Dr. Wright is Assistant Professor of Nursing. She received a B.S.N. and M.S. from the University of Michigan School of Nursing, and a Ph.D. from New York University College of Nursing.
Fay Wright, PhD, RN, APRN-BC joined the Rory Meyers College of Nursing as an assistant professor in 2017, following a T-32 post-doctoral fellowship in Self and Symptom Management at Yale University School of Nursing. Dr. Wright’s research is focused on identifying profiles of patients at risk for higher levels of symptoms with chronic comorbid conditions including cancer, heart disease, and diabetes. By developing risk profiles that incorporate demographic, clinical and genomic characteristics, Dr. Wright plans to develop and test precision interventions to support patients’ self-management of symptoms, and to improve their functional status and quality of life. Dr. Wright has experience conducting clinical research within large urban and community hospital settings using quantitative, biobehavioral and mixed method methodology. Her publications identify characteristics of patients at risk for higher levels of fatigue during chemotherapy.
Prior to joining the faculty at NYU, Dr. Wright the Assistant Director for Evidence-Based Practice and Nursing Research at Northern Westchester Hospital in Mt Kisco NY, a clinical assistant professor at Pace University Lienhard School of Nursing, and NYU College of Nursing.
New York University, PhDNew York University, Post-Master's CertificateUniversity of Michigan School of Nursing, MSUniversity of Michigan School of Nursing, BSN
Honors and awards
Research Pilot Grant International Society of Nurses in Genetics: Daily Diurnal Fatigue Variability and its Association with Genetic Expression of Inflammasome Pathways $2500 (2017)T32 Post-doctoral fellowship, National Institute of Nursing Research (2017)Intramural Research Training Award: Precision Health Boot Camp. National Institute of Nursing (2016)Intramural Research Training Award: Summer genetics Institute. National Institute of Nursing. (2015)Summer Genetics Institute. Intramural Research Training Award National Institute of Nursing Research. (2015)Valedictorian PhD Program New York University College of Nursing (2015)Distinguished PhD Student New York University College of Nursing (2015)Best Dissertation Award New York University College of Nursing (2015)Evidence-Based Practice Excellence Award Maintaining Normothermia in Perioperative Patients Foundation of New York State Nurses Association (2012)Evidence-Based Practice Excellence Award: Developing an Evidence-based Protocol for Sedation in Mechanically Ventilated Critical Care Patients Foundation of New York State Nurses Association (2011)
Eastern Nursing Research Society (Chronic Comorbid Conditions Research Interest Group Co-Chair)Sigma Theta TauOncology Nursing SocietyAmerican Nurses AssociationAssociation of New York State Nurses
Network Analysis of the Multidimensional Symptom Experience of OncologyAbstractPapachristou, N., Barnaghi, P., Cooper, B., Kober, K. M., Maguire, R., Paul, S. M., Hammer, M., Wright, F., Armes, J., Furlong, E. P., McCann, L., Conley, Y. P., Patiraki, E., Katsaragakis, S., Levine, J. D., & Miaskowski, C. (2019). Scientific Reports, 9(1). 10.1038/s41598-018-36973-1Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress. Recent advances in Network Analysis (NA) provide a novel approach to gain insights into the complex nature of co-occurring symptoms and symptom clusters and identify core symptoms. We present findings from the first study that used NA to examine the relationships among 38 common symptoms in a large sample of oncology patients undergoing chemotherapy. Using two different models of Pairwise Markov Random Fields (PMRF), we examined the nature and structure of interactions for three different dimensions of patients’ symptom experience (i.e., occurrence, severity, distress). Findings from this study provide the first direct evidence that the connections between and among symptoms differ depending on the symptom dimension used to create the network. Based on an evaluation of the centrality indices, nausea appears to be a structurally important node in all three networks. Our findings can be used to guide the development of symptom management interventions based on the identification of core symptoms and symptom clusters within a network.
Stability of Symptom Clusters in Patients With Lung Cancer Receiving ChemotherapyAbstractRussell, J., Wong, M. L., Mackin, L., Paul, S. M., Cooper, B. A., Hammer, M., Conley, Y. P., Wright, F., Levine, J. D., & Miaskowski, C. (2019). Journal of Pain and Symptom Management. 10.1016/j.jpainsymman.2019.02.002Context: Patients with lung cancer who undergo chemotherapy (CTX) experience multiple symptoms. Evaluation of how these symptoms cluster together and how these symptom clusters change over time are salient questions in symptom clusters research. Objectives: The purposes of this analysis, in a sample of patients with lung cancer (n = 145) who were receiving CTX, were to 1) evaluate for differences in the number and types of symptom clusters at three time points (i.e., before their next cycle of CTX, the week after CTX, and two weeks after CTX) using ratings of symptom occurrence and severity and 2) evaluate for changes in these symptom clusters over time. Methods: At each assessment, a modified version of the Memorial Symptom Assessment Scale was used to assess the occurrence and severity of 38 symptoms. Exploratory factor analyses were used to extract the symptom clusters. Results: Across the two symptom dimensions (i.e., occurrence and severity) and the three assessments, six distinct symptom clusters were identified; however, only three of these clusters (i.e., lung cancer specific, psychological, nutritional) were relatively stable across both dimensions and across time. Two additional clusters varied by time but not by symptom dimension (i.e., epithelial/gastrointestinal and epithelial). A sickness behavior cluster was identified at each assessment with the exception of the week before CTX using only the severity dimension. Conclusion: Findings provide insights into the most common symptom clusters in patients with lung cancer undergoing CTX. Most common symptoms within each cluster appear to be relatively stable across the two dimensions, as well as across time.
Changes in the Occurrence, Severity, and Distress of Symptoms in Patients With Gastrointestinal Cancers Receiving ChemotherapyAbstractTantoy, I. Y., Cooper, B. A., Dhruva, A., Cataldo, J., Paul, S. M., Conley, Y. P., Hammer, M., Wright, F., Dunn, L. B., Levine, J. D., & Miaskowski, C. (2018). Journal of Pain and Symptom Management. 10.1016/j.jpainsymman.2017.10.004Context: Studies on multiple dimensions of the symptom experience of patients with gastrointestinal cancers are extremely limited. Objective: Purpose was to evaluate for changes over time in the occurrence, severity, and distress of seven common symptoms in these patients. Methods: Patients completed Memorial Symptom Assessment Scale, six times over two cycles of chemotherapy (CTX). Changes over time in occurrence, severity, and distress of pain, lack of energy, nausea, feeling drowsy, difficulty sleeping, and change in the way food tastes were evaluated using multilevel regression analyses. In the conditional models, effects of treatment group (i.e., with or without targeted therapy), age, number of metastatic sites, time from cancer diagnosis, number of prior cancer treatments, cancer diagnosis, and CTX regimen on enrollment levels, as well as the trajectories of symptom occurrence, severity, and distress were evaluated. Results: Although the occurrence rates for pain, lack of energy, feeling drowsy, difficulty sleeping, and change in the way food tastes declined over the two cycles of CTX, nausea and numbness/tingling in hands/feet had more complex patterns of occurrence. Severity and distress ratings for the seven symptoms varied across the two cycles of CTX. Conclusions: Demographic and clinical characteristics associated with differences in enrollment levels as well as changes over time in occurrence, severity, and distress of these seven common symptoms were highly variable. These findings can be used to identify patients who are at higher risk for more severe and distressing symptoms during CTX and to enable the initiation of preemptive symptom management interventions.
Differential expression of genes and differentially perturbed pathways associated with very high evening fatigue in oncology patients receiving chemotherapyAbstractPurpose: Fatigue is the most common symptom associated with cancer and its treatment. Investigation of molecular mechanisms associated with fatigue in oncology patients may identify new therapeutic targets. The objectives of this study were to evaluate the relationships between gene expression and perturbations in biological pathways and evening fatigue severity in oncology patients who received chemotherapy (CTX). Methods: The Lee Fatigue Scale (LFS) and latent class analysis were used to identify evening fatigue phenotypes. We measured 47,214 ribonucleic acid transcripts from whole blood collected prior to a cycle of CTX. Perturbations in biological pathways associated with differential gene expression were identified from public data sets (i.e., Kyoto Encyclopedia Gene and Genomes, BioCarta). Results: Patients were classified into Moderate (n = 65, mean LFS score 3.1) or Very High (n = 195, mean LFS score 6.4) evening fatigue groups. Compared to patients with Moderate fatigue, patients with Very High fatigue exhibited differential expression of 29 genes. A number of the perturbed pathways identified validated prior mechanistic hypotheses for fatigue, including alterations in immune function, inflammation, neurotransmission, energy metabolism, and circadian rhythms. Based on our findings, energy metabolism was further divided into alterations in carbohydrate metabolism and skeletal muscle energy. Alterations in renal function-related pathways were identified as a potential new mechanism. Conclusions: This study identified differential gene expression and perturbed biological pathways that provide new insights into the multiple and likely inter-related mechanisms associated with evening fatigue in oncology patients.
Implementation of Enhanced Recovery After Surgery in a Community Hospital: An Evidence-Based ApproachAbstractPurpose: Enhanced recovery after surgery (ERAS) is an evidence-based practice protocol that has been shown to reduce cost, decrease length of stay (LOS), and improve surgical outcomes. Design: An evidence-based practice improvement project with a multidisciplinary team translated the ERAS protocol into practice at a community hospital. The evidence-based practice improvement design allows integration of evidence into projects to improve clinical outcomes for patients. Methods: Small tests of change using the Plan-Act-Study-Do methodology were used to evaluate the process of implementing one surgical service at a time to ensure effective outcomes. After the process was determined to be effective, patient outcomes (eg, LOS) were measured. Findings: On average, LOS was decreased from 3.2 to 1.7 days. Surgical readmission rate decreased from 3% to 1%. There has been positive feedback and nursing workload has decreased with consistent processes. Conclusions: The ERAS order set continues to be modified based on the evidence and feedback from anesthesia and registered nurses. Monthly reports ensure consistency.
Learning from data to predict future symptoms of oncology patientsAbstractPapachristou, N., Puschmann, D., Barnaghi, P., Cooper, B., Hu, X., Maguire, R., Apostolidis, K., Conley, Y. P., Hammer, M., Katsaragakis, S., Kober, K. M., Levine, J. D., McCann, L., Patiraki, E., Furlong, E. P., Fox, P. A., Paul, S. M., Ream, E., Wright, F., & Miaskowski, C. (2018). PLoS One, 13(12). 10.1371/journal.pone.0208808Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient’s treatment regimen more efficiently and provide more aggressive and timely interventions. Three common and inter-related symptoms in cancer patients are depression, anxiety, and sleep disturbance. In this paper, we elaborate on the efficiency of Support Vector Regression (SVR) and Non-linear Canonical Correlation Analysis by Neural Networks (n-CCA) to predict the severity of the aforementioned symptoms between two different time points during a cycle of chemotherapy (CTX). Our results demonstrate that these two methods produced equivalent results for all three symptoms. These types of predictive models can be used to identify high risk patients, educate patients about their symptom experience, and improve the timing of pre-emptive and personalized symptom management interventions.
Risk Factors Associated With Chemotherapy-Induced Nausea in the Week Before the Next Cycle and Impact of Nausea on Quality of Life OutcomesAbstractSingh, K. P., Kober, K. M., Dhruva, A. A., Flowers, E., Paul, S. M., Hammer, M., Cartwright, F., Wright, F., Conley, Y. P., Levine, J. D., & Miaskowski, C. (2018). Journal of Pain and Symptom Management. 10.1016/j.jpainsymman.2018.05.019Context: Despite current advances in antiemetic treatments, between 19% and 58% of oncology patients experience chemotherapy-induced nausea (CIN). Objectives: Aims of this post hoc exploratory analysis were to determine occurrence, severity, and distress of CIN and evaluate for differences in demographic and clinical characteristics, symptom severity, stress; and quality of life (QOL) outcomes between oncology patients who did and did not report CIN in the week before chemotherapy (CTX). Demographic, clinical, symptom, and stress characteristics associated with CIN occurrence were determined. Methods: Patients (n = 1296) completed questionnaires that provided information on demographic and clinical characteristics, symptom severity, stress, and QOL. Univariate analyses evaluated for differences in demographic and clinical characteristics, symptom severity, stress, and QOL scores between the two patient groups. Multiple logistic regression analysis was used to evaluate for factors associated with nausea group membership. Results: Of the 1296 patients, 47.5% reported CIN. In the CIN group, 15% rated CIN as severe and 23% reported high distress. Factors associated with CIN included less education; having childcare responsibilities; poorer functional status; higher levels of depression, sleep disturbance, evening fatigue, and intrusive thoughts; as well as receipt of CTX on a 14-day CTX cycle and receipt of an antiemetic regimen that contained serotonin receptor antagonist and steroid. Patients in the CIN group experienced clinically meaningful decrements in QOL. Conclusion: This study identified new factors (e.g., poorer functional status, stress) associated with CIN occurrence. CIN negatively impacted patients’ QOL. Pre-emptive and ongoing interventions may alleviate CIN occurrence in high-risk patients.
Characteristics associated with inter-individual differences in the trajectories of self-reported attentional function in oncology outpatients receiving chemotherapyAbstractPurpose: Between 14 and 85 % of patients report noticeable changes in cognitive function during chemotherapy (CTX). The purposes of this study were to determine which demographic, clinical, and symptom characteristics were associated with inter-individual variability in initial levels of attentional function as well as with changes in the trajectories of attentional function in a sample of oncology patients who received two cycles of CTX. Methods: Oncology outpatients (n = 1329) were recruited from two comprehensive cancer centers, one veteran’s affairs hospital, and four community-based oncology programs. The Attentional Function Index (AFI) was used to assess perceived effectiveness in completing daily tasks that required working memory and attention. Hierarchical linear modeling (HLM) was used to evaluate for inter-individual variability in initial levels and in the trajectories of attentional function. Results: Demographic, clinical, and symptom characteristics associated with inter-individual differences of attentional function at enrollment (i.e., intercept) were as follows: employment status, functional status, trait anxiety, depressive symptoms, sleep disturbance, evening fatigue, and morning energy. Gender was the only characteristic associated with inter-individual differences in the trajectories of attentional function. Morning fatigue was the only characteristic associated with both initial levels and the trajectories of attentional function. Conclusions: Prior to their next dose of CTX, patients reported moderate levels of attentional function that persisted over two cycles of CTX. Many of the clinical and symptom characteristics associated with decrements in attentional function are amenable to interventions. Clinicians need to assess patients for changes in attentional function and associated characteristics and recommend evidence-based interventions.
Common and Distinct Characteristics Associated With Trajectories of Morning and Evening Energy in Oncology Patients Receiving ChemotherapyAbstractAbid, H., Kober, K. M., Smoot, B., Paul, S. M., Hammer, M., Levine, J. D., Lee, K., Wright, F., Cooper, B. A., Conley, Y. P., & Miaskowski, C. (2017). Journal of Pain and Symptom Management. 10.1016/j.jpainsymman.2016.12.339Context: Although energy conservation strategies are recommended in clinical practice guidelines, little is known about changes in energy levels in oncology patients undergoing cancer treatment. Objectives: The objective of this study was to identify variations in the trajectories of morning and evening energy levels and determine which characteristics predicted initial levels and the trajectories of morning and evening energy. Methods: Outpatients receiving chemotherapy (CTX) completed demographic and symptom questionnaires six times over two CTX cycles. Energy was assessed using the Lee Fatigue Scale. Hierarchical linear modeling was used to analyze the data. Results: A large amount of interindividual variability was found in the morning and evening energy trajectories. Patients who lived alone, had childcare responsibilities, had a lower functional status, did not exercise on a regular basis, had lower hemoglobin levels, had lower attentional function, higher trait anxiety, and higher sleep disturbance reported lower morning energy levels at enrollment. Variations in the trajectories of morning energy were associated with a higher body mass index and higher levels of morning energy and higher sleep disturbance scores. For evening energy, patients who were female, white, had lower functional status, and had lower attentional function and higher sleep disturbance reported lower evening energy levels at enrollment. Evening energy levels at enrollment were associated with changes in evening energy over time. Conclusion: Patients undergoing CTX experience decrements in both morning and evening energy. The modifiable characteristics associated with these decrements can be used to design intervention studies to increase energy levels in these patients.
Congruence Between Latent Class and K-Modes Analyses in the Identification of Oncology Patients With Distinct Symptom ExperiencesAbstractPapachristou, N., Barnaghi, P., Cooper, B. A., Hu, X., Maguire, R., Apostolidis, K., Armes, J., Conley, Y. P., Hammer, M., Katsaragakis, S., Kober, K. M., Levine, J. D., McCann, L., Patiraki, E., Paul, S. M., Ream, E., Wright, F., & Miaskowski, C. (2017). Journal of Pain and Symptom Management. 10.1016/j.jpainsymman.2017.08.020Context: Risk profiling of oncology patients based on their symptom experience assists clinicians to provide more personalized symptom management interventions. Recent findings suggest that oncology patients with distinct symptom profiles can be identified using a variety of analytic methods. Objectives: The objective of this study was to evaluate the concordance between the number and types of subgroups of patients with distinct symptom profiles using latent class analysis and K-modes analysis. Methods: Using data on the occurrence of 25 symptoms from the Memorial Symptom Assessment Scale, that 1329 patients completed prior to their next dose of chemotherapy (CTX), Cohen's kappa coefficient was used to evaluate for concordance between the two analytic methods. For both latent class analysis and K-modes, differences among the subgroups in demographic, clinical, and symptom characteristics, as well as quality of life outcomes were determined using parametric and nonparametric statistics. Results: Using both analytic methods, four subgroups of patients with distinct symptom profiles were identified (i.e., all low, moderate physical and lower psychological, moderate physical and higher Psychological, and all high). The percent agreement between the two methods was 75.32%, which suggests a moderate level of agreement. In both analyses, patients in the all high group were significantly younger and had a higher comorbidity profile, worse Memorial Symptom Assessment Scale subscale scores, and poorer QOL outcomes. Conclusion: Both analytic methods can be used to identify subgroups of oncology patients with distinct symptom profiles. Additional research is needed to determine which analytic methods and which dimension of the symptom experience provide the most sensitive and specific risk profiles.