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.