Development of a diagnostic model focusing on nutritional indicators for frailty classification in people with chronic heart failure

Frailty has a great impact on the quality of life of patients with chronic heart failure (CHF), which needs to be judged in time. To develop a diagnostic model based on nutritional indicators to judge the frailty status of patients with chronic heart failure (Frailty-CHF).

In the data collection part of this study, questionnaire method and biomedical measurement method were adopted. The trace elements in serum samples were detected by high performance liquid chromatography, chemiluminescence, and inductively coupled plasma mass spectrometry. We used Excel for data consolidation, and then imported the data into R software for modelling. Lasso method was used for variable screening, and Logistics regression fitting model was used after variables were determined. The internal validation of the model was completed by Bootstrap re-sampling. A total of 123 patients were included in this study. After variables’ screening, age, nutritional status-heart failure, New York Heart Association Functional Class (NYHA), micronutrients B12, Ca, folic acid, and Se were included in the model, the c statistic and Brier score of the original model were 0.9697 and 0.0685, respectively. After Bootstrap re-sampling adjustment, the c statistic and Brier score were 0.8503 and 0.1690.

In this study, a diagnostic model of age, nutritional status-heart failure, NYHA, the micronutrients B12, Ca, folic acid, and Se was established. It could help healthcare professionals better identify the frailty status in patients with CHF.