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Prediction of morning fatigue severity in outpatients receiving chemotherapy: less may still be more

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Abstract

Introduction

Fatigue is the most common and debilitating symptom experienced by cancer patients undergoing chemotherapy (CTX). Prediction of symptom severity can assist clinicians to identify high-risk patients and provide education to decrease symptom severity. The purpose of this study was to predict the severity of morning fatigue in the week following the administration of CTX.

Methods

Outpatients (n = 1217) completed questionnaires 1 week prior to and 1 week following administration of CTX. Morning fatigue was measured using the Lee Fatigue Scale (LFS). Separate prediction models for morning fatigue severity were created using 157 demographic, clinical, symptom, and psychosocial adjustment characteristics and either morning fatigue scores or individual fatigue item scores. Prediction models were created using two regression and five machine learning approaches.

Results

Elastic net models provided the best fit across all models. For the EN model using individual LFS item scores, two of the 13 individual LFS items (i.e., “worn out,” “exhausted”) were the strongest predictors.

Conclusions

This study is the first to use machine learning techniques to accurately predict the severity of morning fatigue from prior to through the week following the administration of CTX using total and individual item scores from the Lee Fatigue Scale (LFS). 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 morning fatigue severity.

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Funding

This work was supported by the National Cancer Institute at the National Institute of Health under grant CA134900, grant CA233774, and grant CA082103. Dr. Olshen is partially supported by the Cancer Center Support Grant (P30CA082103). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. Dr. Miaskowski is an American Cancer Society Clinical Research Professor.

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Authors and Affiliations

  1. School of Nursing, University of California, San Francisco, CA, USA

    Kord M. Kober & Christine Miaskowski

  2. Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA

    Kord M. Kober, Ritu Roy, Adam Olshen & Christine Miaskowski

  3. Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA

    Kord M. Kober & Adam Olshen

  4. School of Nursing, University of Pittsburg, Pittsburg, PA, USA

    Yvette Conley

  5. School of Medicine, University of California, San Francisco, CA, USA

    Anand Dhruva, Jon Levine & Adam Olshen

  6. Dana Farber Cancer Institute, Boston, MA, USA

    Marilyn J Hammer

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  1. Kord M. Kober
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Contributions

All authors contributed to the study conception and design. Data analysis was performed by Kord Kober, Ritu Roy, and Adam Olshen. The first draft of the manuscript was written by Kord Kober and Chris Miaskowski, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Kord M. Kober.

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Kober, K.M., Roy, R., Conley, Y. et al. Prediction of morning fatigue severity in outpatients receiving chemotherapy: less may still be more. Support Care Cancer 31, 253 (2023). https://doi.org/10.1007/s00520-023-07723-5

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