A prediction model for moderate to severe cancer-related fatigue in colorectal cancer after chemotherapy: a prospective case‒control study

Attention to cancer-related physical and mental fatigue: Breaking the vicious cycle

Cancer-related fatigue (CRF) presents as a complex interplay between physical and mental fatigue, with mindfulness interventions offering a promising approach to alleviate both. These techniques, including mindfulness-based stress reduction, cognitive therapy, dialectical behavior therapy, and acceptance and commitment therapy, are designed to break the cycle of CRF by addressing its psychological and emotional aspects. This editorial integrates the latest research published by Liu et al, examining the reciprocal and harmful cyclical relationship between physical and mental CRF, and explores the causes and associated mindfulness interventions. We expect that future research will emphasize the identification and management of CRF, particularly focusing on the application of various mindfulness interventions in cancer survivors and patients undergoing cancer treatment, as well as the development of mindfulness in the era of new technologies.

Development and validation of machine learning models for predicting cancer-related fatigue in lymphoma survivors

BACKGROUND

New cases of lymphoma are rising, and the symptom burden, like cancer-related fatigue (CRF), severely impacts the quality of life of lymphoma survivors. However, clinical diagnosis and treatment of CRF are inadequate and require enhancement.

OBJECTIVE

The main objective of this study is to construct machine learning-based CRF prediction models for lymphoma survivors to help healthcare professionals accurately identify the CRF population and better personalize treatment and care for patients.

METHODS

A cross-sectional study in China recruited lymphoma patients from June 2023 to March 2024, dividing them into two datasets for model construction and external validation. Six machine learning algorithms were used in this study: Logistic Regression (LR), Random Forest, Single Hidden Layer Neural Network, Support Vector Machine, eXtreme Gradient Boosting, and Light Gradient Boosting Machine (LightGBM). Performance metrics like the area under the receiver operating characteristic (AUROC) and calibration curves were compared. The clinical applicability was assessed by decision curve, and Shapley additive explanations was employed to explain variable significance.

RESULTS

CRF incidence was 40.7 % (dataset I) and 44.8 % (dataset II). LightGBM showed strong performance in training and internal validation. LR excelled in external validation with the highest AUROC and best calibration. Pain, total protein, physical function, and sleep disturbance were important predictors of CRF.

CONCLUSION

The study presents a machine learning-based CRF prediction model for lymphoma patients, offering dynamic, data-driven assessments that could enhance the development of automated CRF screening tools for personalized management in clinical practice.

Factors associated with anxiety in colorectal cancer survivors: a scoping review

PURPOSE

Anxiety is one of the most common psychological issues among colorectal cancer (CRC) survivors. It can interact with physical symptoms, impacting cancer progression, survival, and quality of life. This scoping review aims to explore the factors associated with anxiety in patients with CRC and the instruments used to measure anxiety.

METHODS

Using Arksey and O'Malley's (2005) framework for the scoping review, studies investigating anxiety in CRC patients published in CINAHL, PubMed, PsycINFO, and Scopus between 2013 and 2024 were included.

RESULTS

We analyzed fifty-one studies for this review. The review identified several risk factors and consequences of anxiety in CRC patients. The risk factors were classified into six domains using Niedzwiedz et al.'s (2019) framework: individual characteristics, social/ contextual factors, prior psychological factors, psychological responses to diagnosis and treatment, characteristics of cancer, and treatment. The consequences of anxiety were classified into three categories: global health status/quality of life, functions, and symptoms/problems. The most frequently used tool was the Hospital Anxiety and Depression Scale, with International Classification of Diseases codes being the second most used.

CONCLUSIONS

This scoping review highlighted the intricate interaction between biological and psychosocial aspects in the lives of CRC survivors. It also identified unique factors associated with anxiety among these individuals. However, the review found some inconsistencies in the results related to anxiety-related factors, potentially due to differences in study populations, designs, measurement tools, and analysis methods.

IMPLICATIONS FOR CANCER SURVIVORS

This review underscores the potential for interventions targeting modifiable factors to prevent or reduce anxiety and enhance the quality of life for CRC survivors.

Development and validation of a nomogram for predicting sever cancer-related fatigue in patients with cervical cancer

OBJECTIVE

Cancer-related fatigue (CRF) has been considered the biggest influencing factor for cancer patients after surgery. This study aimed to develop and validate a nomogram for severe cancer-related fatigue (CRF) patients with cervical cancer (CC).

METHODS

A cross-sectional study was conducted to develop and validate a nomogram (building set = 196; validation set = 88) in the Department of Obstetrics and Gynecology of a Class III hospital in Shenyang, Liaoning Province. We adopted the questionnaire method, including the Cancer Fatigue Scale (CFS), Medical Uncertainty in Illness Scale (MUIS), Medical Coping Modes Questionnaire (MCMQ), Multidimensional Scale of Perceived Social Support (MSPSS), and Sense of Coherence-13 (SOC-13). Binary logistic regression was used to test the risk factors of CRF. The R4.1.2 software was used to develop and validate the nomogram, including Bootstrap resampling method, the ability of Area Under Curve (AUC), Concordance Index (C-Index), Hosmer Lemeshow goodness of fit test, Receiver Operating Characteristic (ROC) curve, Calibration calibration curve, and Decision Curve Analysis curve (DCA).

RESULTS

The regression equation was Logit(P) = 1.276-0.947 Monthly income + 0.989 Long-term passive smoking - 0.952 Physical exercise + 1.512 Diagnosis type + 1.040 Coping style - 0.726 Perceived Social Support - 2.350 Sense of Coherence. The C-Index of the nomogram was 0.921 (95% CI: 0.877∼0.958). The ROC curve showed the sensitivity of the nomogram was 0.821, the specificity was 0.900, and the accuracy was 0.857. AUC was 0.916 (95% CI: 0.876∼0.957). The calibration showed that the predicted probability of the nomogram fitted well with the actual probability. The DCA curve showed when the prediction probability was greater than about 10%, the benefit of the nomogram was positive. The results in the validation group were similar.

CONCLUSION

This nomogram had good identifiability, accuracy and clinical practicality, and could be used as a prediction and evaluation tool for severe cases of clinical patients with CC.

Analysis of the factors influencing moderate to poor performance status in patients with cancer after chemotherapy: a cross-sectional study comparing three models

There are no models for assessing the factors that determine moderate to poor performance status in patients with cancer after chemotherapy. This study investigated the influencing factors and identified the best model for predicting moderate-poor performance status. A convenience sampling method was used. Demographic and clinical data and evaluation results for fatigue, pain, quality of life and Eastern Cooperative Oncology Group status were collected three days after the end of chemotherapy. Decision tree, random forest and logistic regression models were constructed. Ninety-four subjects in the case group had moderate to poor performance status, and 365 subjects in the control group had no or mild activity disorders. The random forest model was the most accurate model. Physical function, total protein, general quality of life within one week before chemotherapy, hemoglobin, pain symptoms and globulin were the main factors. Total protein and hemoglobin levels reflect nutritional status, and globulin levels are an index of liver function. Therefore, physical function, nutritional status, general quality of life and pain symptoms within one week before chemotherapy and liver function can be used to predict moderate-poor performance status. Nurses should pay more attention to patients with poor physical function, poor nutritional status, lower quality of life and pain symptoms after chemotherapy.

Functional impairment is associated with medical debt in male cancer survivors and credit card debt in female cancer survivors

PURPOSE

To examine the associations of functional limitations with medical and credit card debt among cancer survivor families and explore sex differences in these associations.

METHODS

This cross-sectional study used data from the 2019 wave of the Panel Study of Income Dynamics, a nationally representative, population-based survey of individuals and households in the US administered in both English and Spanish and includes all households where either the head of household or spouse/partner reported having been diagnosed with cancer. Participants reported on functional limitations in six instrumental activities of daily living (IADL) and seven activities of daily living (ADL). Functional impairment was categorized as 0, 1-2 and ≥ 3 limitations. Medical debt was defined as self-reported unpaid medical bills. Credit card debt was defined as revolving credit card debt. Multivariable logistic regression analyses were performed.

RESULTS

Credit card debt was more common than medical debt (39.8% vs. 7.6% of cancer survivor families). Families of male cancer survivors were 7.3 percentage points more likely to have medical debt and 16.0 percentage points less likely to have credit card debt compared to families of female cancer survivors. Whereas male cancer survivors with increasing levels of impairment were 24.7 percentage point (p-value = 0.006) more likely to have medical debt, female survivors with more functional impairment were 13.6 percentage points (p-value = 0.010) more likely to have credit card debt.

CONCLUSIONS

More research on medical and credit card debt burden among cancer survivors with functional limitations is needed.