Some medical patients are at greater risk of adverse outcomes than others and may benefit from higher observation hospital units. We constructed and validated a model predicting adverse hospital outcome for patients. Study results may be used to admit patients into planned tiered care units. Adverse outcome comprised death or cardiac arrest during the first 30 days of hospitalisation, or transfer to intensive care within the first 48 h of admission.
The study took place at two tertiary teaching hospitals and two community hospitals in Winnipeg, Manitoba, Canada.
We analysed data from 4883 consecutive admissions at a tertiary teaching hospital to construct the Early Prediction of Adverse Hospital Outcome for Medical Patients (ALERT) model using logistic regression. Robustness of the model was assessed through validation performed across four hospitals over two time periods, including 65 640 consecutive admissions.
Receiver-operating characteristic curves (ROC) and sensitivity and specificity analyses were used to assess the usefulness of the model.
9.3% of admitted patients experienced adverse outcomes. The final model included gender, age, Charlson Comorbidity Index, Activities of Daily Living Score, Glasgow Coma Score, systolic blood pressure, respiratory rate, heart rate and white cell count. The model was discriminative (ROC=0.83) in predicting adverse outcome. ALERT accurately predicted 75% of the adverse outcomes (sensitivity) and 75% of the non-adverse outcomes (specificity). Applying the same model to each validation hospital and time period produced similar accuracy and discrimination to that in the development hospital.
Used during initial assessment of patients admitted to general medical wards, the ALERT scale may complement other assessment measures to better screen patients. Those considered as higher risk by the ALERT scale may then be provided more effective care from action such as planned tiered care units.