Perturbations in neuroinflammatory pathways are associated with paclitaxel-induced peripheral neuropathy in breast cancer survivors.

Paclitaxel is a common chemotherapy drug associated with the development of chronic paclitaxel-induced peripheral neuropathy (PIPN). PIPN is associated with neuroinflammatory mechanisms in pre-clinical studies. Here, we evaluated for differential gene expression (DGE) in peripheral blood between breast cancer survivors with and without PIPN and for neuroinflammatory (NI) related signaling pathways and whole-transcriptome profiles from other experiments. Pathway impact analysis identified 8 perturbed NI related pathways. Expression profile analysis found 15 experiments having similar whole-transcriptome profiles of DGE related to neuroinflammation and PIPN. These findings suggest that perturbations in pathways associated with neuroinflammation are found in cancer survivors with PIPN

Comparing machine learning clustering with latent class analysis on cancer symptoms' data

Symptom Cluster Research is a major topic in Cancer Symptom Science. In spite of the several statistical and clinical approaches in this domain, there is not a consensus on which method performs better. Identifying a generally accepted analytical method is important in order to be able to utilize and process all the available data. In this paper we report a secondary analysis on cancer symptom data, comparing the performance of five Machine Learning (ML) clustering algorithms in doing so. Based on how well they separate specific subsets of symptom measurements we select the best of them and proceed to compare its performance with the Latent Class Analysis (LCA) method. This analysis is a part of an ongoing study for identifying suitable Machine Learning algorithms to analyse and predict cancer symptoms in cancer treatment

The Relationships between Mood Disturbances and Pain, Hope, and Quality of Life in Hospitalized Cancer Patients with Pain on Regularly Scheduled Opioid Analgesic

Objective: The study purposes were to describe the percentage of patients in one of four mood groups (i.e., neither anxiety nor depression [NEITHER], only anxiety [ANX], only depression [DEP], both anxiety and depression [BOTH]) and to evaluate how differences in mood states are related to pain, hope, and quality of life (QOL). Methods: Oncology inpatients (n=225) completed Brief Pain Inventory, Herth Hope Index (HHI), and the European Organization for Research and Treatment of Cancer Core QOL Questionnaire-C30. Research nurses completed Symptom Severity Checklist, Karnofsky Performance Status score, and medical record reviews. Data were analyzed using x^2, Kruskal-Wallis, one-way analyses of variance (ANOVAs), and analyses of covariance (ANCOVA). Results: Thirty-two percent of patients were categorized in the NEITHER group, 12% in the ANX group, 12% in the DEP group, and 44% in the BOTH group. Younger patients and women were more likely to be in the BOTH group. While only minimal differences were found among the mood groups on pain intensity scores, patients in the NEITHER group in general, reported lower pain interference scores than those in the other three groups. Significant differences were found in HHI scores between the patients in the NEITHER group and the BOTH group. In addition, patients with both mood disorders reported significantly poorer QOL scores. Conclusions: Because 44% of the patients had both anxiety and depression, clinicians need to evaluate patients for the co-occurrence of these two symptoms, evaluate its impact on pain management, hope, and QOL, and develop appropriate interventions to manage these symptoms

The complexity of the relationship between chronic pain and quality of life: a study of the general Norwegian population

The aims of this paper were to evaluate the relationship between chronic pain and global quality of life (GQOL) and to explore the effect of possible confounders, mediators, and moderators such as selected demographic variables, chronic illnesses, stress-related symptoms, fatigue, and subjective health of the relationship between chronic pain and GQOL. We used a cross-sectional design, including 1,893 respondents from a population of 4,000 of Norwegian citizens, aged 19–81 years, who were randomly drawn from the National Register by Statistics Norway in November 2000 (48.5%). Pain duration of more than 3 months was categorized as having chronic pain. The Quality of Life Scale, the Fatigue Severity Scale, and the Posttraumatic Stress Scale were used as our main dependent and independent variables, respectively. A series of multiple regression analyses (GLM in SPSS) were applied using GQOL as the dependent variable, entering subsets of independent variables in a theoretically predefined sequence. In the total model, there was no significant relationship between chronic pain and GQOL. The model explained 39% of the variance in GQOL. For direct effect sizes, stress-related symptoms were related most strongly to GQOL, followed by subjective health, fatigue, chronic illnesses, and selected demographic variables. These findings support the assumption of a complex and indirect relationship between chronic pain and GQOL

Moving from the means to the standard deviations in symptom management research

Session presented on Friday, July 24, 2015: Most longitudinal studies of symptoms in patients with chronic medical conditions report means scores and standard deviations to describe changes in symptom occurrence or severity over time. However, most clinicians know that a large amount of inter-individual variability exists in patients\u27 reports of their symptom experiences. For example, in oncology patients receiving chemotherapy, while some patients report very few symptoms, other patients report every conceivable symptom with the highest severity scores. It is important for clinicians to be able to identify these high risk patients in order to target more aggressive symptom management interventions. In order to be able to identify patients are higher risk for a more severe symptom burden, nurse researchers need to use statistical procedures that go beyond the simple reporting of means and standard deviations. Newer approaches to the analysis of longitudinal data, including hierarchical linear modeling and latent class analysis, provide methods to identify patients who are at higher risk for a more severe symptom burden. In addition, the demographic, clinical, and molecular characteristics that are associated with increased risk can be determined. If these risk factors are confirmed in future studies, they can be used to build predictive risk models that will assist clinicians to pre-emptively identify high risk patients. The focus for this presentation is to describe these newer methods of longitudinal data analysis using the symptoms of fatigue and sleep disturbance by oncology patients as the exemplars. Fatigue and sleep disturbance are common symptoms in patients with a variety of a chronic medical conditions. Therefore, using these two symptoms as exemplars will provide information to both clinicians and researchers on the most common phenotypic and molecular characteristics associated with the most severe levels of fatigue and sleep disturbance. As part of this presentation, the purposes for using hierarchical linear modeling and latent class analysis will be compared and contrasted. In addition, approaches for integrating molecular markers into symptom management research will be discussed. This presentation will assist clinicians to perform better assessments of symptoms in patients with chronic conditions. In addition, it should provide essential information to guide the development of future symptom management studies

The relationship between chronic pain and health-related quality of life in long-term social assistance recipients in Norway

PurposeThe purposes of this study were to compare the health-related quality of life (HRQOL) of long-term social assistance recipients (LTRs) with and without chronic pain and determine the effect of select demographic, social, pain, alcohol, and illicit drug use characteristics on the physical and mental components of their HRQOL.MethodsIn this cross-sectional study, which is part of a larger study that evaluated the health and functional abilities of LTRs in Norway, 405 LTRs of which 178 had chronic pain were recruited from 14 of 433 municipalities.ResultsLTRs with chronic pain were older (P < .001), more often married (P = .002), feeling more lonely, (P = .048), and had more problems with alcohol (P = .035). The final regression model explained 41.2% (P < .001) of the variance in PCS scores and 32.2% (P < .001) of the variance in MCS scores. Being in chronic pain (29.7%), being older (4.7%), and never married (2%) predicted worse PCS scores. Feeling lonely (11.9%), having problems with illicit drug use (5.9%), and being in chronic pain (2.9%) predicted worse MCS scores.ConclusionLTRs with chronic pain rated both the physical and mental components of HRQOL lower than LTRs without chronic pain. The MCS score in both groups was negatively effected