The predictive role of identifying frailty in assessing the need for palliative care in the elderly: the application of machine learning algorithm.

backgroundPalliative care is a key component of integrated care to improve care quality and reduce hospitalization costs for patients with chronic obstructive pulmonary disease (COPD). This study aims to use machine learning algorithms to create an effective approach to the early recognition and identification of frailty as a long-term condition in COPD patients.

methodsThe level of frailty in a sample of patients (total n = 140) was assessed using the checklist of frailty assessment, which encompasses five questions: measured decrease in body mass index (BMI), fatigue status, physical activity status, and walking speed. The last question assessed disability through forced expiratory volume in the first second (FEV1) measured using spirometry results. The next checklist was the Palliative Care Needs Assessment Tool, taken from the assessment checklist for palliative care needs in patients with COPD by Thoenesen et al. [28]. We used different machine learning algorithms, with performance assessed using an area under the receiver-operating characteristic curve, sensitivity, and specificity, to develop a validated set of criteria for frailty using machine learning.

resultsStudy findings revealed that the palliative care needs assessment tool categorized 74% of all patients into two groups: those requiring palliative care and those not requiring it. Furthermore, the influential variables that contributed to predicting the need for palliative care included measured BMI reduction, fatigue status, physical activity level, slow walking, and FEV1. The super-learning model demonstrated higher accuracy (92%) than other machine-learning algorithms.

conclusionThe study highlights the need for more collaboration between clinicians and data scientists to use the potential of data collected from COPD patients in clinical settings with the purpose of early identification of frailty as a long-term condition. Predicting palliative care needs accurately is critical in these contexts, as it can lead to better resource allocation, improved healthcare delivery, and enhanced patient outcomes.