Development and Validation of a Clinical Prediction Model for Paclitaxel Hypersensitivity Reaction on the Basis of Real-World Data: Pac-HSR Score.

IF 3.2 Q2 ONCOLOGY JCO Global Oncology Pub Date : 2024-10-01 Epub Date: 2024-10-17 DOI:10.1200/GO-24-00318
Sunatee Sa-Nguansai, Radasar Sukphinetkul
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Abstract

Purpose: Paclitaxel is effective chemotherapy against various cancers but can cause hypersensitivity reaction (HSR). This study aimed to identify predictors associated with paclitaxel HSR and develop a clinical prediction model to guide clinical decisions.

Methods: Data were collected from the medical records database of Rajavithi Hospital. Patients with cancer treated with paclitaxel from 2015 to 2022 were included, and a multivariable logistic regression analysis identified predictors associated with paclitaxel HSR. The scoring system was transformed and calibrated on the basis of diagnostic parameters. Discrimination and calibration performances were assessed. Internal validation was conducted using bootstrap resampling with 1,000 replications.

Results: This study involved 3,708 patients with cancer, with an incidence of paclitaxel HSR of 10.11%. An 11-predictor-based Pac-HSR scoring system was developed, involving the following factors: younger age; poor Eastern Cooperative Oncology Group performance status; previous history of paclitaxel HSR; medication allergy history; chronic obstructive airway disease; lung and cervical cancers; high actual dose of paclitaxel; no diphenhydramine premedication; low hemoglobin level; high WBC count; and high absolute lymphocyte count. The C-statistics was 0.73 (95% CI, 0.70 to 0.76), indicating acceptable discrimination. The P value of the Hosmer-Lemeshow goodness-of-fit test was 0.751. The ratio of observed and expected values was 1.00, indicating good calibration. At a cutoff point of 8, specificity was 75.28% and sensitivity was 57.07%. Internal validation indicated good performance with minimal bias, and decision curve analysis demonstrated improved prediction with the use of this scoring system in clinical decision making.

Conclusion: This study developed the 11-predictor-based Pac-HSR scoring system for predicting paclitaxel HSR in patients with cancer. High-risk patients identified by this score should be prioritized for close monitoring and early treatment prophylaxis.

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基于真实世界数据的紫杉醇超敏反应临床预测模型的开发与验证:Pac-HSR 评分。
目的:紫杉醇是治疗多种癌症的有效化疗药物,但可引起超敏反应(HSR)。本研究旨在确定与紫杉醇超敏反应相关的预测因素,并建立临床预测模型以指导临床决策:方法:从 Rajavithi 医院的病历数据库中收集数据。纳入了2015年至2022年接受紫杉醇治疗的癌症患者,并通过多变量逻辑回归分析确定了与紫杉醇HSR相关的预测因素。根据诊断参数对评分系统进行了转换和校准。评估了识别和校准性能。使用自举重采样法进行了1000次重复,进行了内部验证:这项研究涉及 3708 名癌症患者,紫杉醇 HSR 发生率为 10.11%。研究建立了一个基于 11 个预测因子的 Pac-HSR 评分系统,其中包括以下因素:年龄较小;东部合作肿瘤学组(Eastern Cooperative Oncology Group)表现较差;既往紫杉醇 HSR 病史;药物过敏史;慢性阻塞性气道疾病;肺癌和宫颈癌;紫杉醇实际剂量高;无苯海拉明预处理;低血红蛋白水平;高白细胞计数;高绝对淋巴细胞计数。C 统计量为 0.73(95% CI,0.70 至 0.76),表明区分度可以接受。Hosmer-Lemeshow 拟合优度检验的 P 值为 0.751。观察值与预期值的比值为 1.00,表明校准效果良好。在截断点为 8 时,特异性为 75.28%,灵敏度为 57.07%。内部验证表明该评分系统性能良好,偏差极小,决策曲线分析表明在临床决策中使用该评分系统可提高预测效果:本研究开发了基于 11 个预测因子的 Pac-HSR 评分系统,用于预测癌症患者的紫杉醇 HSR。通过该评分确定的高危患者应优先进行密切监测和早期预防治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JCO Global Oncology
JCO Global Oncology Medicine-Oncology
CiteScore
6.70
自引率
6.70%
发文量
310
审稿时长
7 weeks
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