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延长生存的乳腺癌患者衰弱预测模型的建立与验证。

Development and validation of a prediction model for frailty in breast cancer patients with extended survival.

机构信息

Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei Province, People's Republic of China.

Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

出版信息

Support Care Cancer. 2024 May 29;32(6):393. doi: 10.1007/s00520-024-08501-7.

Abstract

BACKGROUND

Breast cancer (BC) patients with extended survival show a higher incidence of frailty. This study aimed to develop and validate a novel model combining sociodemographic factors (SF) and disease-related factors (DRF) to identify frailty in BC patients with extended survival.

METHODS

This cross-sectional study examined data from 1167 patients admitted to a large urban academic medical centre. Three types of predictive models were constructed in the training set (817 patients): the SF model, the DRF model, and the SF + DRF model (combined model). The model performance and effectiveness were assessed using receiver operating characteristic (ROC) curves, calibration plots and decision curves analysis (DCA). Then the model was subsequently validated on the validation set.

RESULTS

The incidence of frailty in BC patients with extended survival was 35.8%. We identified six independent risk factors including age, health status, chemotherapy, endocrine therapy, number of comorbidities and oral medications. Ultimately, we constructed an optimal model (combined model C) for frailty. The predictive model showed significantly high discriminative accuracy in the training set AUC: 0.754, (95% CI, 0.719-0.789; sensitivity: 76.8%, specificity: 62.2%) and validation set AUC: 0.805, (95% CI, 0.76-0.85), sensitivity: 60.8%, specificity: 87.1%) respectively. A prediction nomogram was constructed for the training and validation sets. Calibration and DCA were performed, which indicated that the clinical model presented satisfactory calibration and clinical utility. Ultimately, we implemented the prediction model into a mobile-friendly web application that provides an accurate and individualized prediction for BC.

CONCLUSIONS

The present study demonstrated that the prevalence of frailty in BC patients with extended survival was 35.8%. We developed a novel model for screening frailty, which may provide evidence for frailty screening and prevention.

摘要

背景

具有延长生存期的乳腺癌(BC)患者出现衰弱的发生率更高。本研究旨在开发和验证一种新模型,该模型结合了社会人口统计学因素(SF)和疾病相关因素(DRF),以识别具有延长生存期的 BC 患者的衰弱情况。

方法

这项横断面研究检查了来自大型城市学术医疗中心的 1167 名患者的数据。在训练集中构建了三种类型的预测模型(817 名患者):SF 模型、DRF 模型和 SF+DRF 模型(联合模型)。使用接收者操作特征(ROC)曲线、校准图和决策曲线分析(DCA)评估模型性能和效果。然后,将模型在验证集中进行验证。

结果

具有延长生存期的 BC 患者的衰弱发生率为 35.8%。我们确定了六个独立的风险因素,包括年龄、健康状况、化疗、内分泌治疗、合并症数量和口服药物。最终,我们构建了一个用于衰弱的最佳模型(联合模型 C)。该预测模型在训练集 AUC 中表现出显著的高区分准确性:0.754(95%CI,0.719-0.789;敏感性:76.8%,特异性:62.2%)和验证集 AUC:0.805(95%CI,0.76-0.85),敏感性:60.8%,特异性:87.1%)。为训练集和验证集构建了预测列线图。进行了校准和 DCA,结果表明临床模型具有令人满意的校准和临床实用性。最终,我们将预测模型应用于一个支持移动设备的网络应用程序中,为 BC 提供了准确和个性化的预测。

结论

本研究表明,具有延长生存期的 BC 患者的衰弱发生率为 35.8%。我们开发了一种用于筛查衰弱的新模型,该模型可能为筛查和预防衰弱提供依据。

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