Early prediction of adult lymphoma-associated haemophagocytic lymphohistiocytosis using an interpretable machine learning model.

Lymphoma-associated haemophagocytic lymphohistiocytosis (LA-HLH) is associated with a high mortality rate, making early diagnosis and appropriate treatment critical for improving patient outcomes. In this study, we enrolled 126 patients diagnosed with LA-HLH and 254 with non-LA-HLH. A machine learning-based predictive model was developed and validated to enable timely differentiation of LA-HLH from other HLH subtypes. The model incorporated 11 predictive variables for early LA-HLH prediction, among which the top five most influential were age, ferritin, monocyte percentage, haemoglobin and platelet count. Among seven machine learning algorithms evaluated, the random forest model demonstrated the best performance, achieving an area under the curve (AUC) of 0.946 on the training set and 0.794 on the validation set. We subsequently evaluated combined models that incorporated disease-specific indicators such as soluble interleukin-2 receptor (sCD25) and PET-CT SUVmax, which resulted in a non-significant increase in AUC on the validation set. Finally, the optimal model was deployed as a web-based tool to support early aetiological differentiation. This may facilitate prompt initiation of targeted examination and appropriate treatment for patients with LA-HLH.