Classification and Regression Tree Analysis in Adult Hemophagocytic Syndrome: Identifying Acute Heart Failure Predictors in ICU Patients.

BACKGROUND

Hemophagocytic syndrome (HPS) is a rare but life-threatening condition often complicated by heart failure (HF). This study aimed to identify predictors of acute heart failure in HPS patients using classification and regression tree (CART) analysis.

METHODS

A retrospective analysis was performed on 146 HPS patients without a diagnosis of HF. Variables such as age, cardiothoracic ratio (CTR), etiology, N-terminal pro-brain natriuretic peptide (NT-proBNP), C-reactive protein (CRP), ferritin, triglyceride, and lactate dehydrogenase (LDH) levels at diagnosis were included. CART analysis was employed to develop models predicting heart failure status. The model's performance was evaluated using sensitivity, specificity, and overall accuracy.

RESULTS

This study identified several key predictors of acute heart failure in HPS patients. CTR emerged as the most significant predictor, with patients exhibiting a higher ratio being at a greater risk of developing heart failure. NT-proBNP levels and CRP levels were also significant predictors, indicating cardiac stress and systemic inflammation. Age and etiology played crucial roles, with older patients and those with rheumatological causes showing a higher susceptibility to heart failure. The CART models demonstrated good accuracy, with CTR being the most important predictor.

CONCLUSION

This study highlights critical factors, such as CTR, NT-proBNP, CRP levels, age, and etiology in predicting acute heart failure in HPS patients. Early identification of these predictors can facilitate timely interventions, potentially improving outcomes and reducing mortality rates. These findings provide valuable insights for clinical practice and pave the way for further research on acute heart failure management in HPS.