Jenny Alderden recently had her manuscript, “Predicting Pressure Injury Among Critical Care Patients: A Machine Learning Model,” published to the highly regarded health care journal, American Journal of Critical Care (AJCC).
The purpose of her research was to develop a model that predicts pressure injury development among surgical critical care patients. This research is important because hospital-acquired pressure injuries (HAPI) are a serious problem among critical care patients. Some HAPI can be prevented using measures such as specialty beds, which are not feasible for every patient due to cost. However, decisions about which patient would benefit most from a specialty bed are problematic because existing pressure injury risk-identification tools identify most critical-care patients as “high risk.” This machine learning approach is different from other available models because it does not require clinicians to input information into a tool, such as the Braden Scale, and instead relies on information readily available in the electronic health record. Next steps for her research include testing an independent sample, followed by calibration to optimize specificity.
AJCC is a peer-review journal that strives to deliver clinical content to its readers and provide information on how to improve the care of patients and their families.
Read “Predicting Pressure Injury Among Critical Care Patients: A Machine Learning Model.”