Episode- and Hospital-Level Modeling of Pan-Resistant Healthcare-Associated Infections (2020–2024) Using TabTransformer and Attention-Based LSTM Forecasting

<b>Background</b>: Pan-drug-resistant (PDR) Acinetobacterinfections are an escalating ICU threat, demanding both patient-level triage and facility-wide forecasting. <b>Objective</b>: The aim of this study was to build a dual-scale AI framework that (i) predicts PDR status at infection onset and (ii) forecasts hospital-level PDR burden through 2027. <b>Methods</b>: We retrospectively analyzed 270 <i>Acinetobacter</i> infection episodes (2020-2024) with 65 predictors spanning demographics, timelines, infection type, resistance-class flags, and a 25-drug antibiogram. TabTransformer and XGBoost were trained on 2020-2023 episodes (<i>n</i> = 210), evaluated by stratified 5-fold CV, and externally tested on 2024 episodes (<i>n</i> = 60). Metrics included AUROC, AUPRC, accuracy, and recall at 90% specificity; AUROC was optimism-corrected via 0.632 + bootstrap and DeLong-tested for drift. SHAP values quantified feature impact. Weekly PDR incidence was forecast with an attention-LSTM model retrained monthly (200 weekly origins, 4-week horizon) and benchmarked against seasonal-naïve, Prophet, and SARIMA models (MAPE and RMSE). Quarterly projections (TFT-lite) extended forecasts to 2027. <b>Results</b>: The CV AUROC was 0.924 (optimism-corrected 0.874); an ensemble of TabTransformer + XGBoost reached 0.958. The 2024 AUROC fell to 0.586 (<i>p</i> < 0.001), coinciding with a PDR prevalence drop (75→38%) and three covariates with PSIs > 1.0. Isotonic recalibration improved the Brier score from 0.326 to 0.207 and yielded a net benefit equivalent to 26 unnecessary isolation-days averted per 100 ICU admissions at a 0.20 threshold. SHAP highlighted Ampicillin/Sulbactam resistance, unknown acquisition mode, and device-related infection as dominant drivers. The attention-LSTM achieved a median weekly MAE of 0.10 (IQR: 0.028-0.985) vs. 1.00 for the seasonal-naïve rule, outperforming it on 48.5% of weeks and surpassing Prophet and SARIMA (MAPE = 6.2%, RMSE = 0.032). TFT-lite projected a ≥ 25% PDR tipping point in 2025 Q1 with a sustained rise in 2027. <b>Conclusions</b>: The proposed framework delivers explainable patient-level PDR risk scores and competitive 4-week and multi-year incidence forecasts despite temporal drift, supporting antimicrobial stewardship and ICU capacity planning. Shrinkage and bootstrap correction were applied to address the small sample size (EPV = 2.1), which poses an overfitting risk. Continuous recalibration and multi-center validation remain priorities.

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Journal
Diagnostics
Published
2025-08-25
DOI
https://doi.org/10.3390/diagnostics15172138
Citations
1
Primary Topic
Antibiotic Resistance in Bacteria
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article
Field-Weighted Citation Impact
0.59
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article

Episode- and Hospital-Level Modeling of Pan-Resistant Healthcare-Associated Infections (2020–2024) Using TabTransformer and Attention-Based LSTM Forecasting

Nicoleta Luchian, Mădălina Duceac, Lăcrămioara Ochiuz, Constantin Marcu et al.
1 citations
Diagnostics
Antibiotic Resistance in Bacteria
0.59
article

Episode- and Hospital-Level Modeling of Pan-Resistant Healthcare-Associated Infections (2020–2024) Using TabTransformer and Attention-Based LSTM Forecasting

Nicoleta Luchian, Mădălina Duceac, Lăcrămioara Ochiuz, Constantin Marcu, Călin Gheorghe Buzea, Dragoș Ioan Rusu, Madălina Nicoleta Matei, Alina Pleșea-Condratovici, Ciprian Adrian Dinu, Eva Maria Elkan, Letiția Doina Duceac, Camer Salim
article en
1 citations

Abstract

<b>Background</b>: Pan-drug-resistant (PDR) Acinetobacterinfections are an escalating ICU threat, demanding both patient-level triage and facility-wide forecasting. <b>Objective</b>: The aim of this study was to build a dual-scale AI framework that (i) predicts PDR status at infection onset and (ii) forecasts hospital-level PDR burden through 2027. <b>Methods</b>: We retrospectively analyzed 270 <i>Acinetobacter</i> infection episodes (2020-2024) with 65 predictors spanning demographics, timelines, infection type, resistance-class flags, and a 25-drug antibiogram. TabTransformer and XGBoost were trained on 2020-2023 episodes (<i>n</i> = 210), evaluated by stratified 5-fold CV, and externally tested on 2024 episodes (<i>n</i> = 60). Metrics included AUROC, AUPRC, accuracy, and recall at 90% specificity; AUROC was optimism-corrected via 0.632 + bootstrap and DeLong-tested for drift. SHAP values quantified feature impact. Weekly PDR incidence was forecast with an attention-LSTM model retrained monthly (200 weekly origins, 4-week horizon) and benchmarked against seasonal-naïve, Prophet, and SARIMA models (MAPE and RMSE). Quarterly projections (TFT-lite) extended forecasts to 2027. <b>Results</b>: The CV AUROC was 0.924 (optimism-corrected 0.874); an ensemble of TabTransformer + XGBoost reached 0.958. The 2024 AUROC fell to 0.586 (<i>p</i> < 0.001), coinciding with a PDR prevalence drop (75→38%) and three covariates with PSIs > 1.0. Isotonic recalibration improved the Brier score from 0.326 to 0.207 and yielded a net benefit equivalent to 26 unnecessary isolation-days averted per 100 ICU admissions at a 0.20 threshold. SHAP highlighted Ampicillin/Sulbactam resistance, unknown acquisition mode, and device-related infection as dominant drivers. The attention-LSTM achieved a median weekly MAE of 0.10 (IQR: 0.028-0.985) vs. 1.00 for the seasonal-naïve rule, outperforming it on 48.5% of weeks and surpassing Prophet and SARIMA (MAPE = 6.2%, RMSE = 0.032). TFT-lite projected a ≥ 25% PDR tipping point in 2025 Q1 with a sustained rise in 2027. <b>Conclusions</b>: The proposed framework delivers explainable patient-level PDR risk scores and competitive 4-week and multi-year incidence forecasts despite temporal drift, supporting antimicrobial stewardship and ICU capacity planning. Shrinkage and bootstrap correction were applied to address the small sample size (EPV = 2.1), which poses an overfitting risk. Continuous recalibration and multi-center validation remain priorities.

DiagnosticsVol. 15(17)
Ovidius University (RO), Grigore T. Popa University of Medicine and Pharmacy (RO), "Dunarea de Jos" University of Galati (RO), Clinica de Pneumologie Iaşi (RO), University of Bacău (RO), National Institute of Research and Development for Technical Physics (RO)
Openalex Percentile: Top 31%
Antibiotic Resistance in Bacteria
0.59
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