Evidence map›Paper›PMID 42377458›Full record

ArticleIntensive care medicine2026

The HLH-Risk-Calculator is a machine learning-based tool to predict course & mortality of secondary hemophagocytic lymphohistiocytosis.

Michael Ruzicka, Hans Christian Stubbe, Josia Fauser, Manuel Trebo, Thomas Wimmer, Lena Horvath, Hans-Joachim Stemmler, Stefanie Susanne Stecher, Hendrik Schulze-Koops, Fabian Hauck and 15 more

Abstract readMulticenter Study
In one paragraph

Article in Intensive care medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Michael RuzickaDepartment of Medicine III, Ludwig Maximilian University (LMU) University Hospital, LMU Munich, Munich, Germany. michael.ruzicka@med.uni-muenchen.de.ORCID http://orcid.org/0000-0002-2451-1070
Hans Christian StubbeDepartment of Medicine II, LMU University Hospital, LMU Munich, Munich, Germany.ORCID http://orcid.org/0000-0002-7873-996X
Josia FauserDepartment of Internal Medicine V, Medical University of Innsbruck, Innsbruck, Austria.ORCID http://orcid.org/0000-0002-1280-7464
Manuel TreboDepartment of Internal Medicine V, Medical University of Innsbruck, Innsbruck, Austria.ORCID http://orcid.org/0000-0001-8597-9729
Thomas WimmerDepartment of Medicine III, Ludwig Maximilian University (LMU) University Hospital, LMU Munich, Munich, Germany.ORCID http://orcid.org/0000-0003-3890-4753
Lena HorvathDepartment of Internal Medicine V, Medical University of Innsbruck, Innsbruck, Austria.
Hans-Joachim StemmlerDepartment of Medicine III, Ludwig Maximilian University (LMU) University Hospital, LMU Munich, Munich, Germany.ORCID http://orcid.org/0000-0001-8751-8555
Stefanie Susanne StecherDepartment of Medicine II, LMU University Hospital, LMU Munich, Munich, Germany.ORCID http://orcid.org/0000-0001-6715-1020
Hendrik Schulze-KoopsDivision of Rheumatology and Clinical Immunology, Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany.ORCID http://orcid.org/0000-0002-1681-491X
Fabian HauckDivision of Pediatric Immunology and Rheumatology, Department of Pediatrics at the Dr. Von Hauner Children's Hospital, LMU Munich, Munich, Germany.
Michael MedingerKlinik für Innere Medizin III, Department of Oncology, Hematology and Palliative Care, Diak Klinikum Landkreis Schwäbisch Hall, Schwäbisch Hall, Germany.
Claire SeydouxDivision of Hematology, University Hospital of Basel, Basel, Switzerland.ORCID http://orcid.org/0000-0003-3263-086X
Michael StarckDepartment of Hematology, Oncology, Immunology, Palliative Care, Infectious Diseases and Tropical Medicine, Munich Clinic Schwabing, Munich, Germany.
Clemens-Martin WendtnerDepartment of Medicine III, Ludwig Maximilian University (LMU) University Hospital, LMU Munich, Munich, Germany.ORCID http://orcid.org/0000-0003-2866-2260
Peter BojkoDepartment of Medicine III, Hematology and Oncology, Red Cross Hospital Munich, Munich, Germany.
Marcus HentrichDepartment of Medicine III, Hematology and Oncology, Red Cross Hospital Munich, Munich, Germany.
Katharina Elisabeth NickelDepartment of Medicine III, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany.
Katharina GoetzeDepartment of Medicine III, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany.
Florian BassermannDepartment of Medicine III, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany.
Sabine Janina EhrlichDepartment of Medicine III, Ludwig Maximilian University (LMU) University Hospital, LMU Munich, Munich, Germany.ORCID http://orcid.org/0009-0006-3283-700X
Marion SubkleweDepartment of Medicine III, Ludwig Maximilian University (LMU) University Hospital, LMU Munich, Munich, Germany.ORCID http://orcid.org/0000-0001-9154-9469
Andreas PircherDepartment of Internal Medicine V, Medical University of Innsbruck, Innsbruck, Austria.
Dominik WolfDepartment of Internal Medicine V, Medical University of Innsbruck, Innsbruck, Austria.
Michael von Bergwelt-Baildon *Department of Medicine III, Ludwig Maximilian University (LMU) University Hospital, LMU Munich, Munich, Germany.ORCID http://orcid.org/0000-0002-1952-052X
Karsten Spiekermann *Department of Medicine III, Ludwig Maximilian University (LMU) University Hospital, LMU Munich, Munich, Germany.ORCID http://orcid.org/0000-0002-5139-4957

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSecondary 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.

methods167 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.

resultsOverall, 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.

conclusionThe 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.

Indexed as

Lymphohistiocytosis, HemophagocyticMachine LearningAdultEuropeFemaleHumansIntensive Care UnitsInterleukin-2 Receptor alpha SubunitMaleMiddle AgedPredictive Learning ModelsPrognosisRandom ForestRetrospective StudiesRisk AssessmentSeverity of Illness IndexInterleukin-2 Receptor alpha SubunitHemophagocytic lymphohistiocytosisHLH-Risk-CalculatorPrognostic scoreSoluble CD25Soluble interleukin-2 receptor

Identifiers

PMID42377458
PMCPMC13341829

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.