The HLH-Risk-Calculator is a machine learning-based tool to predict course & mortality of secondary hemophagocytic lymphohistiocytosis
Year: 2026
Type: article
Abstract: PURPOSE: Secondary hemophagocytic lymphohistiocytosis (sHLH) is a life-threatening hyperinflammatory condition. While few diagnostic scores are established, none exist to predict both clinical course and time-point specific outcome of sHLH patients so far. We present a machine learning (ML)-based tool to predict Initial Disease Severity (IDS; defined as admission to intensive care units (ICU) OR death < 90 days without ICU admission) and mortality across different time points in sHLH patients. METHODS: 167 adult sHLH patients from six study centers across three European countries were included retrospectively. Clinical and demographic features, course, survival, and laboratory data were assessed. Random forest models were trained with two sets of eight clinical and laboratory features: one to predict IDS, and five to predict mortality at distinct time points (30, 60, 90, 180 or 365 days). After calibration, the models were tested against hold-out test sets containing n = 32 (IDS) or n = 43 (mortality) sHLH patients. RESULTS: Overall, the models demonstrated strong discriminatory ability, overall performance, and accurate prediction of risk. Serum levels of the soluble interleukin-2 receptor (sIL-2R) and albumin (for IDS) or sIL-2R and platelet counts (for mortality prediction) showed the strongest contributions to the models' predictions. CONCLUSION: The HLH-Risk-Calculator is an exploratory tool predicting the clinical course of sHLH. External validation is critical to assess its validity, applicability, and robustness for real-world use. To this end, the calculator is available at www.hlh-risk-calculator.com for research use only, and is currently not intended for clinical decision-making.
Source: Intensive Care Medicine
Authors: Michael Ruzicka, Hans Christian Stubbe, Josia Fauser, Manuel Trebo, Thomas Wimmer, Lena Horvath, Hans-Joachim Stemmler, Stefanie Susanne Stecher, Hendrik Schulze-Koops, Fabian Hauck, Michael Medinger, Claire Seydoux, Michael Starck, Clemens-Martin Wendtner, Peter Bojko, Marcus Hentrich, Katharina Elisabeth Nickel, Katharina Goetze, Florian Bassermann, Sabine Janina Ehrlich, Marion Subklewe, Andreas Pircher, Dominik Wolf, Michael von Bergwelt-Baildon, Karsten Spiekermann
Institutions: LMU Klinikum, Deutsches Konsortium für Translationale Krebsforschung, Ludwig-Maximilians-Universität München, German Center for Infection Research, Innsbruck Medical University, Universität Innsbruck, University Hospital of Basel, München Klinik Schwabing, German Red Cross, TUM Klinikum, Technical University of Munich
Language: English
Subfield: Hematology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $5,190
OpenAlex ID: W7166696755