New Model Predicts Early Death in Childhood Hemophagocytic:Lymphohistiocytosis: based on Optimized HScore Parameters (Preprint)
Authors: HLH Research Group li , Xiao Li , Wan Yiran , Xu Ximing , Dou Ying , Guan Xianmin , Guo Yuxia , Wen Xianhao , Meng Yan , Liao Meiling , Hu Qinshi , Yu Jie 
Published: February 2025

BACKGROUND

Hemophagocytic lymphohistiocytosis (HLH) is a rare and life-threatening condition predominantly affecting children, characterized by hyperinflammation and high early mortality. While the HScore criteria, developed by Fardet et al. in 2014, have been widely used to diagnose HLH, recent studies have also explored its potential to predict disease severity in COVID-19 patients, with findings indicating that higher HScore values correlate with increased mortality risk and may identify patients who could benefit from immunomodulatory treatment.However, the potential of HScore-related indicators in predicting early prognosis in patients with HLH has not been reported.

OBJECTIVE

This study aimed to investigate the association between HScore scores and related indicators and early mortality in children with Hemophagocytic Lymphohistiocytosis (HLH), and to develop a Cox prediction model for early mortality based on optimized HScore parameters to assist medical professionals in better risk stratification at the time of diagnosis.

METHODS

This retrospective study included 624 children diagnosed with HLH admitted to the Children's Hospital of Chongqing Medical University between January 2006 and December 2022. Five Cox prediction models were developed: Model 1 used HScore values; Model 2 used original HScore grouping criteria; Model 3 used raw HScore parameters; Model 4 grouped HScore parameters by optimal cut-offs; Model 5 combined optimized HScore parameters with common prognostic indicators (total protein, TC, APTT, BUN, ferritin). Model performance was assessed using AUC and calibration curves. An interactive web application was developed using R and Shiny for clinical use.

RESULTS

The optimal cut-off value for the HScore was determined to be 223, which stratified children into high-risk and low-risk groups. The estimated 30-day survival rates were 77.9% (95% CI: 73.6%-82.5%) for the low-risk group and 65.8% (95% CI: 60.5%-71.7%) for the high-risk group (P < 0.001). Model 5 demonstrated the highest AUC on both training and validation datasets, with values of 0.881 (95% CI: 0.818-0.942) and 0.813 (95% CI: 0.747-0.881), respectively. Factors associated with increased early mortality risk included hemoglobin levels <61 g/L, platelet counts <31×10^9/L, ferritin levels ≥1318 ng/mL, total protein levels ≥52.3 g/L, AST levels >460 U/L, total cholesterol levels <3.23 mmol/L, APTT levels >60.2s, and bone marrow phagocytosis. To facilitate clinical application, we developed a free online calculator tool (https://ranwyr.shinyapps.io/DynNomapp/) based on the optimal model results.

CONCLUSIONS

The HScore alone is insufficient for accurately predicting early mortality risk in children with HLH. Our study provides a novel early mortality prediction tool based on optimized HScore parameters, which may aid clinicians in better risk stratification and decision-making at the time of diagnosis.