Optimising antibiotic switching via forecasting of patient physiology

TL;DR

Using neural processes to model vital sign trajectories probabilistically, predicting switch-readiness by comparing forecasts against clinical guidelines rather than learning from past actions, and ranking patients to prioritise clinical review demonstrate that forecasting patient physiology offers a principled foundation for decision support in antibiotic stewardship.

Abstract

Timely transition from intravenous (IV) to oral antibiotic therapy shortens hospital stays, reduces catheter-related infections, and lowers healthcare costs, yet one in five patients in England remain on IV antibiotics despite meeting switching criteria. Clinical decision support systems can improve switching rates, but approaches that learn from historical decisions reproduce the delays and inconsistencies of routine practice. We propose using neural processes to model vital sign trajectories probabilistically, predicting switch-readiness by comparing forecasts against clinical guidelines rather than learning from past actions, and ranking patients to prioritise clinical review. The design yields interpretable outputs, adapts to updated guidelines without retraining, and preserves clinical judgement. Validated on MIMIC-IV (US intensive care, 6,333 encounters) and UCLH (a large urban academic UK hospital group, 10,584 encounters), the system selects 2.2-3.2$\times$ more relevant patients than random. Our results demonstrate that forecasting patient physiology offers a principled foundation for decision support in antibiotic stewardship.

Figures (8)

• Figure 1: The proposed AI-enabled IVOS system. A) Training a probabilistic neural process model to forecast patient trajectories. B) Using the trained model to generate likely trajectories over the following day and applying clinical criteria to determine the proportion of samples ready to be switched. C) Presentation of switch-readiness probabilities as a ranked list of patients for review.
• Figure 2: Forecasting performance on the MIMIC data for each variable with increasing forecast horizon for NP model and baselines. Note horizontal axis scale is logarithmic. Error bars show 95% confidence. Variable Abbreviations: HR (Heart Rate), RR (Respiratory Rate), $\text{SpO}_2$ (Oxygen Saturation), SBP (Systolic Blood Pressure), Temp. (Temperature). Model abbreviations: GBDT (Gradient Boosted Decision Trees), NP (Neural Process).
• Figure 3: Comparison of model performance curves for both datasets. From left to right the plots show Receiver Operating Characteristic, precision-recall, and calibration curves. For the former two the dashed line shows the performance of a chance model, for the latter it shows a perfect model. For the calibration curve, aggregation bins are the quantiles of the distribution of predicted probabilities; the small proportion of positive examples in MIMIC means the curves do not span the full range of the plot. The shaded region indicates 95% confidence. Model abbreviations: GBDT (Gradient Boosted Decision Trees), NP (Neural Process).
• Figure 4: Ranking performance on the MIMIC data for each variable with increasing forecast horizon for NP models and baselines. Horizontal axis scale is logarithmic. Error bars show 95% confidence. Note that the classes become more imbalanced as the forecast horizon increases, with 20.1%, 12.2%, and 7.8% of the instances being positive for the 6, 12, and 24 hour horizons respectively. Model abbreviations: GBDT (Gradient Boosted Decision Trees), NP (Neural Process).
• Figure 5: Example encounter from the MIMIC test set illustrating the intended functioning of the system. The panels show (from top to bottom): predicted switch-readiness probabilities from the NP-tuned model; labels indicating oral (PO) and IV antibiotic prescriptions and whether clinical criteria are satisfied in each forecast window; measured vital signs with clinical thresholds for switch-readiness overlaid; and antibiotic prescription timelines, with colour and pattern indicating administration route. Grey vertical lines demarcate daily 12-hour forecast windows. Forecasts begin 48 hours after admission to ensure sufficient historical data for prediction. Variable Abbreviations: HR (Heart Rate), RR (Respiratory Rate), $\text{SpO}_2$ (Oxygen Saturation), SBP (Systolic Blood Pressure), Temp. (Temperature). Antibiotic Abbreviations: Sulfameth/Trimeth (Sulfamethoxazole-Trimethoprim).