Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis.
Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis.
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DOI：
10.2196/10275
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发表时间：
2018-08-21
影响因子：
7.4
通讯作者：
Riper H
中科院分区：
文献类型：
--
作者：
Bremer V;Becker D;Kolovos S;Funk B;van Breda W;Hoogendoorn M;Riper H
心理障碍有不同的治疗方法。治疗的临床和成本效益是决策者、治疗师和患者的关键因素，因此在医疗决策中发挥着重要作用。在一项干预措施开始时，往往不清楚哪些具体的个人从特定的干预措施中受益最多，也不清楚如何在单个患者层面上分配成本。这项研究旨在预测基于互联网的干预开始之前患者的个人结果和成本。根据这些预测，可以提供个性化的治疗建议。因此，我们扩大了个性化治疗建议的讨论。结果和成本是根据一项两组随机对照试验中350例患者的基线数据预测的，该试验比较了常规治疗和混合治疗抑郁症的效果。为此，我们评估了各种机器学习技术，比较了这些技术的预测准确性，并揭示了对预测性能贡献最大的特征。然后，我们结合这些预测，并利用增量成本效益比，以便在治疗开始前获得个人治疗建议。预测临床结果和成本是一项具有挑战性的任务，当仅利用基线信息时具有很高的不确定性。然而，我们能够生成比预定义的平均结果和成本值形式的参考指标更准确的预测。包括焦虑或抑郁项目以及关于个人流动性和能量水平的问题的测试有助于预测性能。然后，我们描述了如何将患者单独分配到最合适的治疗类型。对于25，000欧元/质量调整生命年的增量成本效益阈值，我们证明了我们的建议会导致略差的结局（1.98%），但成本降低（5.42%）。我们的研究结果表明，在基线时提供个性化的治疗建议是可行的，从而将患者分配到最有益的治疗类型。这可能会改善决策，为个人带来更好的结果，并降低医疗保健成本。
Different treatment alternatives exist for psychological disorders. Both clinical and cost effectiveness of treatment are crucial aspects for policy makers, therapists, and patients and thus play major roles for healthcare decision-making. At the start of an intervention, it is often not clear which specific individuals benefit most from a particular intervention alternative or how costs will be distributed on an individual patient level. This study aimed at predicting the individual outcome and costs for patients before the start of an internet-based intervention. Based on these predictions, individualized treatment recommendations can be provided. Thus, we expand the discussion of personalized treatment recommendation. Outcomes and costs were predicted based on baseline data of 350 patients from a two-arm randomized controlled trial that compared treatment as usual and blended therapy for depressive disorders. For this purpose, we evaluated various machine learning techniques, compared the predictive accuracy of these techniques, and revealed features that contributed most to the prediction performance. We then combined these predictions and utilized an incremental cost-effectiveness ratio in order to derive individual treatment recommendations before the start of treatment. Predicting clinical outcomes and costs is a challenging task that comes with high uncertainty when only utilizing baseline information. However, we were able to generate predictions that were more accurate than a predefined reference measure in the shape of mean outcome and cost values. Questionnaires that include anxiety or depression items and questions regarding the mobility of individuals and their energy levels contributed to the prediction performance. We then described how patients can be individually allocated to the most appropriate treatment type. For an incremental cost-effectiveness threshold of 25,000 €/quality-adjusted life year, we demonstrated that our recommendations would have led to slightly worse outcomes (1.98%), but with decreased cost (5.42%). Our results indicate that it was feasible to provide personalized treatment recommendations at baseline and thus allocate patients to the most beneficial treatment type. This could potentially lead to improved decision-making, better outcomes for individuals, and reduced health care costs.
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影响因子：
5.1
作者：
Chai, T.;Draxler, R. R.
通讯作者：
Draxler, R. R.
影响因子：
4
作者：
O'Hagan, Anthony;Stevens, John W
通讯作者：
Stevens, John W
影响因子：
3.6
作者：
BLACK, WC
通讯作者：
BLACK, WC
影响因子：
15.8
作者：
Ioannidis JP;Garber AM
通讯作者：
Garber AM
影响因子：
3.5
作者：
Manning, WG;Mullahy, J
通讯作者：
Mullahy, J