life care. Because of the various complications associated with HM, it was reported to be difficult to predict the prognosis for patients with HM. Providing patients with accurate information about prognosis is important for them to consider how to spend their remaining life. Aims: In patients in the terminal phase of HM who received HMC, we aimed to validate the usefulness of two prognostic models: Palliative Prognostic Index (PPI), which is an established prognostic model for patients in the terminal phase of a solid tumor, and the prognostic model reported by Kripp et al., which is a prognostic model for patients with HM in a palliative care unit. In addition, we aimed to determine prognostic factors for patients in the terminal phase of HM who received HMC and to develop a more detailed prognostic scoring system. Methods: We retrospectively evaluated 136 patients in the terminal phase of HM who were receiving HMC provided by 6 clinics between 2008 and 2022. Medical records relevant to prognosis were collected by a chart review. The effects of possible factors associated with overall survival (OS) were determined by the Kaplan-Meier method and univariate and multivariate Cox regression models. This study was approved by the IRB of Hokkaido University Hospital. Results: Patients characteristics were as follows: male/female, 78/58;age, 25 to 94 years;median age, 79 years;AML, 50 patients;B-NHL, 32;MDS, 24;MM, 13;T-NHL 6;ALL, 5;ATL 2, CMML 2, and PV, 2. According to PPI, there was no significant difference in OS between the intermediate-risk group and the low-risk group (panel A;P = 0.15). By using the prognostic model reported by Kripp et al., we could stratify the patients into 3 risk groups with significantly different survival times (panel B;P < 0.01). However, there was a wide range of survival times in the high-risk group (OS, 0 to 125 days;median OS, 24 days). In our investigation of factors associated with OS, multivariate analyses revealed that there were 7 factors associated with poor OS (panel C). For the development of our prognostic scoring system, each variable was weighed according to the value of the hazard ratio (panel C) and 4 risk groups were shown to clearly discriminate survival (P < 0.01): low-risk group (n = 25, median OS of 434 days), intermediate-low risk group (n = 60, median OS of 112 days), intermediate-high risk group (n = 31, median OS of 31 days), and high-risk group (n = 20, median OS of 9 days). (Figure Presented ) Summary/Conclusion: This is the first investigation of prognostic factors that influence the overall survival of patients in the terminal phase of HM who received home medical care. In comparison to previously reported prognostic models, our scoring system could stratify patients in more detail. Providing patients and medical staff with accurate information about prognosis will lead to a higher quality of life in the terminal phase and better support by medical staff.