ArticleTherapeutic advances in infectious disease
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.
Article in Therapeutic advances in infectious disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Managing multidrug resistant Gram-negative bacteraemia in immunocompromised hosts.Current opinion in infectious diseases · 2026Review
- Artificial Intelligence in Febrile Neutropenia: From Risk Scores to Real-Time Clinical Decision Support.Cureus · 2026Article
- ASP-COMPLEX: redefining antimicrobial stewardship to improve outcomes in high-risk patients with severe infections.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2026Review
- Antibiotic therapy in febrile neutropenia in haematological patients: current considerations and future challenges.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Empirical antibiotic therapy (EAT) in febrile neutropenia (FN) remains challenging due to multidrug-resistant (MDR) Gram-negative bacteria, often leading to inappropriate empirical antibiotic therapy (IEAT). Objective: To demonstrate that risk stratification based on machine learning (ML) and prior colonisation with MDR bacteria may support the tailoring of EAT in patients with haematological malignancies. Design: Retrospective proof-of-concept cohort study. Methods: All consecutive FN episodes in patients with haematological malignancies were retrospectively included from January 2020 to March 2023 at a tertiary-level university hospital. We compared real-world, clinician-driven empirical antibiotic use with a simulated approach guided by an ML-based risk stratification model combined with prior colonisation data. The main outcomes were antibiotic selection and rates of IEAT. Results: A total of 553 FN episodes in 398 haematological patients were analysed. Bloodstream infection (BSI) occurred in 141/553 episodes (25.5%). Anti-pseudomonal (PsA) beta-lactams were prescribed in 515/553 episodes (93.1%), with carbapenems in 406/553 (73.4%). The clinician-driven approach resulted in 16/70 (22.9%) GNB-BSI episodes receiving IEAT. The ML plus colonisation-guided approach would have reduced the use of meropenem by 29.7% (-2.08 days; 95% CI, -2.42 to -1.73; Conclusion: ML-based risk stratification combined with colonisation status would allow for personalised antibiotic therapy in FN, potentially reducing IEAT and improving antimicrobial use. These results support integrating these tools into clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.