预测乌司替库单抗对中重度克罗恩病患者的短期疗效

IF 4.2 2区 医学 Q2 IMMUNOLOGY
Journal of Inflammation Research Pub Date : 2024-11-20 eCollection Date: 2024-01-01 DOI:10.2147/JIR.S479618
Tao Su, Ling Liu, Fan Meng, Hongzhen Wu, Tao Liu, Jun Deng, Xiang Peng, Min Zhi, Jiayin Yao
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引用次数: 0

摘要

背景优特克单抗(UST)被推荐为中重度克罗恩病(CD)患者的一线治疗药物。然而,某些患者的疗效可能并不理想,因此需要强化治疗或修改治疗方案。我们试图建立一个提名图模型来预测 UST 对中重度 CD 患者的短期疗效:方法:我们建立了一个衍生队列,包括 2020 年 5 月至 2023 年 7 月期间在中山大学附属第六医院确诊为 CD 并接受 UST 治疗的患者。系统收集了患者数据,包括人口统计学特征、临床特征和治疗细节。诱导治疗后达到临床缓解(定义为克罗恩病活动指数CDAI<150,未使用皮质类固醇)是随访观察的终点。通过最小绝对收缩和选择操作器(LASSO)回归分析确定了潜在的预测因素。随后,进行了多变量逻辑回归分析,构建了一个提名图模型。该模型的预测准确性和鉴别力通过接收者操作特征曲线(ROC)和校准曲线进行评估。采用决策曲线分析(DCA)评估模型的临床应用价值:结果:162 名患者被纳入推导队列。预测因子的选择符合最低标准。确定了包括病程、体重指数(BMI)、吸烟、肠道外表现(EIMs)、肛周病变(P)、维多珠单抗治疗史和白蛋白水平(ALB)在内的预后因素,并将其纳入提名图。该模型在基于引导法的内部验证中显示出良好的区分度和校准性(C 指数:0.843,95% 置信区间:0.768-0.903)。此外,DCA 显示该提名图对临床有益:我们构建了一个实用的工具,帮助临床医生识别预计对UST有良好临床反应的中重度CD患者,促进了个性化治疗和精准医学的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of the Short-Term Effectiveness of Ustekinumab in Patients with Moderate to Severe Crohn's Disease.

Background: Ustekinumab (UST) is recommended as the first-line treatment for patients with moderate to severe Crohn's disease (CD). However, the efficacy of certain patients may be suboptimal and necessitate intensive treatment or modification of the treatment regimen. We sought to establish a nomogram model to predict the short-term effectiveness of UST in moderate to severe CD patients.

Methods: We established a derivation cohort comprising patients diagnosed with CD and treated with UST at the Sixth Affiliated Hospital of Sun Yat-sen University from May 2020 to July 2023. The patient data, including demographic and clinical characteristics as well as treatment details, were systematically collected. The achievement of clinical remission (defined as Crohn's Disease Activity Index, CDAI < 150, without corticosteroid usage) after induction therapy was the endpoint observed during follow-up. Potential predictors were identified through the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Subsequently, a multivariate logistic regression analysis was conducted to construct a nomogram model. The predictive accuracy and discriminative power of the model were assessed by Receiver Operating Characteristics (ROC) curves and calibration curves. Decision curve analysis (DCA) was employed to assess the clinical application value of the model.

Results: 162 patients were included in the derivation cohort. The predictor's selection was according to the minimum criteria. Prognostic factors, including duration, body mass index (BMI), smoking, extraintestinal manifestations (EIMs), perianal lesions (P), history of Vedolizumab therapy, and albumin levels (ALB), were identified and included in the nomogram. The model showed good discrimination and calibration on internal validation based on the bootstrap method (C-index: 0.843, 95% confidence interval: 0.768-0.903). Moreover, DCA demonstrated that the nomogram was clinically beneficial.

Conclusion: We constructed a practical tool to assist clinicians in identifying moderate to severe CD patients who are expected to have a good clinical response to UST, promoting personalized treatment and the development of precision medicine.

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来源期刊
Journal of Inflammation Research
Journal of Inflammation Research Immunology and Microbiology-Immunology
CiteScore
6.10
自引率
2.20%
发文量
658
审稿时长
16 weeks
期刊介绍: An international, peer-reviewed, open access, online journal that welcomes laboratory and clinical findings on the molecular basis, cell biology and pharmacology of inflammation.
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