Optimising personalised antibiotic treatment for methicillin-resistant Staphylococcus aureus bloodstream infections in ICU patients using a deep learning–based causal inference approach
Bibliographic record
Abstract
OBJECTIVE: Methicillin‑resistant Staphylococcus aureus (MRSA) bloodstream infections (BSIs) in intensive care units (ICUs) carry high mortality, and although vancomycin remains standard treatment, daptomycin and linezolid may benefit specific subgroups. This study evaluates the mortality reduction associated with vancomycin, daptomycin, and linezolid using a deep learning-based causal inference model. METHODS: Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-III and MIMIC-IV databases, including 270 ICU patients with MRSA BSI. A deep learning-based causal inference model was used to assess the treatment effect of linezolid, daptomycin, and vancomycin on in-hospital mortality. Multivariable logistic regression was employed to identify patient characteristics associated with the effectiveness of each antibiotic. RESULTS: The deep learning-based model predicted that vancomycin, daptomycin, and linezolid reduced mortality by 15.86% (17.90% to 13.82%), 9.68% (11.83% to 7.53%), and 10.74% (12.64% to 8.84%), respectively, with vancomycin showing the greatest reduction. The average treatment effect for in-hospital mortality reduction with vancomycin was significantly greater than that with linezolid and daptomycin (both P < 0.001). Multivariable logistic regression for treatment effects revealed that vancomycin was particularly effective in patients of advanced age, those with chronic liver disease, and those with end-stage kidney disease, while it was less effective in patients with congestive heart failure or cancer. Daptomycin exhibited superior efficacy over vancomycin in patients with cancer, and linezolid was more effective in patients with cancer, hypertension, and congestive heart failure. CONCLUSION: This study highlights linezolid and daptomycin treatment in select subgroups, while a deep learning-based model enables personalised antibiotic recommendations for ICU treatment strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".