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

Alejandro Rodríguez; Josep Gómez; Ignacio Martín-Loeches; Laura Claverias; Emili Díaz; Rafael Zaragoza; Marcio Borges-Sa; Frederic Gómez- Bertomeu; Álvaro Franquet; Sandra Trefler; Carlos González Garzón; Lisset Cortes; Florencia Alés; Susana Sancho; Jordi Solé-Violán; Ángel Estella; Julen Berrueta; Alejandro García-Martínez; Borja Suberviola; Juan José Guardiola; María Bodí

doi:10.20944/preprints202409.1210.v1

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 13 September 2024 / Approved: 16 September 2024 / Online: 16 September 2024 (10:07:03 CEST)

Background: Bacterial/fungal coinfections (BFC) 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 on ICU admission. Methods: 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). Performance was assessed by area under the ROC curve (AUC) and out-of-bag (OOB) for MLR and RF, respectively. Results: Of the 8,902 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 predictive models showed modest performance with 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. Conclusion: Machine learning models for predicting respiratory BFC in patients with pandemic viruses are not able to predict adequately. 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.

Influenza A(H1N1); COVID-19; Bacterial coinfection; fungal coinfection; machine learning

Medicine and Pharmacology, Epidemiology and Infectious Diseases

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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

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