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乌司奴单抗治疗中重度克罗恩病患者短期疗效的预测

Prediction of the Short-Term Effectiveness of Ustekinumab in Patients with Moderate to Severe Crohn's Disease.

作者信息

Su Tao, Liu Ling, Meng Fan, Wu Hongzhen, Liu Tao, Deng Jun, Peng Xiang, Zhi Min, Yao Jiayin

机构信息

Department of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, People's Republic of China.

Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, People's Republic of China.

出版信息

J Inflamm Res. 2024 Nov 20;17:9181-9191. doi: 10.2147/JIR.S479618. eCollection 2024.

Abstract

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.

摘要

背景

乌司奴单抗(UST)被推荐作为中度至重度克罗恩病(CD)患者的一线治疗药物。然而,部分患者的疗效可能欠佳,需要强化治疗或调整治疗方案。我们试图建立一个列线图模型,以预测UST在中度至重度CD患者中的短期疗效。

方法

我们建立了一个推导队列,纳入2020年5月至2023年7月在中山大学附属第六医院被诊断为CD并接受UST治疗的患者。系统收集患者数据,包括人口统计学和临床特征以及治疗细节。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)是随访期间观察的终点。通过最小绝对收缩和选择算子(LASSO)回归分析确定潜在预测因素。随后,进行多因素逻辑回归分析以构建列线图模型。通过受试者操作特征(ROC)曲线和校准曲线评估模型的预测准确性和判别力。采用决策曲线分析(DCA)评估模型的临床应用价值。

结果

推导队列纳入了162例患者。预测因素的选择依据最低标准。确定了包括病程、体重指数(BMI)、吸烟、肠外表现(EIMs)、肛周病变(P)、维得利珠单抗治疗史和白蛋白水平(ALB)等预后因素,并纳入列线图。基于自举法的内部验证显示,该模型具有良好的判别力和校准性(C指数:0.843,95%置信区间:0.768 - 0.903)。此外,DCA表明该列线图具有临床应用价值。

结论

我们构建了一个实用工具,以帮助临床医生识别预期对UST有良好临床反应的中度至重度CD患者,促进个性化治疗和精准医学的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/deb3/11586492/015978df2441/JIR-17-9181-g0001.jpg

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