Validation of Predictors of Fall Events in Hospitalized Patients With Cancer

A seven-item cancer-specific fall risk tool (Cleveland Clinic Capone-Albert [CC-CA] Fall Risk Score) was shown to have a strong concordance index for predicting falls; however, validation of the model is needed.

The aims of this study were to validate that the CC-CA Fall Risk Score, made up of six factors, predicts falls in patients with cancer and to determine if the CC-CA Fall Risk Score performs better than the Morse Fall Tool.

Using a prospective, comparative methodology, data were collected from electronic health records of patients hospitalized for cancer care in four hospitals. Risk factors from each tool were recorded, when applicable. Multivariable models were created to predict the probability of a fall. A concordance index for each fall tool was calculated.

The CC-CA Fall Risk Score provided higher discrimination than the Morse Fall Tool in predicting fall events in patients hospitalized for cancer management.