Multivariate joint model under competing risks to predict death of hospitalized patients for SARS-CoV-2 infection.

During the coronavirus disease 2019 (COVID-19) pandemic, several clinical prognostic scores have been proposed and evaluated in hospitalized patients, relying on variables available at admission. However, capturing data collected from the longitudinal follow-up of patients during hospitalization may improve prediction accuracy of a clinical outcome. To answer this question, 327 patients diagnosed with COVID-19 and hospitalized in an academic French hospital between January and July 2020 are included in the analysis. Up to 59 biomarkers were measured from the patient admission to the time to death or discharge from hospital. We consider a joint model with multiple linear or nonlinear mixed-effects models for biomarkers evolution, and a competing risks model involving subdistribution hazard functions for the risks of death and discharge. The links are modeled by shared random effects, and the selection of the biomarkers is mainly based on the significance of the link between the longitudinal and survival parts. Three biomarkers are retained: the blood neutrophil counts, the arterial pH, and the C-reactive protein. The predictive performances of the model are evaluated with the time-dependent area under the curve (AUC) for different landmark and horizon times, and compared with those obtained from a baseline model that considers only information available at admission. The joint modeling approach helps to improve predictions when sufficient information is available. For landmark 6 days and horizon of 30 days, we obtain AUC [95% CI] 0.73 [0.65, 0.81] and 0.81 [0.73, 0.89] for the baseline and joint model, respectively (p = 0.04). Statistical inference is validated through a simulation study.

Aspergillus detection in airways of ICU COVID-19 patients: To treat or not to treat?

It is now well known that patients with severe COVID-19 are at risk for developing invasive pulmonary aspergillosis (IPA). Nevertheless, the symptomatology of IPA is often atypical in mechanically ventilated patients and the radiological aspects of SARS CoV-2 pneumonia and IPA are difficult to differentiate. In this context, the significance of the presence of Aspergillus in respiratory tract samples (detected by culture, galactomannan antigen, or specific PCR) is not yet fully understood. Here we report two cases of intubated and mechanically ventilated ICU patients with SARS-CoV-2 pneumonia, in whom Aspergillus was detected in respiratory samples, who had a favorable outcome in the absence of antifungal treatment. These two cases highlight the difficulty of using the new definitions of COVID-19 associated pulmonary aspergillosis for routine management of patients.

A review of significance of Aspergillus detection in airways of ICU COVID-19 patients.

It is now well known that patients with SARS-CoV-2 infection admitted in ICU and mechanically ventilated are at risk of developing invasive pulmonary aspergillosis (IPA). Nevertheless, symptomatology of IPA is often atypical in mechanically ventilated patients, and radiological aspects in SARS-CoV-2 pneumonia and IPA are difficult to differentiate. In this context, the significance of the presence of Aspergillus in airway specimens (detected by culture, galactomannan antigen or specific PCR) remains to be fully understood. To decipher the relevance of the detection of Aspergillus, we performed a comprehensive review of all published cases of respiratory Aspergillus colonisation and IPA in COVID-19 patients. The comparison of patients receiving or not antifungal treatment allowed us to highlight the most important criteria for the decision to treat. The comparison of surviving and non-surviving patients made it possible to unveil criteria associated with mortality that should be taken into account in the treatment decision.