Coinfections in Patients With Cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) Study.

Journal: Open Forum Infectious Diseases
Published:
Abstract

Background: The frequency of coinfections and their association with outcomes have not been adequately studied among patients with cancer and coronavirus disease 2019 (COVID-19), a high-risk group for coinfection.

Methods: We included adult (≥18 years) patients with active or prior hematologic or invasive solid malignancies and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) infection, using data from the COVID-19 and Cancer Consortium (CCC19, NCT04354701). We captured coinfections within ±2 weeks from diagnosis of COVID-19, identified factors cross-sectionally associated with risk of coinfection, and quantified the association of coinfections with 30-day mortality.

Results: Among 8765 patients (hospitalized or not; median age, 65 years; 47.4% male), 16.6% developed coinfections: 12.1% bacterial, 2.1% viral, 0.9% fungal. An additional 6.4% only had clinical diagnosis of a coinfection. The adjusted risk of any coinfection was positively associated with age >50 years, male sex, cardiovascular, pulmonary, and renal comorbidities, diabetes, hematologic malignancy, multiple malignancies, Eastern Cooperative Oncology Group Performance Status, progressing cancer, recent cytotoxic chemotherapy, and baseline corticosteroids; the adjusted risk of superinfection was positively associated with tocilizumab administration. Among hospitalized patients, high neutrophil count and C-reactive protein were positively associated with bacterial coinfection risk, and high or low neutrophil count with fungal coinfection risk. Adjusted mortality rates were significantly higher among patients with bacterial (odds ratio [OR], 1.61; 95% CI, 1.33-1.95) and fungal (OR, 2.20; 95% CI, 1.28-3.76) coinfections.

Conclusions: Viral and fungal coinfections are infrequent among patients with cancer and COVID-19, with the latter associated with very high mortality rates. Clinical and laboratory parameters can be used to guide early empiric antimicrobial therapy, which may improve clinical outcomes.

Authors
Gowri Satyanarayana, Kyle Enriquez, Tianyi Sun, Elizabeth Klein, Maheen Abidi, Shailesh Advani, Joy Awosika, Ziad Bakouny, Babar Bashir, Stephanie Berg, Marilia Bernardes, Pamela Egan, Arielle Elkrief, Lawrence Feldman, Christopher Friese, Shipra Goel, Cyndi Gomez, Keith Grant, Elizabeth Griffiths, Shuchi Gulati, Shilpa Gupta, Clara Hwang, Jayanshu Jain, Chinmay Jani, Anna Kaltsas, Anup Kasi, Hina Khan, Natalie Knox, Vadim Koshkin, Daniel Kwon, Chris Labaki, Gary Lyman, Rana Mckay, Christopher Mcnair, Gayathri Nagaraj, Elizabeth Nakasone, Ryan Nguyen, Taylor Nonato, Adam Olszewski, Orestis Panagiotou, Matthew Puc, Pedram Razavi, Elizabeth Robilotti, Miriam Santos Dutra, Andrew Schmidt, Dimpy Shah, Sumit Shah, Kendra Vieira, Lisa Weissmann, Trisha Wise Draper, Ulysses Wu, Julie Wu, Toni Choueiri, Sanjay Mishra, Jeremy Warner, Benjamin French, Dimitrios Farmakiotis