Affiliation:
1. Third Affiliated Hospital of Soochow University: Changzhou First People's Hospital
2. Nanjing Medical University affiliated Nanjing Hospital: Nanjing First Hospital
Abstract
Abstract
Aims
The aim of this study is to analyze the sarcopenia index (SI), based on serum creatinine to cystatin C ratio, in heart failure (HF) patients, especially HF with preserved ejection fraction (HFpEF) patients, and to develop a prediction model for the diagnosis of HFpEF.
Methods
There were 229 HF patients and 73 healthy controls (HCs) enrolled in this study. Binary logistic regression model was used to analyze the influence factors of HFpEF. A prediction model was constructed and optimized based on the least absolute shrinkage and selection operator (LASSO), displayed by nomogram and verified internally by the bootstrap sampling method (Bootstrap).
Results
SI was significantly different between the HF and HC groups (67.9 ± 13.0 vs. 98.6 ± 31.5). Atrial fibrillation (AF) (OR 6.336, 95% CI 2.511-15.988, P < 0.001) and SI (OR0.948, 95% CI 0.914-0.983, P = 0.004) were independently associated with HFpEF. Nine indicators, including SI, were included in the prediction model. The area under the curve (AUC) was 0.902. In Bootstrap (500 resamples), the calibration curve was distributed approximately along the reference line. The prediction models with the additional features of AF and SI showed a significantly higher value of AUC (0.902 vs. 0.855, P < 0.01).
Conclusions
Low SI is an independent risk factor for hospitalized HF patients, especially HFpEF patients. HFpEF was better identified using this diagnostic prediction model, and the diagnostic efficacy of the model was significantly improved by two features, including SI and AF.
Publisher
Research Square Platform LLC