Latent class symptom profiles of colorectal cancer survivors with cancer-related cognitive impairment

Abstract

Purpose. Colorectal cancer (CRC) survivors experience cancer-related cognitive impairment and cooccurring symptoms after cancer treatments. There has been little data to inform the risk factors of complex symptom phenotypes in CRC survivors.Objectives.To determine if subgroups of CRC survivors after cancer treatments could be identi ed based on the cognitive impairment and common co-occurring symptoms (depression, anxiety, sleep disturbance, fatigue, and pain); and to explore risk factors (sociodemographic and clinical characteristics, perceived stress, and social support) of these subgroups.Methods. Latent class pro le analysis (LCPA) was used to identify subgroups based on self-reported symptoms in 64 CRC survivors. Cognitive impairment was measured by assessing subjective cognitive function using the Patient-Reported Outcome Measurement Information System (PROMIS) measure. The Kruskal-Wallis test and regression analyses were performed.Results.Three distinct latent classes were identi ed (Class 1: All Low '28.1%'; Class 2: High Psychological Symptoms (depression/anxiety) '25%'; Class 3: High Somatic Symptoms (fatigue, sleep disturbance, and pain) with High Cognitive Impairment'46.9%'). Pain was the most distinguishable symptom across the latent classes. The high symptom burden group was associated with less time since cancer diagnosis, higher perceived stress levels, and poor emotional social support. Conclusion.Our study adds to the information on interindividual variability in symptom experience of CRC survivors with cognitive impairment. Findings suggest a need for increased attention to screening for cooccurring symptoms (e.g., high pain) and future interventions focused on stress management and social supports.Participants who agreed and signed online consent and HIPAA forms provided their contact information.Participants were then prompted to complete online questionnaires via a secure encrypted RedCap database. Following survey completion, an online gift card was sent to the participant's email. For this secondary analysis, we included CRC survivors, excluding those with: (1) stage IV CRC cancer at the time of consenting; and (2) inability to read and understand English to complete survey questionnaires. MeasuresSociodemographic and clinical characteristics. Sociodemographic, medical, and treatment data were collected via a self-report online questionnaire, including age at diagnosis and current age at data collection, marital status, education, income, race/ethnicity, speci c comorbid conditions, number of comorbidities, diagnosis date and stage, and types of cancer treatments. Cognitive impairment and common co-occurring symptoms Cognitive Impairment, Depression, Anxiety, Fatigue, and Sleep Disturbance. We used the Patient-Reported Outcome Measurement Information System (PROMIS) short-form subscales, each containing 8 items (a 5-point Likert scale "1 = not at all" to "5 = very much.") version 2.0 for Cognitive Function [20], and version 1.0 for Depression [21], Anxiety [21], Fatigue [22], and Sleep Dis...

Identification of distinct symptom profiles in prostate cancer patients with cancer-related cognitive impairment undergoing androgen deprivation therapy: A latent class analysis

“…This finding is in line with previous research on cancer that found that higher levels of fatigue, pain, and sleep disturbance were correlated with worse cognitive function. 7 , 9 Moreover, previous reports have confirmed the existence of subgroups of multiple psychoneurological symptoms and suggest inter-individual variability of symptom experience in cancer populations. 8 Notably, cognitive impairment and common co-occurring symptoms may share common biological mechanisms.…”

“… 5 , 6 Research of symptom clusters from other cancer populations indicates robust associations between co-occurring symptoms (fatigue, sleep disturbances, pain, depression) and cognitive impairment and that patients with cognitive impairment experienced distinct co-occurring symptom profiles. 7 , 8 , 9 These symptoms are also prevalently reported among PC patients with cognitive impairment, especially following ADT. 6 , 10 However, exploring symptom clusters of cognitive impairment and common co-occurring symptoms remains poorly understood in PC patients undergoing ADT.…”

A longitudinal study of symptom cluster latent profiles in ovarian cancer patients undergoing chemotherapy

“…Some of these studies have primarily focused on diverse cancer populations, failing to account for the unique subpopulation differences within the symptom cluster of fatigue, pain, and sleep disturbance 10–12 . Studies have also emerged for specific subpopulations, such as breast, lung, and pediatric cancer, 13–15 that used LPA to classify patients into latent classes based on their unique experiences with some of the common co‐occurring symptoms during chemotherapy 16,17 . Notably, these studies have identified a subgroup of individuals with high‐symptom burden.…”

“… 10 , 11 , 12 Studies have also emerged for specific subpopulations, such as breast, lung, and pediatric cancer, 13 , 14 , 15 that used LPA to classify patients into latent classes based on their unique experiences with some of the common co‐occurring symptoms during chemotherapy. 16 , 17 Notably, these studies have identified a subgroup of individuals with high‐symptom burden. This subgroup may represent a high‐risk category with a distinct symptom phenotype.…”

Comparing Symptom Clusters in Cancer Survivors by Cancer Diagnosis: A Latent Class Profile Analysis

“…The latent class analysis is bene cial for symptom research where data often include heterogeneous groups of individuals with cancer experiencing multiple symptoms, and for identifying distinct subgroups of patients based on their symptoms [10,11]. Although latent class modeling was used in a few studies in cancer survivors to identify symptom clusters [12][13][14], no published symptom cluster study using latent class analysis has compared distinct symptom subgroups across different cancer types. Comprehensive and representative samples of cancer survivors using the latent class approach in conjunction with well-validated and reliable patient-reported outcomes might be helpful in clarifying how subgroups of survivors may differ across different cancer diagnosis based on a symptom cluster.…”