Person:

Hong, Fangxin

Overview: Patient–provider communication is vital to quality patient care in oncology settings and impacts health outcomes. Newer communication datasets contain patient symptom reports, real-time audiofiles of visits, coded communication data, and visit outcomes. The purpose of this paper is to: (1) review the complex communication processes during patient–provider interaction during oncology care; (2) describe methods of gathering and coding communication data; (3) suggest logical approaches to analyses; and (4) describe one new dataset that allows linking of patient symptoms and communication processes with visit outcomes. Challenges: Patient–provider communication research is complex due to numerous issues, including human subjects’ concerns, methods of data collection, numerous coding schemes, and varying analytic techniques. Data collection and coding: Coding of communication data is determined by the research question(s) and variables of interest. Subsequent coding and timestamping the behaviors provides categorical data and determines the interval between and patterns of behaviors. Analytic approaches: Sequential analyses move from descriptive statistics to explanatory analyses to direct analyses and conditional probabilities. In the final stage, explanatory modeling is used to predict outcomes from communication elements. Examples of patient and provider communication in the ambulatory oncology setting are provided from the new Electronic Self Report Assessment-Cancer II dataset. Summary: More complex communication data sets provide opportunities to link elements of patient–provider communication with visit outcomes. Given more complex datasets, a step-wise approach is necessary to analyze and identify predictive variables. Sequential analyses move from descriptive results to predictive models with communication data, creating links between patient symptoms and concerns, real-time audiotaped communication, and visit outcomes. The results of these analyses will be useful in developing evidence-based interventions to enhance communication and improve psychosocial outcomes in oncology settings.

Purpose Evaluate baseline factors that may explain the influence of study site on decisional conflict (DC) in men from the Personal Patient Profile: Prostate (P3P) randomized trial. Materials and methods 476 cases from 5 P3P sites were included. Participants completed baseline demographic assessments, 4 subscales of the DC scale at baseline (uncertainty, informed, values clarity, and support), the Expanded Prostate Cancer Index Composite (short form) and the State-Trait Anxiety Inventory. Site data regarding typical practices were collected. Linear regressions were used to model the relation between baseline DC scores and study site adjusting for the list of variables. Results: Baseline decisional uncertainly (p = 0.001) and informed (p = 0.03) subscales were significantly different across sites. Participant demographic and baseline measures were significantly different (p < 0.05) between sites except for trait anxiety. We identified participant level factors that explained study site differences at baseline for the decisional uncertainty and values clarity subscales: a preferred treatment choice at study entry, whether the study program was accessed at home vs. in clinic, number of doctors consulted pre-study, working status, state anxiety, information from the media or a health care provider, and perceived knowledge level. State anxiety was associated with higher DC across all subscales. Conclusions: Individual characteristics of men seeking consultation for LPC were associated with DC at baseline, not the site alone; anxiety contributed to higher conflict. These findings will inform future development and implementation of the P3P and other decision support interventions. Trial registration NCT00692653.

Background: Therapeutic cancer chemotherapy is most successful when complete dosing is achieved. Because many newer therapeutic agents are oral and self-administered by the patient, adherence is a concern. The purpose of our analysis was to explore relationships between adherence, patient characteristics, and barriers to adherence. Methods: This secondary analysis utilized self-reported data from a randomized trial of self-care management conducted at two cancer centers in the US. Symptom distress was measured using the 15-item Symptom Distress Scale (SDS-15) and depression with the Patient Health Questionnaire-9 (PHQ-9). Adherence to oral medication was self-reported using the 8-item Morisky Medication Adherence Scale (MMAS-8). Measures were collected via Web-based, study-specific software ~8 weeks after treatment start date. Odds of low/medium adherence (score <8) were explored using univariate logistic regression. Given the number of factors and possible relationships among factors, a classification tree was built in lieu of a multivariable logistic regression model. Results: Of the eligible participants enrolled, 77 were on oral therapy and 70 had an MMAS score. Forty-nine (70%) reported a high adherence score (=8). Higher odds of low/medium adherence were associated with greater symptom distress (P=0.09), more depression (P=0.05), chemotherapy vs hormonal oral medication (P=0.03), being female (P=0.02), and being randomized to the control group in the parent trial (P=0.09). Conversely, high adherence was associated with working (P=0.08), being married/partnered (P=0.004), and being older (P=0.02). Factors identified as significantly related to low/medium adherence from the univariate logistic regression analyses were supported by the classification tree results. Conclusion: Nonadherence to therapeutic oral medications in patients with cancer was associated with being unmarried/unpartnered, symptom distress, younger age, not working, and female sex. These findings may help to identify patients at risk for nonadherence and for whom supportive interventions to enhance adherence may be needed.

Killer-cell immunoglobulin-like receptors (KIRs) are a family of glycoproteins expressed primarily on natural killer cells that can regulate their function. Inhibitory KIRs recognize MHC class I molecules (KIR-ligands) as ligands. We have reported associations of KIRs and KIR-ligands for patients in two monoclonal antibody (mAb)-based trials: (1) A Children’s Oncology Group (COG) trial for children with high-risk neuroblastoma randomized to immunotherapy treatment with dinutuximab (anti-GD2 mAb) + GM-CSF + IL-2 + isotretinion or to treatment with isotretinoin alone and (2) An Eastern Cooperative Oncology Group (ECOG) trial for adults with low-tumor burden follicular lymphoma responding to an induction course of rituximab (anti-CD20 mAb) and randomized to treatment with maintenance rituximab or no-maintenance rituximab. In each trial, certain KIR/KIR-ligand genotypes were associated with clinical benefit for patients randomized to immunotherapy treatment (immunotherapy in COG; maintenance rituximab in ECOG) as compared to patients that did not receive the immunotherapy [isotretinoin alone (COG); no-maintenance (ECOG)]. Namely, patients with both KIR3DL1 and its HLA-Bw4 ligand (KIR3DL1+/HLA-Bw4+ genotype) had improved clinical outcomes if randomized to immunotherapy regimens, as compared to patients with the KIR3DL1+/HLA-Bw4+ genotype randomized to the non-immunotherapy regimen. Conversely, patients that did not have the KIR3DL1+/HLA-Bw4+ genotype showed no evidence of a difference in outcome if receiving the immunotherapy vs. no-immunotherapy. For each trial, HLA-Bw4 status was determined by assessing the genotypes of three separate isoforms of HLA-Bw4: (1) HLA-B-Bw4 with threonine at amino acid 80 (B-Bw4-T80); (2) HLA-B-Bw4 with isoleucine at amino acid 80 (HLA-B-Bw4-I80); and (3) HLA-A with a Bw4 epitope (HLA-A-Bw4). Here, we report on associations with clinical outcome for patients with KIR3DL1 and these separate isoforms of HLA-Bw4. Patients randomized to immunotherapy with KIR3DL1+/A-Bw4+ or with KIR3DL1+/B-Bw4-T80+ had better outcome vs. those randomized to no-immunotherapy, whereas for those with KIR3DL1+/B-Bw4-I80+ there was no evidence of a difference based on immunotherapy vs. no-immunotherapy. Additionally, we observed differences within treatment types (either within immunotherapy or no-immunotherapy) that were associated with the genotype status for the different KIR3DL1/HLA-Bw4-isoforms. These studies suggest that specific HLA-Bw4 isoforms may differentially influence response to these mAb-based immunotherapy, further confirming the involvement of KIR-bearing cells in tumor-reactive mAb-based cancer immunotherapy.

Introduction: Frequently reported symptoms and treatment side effects may not be the most bothersome issues to patients with cancer. The purpose of this study was to investigate patient-reported symptom distress and bothersome issues among participants with cancer. Methods: Participants completed the Symptom Distress Scale-15 before treatment (T1) and during cancer treatment (T2) and reported up to two most bothersome issues among symptoms rated with moderate-to-severe distress. We compared symptom ratings and perceived bother and explored two approaches predicting patients’ most bothersome issues: worst absolute symptom score or worst change from pretreatment. Results: Significantly, (P≤0.0002) more patients reported moderate-to-severe distress at T2 for eight of 13 symptoms. At T1, 81% of patients reported one and 56% reported multiple symptoms with moderate-to-severe distress, while at T2, 89% reported one and 69% reported multiple symptoms with moderate-to-severe distress. Impact on sexual activity/interest, pain, fatigue, and insomnia were the most prevalent symptoms with moderate-to-severe distress. Fatigue, pain, and insomnia were perceived most often as bothersome. When one symptom was rated moderate-to-severe, predictive accuracy of the absolute score was 46% and 48% (T1 & T2) and 38% with the change score (T2–T1). When two or more symptoms were rated moderate-to-severe, predictive accuracy of the absolute score was 76% and 79% (T1 & T2) and 70% with the change score (T2–T1). Conclusion: More patients experienced moderate-to-severe symptom distress after treatment initiation. Patient identification of bothersome issues could not be assumed based on prevalence of symptoms reported with moderate-to-severe distress. The absolute symptom distress scores identified patients’ most bothersome issues with good accuracy, outperforming change scores.

Abstract Motivation: The Rank Product (RP) is a statistical technique widely used to detect differentially expressed features in molecular profiling experiments such as transcriptomics, metabolomics and proteomics studies. An implementation of the RP and the closely related Rank Sum (RS) statistics has been available in the RankProd Bioconductor package for several years. However, several recent advances in the understanding of the statistical foundations of the method have made a complete refactoring of the existing package desirable. Results: We implemented a completely refactored version of the RankProd package, which provides a more principled implementation of the statistics for unpaired datasets. Moreover, the permutation-based P-value estimation methods have been replaced by exact methods, providing faster and more accurate results. Availability and implementation: RankProd 2.0 is available at Bioconductor (https://www.bioconductor.org/packages/devel/bioc/html/RankProd.html) and as part of the mzMatch pipeline (http://www.mzmatch.sourceforge.net). Contact: rainer.breitling@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.