ChronoSynthNet: a dual-task deep learning model development and validation study for predicting real-time norepinephrine dosage and the early detection of hypotension in patients with septic shock.

BACKGROUND

In intensive care units (ICUs), managing septic shock requires maintaining adequate tissue perfusion with vasopressors, most commonly norepinephrine, while avoiding under or over-dosing that can worsen hypotension, organ injury, and adverse effects. Bedside vasopressor titration often depends on clinician judgment and simple rules, with limited tools providing individualized, time-aware guidance or early warning of impending hypotension. ChronoSynthNet aimed to create a data-driven model that learns from routine electronic health record (EHR) time-series data to personalize vasopressor therapy and anticipate deterioration. To develop and validate a dual-task deep learning model that predicts real-time norepinephrine requirements and detects hypotension early in adults with septic shock.

METHODS

We performed a retrospective cohort analysis using the Medical Information Mart for Intensive Care [MIMIC-IV (2008-2019)] database. Eligible adult ICU stays met Sepsis-3 criteria, received norepinephrine, and had adequate time-series data. ChronoSynthNet integrates a shared Transformer encoder, long short-term memory (LSTM) layers, and a dynamic feature-weighting network to learn cross-variable and temporal relationships. The dataset was split 80/20 into training and internal test sets, with five-fold cross-validation on training data. Classification performance for early hypotension detection was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), precision, recall, and specificity; norepinephrine rate prediction performance was assessed using mean squared error (MSE). Ninety-five percent confidence intervals (95% CIs) were calculated for AUROC, recall, and specificity on the internal test set using bootstrap and Wilson methods.

RESULTS

ChronoSynthNet achieved AUROC of 0.89 (95% CI: 0.836-0.938) for hypotension classification and MSE of 0.0213 (95% CI: 0.0192-0.0234) for predicting the norepinephrine infusion rate. The model demonstrated high specificity (97%, 95% CI: 96.3-98.3%) and precision (92%, 95% CI: 90.3-93.7%), with a recall of 74% (95% CI: 71.3-76.7%). Hypotension events were predicted a median of 3.5 hours in advance.

CONCLUSIONS

ChronoSynthNet demonstrated strong performance in early hypotension detection and norepinephrine dose forecasting in ICU patients with septic shock. These findings support its potential role in aiding real-time vasopressor titration and early recognition of hemodynamic instability; prospective multicenter validation is needed before clinical deployment.