Development and Validation of a NEWS2-Enhanced Multivariable Prediction Model for Clinical Deterioration and In-Hospital Mortality in Hospitalized Adults

Background and Objectives: Early identification of patients at risk of clinical deterioration is essential for optimizing therapeutic management and improving outcomes in general medicine wards. The National Early Warning Score 2 (NEWS2) is a validated tool for predicting patient worsening but integrating it with additional clinical and demographic data can enhance its predictive accuracy and support timely clinical decisions. Material and methods: In this retrospective cohort study, 2108 patients admitted to the general medicine department of the University Hospital of Siena were analyzed. Logistic regression models incorporating NEWS2 alongside key clinical variables-including age, presence of central venous catheter (CVC), and functional status measured by the Barthel Index-were developed to predict high clinical risk (HCR) and mortality. Model performance was assessed using the area under the ROC curve (AUC). Results: High clinical risk status developed in 29% of patients. Older age, presence of CVC, lower Barthel Index, and higher NEWS2 scores were significantly associated with both HCR and mortality. The integrated predictive model demonstrated good accuracy, with an AUC of 0.798 for HCR and 0.716 for mortality prediction. Conclusions: This study suggests that NEWS2, when combined with additional patient-specific variables from the electronic health record, can become a more sophisticated tool for early risk stratification. Such a tool has the potential to support timely clinical intervention and optimized therapeutic management, potentially contributing to improved patient outcomes. While the model may indirectly support nurse workload balancing by identifying patients requiring intensified care, its ultimate impact on patient outcomes requires confirmation through prospective studies.