Derivation and Validation of a Clinical Deterioration Early Warning System (Cdews) Score in Hospital Inpatients

Electronic health records allow access to data on potential predictors of clinical deterioration in hospital inpatients beyond vital signs, e.g., lab test results and comorbidities. Using such data, we aimed to develop and validate a model to predict clinical deterioration. A retrospective cohort study was conducted with information on vital signs, comorbidities, and lab test results for consecutive over-17-year-olds hospitalized in Galdakao-Usansolo University Hospital. Deterioration was defined as death or unplanned admission to an intermediate/intensive care unit. A multivariate generalized linear mixed model was constructed using a derivation dataset, adjusting for the random effect of the patient. We calculated risk scores and categories and the area under the receiver operating characteristic curve (AUC) for this cohort, and also for an external validation cohort, to test their validity with external data. The model was calibrated in the derivation dataset considering 10 decile-based groups. The score was calibrated in both datasets. Overall, 6,372 hospitalizations of 9,084 patients (70.5%) were included in the derivation dataset and 7,812 of 10,531 patients (74.2%) in the validation dataset. In these sets, 5% and 6% of patients respectively reached the composite endpoint ( p value = 0.02). Older patients, and those with polypharmacy and/or neoplasms were more likely to deteriorate. Deterioration was associated with higher blood pressure and C-reactive protein and lower oxygen saturation, glycemia, and potassium levels. The final model´s AUC was 0.83 (0.81-0.85). We provide a reliable easy-to-implement score to predict death or unexpected ICU admission; further research is needed to assess potential clinical benefits. ClinicalTrials.gov ID: NCT06499376Registered at 2024-07-12 (retrospectively registered)