ResPredAI：用机器学习预测革兰氏阴性菌血流感染的抗生素耐药性

ResPredAI is an open-source machine learning framework that predicts antibiotic resistance of Gram-negative bloodstream infection (GN-BSI) based on patient clinical data and MALDI-TOF pathogen identification results. It aims to provide timely guidance for clinicians in empirical antibiotic selection, addressing the critical gap between pathogen identification and traditional药敏 test results. Key info: Published in npj Digit. Med. 2025, maintained by Ettore Rocchi, source on GitHub (https://github.com/EttoreRocchi/ResPredAI) and project website (https://ettorerocchi.github.io/respredai-website/).

GN-BSI is a severe hospital-acquired infection with high morbidity and mortality. Timely correct empirical antibiotic treatment is crucial—delays over 12h increase death risk. However, traditional药敏 tests take 48-72h, forcing clinicians to use empirical treatment: too narrow may fail, too broad accelerates resistance and adverse effects. The key problem is predicting resistance patterns between pathogen identification and药敏 results.

ResPredAI is a reproducible ML framework predicting resistance to 4 common antibiotics: Fluoroquinolones, 3rd generation cephalosporins, β-lactam/β-lactamase inhibitors (BL/BLI), Carbapenems. It's based on a 2025 study with 2552 GN-BSI patient data.

Tech Architecture: Uses nested cross-validation for robust evaluation. Supports multiple models: Logistic Regression (interpretable), Random Forest (nonlinear), XGBoost (structured data), CatBoost (category-friendly), TabPFN (Transformer-based, optional).

Key Predictors: Rectal拭子阳性 (strongest for carbapenem/fluoroquinolone/BL/BLI resistance),菌种 (Klebsiella pneumoniae linked to resistance; E. coli/Proteus spp. often carbapenem-sensitive), patient clinical features (comorbidities, infection severity, prior antibiotic exposure).

Performance: 10-fold external CV results:

Antibiotic	AUROC	F1 (Resistant)	False Negative Rate
Carbapenems	0.921±0.013	0.626	0.035
BL/BLI	0.786±0.033	0.560	0.156
3rd Gen Cephalosporins	0.737±0.022	-	0.262
Fluoroquinolones	0.732±0.029	0.639	0.341

Notably, carbapenem resistance prediction has best AUROC (0.921) and low false negative rate (3.5%).

Installation:

• PyPI: pip install respredai
• Source: git clone https://github.com/EttoreRocchi/ResPredAI.git && cd ResPredAI && pip install -e ".[dev]"

Configuration: Use INI file (e.g., data path, models, folds).

Advanced Features: Probability calibration (sigmoid/isotonic), threshold optimization (Youden index/F1/cost-sensitive), uncertainty quantification (conformal prediction), subgroup analysis (ward/gender), temporal validation (prospective simulation).

Optimize Empirical Treatment: After MALDI-TOF identification (12h post blood culture positive), ResPredAI provides resistance risk to help: identify high-risk patients needing antibiotic upgrade, avoid overusing broad-spectrum for low-risk, make informed decisions while waiting for药敏.

Complement Rapid Tests: Supplements genotypic tests (which can't explain phenotype expression, have limitations in non-fermenters like Pseudomonas aeruginosa).

Support Antibiotic Stewardship: Helps centers without 24/7 microbiologist/infectious disease expert access, implement precise stewardship, reduce unnecessary broad-spectrum use.

Limitations: 1. Regional specificity (trained on Italian single-center data; needs local validation in other epidemiological settings). 2. Time drift (resistance patterns change over time; model needs regular updates).3. Data dependency (relies on accurate MALDI-TOF and complete clinical data).

Future: Open-source workflow encourages other institutions to validate with local data; aim to improve model adaptability and maintain accuracy over time.