Development and Validation of VC-MAES and VC-SEPS: Deep Learning-Based Early Warning Systems for Hospitalized Patients

The timely detection of ward deterioration--including unplanned intensive care unit (ICU) transfer, cardiac arrest, death, and sepsis--remains an unmet need. Although rule-based early warning scores and newer machine-learning models have been introduced, their clinical adoption is limited owing to challenges such as low predictive performance, excessive false alarms, and lack of generalizability. This study aimed to develop two deep-learning models for patients in general wards using a bidirectional long short-term memory neural network architecture: (i) the VitalCare-Major Adverse Event Score (VC-MAES), which predicts clinical deterioration events (CDEs)-- unplanned ICU transfer, cardiac arrest, or in-hospital death--within 6 h and (ii) the VitalCare-SEPsis Score (VC-SEPS), which predicts sepsis onset within 4 h. Additionally, we sought to externally validate the performance of the models in an independent cohort. This study was conducted in two sequential phases. First, the VC-MAES and VC-SEPS models were developed using a large retrospective cohort from Yonsei Severance Hospital, Seoul, Republic of Korea. Second, external validation was performed in a single-center cohort at National Health Insurance Service Ilsan Hospital (NHIS Ilsan Hospital), Ilsan, Republic of Korea. Both algorithms incorporated patient age, vital signs, laboratory results, and the Glasgow Coma Scale scores. VC-MAES performance was compared with the Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS), whereas VC-SEPS performance was compared with the Sequential Organ Failure Assessment (SOFA), quick SOFA (qSOFA), and NEWS, using the area under the receiver operating characteristic curve (AUROC) as the primary performance metric. The derivation cohort comprised 357,009 adult general-ward admissions at Yonsei Severance Hospital (2013-2017), and the external validation cohort included 22,073 admissions at NHIS Ilsan Hospital (2017). In the external validation cohort, the VC-MAES predicted CDEs within 6 h with an AUROC of 0.918 (95% confidence interval [CI], 0.909-0.927), outperforming the MEWS (0.834; 95% CI, 0.820-0.849) and NEWS (0.883; 95% CI, 0.869- 0.896). VC-SEPS predicted sepsis onset within 4 h, with an AUROC of 0.941 (95% CI, 0.934-0.947), surpassing the SOFA (0.559; 95% CI, 0.546-0.571), qSOFA (0.687; 95% CI, 0.671-0.704), and NEWS (0.767; 95% CI, 0.748-0.785). Both models maintained AUROC values above 0.86 across all age and sex categories. The VC-MAES and VC-SEPS outperformed conventional early warning scores in predicting clinical deterioration events and sepsis. These models can enable earlier and more precise interventions, enhancing patient care and optimizing resource use, ultimately leading to better patient outcomes without overwhelming healthcare providers.