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用于识别噬血细胞性淋巴组织细胞增生症继发潜在疾病的预测模型。

A predictive model for identifying secondary underlying diseases of hemophagocytic lymphohistiocytosis.

机构信息

Department of Emergency, Peking University People's Hospital, Beijing, China.

Peking University People's Hospital, Peking University Institute of Haematology, Beijing, China.

出版信息

Front Immunol. 2023 Apr 28;14:1143181. doi: 10.3389/fimmu.2023.1143181. eCollection 2023.

Abstract

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 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), =0.001], younger age [OR 6.773 (95% CI, 2.706-16.952), <0.001], higher PLT level [OR 6.674 (95% CI, 2.838-15.694), <0.001], higher ferritin level [OR 5.269 (95% CI, 1.995-13.920), =0.001], and EBV negativity [OR 27.656 (95% CI, 4.499-169.996), <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.

摘要

背景

继发性噬血细胞性淋巴组织细胞增生症(HLH)是一种罕见的、危及生命的免疫过度激活疾病,由感染、炎症或肿瘤触发。本研究旨在通过验证临床和实验室发现,建立一个预测模型,以对导致 HLH 的基础疾病进行及时的鉴别诊断,从而进一步提高 HLH 的治疗效果。

方法

我们回顾性纳入了 175 例继发性 HLH 患者,其中 92 例为血液病患者,83 例为风湿病患者。回顾性分析所有确诊患者的病历资料,建立预测模型。我们还采用多元分析,根据回归系数值的比例加权点,建立早期风险评分,并计算其对诊断导致 HLH 的基础疾病的灵敏度和特异度。

结果

多变量逻辑分析显示,较低的血红蛋白和血小板(PLT)水平、较低的铁蛋白水平、脾肿大和 EBV 阳性与血液病相关,而年轻年龄和女性与风湿病相关。风湿病导致 HLH 的危险因素为女性[比值比(OR)4.434(95%置信区间,1.88910.407), =0.001]、年龄较小[OR 6.773(95%置信区间,2.70616.952), <0.001]、PLT 水平较高[OR 6.674(95%置信区间,2.83815.694), <0.001]、铁蛋白水平较高[OR 5.269(95%置信区间,1.99513.920), =0.001]和 EBV 阴性[OR 27.656(95%置信区间,4.499169.996), <0.001]。风险评分包括对女性、年龄、PLT 计数、铁蛋白水平和 EBV 阴性的评估,可用于预测风湿病继发 HLH,AUC 为 0.844(95%置信区间,0.8360.932)。

结论

本研究所建立的预测模型旨在帮助临床医生在常规实践中诊断继发性 HLH 的基础疾病,从而通过及时治疗基础疾病改善预后。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecd7/10175773/2d5885b593ad/fimmu-14-1143181-g001.jpg

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