A Machine Learning Approach to Determine Risk Factors for Respiratory Bacterial/Fungal Coinfection in Critically Ill Patients with Influenza and SARS-CoV-2 Infection: A Spanish Perspective

<b>Background</b>: Bacterial/fungal coinfections (COIs) are associated with antibiotic overuse, poor outcomes such as prolonged ICU stay, and increased mortality. Our aim was to develop machine learning-based predictive models to identify respiratory bacterial or fungal coinfections upon ICU admission. <b>Methods</b>: We conducted a secondary analysis of two prospective multicenter cohort studies with confirmed influenza A (H1N1)pdm09 and COVID-19. Multiple logistic regression (MLR) and random forest (RF) were used to identify factors associated with BFC in the overall population and in each subgroup (influenza and COVID-19). The performance of these models was assessed by the area under the ROC curve (AUC) and out-of-bag (OOB) methods for MLR and RF, respectively. <b>Results</b>: Of the 8902 patients, 41.6% had influenza and 58.4% had SARS-CoV-2 infection. The median age was 60 years, 66% were male, and the crude ICU mortality was 25%. BFC was observed in 14.2% of patients. Overall, the predictive models showed modest performances, with an AUC of 0.68 (MLR) and OOB 36.9% (RF). Specific models did not show improved performance. However, age, procalcitonin, CRP, APACHE II, SOFA, and shock were factors associated with BFC in most models. <b>Conclusions</b>: Machine learning models do not adequately predict the presence of co-infection in critically ill patients with pandemic virus infection. However, the presence of factors such as advanced age, elevated procalcitonin or CPR, and high severity of illness should alert clinicians to the need to rule out this complication on admission to the ICU.

Authors

Institutions

Publication Details

Journal
Antibiotics
Published
2024-10-14
DOI
https://doi.org/10.3390/antibiotics13100968
Citations
4
Primary Topic
Antibiotic Use and Resistance
Type
article
Field-Weighted Citation Impact
1.19
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Machine Learning Approach to Determine Risk Factors for Respiratory Bacterial/Fungal Coinfection in Critically Ill Patients with Influenza and SARS-CoV-2 Infection: A Spanish Perspective

Marcío Borges-Sa, Sandra Trefler, María Bodi, Rafael Zaragoza et al.
4 citations
Antibiotics
Antibiotic Use and Resistance
1.19
article

A Machine Learning Approach to Determine Risk Factors for Respiratory Bacterial/Fungal Coinfection in Critically Ill Patients with Influenza and SARS-CoV-2 Infection: A Spanish Perspective

Marcío Borges-Sa, Sandra Trefler, María Bodi, Rafael Zaragoza, Alejandro Rodríguez, Juan Guardiola, Ignacio Martín‐Loeches, Borja Suberviola, Julen Berrueta, Josep Gómez, Jordi Solé‐Violán, Frederic Gómez‐Bertomeu, Susana Sancho, Emili Dı́az, Ángel Estella, Álvaro Franquet, Laura Claverías, Florencia Alés, Carlos González Garzón, Leonardo Afonso Côrtes, Alejandro García-Martínez
article en
4 citations

Abstract

<b>Background</b>: Bacterial/fungal coinfections (COIs) are associated with antibiotic overuse, poor outcomes such as prolonged ICU stay, and increased mortality. Our aim was to develop machine learning-based predictive models to identify respiratory bacterial or fungal coinfections upon ICU admission. <b>Methods</b>: We conducted a secondary analysis of two prospective multicenter cohort studies with confirmed influenza A (H1N1)pdm09 and COVID-19. Multiple logistic regression (MLR) and random forest (RF) were used to identify factors associated with BFC in the overall population and in each subgroup (influenza and COVID-19). The performance of these models was assessed by the area under the ROC curve (AUC) and out-of-bag (OOB) methods for MLR and RF, respectively. <b>Results</b>: Of the 8902 patients, 41.6% had influenza and 58.4% had SARS-CoV-2 infection. The median age was 60 years, 66% were male, and the crude ICU mortality was 25%. BFC was observed in 14.2% of patients. Overall, the predictive models showed modest performances, with an AUC of 0.68 (MLR) and OOB 36.9% (RF). Specific models did not show improved performance. However, age, procalcitonin, CRP, APACHE II, SOFA, and shock were factors associated with BFC in most models. <b>Conclusions</b>: Machine learning models do not adequately predict the presence of co-infection in critically ill patients with pandemic virus infection. However, the presence of factors such as advanced age, elevated procalcitonin or CPR, and high severity of illness should alert clinicians to the need to rule out this complication on admission to the ICU.

AntibioticsVol. 13(10)
Universitat Autònoma de Barcelona (ES), University of Louisville (US), Hospital Universitari i Politècnic La Fe (ES), Universidad de Cádiz (ES), Hospital Son Llatzer (ES), Hospital Universitario de Gran Canaria Doctor Negrín (ES), Marqués de Valdecilla University Hospital (ES), Institut de Recerca Biomèdica Catalunya Sud (ES), Louisville VA Medical Center (US), Centre for Biomedical Network Research on Rare Diseases (ES), Hospital Universitario Doctor Peset (ES), Fundación Universitaria de Ciencias de la Salud (CO), Hospital Universitari Joan XXIII de Tarragona (ES), Hospital Jerez Puerta del Sur (ES), Hospital General de Niños Ricardo Gutierrez (AR), Deleted Institution, Universitat Rovira i Virgili (ES)
Good health and well-being
Openalex Percentile: Top 15%
Antibiotic Use and Resistance
1.19
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.