Research progress on preclinical efficacy evaluation of COVID-19 complicated with cytokine storm

Studies have shown that COVID-19 patients infected with SARS-CoV-2 have severe pulmonary inflammation and cytokine storm, so the treatment of cytokine storm is an important part of rescuing critically ill patients with COVID-19. As an important cause of death, the preclinical study of cytokine storm is essential, and related experiments in vivo and in vitro are also the only way to develop new drugs for COVID-19 in the future. This paper reviews the in vitro and in vivo experimental methods of cytokine storm research articles at home and abroad in recent years, including the establishment of animal models, cell evaluation methods, pharmacodynamic evaluation indicators, etc., in order to provide reference and guidance for the experimental design methods of cytokine storm. © 2021 Publication Centre of Anhui Medical University. All rights reserved.

Waitlist outcomes for exception candidates following the implementation of MMAT/250 score

Purpose: Starting in May 2019, exception points were allocated based on DSA-level median-MELD-at-transplant (MMaT/DSA). In February 2020, the exception point system was further modified based on MMaT within 250 nautical miles (MMaT/250). Our aim was to describe subsequent changes in transplant and mortality rates for exception and non-exception candidates. Methods: Using SRTR data on 26,992 adult, first-time, active, DDLT waitlist registrants, we compared DDLT rates in non-HCC exception vs. HCC-exception vs. non-exception candidates using Cox regression in three eras: Pre-MMat (02/01/2018-01/31/2019), MMaT/DSA (05/01/2019-02/03/2020), and MMaT/250 (02/04/2020-08/30/2020). In addition, we compared waitlist mortality/dropout risk among non-HCC exception, HCC-exception, non-exception candidates of the same allocation priority using Fine and Gray method after accounting for the competing risk of transplantation. Results: During pre-MMat era, Non-HCC exception candidates had 15% (aHR= 0.75 0.85 0.96) lower access to DDLT compared to non-exception candidates;similar DDLT rate in MMaT/DSA-era (aHR= 0.86 1.04 1.24) and then attenuated to 38% lower access to DDLT in MMaT/250 era (aHR=0.48 0.62 0.80) (Figure 1). HCC-exception candidates had 52 % (aHR=0.44 0.48 0.53), 69% (aHR=0.27 0.31 0.35) and 79% (aHR=0.7 0.21 0.25) lower access to DDLT compared to non-exception candidates during pre- MMat , MMaT/DSA and MMaT/250 era , respectively. Non-HCC exception and non-exception candidates with the same allocation MELD had comparable risk of death/dropout during pre-MMaT and MMaT/DSA eras (Figure 2). However, under MMaT/250, non-HCC exception patients had twice as much risk of death/dropout compared to non-exception patients (asHR= 1.06 2.08 4.06);risk was potentially elevated for HCC exception patients although not statistically significant (asHR = 0.93 1.67 3.01) (Figure 2). Conclusions: Following the implementation of MMaT/250 score, access to DDLT was attenuated for both Non-HCC exception and HCC exception candidates. In addition, the policy change appeared to have substantially increased risk of death/ dropout for non-HCC exception candidates compared to non-exception candidates. The COVID-19 pandemic may have influenced death/dropout in the MMaT/250 era.

Development of a point-of-care mortality risk score for hospitalized COVID-19 patients

Rationale: While several COVID-19-specific mortality risk scores exist, they lack the ease of use given their dependence on online calculators and algorithms. Objectives: The objectives of this study were (1) to design, validate, and calibrate a simple, easy-to-use mortality risk score in a hospitalized COVID-19 population. Methods: Multi-hospital health system in New York City. Patients (n=4840) with laboratory-confirmed SARS-CoV2 infection who were admitted between March 1 and April 28, 2020. Gray's K-sample test for the cumulative incidence of a competing risk was used to assess and rank 48 different variables' associations with mortality. Candidate variables were added to the composite score using DeLong's test to evaluate their effect on predictive performance (AUC) of in-hospital mortality. Final AUCs for the new score, SOFA, qSOFA, and CURB-65 were assessed on an independent test set. Results: Of 48 variables investigated, 36 (75%) displayed significant (p<0.05 by Gray's test) associations with mortality. The variables selected for the final score were (1) oxygen support level, (2) troponin, (3) blood urea nitrogen, (4) lymphocyte percentage, (5) Glasgow Coma Score, and (6) age. The new score, COBALT, outperforms SOFA, qSOFA, and CURB-65 at predicting mortality in this COVID-19 population: AUCs for initial, maximum, and mean COBALT scores were 0.81, 0.91, and 0.92, compared to 0.77, 0.87, and 0.87 for SOFA. Conclusions: The COBALT score provides a point-of-care tool to estimate mortality in hospitalized COVID-19 patients with superior performance to SOFA and other scores currently in widespread use.

Cardiovascular disease and severe hypoxemia are associated with higher rates of non-invasive respiratory support failure in COVID-19 pneumonia

RATIONALE Acute hypoxemic respiratory failure (AHRF) is the major complication of coronavirus disease 2019 (COVID-19), yet optimal respiratory support strategies are uncertain. We aimed to describe outcomes with highflow oxygen delivered through nasal cannula (HFNC) and non-invasive positive pressure ventilation (NIPPV) in COVID-19 AHRF and identify individual factors associated with non-invasive respiratory support failure. METHODS We conducted a retrospective cohort study of hospitalized adults with COVID-19 within a large academic health system in New York City early in the pandemic to describe outcomes with HFNC and NIPPV. Patients were categorized into the HFNC cohort if they received HFNC but not NIPPV, whereas the NIPPV cohort included patients who received NIPPV with or without HFNC. We described rates of HFNC and NIPPV success, defined as live discharge without endotracheal intubation (ETI). Further, using Fine-Gray sub-distribution hazard models, we identified demographic and patient characteristics associated with HFNC and NIPPV failure, defined as the need for ETI and/or in-hospital mortality. RESULTS Of the 331 patients in the HFNC cohort, 154 (46.5%) patients were successfully discharged without requiring ETI. Of the 177 (53.5%) who experienced HFNC failure, 100 (56.5%) required ETI and 135 (76.3%) patients ultimately died. Among the 747 patients in the NIPPV cohort, 167 (22.4%) patients were successfully discharged without requiring ETI, and 8 (1.1%) were censored. Of the 572 (76.6%) patients who failed NIPPV, 338 (59.1%) required ETI and 497 (86.9%) ultimately died. In adjusted models, significantly increased risk of HFNC and NIPPV failure was observed among patients with co-morbid cardiovascular disease (sub-distribution hazard ratio (sHR) 1.82;95% confidence interval (CI), 1.17-2.83 and sHR 1.40;95% CI 1.06-1.84, respectively). Conversely, a higher oxygen saturation to fraction of inspired oxygen ratio (SpO2/FiO2) at HFNC and NIPPV initiation was associated with reduced risk of failure (sHR, 0.32;95% CI 0.19-0.54, and sHR 0.34;95% CI 0.21-0.55, respectively). CONCLUSIONS A subset of patients with COVID-19 AHRF was effectively managed with non-invasive respiratory modalities and achieved successful hospital discharge without requiring ETI. Notably, patients with co-morbid cardiovascular disease and more severe hypoxemia experienced lower success rates with both HFNC and NIPPV. Identification of specific patient factors may help inform more selective use of non-invasive respiratory strategies, and allow for a more personalized approach to the management of COVID-19 AHRF in pandemic settings.

Machine learning to predict ICU mortality from vital signs for different diagnoses and COVID-19

INTRODUCTION: Vital signs (VS) are important indicators of disease severity and clinical deterioration. However, the predictive scope of VS for ICU mortality is unknown and there are no validated system for early and real-time prediction of ICU mortality from VS data alone. In this study we aimed to develop and validate a Machine Learning (ML) classifier to predict ICU mortality from continuous VS data. METHODS: We used de-identified patient VS data obtained from our eSearch (Philips Healthcare) database to encode 7 continuous VS time series and use of 5 VS monitoring devices. Mean, standard deviation, autocorrelation, and the trend of the mean were used to encode VS time series variations and were adjusted to the entire ICU stay, and 6, 12, or 24 hours before death. Our approach did not encode diagnoses but agnostically classified based on VS features. Performance of the models was determined on a naïve cohort and an independent sample of patients with COVID-19. RESULTS: A total of 19,266 ICU stays prior to COVID were studied including 17,339 in the training cohort, and 1,927 in the naïve validation cohort with ICU mortalities of 9%. An independent sample of 548 patients with COVID-19 with mortality of 22% was also used for validation. For the entire stay, and 6-, 12-, and 24-hours in advance, the ML classifier achieved AUCs and PRCs of 0.97 - 0.81 and 0.78 - 0.40, respectively in the naïve population obtained prior to COVID, and AUCs and PRCs of 0.92 - 0.80 and 0.81 - 0.58, respectively, for the COVID cohort. Notably, a differential ranking of features was found for mortality predictions in the COVID-19 sample, as well as in 9 other specific diagnoses. The effectiveness of this approach compared favorably with six other ML methods and with the DRS (Philips) mortality predictions. CONCLUSIONS: A data-driven ML algorithm developed from composite vital sign data alone made ICU mortality predictions with model performance on a naïve ICU test population, as well as on a COVID-19 patient population, that rivals other prediction models using more complex data domains. Shapley Additive exPlanations provided interpretability and clinical validation of the ML model related to the specific features in the ICU subpopulations.

Epidemiological features of coronavirus disease 2019 in children: a meta-analysis.

OBJECTIVE: Many studies have been published recently on the characteristics of the clinical manifestations of COVID-19 in children. The quality scores of literature are different, and the incidence of clinical manifestations and laboratory tests results vary greatly. Therefore, a systematic retrospective meta-analysis is needed to determine the incidence of the clinical manifestations of COVID-19 in children. MATERIALS AND METHODS: Data from databases, such as PubMed, Web of science, EMBASE, Johns Hopkins University, and Chinese databases were analysed from January 31, 2020 to October 20, 2020. High-quality articles were selected for analysis based on a quality standard score. A meta-analysis of random effects was used to determine the prevalence of comorbidities and subgroup meta-analysis to examine the changes in the estimated prevalence in different subgroups. RESULTS: Seventy-one articles involving 11,671 children were included in the study. The incidence of fever, respiratory symptoms, gastrointestinal symptoms, asymptomatic patients, nervous system symptoms, and chest tightness was 55.8%, 56.8%, 14.4%, 21.1%, 6.7%, and 6.1%, respectively. The incidence of multisystem inflammatory syndrome was 6.2%. Laboratory examination results showed that lymphocytes decreased in 12% and leukocytes decreased in 8.8% of patients, whereas white blood cells increased in 7.8% of patients. Imaging showed abnormalities in 66.5%, and ground-glass opacities were observed in 36.9% patients. Epidemiological history was present in 85.2% cases; severe disease rate was 3.33%. The mortality rate was 0.28%. CONCLUSIONS: The clinical symptoms of COVID-19 in children are mild, and laboratory indicators and imaging manifestations are atypical. While screening children for COVID-19, in addition to assessing patients for symptoms as the first step of screening, the epidemiological history of patients should be obtained.

Identifying scenarios of benefit or harm from kidney transplantation during the COVID-19 pandemic: A simulation study

Background: Clinical decision-making in kidney transplantation (KT) during the COVID-19 pandemic is a challenge: both candidates and recipients may face increased acquisition risks and case fatality rates (CFRs). Given our poor understanding of these risks, many centers have paused or reduced KT activity, yet data to inform such decisions are lacking. Methods: To quantify the benefit/harm of KT in this context, we conducted a Markov simulation study of immediate-KT vs delay-until-after-pandemic for different patient phenotypes under a variety of potential COVID-19 scenarios (Figure 1), simulating expected life-months gained from transplant over 5 years. A calculator was implemented (http://www.transplantmodels.com/covid-sim), and machine learning approaches were used to evaluate the important aspects of our modeling. Results: Characteristics of the pandemic (acquisition risk, CFR) and length of delay (length of pandemic, waitlist priority for DDKT) had greatest influence on benefit/ harm (Figure 2). In most scenarios of COVID-19 dynamics and patient characteristics, immediate-KT provided survival benefit;KT only began showing evidence of harm in scenarios where CFRs were substantially higher for KT recipients (e.g. ≥50% fatality) than for waitlist registrants. Conclusions: Our simulations suggest that KT remains beneficial under COVID-19 in many scenarios. Our calculator can help identify patients who would benefit most. As the pandemic evolves, our calculator can update these predictions.