Janet H Van Cleave

Abstract

Background: Annually, over 65,000 persons are diagnosed with head and neck cancer in the United States. During treatment, up to 50% of patients become severely symptomatic with pain, fatigue, mouth sores, and inability to eat. Long term complications are lymphedema, fibrosis, dysphagia, and musculoskeletal impairment. Patients' ability to perform daily activities and to interact socially may be impaired, resulting in poor quality of life. A pragmatic, clinically useful assessment is needed to ensure early detection and intervention for patients to report symptoms and functional limitations over time. We developed the Electronic Patient Visit Assessment (ePVA) that enables patients to report 42 symptoms related to head and neck cancer and 17 limitations of functional status. This manuscript reports (I) the development of the ePVA, (II) the content validity of the ePVA, and (III) the usability and reliability of the ePVA.Methods: Usability was evaluated using the "Think Aloud" technique to guide the iterative process to refine the ePVA based on participants' evaluations. After signing the informed consent, 30 participants with head and neck cancer completed the ePVA using digital tablet devices while thinking aloud about ease of use. All patient conversations were recorded and professionally transcribed. Reliability of the ePVA symptom and functional limitation measures was estimated using the Kuder-Richardson test. Convergent validity of the ePVA was evaluated using the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30 global QoL/health scale. Transcribed qualitative data were analyzed using directed content analysis approach. Quantitative analyses consisted of descriptive statistics and correlation analyses.Results: Among participants, 90% strongly agreed or agreed that the ePVA system was easy to use and 80% were very satisfied. Only minor usability problems were reported due to formatting and software "bugs". Reporting of usability problems decreased in frequency over the study period and no usability problems were reported by the last 3 participants who completed the ePVA. Based on participants' suggestions during the iterative process, refinement of the ePVA included increased touch sensitivity of the touch screen technology and customized error messages to improve ease of use. The ePVA also recorded patient reported symptoms (mouth symptoms: 93%, fibrosis: 60%, fatigue: 60%). The ePVA demonstrated acceptable reliability (alpha =0.82-0.85) and convergent validity (ePVA total number of reported symptoms and function limitations was negatively correlated with EORTC QLQ-C30 global QOL/health scale: r=-0.55038, P

Abstract

Background: Abnormal blood glucose (BG) levels during hematopoietic cell transplantation (HCT) are associated with increased infections, delayed engraftment, and prolonged hospitalization, though little is known about these associations. Materials and Methods: We retrospectively evaluated mean BG levels in the week prior to HCT and subsequent outcomes for 852 HCTs at our hospital from 1/2009 � 12/2013 pertaining to 745 patients. Outcomes included infections (pneumonia, C. difficile, positive cultures, administration of antimicrobials, or neutropenic fever), time-to-engraftment (TTE), and quality indicators (30- and 90-day readmission rates [RR] and median length-of-stay [LOS]). Results: We retrospectively evaluated mean BG levels in the week prior to HCT and subsequent outcomes for 852 HCTs at our hospital from 1/2009 � 12/2013 pertaining to 745 patients. Outcomes included infections (pneumonia, C. difficile, positive cultures, administration of antimicrobials, or neutropenic fever), time-to-engraftment (TTE), and quality indicators (30- and 90-day readmission rates [RR] and median length-of-stay [LOS]). Conclusion: Pre-HCT BG trends may be a prognostic biomarker for adverse outcomes, and thus can help improve quality of care for HCT patients.

Abstract

Background: Awareness of disease status has been identified as a factor in the treatment decision-making process. Women with recurrent ovarian cancer are facing the challenge of making treatment decisions throughout the disease trajectory. It is not understood how women with ovarian cancer perceive their disease and subsequently make treatment decisions. Purpose: The purpose of this phenomenological study was to understand the lived experience of women with recurrent ovarian cancer, how they understood their disease and made their treatment decisions. Methods: A qualitative design with a descriptive phenomenological method was used to conduct 2 in-depth interviews with 12 women (n = 24 interviews). Each interview was ∼60 minutes and was digitally recorded and professionally transcribed. Data collection focused on patients' understanding of their disease and how patients participated in treatment decisions. A modified version of Colaizzi's method of phenomenological reduction guided data analysis. Results: Three themes emerged to describe the phenomenon of being aware of disease status: (1) perceiving recurrent ovarian cancer as a chronic illness, (2) perceived inability to make treatment decisions, and (3) enduring emotional distress. Conclusions and Implications: This study revealed how 12 women conceptualized recurrent ovarian cancer as a chronic disease and their perceived inability to make treatment decisions because of lack of information and professional qualifications, resulting in enduring emotional distress. Future research should replicate the study to confirm the persistence of the themes for racially, ethnically, and religiously diverse patient samples and to improve understanding of awareness of disease status and decision-making processes of patients.

Abstract

PROBLEM IDENTIFICATION: The impact of mental health disorders (MHDs) and substance use disorders (SUDs) on healthcare utilization (HCU) in patients with cancer is an understudied phenomenon. LITERATURE SEARCH: A literature search of studies published prior to January 2018 that examined HCU in patients with preexisting MHDs or SUDs diagnosed with cancer was conducted. DATA EVALUATION: The research team evaluated 22 studies for scientific rigor and examined significant trends in HCU, as well as types of the MHD, SUD, and cancer studied. SYNTHESIS: The heterogeneity of HCU outcome measures, MHD, SUD, sample sizes, and study settings contributed to inconsistent study findings. However, study trends indicated higher rates of HCU by patients with depression and lower rates of HCU by patients with schizophrenia. In addition, the concept of HCU measures is evolving, addressing not only volume of health services, but also quality and efficacy. IMPLICATIONS FOR RESEARCH: Oncology nurses are essential to improving HCU in patients with MHDs and SUDs because of their close connections with patients throughout the stages of cancer care. Additional prospective studies are needed to examine specific MHDs and different types of SUDs beyond alcohol use, improving cancer care and the effectiveness of HCU in this vulnerable population.

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Frailty is defined as a disability in those of advanced age, often with comorbidities, poor nutritional status, cognitive decline, and reduced functional status. OBJECTIVES: The purpose of this article is to discuss the concept of frailty, assess the use of a comprehensive geriatric assessment (CGA), and understand the implications for treatment to maintain or enhance physical, functional, and cognitive health of older adult patients with cancer. METHODS: Literature about frailty in older adult patients diagnosed with cancer was reviewed to determine evidence-based assessment and treatment options. FINDINGS: About half of all older adult patients with cancer experience some degree of frailty. CGA is a useful way to evaluate frailty and the extent of limitations. Many frailty-specific tools have been developed. Evidence-based strategies are available to address limitations associated with frailty in older adult patients with cancer.

Abstract

Background: In the digital era when mHealth has emerged as an important venue for health care, the application of computer science, such as machine learning, has proven to be a powerful tool for health care in detecting or predicting various medical conditions by providing improved accuracy over conventional statistical or expert-based systems. Symptoms are often indicators for abnormal changes in body functioning due to illness or side effects from medical treatment. Real-time symptom report refers to the report of symptoms that patients are experiencing at the time of reporting. The use of machine learning integrating real-time patient-centered symptom report and real-time clinical analytics to develop real-time precision prediction may improve early detection of lymphedema and long term clinical decision support for breast cancer survivors who face lifelong risk of lymphedema. Lymphedema, which is associated with more than 20 distressing symptoms, is one of the most distressing and dreaded late adverse effects from breast cancer treatment. Currently there is no cure for lymphedema, but early detection can help patients to receive timely intervention to effectively manage lymphedema. Because lymphedema can occur immediately after cancer surgery or as late as 20 years after surgery, real-time detection of lymphedema using machine learning is paramount to achieve timely detection that can reduce the risk of lymphedema progression to chronic or severe stages. This study appraised the accuracy, sensitivity, and specificity to detect lymphedema status using machine learning algorithms based on real-time symptom report.Methods: A web-based study was conducted to collect patients' real-time report of symptoms using a mHealth system. Data regarding demographic and clinical information, lymphedema status, and symptom features were collected. A total of 355 patients from 45 states in the US completed the study. Statistical and machine learning procedures were performed for data analysis. The performance of five renowned classification algorithms of machine learning were compared: Decision Tree of C4.5, Decision Tree of C5.0, gradient boosting model (GBM), artificial neural network (ANN), and support vector machine (SVM). Each classification algorithm has certain user-definable hyper parameters. Five-fold cross validation was used to optimize these hyper parameters and to choose the parameters that led to the highest average cross validation accuracy.Results: Using machine leaning procedures comparing different algorithms is feasible. The ANN achieved the best performance for detecting lymphedema with accuracy of 93.75%, sensitivity of 95.65%, and specificity of 91.03%.Conclusions: A well-trained ANN classifier using real-time symptom report can provide highly accurate detection of lymphedema. Such detection accuracy is significantly higher than that achievable by current and often used clinical methods such as bio-impedance analysis. Use of a well-trained classification algorithm to detect lymphedema based on symptom features is a highly promising tool that may improve lymphedema outcomes.

Abstract

In an attempt to address the issue of undertreated pain, the Pain as the Fifth Vital Sign (P5VS) Initiative was established to improve the quality of pain care across clinical settings. This initiative included policy efforts such as mandatory pain screening and the implementation of pain-related questions on patient satisfaction surveys. These policies have failed to enhance the treatment of pain and may have unintentionally contributed, in part, to the opioid epidemic. To assess pain more effectively, an inter-professional team approach using multi-dimensional pain assessment tools is needed. The inter-professional team can use these multi-dimensional tools to conduct comprehensive assessments to measure aspects of the pain experience (e.g., psychological, spiritual and socio-emotional pain; impact on daily functioning) beyond its sensory component and establish realistic goals that align with patients' needs. To implement multi-dimensional pain assessments in busy clinical practices, nurses will need to play a central role. Nurses can work to ensure that patients complete the questionnaires prior to the visit. Nurses can also take the lead in the use of new technologies in the form of tablets, smart phones, and mobile apps to facilitate collecting patient-level data in the home or in a waiting room before their visits.

Abstract

Preventing adverse events among chronically ill older adults living in the community is a national health priority. The purpose of this study was to generate distinct risk profiles and compare these profiles in time to: hospitalization, emergency department (ED) visit or death in 371 community-dwelling older adults enrolled in a Medicare demonstration project. Guided by the Behavioral Model of Health Service Use, a secondary analysis was conducted using Latent Class Analysis to generate the risk profiles with Kaplan Meier methodology and log rank statistics to compare risk profiles. The Vuong-Lo-Mendell-Rubin Likelihood Ratio Test demonstrated optimal fit for three risk profiles (High, Medium, and Low Risk). The High Risk profile had significantly shorter time to hospitalization, ED visit, and death (p < 0.001 for each). These findings provide a road map for generating risk profiles that could enable more effective targeting of interventions and be instrumental in reducing health care costs for subgroups of chronically ill community-dwelling older adults.