Pontes Beatriz, Núñez Francisco, Rubio Cristina, Moreno Alberto, Nepomuceno Isabel, Moreno Jesús, Cacicedo Jon, Praena-Fernandez Juan Manuel, Rodriguez German Antonio Escobar, Parra Carlos, León Blas David Delgado, Del Campo Eleonor Rivin, Couñago Felipe, Riquelme Jose, Guerra Jose Luis Lopez
Department of Computer Language and Systems, Universidad de Sevilla, Seville, Spain.
Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville (IBIS)/Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, Spain.
Rep Pract Oncol Radiother. 2021 Dec 30;26(6):839-848. doi: 10.5603/RPOR.a2021.0088. eCollection 2021.
A clinical decision support system (CDSS ) has been designed to predict the outcome (overall survival) by extracting and analyzing information from routine clinical activity as a complement to clinical guidelines in lung cancer patients.
Prospective multicenter data from 543 consecutive (2013-2017) lung cancer patients with 1167 variables were used for development of the CDSS. Data Mining analyses were based on the XGBoost and Generalized Linear Models algorithms. The predictions from guidelines and the CDSS proposed were compared.
Overall, the highest (> 0.90) areas under the receiver-operating characteristics curve AUCs for predicting survival were obtained for small cell lung cancer patients. The AUCs for predicting survival using basic items included in the guidelines were mostly below 0.70 while those obtained using the CDSS were mostly above 0.70. The vast majority of comparisons between the guideline and CDSS AUCs were statistically significant (p < 0.05). For instance, using the guidelines, the AUC for predicting survival was 0.60 while the predictive power of the CDSS enhanced the AUC up to 0.84 (p = 0.0009). In terms of histology, there was only a statistically significant difference when comparing the AUCs of small cell lung cancer patients (0.96) and all lung cancer patients with longer (≥ 18 months) follow up (0.80; p < 0.001).
The CDSS successfully showed potential for enhancing prediction of survival. The CDSS could assist physicians in formulating evidence-based management advice in patients with lung cancer, guiding an individualized discussion according to prognosis.
设计了一种临床决策支持系统（CDSS），通过从常规临床活动中提取和分析信息来预测肺癌患者的预后（总生存期），作为临床指南的补充。
使用来自543例连续（2013 - 2017年）肺癌患者的前瞻性多中心数据，共1167个变量，用于开发CDSS。数据挖掘分析基于XGBoost和广义线性模型算法。比较了指南的预测结果和CDSS提出的预测结果。
总体而言，小细胞肺癌患者预测生存期的受试者操作特征曲线下面积（AUC）最高（> 0.90）。使用指南中包含的基本项目预测生存期的AUC大多低于0.70，而使用CDSS获得的AUC大多高于0.70。指南和CDSS的AUC之间的绝大多数比较具有统计学意义（p < 0.05）。例如，使用指南时，预测生存期的AUC为0.60，而CDSS的预测能力将AUC提高到0.84（p = 0.0009）。在组织学方面，比较小细胞肺癌患者（0.96）和所有随访时间较长（≥18个月）的肺癌患者（0.80；p < 0.001）的AUC时，仅存在统计学显著差异。
CDSS成功显示出增强生存期预测的潜力。CDSS可以帮助医生为肺癌患者制定基于证据的管理建议，根据预后指导个体化讨论。
