Dataset Information

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

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.

SUBMITTER: Kober KM

PROVIDER: S-EPMC8262130 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Fatigue, a highly prevalent and distressing symptom during chemotherapy (CTX), demonstrates diurnal and interindividual variability in severity. Little is known about the associations between variations in genes involved in inflammatory processes and morning and evening fatigue severity during CTX. The purposes of this study, in a sample of oncology patients (N=543) with breast, gastrointestinal (GI), gynecological (GYN), or lung cancer who received two cycles of CTX, were to determine whether variations in genes involved in inflammatory processes were associated with inter-individual variability in initial levels as well as in the trajectories of morning and evening fatigue. Patients completed the Lee Fatigue Scale to determine morning and evening fatigue severity a total of six times over two cycles of CTX. Using a whole exome array, 309 single nucleotide polymorphisms SNPs among the 64 candidate genes that passed all quality control filters were evaluated using hierarchical linear modeling (HLM). Based on the results of the HLM analyses, the final SNPs were evaluated for their potential impact on protein function using two bioinformational tools. The following inflammatory pathways were represented: chemokines (3 genes); cytokines (12 genes); inflammasome (11 genes); Janus kinase/signal transducers and activators of transcription (JAK/STAT, 10 genes); mitogen-activated protein kinase/jun amino-terminal kinases (MAPK/JNK, 3 genes); nuclear factor-kappa beta (NFkB, 18 genes); and NFkB and MAP/JNK (7 genes). After controlling for self-reported and genomic estimates of race and ethnicity, polymorphisms in six genes from the cytokine (2 genes); inflammasome (2 genes); and NFkB (2 genes) pathways were associated with both morning and evening fatigue. Polymorphisms in six genes from the inflammasome (1 gene); JAK/STAT (1 gene); and NFkB (4 genes) pathways were associated with only morning fatigue. Polymorphisms in three genes from the inflammasome (2 genes) and the NFkB (1 gene) pathways were associated with only evening fatigue. Taken together, these findings add to the growing body of evidence that suggests that morning and evening fatigue are distinct symptoms.