ABSTRACT
Nebulized thrombolysis offers locally targeted therapy with potentially lower bleeding risk than systemic administration for coronavirus disease 2019 (COVID-19) respiratory failure. In a proof-of-concept safety study, adult patients with COVID-19-induced respiratory failure and a <300mmHg PaO2/FiO2 (P/F) ratio, requiring invasive mechanical ventilation (IMV) or non-invasive respiratory support (NIRS) received nebulized rt-PA in two cohorts (C1 and C2), alongside standard of care during the first two UK COVID-19 waves. Matched historical controls (MHC; n=18) were used in C1. Safety co-primary endpoints were treatment-related bleeds and fibrinogen reduction to <1.0–1.5 g/L. A dose escalation strategy for improved efficacy with the least safety concerns was determined in C1 for use in C2; patients were stratified by ventilation type to receive 40–60 mg rt-PA per day for ≤14 days. Nine patients in C1 (IMV, 6/9; NIRS, 3/9) and 26 in C2 (IMV, 12/26; NIRS, 14/26) received nebulized rt-PA for a mean (SD) of 6.7 (4.6) and 9.1(4.6) days, respectively. Four bleeding events (one severe and three mild) in three patients were considered treatment-related. No significant fibrinogen reductions were reported. Greater improvement in mean P/F ratio from baseline to end of study was observed in C1 compared with MHC [C1; 154 to 299 vs MHC; 154 to 212). In C2, there was no difference in the baseline P/F ratio of NIRS and IMV patients. However, a larger improvement in P/F ratio was observed in NIRS patients [NIRS; 126 to 240 vs IMV; 120 to 188) and they required fewer treatment days (NIRS; 7.86 vs IMV; 10.5). Nebulized rt-PA appears to be well-tolerated, showing a trend of improved oxygenation and faster recovery in patients with acute COVID-19-induced respiratory failure requiring respiratory support; this effect was more pronounced in the NIRS group. Further investigation is required to study the potential of this novel treatment approach.
Subject(s)
Hemorrhage , Neoplasm Invasiveness , COVID-19 , Respiratory InsufficiencyABSTRACT
Prognostic models to predict the risk of clinical deterioration in acute COVID-19 are required to inform clinical management decisions. Among 75,016 consecutive adults across England, Scotland and Wales prospectively recruited to the ISARIC Coronavirus Clinical Characterisation Consortium (ISARIC4C) study, we developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) using 11 routinely measured variables. We used internal-external cross-validation to show consistent measures of discrimination, calibration and clinical utility across eight geographical regions. We further validated the final model in held-out data from 8,252 individuals in London, with similarly consistent performance (C-statistic 0.77 (95% CI 0.75 to 0.78); calibration-in-the-large 0.01 (-0.04 to 0.06); calibration slope 0.96 (0.90 to 1.02)). Importantly, this model demonstrated higher net benefit than using other candidate scores to inform decision-making. Our 4C Deterioration model thus demonstrates unprecedented clinical utility and generalisability to predict clinical deterioration among adults hospitalised with COVID-19.
Subject(s)
COVID-19 , DeathABSTRACT
BackgroundThe number of proposed prognostic models for COVID-19, which aim to predict disease outcomes, is growing rapidly. It is not known whether any are suitable for widespread clinical implementation. We addressed this question by independent and systematic evaluation of their performance among hospitalised COVID-19 cases. MethodsWe conducted an observational cohort study to assess candidate prognostic models, identified through a living systematic review. We included consecutive adults admitted to a secondary care hospital with PCR-confirmed or clinically diagnosed community-acquired COVID-19 (1st February to 30th April 2020). We reconstructed candidate models as per their original descriptions and evaluated performance for their original intended outcomes (clinical deterioration or mortality) and time horizons. We assessed discrimination using the area under the receiver operating characteristic curve (AUROC), and calibration using calibration plots, slopes and calibration-in-the-large. We calculated net benefit compared to the default strategies of treating all and no patients, and against the most discriminating predictor in univariable analyses, based on a limited subset of a priori candidates. ResultsWe tested 22 candidate prognostic models among a cohort of 411 participants, of whom 180 (43.8%) and 115 (28.0%) met the endpoints of clinical deterioration and mortality, respectively. The highest AUROCs were achieved by the NEWS2 score for prediction of deterioration over 24 hours (0.78; 95% CI 0.73-0.83), and a novel model for prediction of deterioration <14 days from admission (0.78; 0.74-0.82). Calibration appeared generally poor for models that used probability outcomes. In univariable analyses, admission oxygen saturation on room air was the strongest predictor of in-hospital deterioration (AUROC 0.76; 0.71-0.81), while age was the strongest predictor of in-hospital mortality (AUROC 0.76; 0.71-0.81). No prognostic model demonstrated consistently higher net benefit than using the most discriminating univariable predictors to stratify treatment, across a range of threshold probabilities. ConclusionsOxygen saturation on room air and patient age are strong predictors of deterioration and mortality among hospitalised adults with COVID-19, respectively. None of the prognostic models evaluated offer incremental value for patient stratification to these univariable predictors.