Prediction of evening fatigue severity in outpatients receiving chemotherapy: less may be more.

IF 2.2 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
Kord M Kober, Ritu Roy, Anand Dhruva, Yvette P Conley, Raymond J Chan, Bruce Cooper, Adam Olshen, Christine Miaskowski
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引用次数: 8

Abstract

Background: Fatigue is the most common and debilitating symptom experienced by oncology patients undergoing chemotherapy. Little is known about patient characteristics that predict changes in fatigue severity over time.

Purpose: To predict the severity of evening fatigue in the week following the administration of chemotherapy using machine learning approaches.

Methods: Outpatients with breast, gastrointestinal, gynecological, or lung cancer (N=1217) completed questionnaires one week prior to and one week following administration of chemotherapy. Evening fatigue was measured with the Lee Fatigue Scale (LFS). Separate prediction models for evening fatigue severity were created using clinical, symptom, and psychosocial adjustment characteristics and either evening fatigue scores or individual fatigue item scores. Prediction models were created using two regression and three machine learning approaches.

Results: Random forest (RF) models provided the best fit across all models. For the RF model using individual LFS item scores, two of the 13 individual LFS items (i.e., "worn out", "exhausted") were the strongest predictors.

Conclusion: This study is the first to use machine learning techniques to predict evening fatigue severity in the week following chemotherapy from fatigue scores obtained in the week prior to chemotherapy. Our findings suggest that the language used to assess clinical fatigue in oncology patients is important and that two simple questions may be used to predict evening fatigue severity.

门诊化疗患者夜间疲劳程度预测:少则可能多。
背景:疲劳是肿瘤患者接受化疗时最常见的虚弱症状。人们对预测疲劳严重程度随时间变化的患者特征知之甚少。目的:利用机器学习方法预测化疗后一周内夜间疲劳的严重程度。方法:1217例乳腺癌、胃肠道、妇科或肺癌门诊患者在化疗前一周和化疗后一周完成问卷调查。采用李疲劳量表(LFS)测量夜间疲劳。使用临床、症状和心理社会调节特征以及夜间疲劳评分或个人疲劳项目评分,分别建立了夜间疲劳严重程度的预测模型。使用两种回归和三种机器学习方法创建了预测模型。结果:随机森林(RF)模型在所有模型中提供了最佳拟合。对于使用单个LFS项目得分的RF模型,13个单独LFS项目中的两个(即“磨损”,“耗尽”)是最强的预测因子。结论:本研究首次使用机器学习技术,根据化疗前一周获得的疲劳评分,预测化疗后一周的夜间疲劳严重程度。我们的研究结果表明,用于评估肿瘤患者临床疲劳的语言是重要的,两个简单的问题可以用来预测夜间疲劳的严重程度。
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来源期刊
Fatigue-Biomedicine Health and Behavior
Fatigue-Biomedicine Health and Behavior MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
5.20
自引率
7.10%
发文量
16
期刊介绍: Fatigue: Biomedicine, Health and Behavior is an international, interdisciplinary journal that addresses the symptom of fatigue in medical illnesses, behavioral disorders, and specific environmental conditions. These broadly conceived domains, all housed in one journal, are intended to advance research on causation, pathophysiology, assessment, and treatment. The list of topics covered in Fatigue will include fatigue in diseases including cancer, autoimmune diseases, multiple sclerosis, pain conditions, mood disorders, and circulatory diseases. The journal will also publish papers on chronic fatigue syndrome, fibromyalgia and related illnesses. In addition, submissions on specific issues involving fatigue in sleep, aging, exercise and sport, and occupations are welcomed. More generally, the journal will publish on the biology, physiology and psychosocial aspects of fatigue. The Editor also welcomes new topics such as clinical fatigue education in medical schools and public health policy with respect to fatigue.
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