ArticleInfectious diseases and therapy2021
Machine Learning to Assess the Risk of Multidrug-Resistant Gram-Negative Bacilli Infections in Febrile Neutropenic Hematological Patients.
Article in Infectious diseases and therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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20 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Artificial intelligence in antimicrobial stewardship: a systematic review and meta-analysis of predictive performance and diagnostic accuracy.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2025Pooled it
- Application of machine learning to identify risk factors for bloodstream infections caused by different antimicrobial resistance phenotypes in chemotherapy patients.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Artificial Intelligence in Infectious Diseases and Clinical Microbiology.Infectious diseases & clinical microbiology · 2026Review
- Development Process of a Clinical Decision Support System for Empiric Antibiotic Therapies in Patients With Sepsis: Case Study.JMIR medical informatics · 2026Article
- Short-chain fatty acid-producing microbes differentiate non-infectious and infectious neutropenic fever in leukemia.mSystems · 2026Article
- Comparison and validation of multiple machine learning algorithms for predicting MDRO infection in catheter-related bloodstream patients: a multicenter cohort study.Microbiology spectrum · 2026Article
- Role of artificial intelligence in ICU therapeutic decision-making for severe infections.Current opinion in critical care · 2025Review
- Epidemiology of Bacteremia in Patients with Hematological Malignancies and Hematopoietic Stem Cell Transplantation and the Impact of Antibiotic Resistance on Mortality: Data from a Multicenter Study in Argentina.Pathogens (Basel, Switzerland) · 2024Observational
- Retrospective validation study of a machine learning-based software for empirical and organism-targeted antibiotic therapy selection.Antimicrobial agents and chemotherapy · 2024Observational
- Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship.PLOS digital health · 2024Article
- Unbiased identification of risk factors for invasive Escherichia coli disease using machine learning.BMC infectious diseases · 2024Article
- Prediction of carbapenem-resistant gram-negative bacterial bloodstream infection in intensive care unit based on machine learning.BMC medical informatics and decision making · 2024Article
- An Emergent Change in Epidemiologic and Microbiological Characteristics of Bloodstream Infections in Adults With Febrile Neutropenia Resulting From Chemotherapy for Acute Leukemia and Lymphoma at Reference Centers in Chile, Ecuador, and Peru.Open forum infectious diseases · 2024Article
- A machine-learning model for prediction of Acinetobacter baumannii hospital acquired infection.PloS one · 2024Article
- Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria.Diagnostics (Basel, Switzerland) · 2023Review
- High Rate of Inappropriate Antibiotics in Patients with Hematologic Malignancies and Pseudomonas aeruginosa Bacteremia following International Guideline Recommendations.Microbiology spectrum · 2023Article
- Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review.Antibiotics (Basel, Switzerland) · 2023Review
- Identifying the Optimal Look-back Period for Prior Antimicrobial Resistance Clinical Decision Support.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Application of machine learning in the prediction of deficient mismatch repair in patients with colorectal cancer based on routine preoperative characterization.Frontiers in oncology · 2022Article
- Towards personalised empirical antibiotic therapy in febrile neutropenia: a theoretical model based on machine learning and prior colonisation with multidrug-resistant gram-negative bacilli - a retrospective proof-of-concept cohort study.Therapeutic advances in infectious diseaseArticle
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Authors and funding
16 authors at 2 institutions in 1 country.
Funding
Abstract
introductionWe aimed to assess risk factors for multidrug-resistant Gram-negative bacilli (MDR-GNB) from a large amount of data retrieved from electronic health records (EHRs) and determine whether machine learning (ML) may be useful in assessing the risk of MDR-GNB infection at febrile neutropenia (FN) onset.
methodsRetrospective study of almost 7 million pieces of structured data from all consecutive episodes of FN in hematological patients in a tertiary hospital in Barcelona (January 2008-December 2017). Conventional multivariate analysis and ML algorithms (random forest, gradient boosting machine, XGBoost, and GLM) were done.
resultsA total of 3235 episodes of FN in 349 patients were documented; MDR-GNB caused 180 (5.6%) infections in 132 patients. The most frequent MDR-GNBs were MDR-Pseudomonas aeruginosa (53%) and extended-spectrum beta-lactamase-producing Enterobacterales (46%). According to conventional logistic regression analysis, independent factors associated with MDR-GNB infection were age older than 45 years (OR 2.07; 95% CI 1.31-3.24), prior antibiotics (2.62; 1.39-4.92), first-ever FN in this hospitalization (2.94; 1.33-6.52), prior hospitalizations for FN (1.72; 1.02-2.89); at least 15 prior hospital visits (2.65; 1.31-5.33), high-risk hematological diseases (3.62; 1.12-11.67), and hospitalization in a room formerly occupied by patients with MDR-GNB isolation (1.69; 1.20-2.38). ML algorithms achieved the following AUC and F1 score for MDR-GNB prediction: random forest, 0.79-0.9711; GMB, 0.79-0.9705; XGBoost, 0.79-0.9670; and GLM, 0.78-0.9716.
conclusionData generated in EHRs proved useful in assessing risk factors for MDR-GNB infections in patients with FN. The great number of analyzed variables allowed us to identify new factors related to MDR infection, as well as to train ML algorithms for infection predictions. This information may be used by clinicians to make better clinical decisions.
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