Coronavirus Disease 2019-Associated Pulmonary Aspergillosis: A Noninvasive Screening Model for Additional Diagnostics

Nitipong Permpalung; Teresa Po-Yu Chiang; Robin K Avery; Darin Ostrander; Kausik Datta; Dorry L Segev; Christine M Durand; Sean X Zhang; Allan B Massie; Kieren A Marr

doi:10.1093/ofid/ofad155

Coronavirus Disease 2019-Associated Pulmonary Aspergillosis: A Noninvasive Screening Model for Additional Diagnostics

Open Forum Infect Dis. 2023 Mar 21;10(4):ofad155. doi: 10.1093/ofid/ofad155. eCollection 2023 Apr.

Authors

Nitipong Permpalung^{1

2}, Teresa Po-Yu Chiang^{3

4}, Robin K Avery¹, Darin Ostrander^{1

3}, Kausik Datta⁵, Dorry L Segev^{3

4

6}, Christine M Durand¹, Sean X Zhang⁷, Allan B Massie^{3

4

6}, Kieren A Marr^{1

5}

Affiliations

¹ Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
² Department of Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
³ Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
⁴ Department of Surgery, New York University Grossman School of Medicine, NYU Langone Health, New York, USA.
⁵ Pearl Diagnostics, Baltimore, Maryland, USA.
⁶ Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, Maryland, USA.
⁷ Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Abstract

Background: Coronavirus disease 2019 (COVID-19)-associated pulmonary aspergillosis (CAPA) is likely underdiagnosed, and current diagnostic tools are either invasive or insensitive.

Methods: A retrospective study of mechanically ventilated patients with COVID-19 admitted to 5 Johns Hopkins hospitals between March 2020 and June 2021 was performed. Multivariable logistic regression was used for the CAPA prediction model building. Performance of the model was assessed using the area under the receiver operating characteristic curve (AUC).

Results: In the cohort of 832 patients, 98 (11.8%) met criteria for CAPA. Age, time since intubation, dexamethasone for COVID-19 treatment, underlying pulmonary circulatory diseases, human immunodeficiency virus, multiple myeloma, cancer, or hematologic malignancies were statistically significantly associated with CAPA and were included in the CAPA prediction model, which showed an AUC of 0.75 (95% confidence interval, .70-.80). At a screening cutoff of ≥0.085, it had a sensitivity of 82%, a specificity of 51%, a positive predictive value of 18.6%, and a negative predictive value of 95.3%. (The CAPA screening score calculator is available at www.transplantmodels.com).

Conclusions: We developed a CAPA risk score as a noninvasive tool to aid in CAPA screening for patients with severe COVID-19. Our score will also identify a group of patients who are unlikely to have CAPA and who therefore need not undergo additional diagnostics and/or empiric antifungal therapy.

Keywords: CAPA; COVID-19; SARS-CoV-2; aspergillosis; risk prediction.