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A predictive model for identifying secondary underlying diseases of hemophagocytic lymphohistiocytosis

BACKGROUND: Secondary hemophagocytic lymphohistiocytosis (HLH) is a rare, life-threatening disease of immune hyperactivation that arises in the context of infectious, inflammatory, or neoplastic triggers. The aim of this study was to establish a predictive model for the timely differential diagnosis...

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Autores principales: Gao, Wei-bo, Hu, Li-juan, Ma, Xiao-lu, Shi, Mao-jing, Wang, Chun-yu, Ma, Yong, Song, Xiao-jing, Zhu, Ji-hong, Wang, Tian-bing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10175773/
https://www.ncbi.nlm.nih.gov/pubmed/37187741
http://dx.doi.org/10.3389/fimmu.2023.1143181
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author Gao, Wei-bo
Hu, Li-juan
Ma, Xiao-lu
Shi, Mao-jing
Wang, Chun-yu
Ma, Yong
Song, Xiao-jing
Zhu, Ji-hong
Wang, Tian-bing
author_facet Gao, Wei-bo
Hu, Li-juan
Ma, Xiao-lu
Shi, Mao-jing
Wang, Chun-yu
Ma, Yong
Song, Xiao-jing
Zhu, Ji-hong
Wang, Tian-bing
author_sort Gao, Wei-bo
collection PubMed
description BACKGROUND: Secondary hemophagocytic lymphohistiocytosis (HLH) is a rare, life-threatening disease of immune hyperactivation that arises in the context of infectious, inflammatory, or neoplastic triggers. The aim of this study was to establish a predictive model for the timely differential diagnosis of the original disease resulting in HLH by validating clinical and laboratory findings to further improve the efficacy of therapeutics for HLH. METHODS: We retrospectively enrolled 175 secondary HLH patients in this study, including 92 patients with hematologic disease and 83 patients with rheumatic disease. The medical records of all identified patients were retrospectively reviewed and used to generate the predictive model. We also developed an early risk score using multivariate analysis weighted points proportional to the β regression coefficient values and calculated its sensitivity and specificity for the diagnosis of the original disease resulting in HLH. RESULTS: The multivariate logistic analysis revealed that lower levels of hemoglobin and platelets (PLT), lower levels of ferritin, splenomegaly and Epstein−Barr virus (EBV) positivity were associated with hematologic disease, but young age and female sex were associated with rheumatic disease. The risk factors for HLH secondary to rheumatic diseases were female sex [OR 4.434 (95% CI, 1.889-10.407), P =0.001], younger age [OR 6.773 (95% CI, 2.706-16.952), P<0.001], higher PLT level [OR 6.674 (95% CI, 2.838-15.694), P<0.001], higher ferritin level [OR 5.269 (95% CI, 1.995-13.920), P =0.001], and EBV negativity [OR 27.656 (95% CI, 4.499-169.996), P<0.001]. The risk score included assessments of female sex, age, PLT count, ferritin level and EBV negativity, which can be used to predict HLH secondary to rheumatic diseases with an AUC of 0.844 (95% CI, 0.836~0.932). CONCLUSION: The established predictive model was designed to help clinicians diagnose the original disease resulting in secondary HLH during routine practice, which might be improve prognosis by enabling the timely treatment of the underlying disease.
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spelling pubmed-101757732023-05-13 A predictive model for identifying secondary underlying diseases of hemophagocytic lymphohistiocytosis Gao, Wei-bo Hu, Li-juan Ma, Xiao-lu Shi, Mao-jing Wang, Chun-yu Ma, Yong Song, Xiao-jing Zhu, Ji-hong Wang, Tian-bing Front Immunol Immunology BACKGROUND: Secondary hemophagocytic lymphohistiocytosis (HLH) is a rare, life-threatening disease of immune hyperactivation that arises in the context of infectious, inflammatory, or neoplastic triggers. The aim of this study was to establish a predictive model for the timely differential diagnosis of the original disease resulting in HLH by validating clinical and laboratory findings to further improve the efficacy of therapeutics for HLH. METHODS: We retrospectively enrolled 175 secondary HLH patients in this study, including 92 patients with hematologic disease and 83 patients with rheumatic disease. The medical records of all identified patients were retrospectively reviewed and used to generate the predictive model. We also developed an early risk score using multivariate analysis weighted points proportional to the β regression coefficient values and calculated its sensitivity and specificity for the diagnosis of the original disease resulting in HLH. RESULTS: The multivariate logistic analysis revealed that lower levels of hemoglobin and platelets (PLT), lower levels of ferritin, splenomegaly and Epstein−Barr virus (EBV) positivity were associated with hematologic disease, but young age and female sex were associated with rheumatic disease. The risk factors for HLH secondary to rheumatic diseases were female sex [OR 4.434 (95% CI, 1.889-10.407), P =0.001], younger age [OR 6.773 (95% CI, 2.706-16.952), P<0.001], higher PLT level [OR 6.674 (95% CI, 2.838-15.694), P<0.001], higher ferritin level [OR 5.269 (95% CI, 1.995-13.920), P =0.001], and EBV negativity [OR 27.656 (95% CI, 4.499-169.996), P<0.001]. The risk score included assessments of female sex, age, PLT count, ferritin level and EBV negativity, which can be used to predict HLH secondary to rheumatic diseases with an AUC of 0.844 (95% CI, 0.836~0.932). CONCLUSION: The established predictive model was designed to help clinicians diagnose the original disease resulting in secondary HLH during routine practice, which might be improve prognosis by enabling the timely treatment of the underlying disease. Frontiers Media S.A. 2023-04-28 /pmc/articles/PMC10175773/ /pubmed/37187741 http://dx.doi.org/10.3389/fimmu.2023.1143181 Text en Copyright © 2023 Gao, Hu, Ma, Shi, Wang, Ma, Song, Zhu and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Gao, Wei-bo
Hu, Li-juan
Ma, Xiao-lu
Shi, Mao-jing
Wang, Chun-yu
Ma, Yong
Song, Xiao-jing
Zhu, Ji-hong
Wang, Tian-bing
A predictive model for identifying secondary underlying diseases of hemophagocytic lymphohistiocytosis
title A predictive model for identifying secondary underlying diseases of hemophagocytic lymphohistiocytosis
title_full A predictive model for identifying secondary underlying diseases of hemophagocytic lymphohistiocytosis
title_fullStr A predictive model for identifying secondary underlying diseases of hemophagocytic lymphohistiocytosis
title_full_unstemmed A predictive model for identifying secondary underlying diseases of hemophagocytic lymphohistiocytosis
title_short A predictive model for identifying secondary underlying diseases of hemophagocytic lymphohistiocytosis
title_sort predictive model for identifying secondary underlying diseases of hemophagocytic lymphohistiocytosis
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10175773/
https://www.ncbi.nlm.nih.gov/pubmed/37187741
http://dx.doi.org/10.3389/fimmu.2023.1143181
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