Machine learning of laboratory parameters to predict mortality risk in pediatric hemophagocytic lymphohistiocytosis: A retrospective single-center study

Machine learning of laboratory parameters to predict mortality risk in pediatric hemophagocytic lymphohistiocytosis: A retrospective single-center study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Machine learning of laboratory parameters to predict mortality risk in pediatric hemophagocytic lymphohistiocytosis: A retrospective single-center study Chuncan Wu, Weijun Huang, Huidie Liang, Lili Liu, Zhonglv Ye, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8979046/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Apr, 2026 Read the published version in BMC Pediatrics → Version 1 posted 19 You are reading this latest preprint version Abstract Background This study aims to screen key peripheral blood laboratory indicators using machine learning algorithms to develop and validate a prediction model for the 30-day mortality risk following the diagnosis of hemophagocytic lymphohistiocytosis (HLH) in children. This seeks to provide a scientific basis for the early clinical identification of high-risk patients. Methods A retrospective cohort study was conducted, encompassing 133 children diagnosed with HLH at the Children's Medical Center of the Affiliated Hospital of Guangdong Medical University between January 1, 2015, and December 30, 2024. Based on the survival outcome within 30 days post-diagnosis, the primary observation endpoint was categorized into a mortality group (n = 29) and a survival group (n = 104). Baseline laboratory indicators from the day of diagnosis or within the preceding 24 hours were collected. The dataset was randomly partitioned into a training set and a validation set at a 7:3 ratio. Initial screening was performed via univariate analysis, followed by principal component analysis (PCA) and variance inflation factor (VIF) assessments to eliminate redundancy and isolate key predictors. Six machine learning models, including LightGBM, XGBoost, and logistic regression, were constructed using the optimized features. Model performance was evaluated using metrics such as the area under the curve (AUC) and F1 scores. The SHapley Additive exPlanations (SHAP) method was introduced for model interpretation, culminating in the construction of a visual nomogram and an online risk calculator. Results Five core predictive variables were identified: procalcitonin (PCT), mean corpuscular hemoglobin (MCH), aspartate aminotransferase (AST), C-reactive protein (CRP), and activated partial thromboplastin time (APTT). Among the six evaluated models, LightGBM demonstrated optimal robustness in the feature decrement experiment (validation set AUC = 0.823). SHAP visual analysis revealed that APTT and MCH contributed most significantly to the predictive outcomes; specifically, high expression levels of APTT, AST, PCT, and CRP, coupled with a low expression level of MCH, were indicative of a high mortality risk. The risk stratification tool derived from this model successfully and significantly distinguished between high-risk and low-risk patients in both the training and validation datasets. Conclusions A prediction model constructed using PCA feature screening and the LightGBM algorithm can effectively utilize routine peripheral blood indicators to quantitatively assess early mortality risk in pediatric HLH. The developed online calculator demonstrates substantial clinical value for auxiliary decision-making. Hemophagocytic lymphohistiocytosis Children Machine learning LightGBM model 30-day mortality risk Principal component analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Apr, 2026 Read the published version in BMC Pediatrics → Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviews received at journal 20 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 15 Mar, 2026 Reviewers agreed at journal 14 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers invited by journal 11 Mar, 2026 Editor invited by journal 02 Mar, 2026 Editor assigned by journal 02 Mar, 2026 Submission checks completed at journal 02 Mar, 2026 First submitted to journal 26 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8979046","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605283092,"identity":"ac2fe3bf-3a3f-44c1-99b9-54b1eb7d528d","order_by":0,"name":"Chuncan Wu","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chuncan","middleName":"","lastName":"Wu","suffix":""},{"id":605283101,"identity":"3ecdb265-5ddd-47cc-9857-49d4e3bbce8b","order_by":1,"name":"Weijun Huang","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical 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This seeks to provide a scientific basis for the early clinical identification of high-risk patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was conducted, encompassing 133 children diagnosed with HLH at the Children's Medical Center of the Affiliated Hospital of Guangdong Medical University between January 1, 2015, and December 30, 2024. Based on the survival outcome within 30 days post-diagnosis, the primary observation endpoint was categorized into a mortality group (n\u0026thinsp;=\u0026thinsp;29) and a survival group (n\u0026thinsp;=\u0026thinsp;104). Baseline laboratory indicators from the day of diagnosis or within the preceding 24 hours were collected. The dataset was randomly partitioned into a training set and a validation set at a 7:3 ratio. Initial screening was performed via univariate analysis, followed by principal component analysis (PCA) and variance inflation factor (VIF) assessments to eliminate redundancy and isolate key predictors. Six machine learning models, including LightGBM, XGBoost, and logistic regression, were constructed using the optimized features. Model performance was evaluated using metrics such as the area under the curve (AUC) and F1 scores. The SHapley Additive exPlanations (SHAP) method was introduced for model interpretation, culminating in the construction of a visual nomogram and an online risk calculator.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFive core predictive variables were identified: procalcitonin (PCT), mean corpuscular hemoglobin (MCH), aspartate aminotransferase (AST), C-reactive protein (CRP), and activated partial thromboplastin time (APTT). Among the six evaluated models, LightGBM demonstrated optimal robustness in the feature decrement experiment (validation set AUC\u0026thinsp;=\u0026thinsp;0.823). SHAP visual analysis revealed that APTT and MCH contributed most significantly to the predictive outcomes; specifically, high expression levels of APTT, AST, PCT, and CRP, coupled with a low expression level of MCH, were indicative of a high mortality risk. The risk stratification tool derived from this model successfully and significantly distinguished between high-risk and low-risk patients in both the training and validation datasets.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA prediction model constructed using PCA feature screening and the LightGBM algorithm can effectively utilize routine peripheral blood indicators to quantitatively assess early mortality risk in pediatric HLH. 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