Development and Validation of a Machine Learning Model for the Prediction of Bloodstream Infections in Patients with Hematological Malignancies and Febrile Neutropenia
Year: 2024
Type: article
Study design: Observational Study
Abstract: Background/Objectives: The rise of multidrug-resistant (MDR) infections demands personalized antibiotic strategies for febrile neutropenia (FN) in hematological malignancies. This study investigates machine learning (ML) for identifying patient profiles with increased susceptibility to bloodstream infections (BSI) during FN onset, aiming to tailor treatment approaches. Methods: From January 2020 to June 2022, we used the unsupervised ML algorithm KAMILA to analyze data from hospitalized hematological malignancy patients. Eleven features categorized clinical phenotypes and determined BSI and multidrug-resistant Gram-negative bacilli (MDR-GNB) prevalences at FN onset. Model performance was evaluated with a validation cohort from July 2022 to March 2023. Results: Among 462 FN episodes analyzed in the development cohort, 116 (25.1%) had BSIs. KAMILA’s stratification identified three risk clusters: Cluster 1 (low risk), Cluster 2 (intermediate risk), and Cluster 3 (high risk). Cluster 2 (28.4% of episodes) and Cluster 3 (43.7%) exhibited higher BSI rates of 26.7% and 37.6% and GNB BSI rates of 13.4% and 19.3%, respectively. Cluster 3 had a higher incidence of MDR-GNB BSIs, accounting for 75% of all MDR-GNB BSIs. Cluster 1 (27.9% of episodes) showed a lower BSI risk (<1%) with no GNB infections. Validation cohort results were similar: Cluster 3 had a BSI rate of 38.1%, including 78% of all MDR-GNB BSIs, while Cluster 1 had no GNB-related BSIs. Conclusions: Unsupervised ML-based risk stratification enhances evidence-driven decision-making for empiric antibiotic therapies at FN onset, crucial in an era of rising multi-drug resistance.
Source: Antibiotics
Institutions: Hospital Clínic de Barcelona, Consorci Institut D'Investigacions Biomediques August Pi I Sunyer, Universitat de Barcelona, Assistance Publique – Hôpitaux de Paris, Hôpital Saint-Louis, Inserm, Sorbonne Université, Institut Pierre Louis d‘Épidémiologie et de Santé Publique, Centro de Investigación Biomédica en Red, Centro de Investigación Biomédica en Red Enfermedades Infecciosas
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,296
Awards: G1003179276, G1277151371, G1316141496, G1887536298, G2342717489, Impacto en el pronóstico de los pacientes con COVID-19 de un centro de control informatizado para el tratamiento precoz y personalizado, G3971430353, G4138783966, G4505316813, Personalización del tratamiento antibiótico empírico de la neutropenia febril en los pacientes hematológicos, G5666476609, G5681804992, G5808425043, G6086457742, G6316193582, G6377077739, G6828198881, G7189928660, G7210523759, G7255958097, G7256813995, G7965051849, G8156371194, G8553640947, G8697300168, G8852039387, G8872133608, G992461360
OpenAlex ID: W4405945330