A risk score for predicting hospitalization for community-acquired pneumonia in ITP using nationally representative data.-猫眼课题宝

DOI：

10.1182/bloodadvances.2020003074

发表时间：

2020-11

期刊：

Blood advances

影响因子：

7.5

通讯作者：

中科院分区：

医学1区

文献类型：

作者：

Ye-Jun Wu;M. Hou;Hui-Xin Liu;J. Peng;Liang-ming Ma;Linhua Yang;R. Feng;Hui Liu;Yi Liu;Jia Feng;Hong-Yu Zhang;Zen-Ping Zhou;Wen-sheng Wang;Xu-liang Shen;P. Zhao;H. Fu;Q. Zeng;Xingyi Wang;Qiu-Sha Huang;Yun He;Q. Jiang;Hao Jiang;Jin Lu;Xiang-Yu Zhao;Xiao-Su Zhao;Yingjun Chang;Lanping Xu;Yueying Li;Qian-fei Wang;Xiao-hui Zhang

感染是免疫性血小板减少症（ITP）死亡的主要原因之一，肺部是最常见的感染部位。我们确定了非脾切除成人ITP患者因社区获得性肺炎（CAP）住院治疗的相关因素，并建立了抗瓜氨酸肽抗体（ACPA）预测模型来预测CAP住院治疗的发生率。这是一项回顾性研究，来自中国10个大型医疗中心的非脾切除成人ITP患者。推导队列包括来自5个医疗中心的145名患有CAP的ITP住院患者和1360名未患有CAP的住院患者，验证队列包括来自其他5个中心的其余63名患有CAP的ITP住院患者和526名未患有CAP的住院患者。通过对衍生队列进行多变量分析，建立了4项ACPA模型，其中包括年龄、Charlson Comorbid指数评分、初始血小板计数和初始淋巴细胞绝对计数。进行了内部和外部验证，以评估模型的性能。ACPA模型在推导队列中的曲线下面积为0.853（95%置信区间[CI]，0.818-0.889），在验证队列中为0.862（95% CI，0.807-0.916），这表明该模型具有良好的区分能力。校准图显示，估计和观察到的概率之间的高度一致性。决策曲线分析表明，ITP患者可以从ACPA模型的临床应用中获益。总之，开发并验证了ACPA模型来预测CAP住院的发生，这可能有助于识别因CAP住院高风险的ITP患者。

Infection is one of the primary causes of death from immune thrombocytopenia (ITP), and the lungs are the most common site of infection. We identified the factors associated with hospitalization for community-acquired pneumonia (CAP) in nonsplenectomized adults with ITP and established the anti-citrullinated peptide antibody (ACPA) prediction model to predict the incidence of hospitalization for CAP. This was a retrospective study of nonsplenectomized adult patients with ITP from 10 large medical centers in China. The derivation cohort included 145 ITP inpatients with CAP and 1360 inpatients without CAP from 5 medical centers, and the validation cohort included the remaining 63 ITP inpatients with CAP and 526 inpatients without CAP from the other 5 centers. The 4-item ACPA model, which included age, Charlson Comorbidity Index score, initial platelet count, and initial absolute lymphocyte count, was established by multivariable analysis of the derivation cohort. Internal and external validation were conducted to assess the performance of the model. The ACPA model had an area under the curve of 0.853 (95% confidence interval [CI], 0.818-0.889) in the derivation cohort and 0.862 (95% CI, 0.807-0.916) in the validation cohort, which indicated the good discrimination power of the model. Calibration plots showed high agreement between the estimated and observed probabilities. Decision curve analysis indicated that ITP patients could benefit from the clinical application of the ACPA model. To summarize, the ACPA model was developed and validated to predict the occurrence of hospitalization for CAP, which might help identify ITP patients with a high risk of hospitalization for CAP.

A risk score for predicting hospitalization for community-acquired pneumonia in ITP using nationally representative data.

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