Kord M Kober, Ritu Roy, Anand Dhruva, Yvette P Conley, Raymond J Chan, Bruce Cooper, Adam Olshen, Christine Miaskowski
{"title":"Prediction of evening fatigue severity in outpatients receiving chemotherapy: less may be more.","authors":"Kord M Kober, Ritu Roy, Anand Dhruva, Yvette P Conley, Raymond J Chan, Bruce Cooper, Adam Olshen, Christine Miaskowski","doi":"10.1080/21641846.2021.1885119","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Purpose: </strong>To predict the severity of evening fatigue in the week following the administration of chemotherapy using machine learning approaches.</p><p><strong>Methods: </strong>Outpatients with breast, gastrointestinal, gynecological, or lung cancer (<i>N</i>=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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":44745,"journal":{"name":"Fatigue-Biomedicine Health and Behavior","volume":"9 1","pages":"14-32"},"PeriodicalIF":2.2000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/21641846.2021.1885119","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fatigue-Biomedicine Health and Behavior","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21641846.2021.1885119","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/2/16 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
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.
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.