Abstract
Background: Hypertrophic cardiomyopathy (HCM) is a prevalent condition that often coexists with chronic kidney disease (CKD), significantly impacting patient prognosis. This study aimed to investigate the predictive value of complete blood cell counts derived inflammatory indicators in assessing CKD risk in HCM patients. Methods: This study enrolled HCM patients and categorized them into CKD and non-CKD group based on discharge diagnoses. Analyzed indicators included systemic inflammation response index (SIRI), systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). Least absolute shrinkage and selection operator (LASSO) logistic and multivariable logistic regression were employed to identified independent risk factors for CKD, which were subsequently utilized to develop a nomogram. Results: A total of 1795 HCM patients were included, including 112 (6.24%) individuals assigned to the CKD group. In univariate analyses, NLR (AUC: 0.755; 95%CI: 0.711-0.800) demonstrated superior accuracy compared to others. Eighteen baseline characteristics exhibiting statistical difference were incorporated into LASSO-logistic regression. Six factors were further selected by multivariable logistic regression. The results identified male gender (OR: 2.622; 95% CI: 1.565-4.393, p < 0.001), Hb (OR: 0.972; 95% CI: 0.962-0.981, p < 0.001), Pro-BNP (OR: 1.000; 95% CI: 1.000-1.000, p < 0.001), SIRI (OR: 1.037; 95% CI: 1.026-1.049, p < 0.001), and SII (OR: 1.000; 95% CI: 1.000-1.001, p = 0.003) as risk factors. These five factors were used to construct a nomogram, which exhibited good accuracy and consistency. Conclusions: Male gender, Hb, Pro-BNP, SIRI, and SII were identified as risk factors for CKD risk in HCM patients. A nomogram was developed using these factors, which may facilitate early identification and management of high-risk individuals.