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. 2024 Dec 20;24(1):721.
doi: 10.1186/s12872-024-04359-z.

Serum creatinine to cystatin C ratio in relation to heart failure with preserved ejection fraction

Ruting Wang #  1 , Kai Huang #  1 , Hangfeng Ying  1 , Jiahao Duan  1 , Qinwen Feng  1 , Xinying Zhang  2 , Zifeng Wu  2 , Riyue Jiang  3 , Bin Zhu  4 , Ling Yang  5 , Chun Yang  6
Affiliations

Serum creatinine to cystatin C ratio in relation to heart failure with preserved ejection fraction

Ruting Wang et al. BMC Cardiovasc Disord. .

Abstract

Background: Sarcopenia was recognized to be one of the common comorbidities in heart failure (HF). The sarcopenia index (SI), based on serum creatinine to cystatin C ratio, was developed as a simple tool to evaluate skeletal muscle mass but has not been well-studied in the correlation of left ventricular ejection fraction (LVEF). The aim of this study is to analyze the SI in patients with HF, especially patients with HF with preserved ejection fraction (HFpEF), and to develop a prediction model for HFpEF.

Methods: The training cohort included 229 hospitalized patients with HF and 73 healthy controls (HCs) from the Third Affiliated Hospital of Soochow University between December 2019 and February 2022. An additional 78 patients with HF hospitalized at the same hospital between March 2022 to June 2023 were considered as an external validation cohort. 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 Bootstrap with 500 resamples.

Results: SI was significantly different between the HF and HC groups (67.95 ± 13.07 vs. 98.57 ± 31.51) and had a significant negative correlation with LVEF. Multivariate logistic regression demonstrated that SI (OR 0.948, 95% CI 0.914-0.983, P = 0.004) was independently associated with HFpEF. The area under the curve (AUC) for the nomogram constructed based on SI was 0.902. The calibration curve was approximately distributed along the reference line in Bootstrap and the decision curve analysis demonstrated significantly better net benefit in the model. The external validation proved the good predictive performance of the model.

Conclusions: Lower SI is an independent factor associated with hospitalized patients with HF, especially patients with HFpEF. A prediction nomogram based on SI has good predictive power for HFpEF.

Trial registration: The study was registered with the China Clinical Trials Centre Registry (registration number: ChiCTR2200063401).

Keywords: Heart failure; Prediction model; Preserved ejection fraction; Sarcopenia index.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The study was approved by the Ethics Committee of the Third Affiliated Hospital of Soochow University (20/07/2021, registration number: 2021 Scientific Research Project No. 139) and registered with the China Clinical Trials Centre Registry (06/09/2022, registration number: ChiCTR2200063401). All methods were carried out in accordance with the Declaration of Helsinki and written informed consents were obtained from all participants. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
LASSO for predictors associated with HFpEF in patients with HF. LASSO, least absolute shrinkage and selection operator; HF, heart failure; HFpEF, heart failure with preserved ejection fraction
Fig. 2
Fig. 2
Nomogram for the prediction model of HFpEF. AF, atrial fibrillation; Hb, hemoglobin; HFpEF, heart failure with preserved ejection fraction; LnNT-proBNP, natural logarithm of N-terminal brain natriuretic peptide precursor; SBP, systolic blood pressure; SI, sarcopenia index
Fig. 3
Fig. 3
Evaluation and validation of the prediction model. A The ROC curve of the training cohort. B The ROC curve of the validation cohort. C The calibration curve of the training cohort. D The calibration curve of the validation cohort. E The DCA curve of the training cohort. F The DCA curve of the validation cohort. DCA, decision curve analysis; ROC, receiver-operating-characteristics
See this image and copyright information in PMC

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