Open forum infectious diseases · 2024
Predicting COVID 19-Associated Pulmonary Aspergillosis Risk in Low- and Middle-Income Countries: A Matched Case-Control Study.
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Moni Merlin, Sathyapalan Dipu T, Edathadathil Fabia, Razak M Abdul, Nair Sivapriya G, Nair Chithira V, Samban Swathy S, Prasanna Preetha, Kulirankal Kiran G, Purushothaman Shyam Sundar, Gutjahr Georg, Ying Jiang, John Teny M
Аннотация
BACKGROUND: Coronavirus disease 2019 (COVID-19)-associated pulmonary aspergillosis (CAPA) is a life-threatening fungal infection. Studies focusing on CAPA in low- and middle-income countries are limited.
METHODS: This retrospective matched case-control study was conducted at a tertiary care center in South India. Cases of CAPA were classified using the 2020 European Confederation of Medical Mycology/International Society for Human and Animal Mycology consensus criteria. A total of 95 cases were matched 1:1 with COVID-19 patients without CAPA. Matching was done based on age and period of admission. Inverse probability weighting was used to account for imbalances in COVID-19 severity and intensive care unit (ICU) admission. Data on demographics, clinical details, microbiologic and radiologic data, and treatment outcomes were collected. A predictive score for CAPA was developed from baseline risk factors.
RESULTS: The predictive score identified lymphopenia, European Organisation for Research and Treatment of Cancer risk factors, and broad-spectrum antibiotic use as the main risk factors for CAPA. Positivity for bacterial pathogens in blood or bronchoalveolar lavage samples reduced the risk of CAPA. The predictive model performed well in cross-validation, with an area under the curve value of 82%. CAPA diagnosis significantly increased mortality and shift to ICU.
CONCLUSIONS: The predictive model derived from the current study offers a valuable tool for clinicians, especially in high-endemic low- and middle-income countries, for the early identification and treatment of CAPA. With further validation, this risk score could improve patient outcomes.
METHODS: This retrospective matched case-control study was conducted at a tertiary care center in South India. Cases of CAPA were classified using the 2020 European Confederation of Medical Mycology/International Society for Human and Animal Mycology consensus criteria. A total of 95 cases were matched 1:1 with COVID-19 patients without CAPA. Matching was done based on age and period of admission. Inverse probability weighting was used to account for imbalances in COVID-19 severity and intensive care unit (ICU) admission. Data on demographics, clinical details, microbiologic and radiologic data, and treatment outcomes were collected. A predictive score for CAPA was developed from baseline risk factors.
RESULTS: The predictive score identified lymphopenia, European Organisation for Research and Treatment of Cancer risk factors, and broad-spectrum antibiotic use as the main risk factors for CAPA. Positivity for bacterial pathogens in blood or bronchoalveolar lavage samples reduced the risk of CAPA. The predictive model performed well in cross-validation, with an area under the curve value of 82%. CAPA diagnosis significantly increased mortality and shift to ICU.
CONCLUSIONS: The predictive model derived from the current study offers a valuable tool for clinicians, especially in high-endemic low- and middle-income countries, for the early identification and treatment of CAPA. With further validation, this risk score could improve patient outcomes.