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Neutralizing type-I interferon autoantibodies are associated with delayed viral clearance and intensive care unit admission in patients with COVID-19.

ABSTRACT: Type-I interferons (IFNs) mediate antiviral activity and have emerged as important immune mediators during coronavirus disease 19 (COVID-19). Several lines of evidence suggest that impaired type-I IFN signaling may predispose to severe COVID-19. However, the pathophysiologic mechanisms that contribute to illness severity remain unclear. In this study, our goal was to gain insight into how type-I IFNs influence outcomes in patients with COVID-19. To achieve this goal, we compared clinical outcomes between 26 patients with neutralizing type-I IFN autoantibodies (AAbs) and 192 patients without AAbs who were hospitalized for COVID-19 at three Italian hospitals. The presence of circulating AAbs to type-I IFNs was associated with an increased risk of admission to the intensive care unit and a delayed time to viral clearance. However, survival was not adversely affected by the presence of type-I IFN AAbs. Our findings provide further support for the role of type-I IFN AAbs in impairing host antiviral defense and promoting the development of critical COVID-19 pneumonia in severe acute respiratory syndrome coronavirus 2-infected individuals.

SUBMITTER: Abers MS

PROVIDER: S-EPMC8444766 | biostudies-literature |

REPOSITORIES: biostudies-literature

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[Nationwide exposure model for COVID-19 intensive care unit admission].

Project description:BackgroundForecasting models for intensive care occupancy of coronavirus disease 2019 (COVID-19) patients are important in the current pandemic for strategic planning of patient allocation and avoidance of regional overcrowding. They are often trained entirely on retrospective infection and occupancy data, which can cause forecast uncertainty to grow exponentially with the forecast horizon.MethodologyWe propose an alternative modeling approach in which the model is created largely independent of the occupancy data being simulated. The distribution of bed occupancies for patient cohorts is calculated directly from occupancy data from "sentinel clinics". By coupling with infection scenarios, the prediction error is constrained by the error of the infection dynamics scenarios. The model allows systematic simulation of arbitrary infection scenarios, calculation of bed occupancy corridors, and sensitivity analyses with respect to protective measures.ResultsThe model was based on hospital data and by adjusting only two parameters of data in the Aachen city region and Germany as a whole. Using the example of the simulation of the respective bed occupancy rates for Germany as a whole, the loading model for the calculation of occupancy corridors is demonstrated. The occupancy corridors form barriers for bed occupancy in the event that infection rates do not exceed specific thresholds. In addition, lockdown scenarios are simulated based on retrospective events.DiscussionOur model demonstrates that a significant reduction in forecast uncertainty in occupancy forecasts is possible by selectively combining data from different sources. It allows arbitrary combination with infection dynamics models and scenarios, and thus can be used both for load forecasting and for sensitivity analyses for expected novel spreading and lockdown scenarios.

| S-EPMC7856858 | biostudies-literature

Predictors of intensive care unit admission in patients with hematologic malignancy.

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Intensive care unit admission in multiple sclerosis: increased incidence and increased mortality.

Project description:ObjectiveTo compare the incidence of, and mortality after, intensive care unit (ICU) admission as well as the characteristics of critical illness in the multiple sclerosis (MS) population vs the general population.MethodsWe used population-based administrative data from the Canadian province of Manitoba for the period 1984 to 2010 and clinical data from 93% of admissions to provincial high-intensity adult ICUs. We identified 5,035 prevalent cases of MS and a cohort from the general population matched 5:1 on age, sex, and region of residence. We compared these populations using incidence rates and multivariable regression models adjusting for age, sex, comorbidity, and socioeconomic status.ResultsFrom January 2000 to October 2009, the age- and sex-standardized annual incidence of ICU admission among prevalent cohorts was 0.51% to 1.07% in the MS population and 0.34% to 0.51% in matched controls. The adjusted risk of ICU admission was higher for the MS population (hazard ratio 1.45; 95% confidence interval [CI] 1.19-1.75) than for matched controls. The MS population was more likely to be admitted for infection than the matched controls (odds ratio 1.82; 95% CI 1.10-1.32). Compared with the matched controls admitted to ICUs, 1-year mortality was higher in the MS population (relative risk 2.06; 95% CI 1.32-3.07) and was particularly elevated in patients with MS who were younger than 40 years (relative risk 3.77; 95% CI 1.45-8.11). Causes of death were MS (9.3%), infections (37.0%), and other causes (52.9%).ConclusionsCompared with the general population, the risk of ICU admission is higher in MS, and 1-year mortality after admission is higher. Greater attention to preventing infection and managing comorbidity is needed in the MS population.

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Risk Factors for Intensive Care Unit Admission in Patients with Autoimmune Encephalitis.

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