Elevated free interleukin-18 associated with severity and mortality in prospective cohort study of 206 hospitalised COVID-19 patients

BACKGROUND

Divergence between deterioration to life-threatening COVID-19 or clinical improvement occurs for most within the first 14 days of symptoms. Life-threatening COVID-19 shares clinical similarities with Macrophage Activation Syndrome, which can be driven by elevated Free Interleukin-18 (IL-18) due to failure of negative-feedback release of IL-18 binding protein (IL-18bp). We, therefore, designed a prospective, longitudinal cohort study to examine IL-18 negative-feedback control in relation to COVID-19 severity and mortality from symptom day 15 onwards.

METHODS

662 blood samples, matched to time from symptom onset, from 206 COVID-19 patients were analysed by enzyme-linked immunosorbent assay for IL-18 and IL-18bp, enabling calculation of free IL-18 (fIL-18) using the updated dissociation constant (K_d) of 0.05 nmol. Adjusted multivariate regression analysis was used to assess the relationship between highest fIL-18 and outcome measures of COVID-19 severity and mortality. Re-calculated fIL-18 values from a previously studied healthy cohort are also presented.

RESULTS

Range of fIL-18 in COVID-19 cohort was 10.05-1157.7 pg/ml. Up to symptom day 14, mean fIL-18 levels increased in all patients. Levels in survivors declined thereafter, but remained elevated in non-survivors. Adjusted regression analysis from symptom day 15 onwards showed a 100 mmHg decrease in PaO₂/FiO₂ (primary outcome) for each 37.7 pg/ml increase in highest fIL-18 (p < 0.03). Per 50 pg/ml increase in highest fIL-18, adjusted logistic regression gave an odds-ratio (OR) for crude 60-day mortality of 1.41 (1.1-2.0) (p < 0.03), and an OR for death with hypoxaemic respiratory failure of 1.90 [1.3-3.1] (p < 0.01). Highest fIL-18 was associated also with organ failure in patients with hypoxaemic respiratory failure, with an increase of 63.67 pg/ml for every additional organ supported (p < 0.01).

CONCLUSIONS

Elevated free IL-18 levels from symptom day 15 onwards are associated with COVID-19 severity and mortality. ISRCTN: #13450549; registration date: 30/12/2020.

No country for old livers? Examining and optimizing the utilization of elderly liver grafts

Of the 1.6 million patients >70 years of age who died of stroke since 2002, donor livers were retrieved from only 2402 (0.15% yield rate). Despite reports of successful liver transplantation (LT) with elderly grafts (EG), advanced donor age is considered a risk for poor outcomes. Centers for Medicare and Medicaid Services definitions of an "eligible death" for donation excludes patients >70 years of age, creating disincentives to donation. We investigated utilization and outcomes of recipients of donors >70 through analysis of a United Network for Organ Sharing Standard Transplant Analysis and Research-file of adult LTs from 2002 to 2014. Survival analysis was conducted using Kaplan-Meier curves, and Cox regression was used to identify factors influencing outcomes of EG recipients. Three thousand one hundred four livers from donors >70, ≈40% of which were used in 2 regions: 2 (520/3104) and 9 (666/3104). Unadjusted survival was significantly worse among recipients of EG compared to recipients of younger grafts (P < .0001). Eight independent negative predictors of survival in recipients of EG were identified on multivariable analysis. Survival of low-risk recipients who received EG was significantly better than survival of recipients of younger grafts (P = .04). Outcomes of recipients of EG can therefore be optimized to equal outcomes of younger grafts. Given the large number of stroke deaths in patients >70 years of age, the yield rate of EGs can be maximized and disincentives removed to help resolve the organ shortage crisis.

One Size Does Not Fit All--Regional Variation in the Impact of the Share 35 Liver Allocation Policy

Allocation policies for liver transplantation underwent significant changes in June 2013 with the introduction of Share 35. We aimed to examine the effect of Share 35 on regional variation in posttransplant outcomes. We examined two patient groups from the United Network for Organ Sharing dataset; a pre-Share 35 group composed of patients transplanted between June 17, 2012, and June 17, 2013 (n = 5523), and a post-Share group composed of patients transplanted between June 18, 2013, and June 18, 2014 (n = 5815). We used Kaplan-Meier and Cox multivariable analyses to compare survival. There were significant increases in allocation Model for End-stage Liver Disease (MELD) scores, laboratory MELD scores, and proportions of patients in the intensive care unit and on mechanical, ventilated, or organ-perfusion support at transplant post-Share 35. We also observed a significant increase in donor risk index in this group. We found no difference on a national level in survival between patients transplanted pre-Share 35 and post-Share 35 (p = 0.987). Regionally, however, posttransplantation survival was significantly worse in the post-Share 35 patients in regions 4 and 10 (p = 0.008 and p = 0.04), with no significant differences in the remaining regions. These results suggest that Share 35 has been associated with transplanting "sicker patients" with higher MELD scores, and although no difference in survival is observed on a national level, outcomes appear to be concerning in some regions.

A stochastic model dissects cell states in biological transition processes

Many biological processes, including differentiation, reprogramming, and disease transformations, involve transitions of cells through distinct states. Direct, unbiased investigation of cell states and their transitions is challenging due to several factors, including limitations of single-cell assays. Here we present a stochastic model of cellular transitions that allows underlying single-cell information, including cell-state-specific parameters and rates governing transitions between states, to be estimated from genome-wide, population-averaged time-course data. The key novelty of our approach lies in specifying latent stochastic models at the single-cell level, and then aggregating these models to give a likelihood that links parameters at the single-cell level to observables at the population level. We apply our approach in the context of reprogramming to pluripotency. This yields new insights, including profiles of two intermediate cell states, that are supported by independent single-cell studies. Our model provides a general conceptual framework for the study of cell transitions, including epigenetic transformations.