A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making.
A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making.
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DOI：
10.1016/j.radonc.2014.04.012
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Lambin P
中科院分区：
文献类型：
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作者：
Oberije C;Nalbantov G;Dekker A;Boersma L;Borger J;Reymen B;van Baardwijk A;Wanders R;De Ruysscher D;Steyerberg E;Dingemans AM;Lambin P
基于统计预测模型的决策支持系统有可能改变医学的实践方式，但其应用目前因缺乏影响研究而受到阻碍。显示使用这些模型的理论好处可以刺激这些研究的电导。此外，它将为开发基于基因组学、蛋白质组学和成像信息的更先进模型铺平道路，以进一步提高模型的性能。在这项前瞻性单中心研究中，使用先前开发和验证的统计模型预测接受化疗和放疗的肺癌患者的2年生存期（2年）、呼吸困难（DPN）和吞咽困难（DPH）结局。这些预测与医生提供的概率和目前使用的基于指南的建议进行了比较。我们假设模型预测的结果会明显优于医生的预测。经验丰富的放射肿瘤学家（RO）在两个时间点预测所有结果：1）首次咨询患者后，2）制定放射治疗计划后。使用曲线下面积（AUC）分析评估医生和模特的表现差异。共纳入155例患者。在时间点#1，RO和模型之间AUC的差异分别为0.15、0.17和0.20（2年、DPN和DPH分别为），p值为0.02、0.07和0.03。由于患者数量有限，时间点#2的可比差异无统计学显著性。与基于指南的建议进行比较也有利于模型。该模型大大优于罗斯的预测和目前在临床实践中使用的基于指南的建议。根据模型确定风险群体有助于个体化治疗，应在临床影响研究中进一步研究。
Decision Support Systems, based on statistical prediction models, have the potential to change the way medicine is being practiced, but their application is currently hampered by the astonishing lack of impact studies. Showing the theoretical benefit of using these models could stimulate conductance of such studies. In addition, it would pave the way for developing more advanced models, based on genomics, proteomics and imaging information, to further improve the performance of the models. In this prospective single-center study, previously developed and validated statistical models were used to predict the two-year survival (2yrS), dyspnea (DPN), and dysphagia (DPH) outcomes for lung cancer patients treated with chemo radiation. These predictions were compared to probabilities provided by doctors and guideline-based recommendations currently used. We hypothesized that model predictions would significantly outperform predictions from doctors. Experienced radiation oncologists (ROs) predicted all outcomes at two timepoints: 1) after the first consultation of the patient, and 2) after the radiation treatment plan was made. Differences in the performances of doctors and models were assessed using Area under the Curve (AUC) analysis. A total number of 155 patients were included. At timepoint #1 the differences in AUCs between the ROs and the models were 0.15, 0.17, and 0.20 (for 2yrS, DPN, and DPH respectively), with p-values of 0.02, 0.07, and 0.03. Comparable differences at timepoint #2 were not statistically significant due to the limited number of patients. Comparison to guideline-based recommendations also favored the models. The models substantially outperformed ROs’ predictions and guideline-based recommendations currently used in clinical practice. Identification of risk groups on the basis of the models facilitates individualized treatment, and should be further investigated in clinical impact studies.