Development of an early mortality risk prediction model for pediatric patients with secondary hemophagocytic lymphohistiocytosis.

Background: To establish a visual prediction model for the risk of early mortality in pediatric patients with secondary hemophagocytic lymphohistiocytosis (sHLH).

Methods: A retrospective analysis was conducted on 128 pediatric sHLH cases diagnosed at the Affiliated Hospital of Zunyi Medical University between January 2012 and April 2023. Logistic regression analysis was employed to identify prognostic factors for pediatric patients with sHLH. A nomogram prediction model was constructed using R software. The receiver operating characteristic (ROC) curve, calibration curve, decision clinical curve (DCA), and clinical impact curve (CIC) were plotted to evaluate the discriminative power and clinical application value of the prediction model.

Results: Among the 128 pediatric patients with sHLH included in the study, 90 (70.31%) were associated with infections, and 37 (28.9%) died within 30 days after admission. In the fully adjusted model, central nervous system involvement (CNSI) (OR = 9.496, 95% CI = 2.965-30.410), prognostic nutrition index (PNI) (OR = 0.931, 95% CI = 0.872-0.994), and activated partial thromboplastin time (APTT) (OR = 1.029, 95% CI = 1.007-1.052) were identified as independent risk factors affecting early mortality risk in pediatric patients with sHLH, while the use of blood purification combined with HLH-94/2004 treatment (OR = 0.097, 95% CI = 0.024-0.395) was an independent protective factor. Based on these independent prognostic factors, a nomogram prediction model was constructed. The ROC curve (AUC = 0.880) and calibration curve (χ² = 10.12, P = 0.257) demonstrated good discriminability and fit. The DCA curve and CIC curve indicated that the model had good clinical applicability.

Conclusion: The nomogram model has good discriminability and accuracy in predicting the risk of early death in pediatric patients with sHLH.