Kinetic and Thermodynamic Studies of Copper-Catalyzed Atom Transfer Radical Processes in the Presence of Free-Radical Diazo Initiators as Reducing Agents

The first part of this dissertation focuses on the kinetic aspects of atom transfer radical addition (ATRA) in the presence of reducing agents. The rate of alkene consumption was found to be dependent on the initial concentration of the radical initiator and its decomposition and termination rate constants but not on the concentrations of CuI and CuII, which was contrary to the rate law for copper-catalyzed ATRA in the absence of a reducing agent. Kinetic experiments showed that the observed rate of ATRA (kobs) was indeed not dependent on the concentration of the catalyst, which supported the newly derived rate law. However, product selectivity was highly dependent on the nature of the catalyst. The activation (ka,AIBN) and deactivation (kd,AIBN) rate constants of various CuII/AIBN systems were determined through a combination of experimental and theoretical methods and were found to control the overall concentrations of CuI and CuII at equilibrium. The effect of the catalyst, alkyl halide, and free radical initiator concentrations on the percent conversion and yield of monoadduct were also investigated. Lower catalyst loadings in ATRA reactions involving reactive monomers led to a decrease in monoadduct yield due to competing polymerization reactions. Low-temperature ATRA reactions were found to significantly increase the formation of the monoadduct as a result of the lowering of the rate constant of propagation (kp). Reactions of less active halides were more affected by increased alkyl halide concentrations than that of the more active alkyl halides. Higher free radical initiator concentration led to an increase in AIBN-initiated polymer formation. The second part explores the role of thermodynamic factors on the product selectivity of atom transfer radical cyclization (ATRC). Various derivatives of alkenyl bromoacetate and trichloroacetate were synthesized and characterized by 1H NMR spectroscopy. Theoretical calculation of the relative energies of the s-trans and s-cis conformers revealed that the presence of bulky substituents on the carbon atom adjacent to the acetate moiety stabilizes the s-cis conformation and, thus, promotes cyclization. This was experimentally confirmed in the ATRC reactions of the synthesized alkenyl haloacetates in which the addition of bulky groups increased the yields of cyclic products

Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimization

While there has been recent progress in abstractive summarization as applied to different domains including news articles, scientific articles, and blog posts, the application of these techniques to clinical text summarization has been limited. This is primarily due to the lack of large-scale training data and the messy/unstructured nature of clinical notes as opposed to other domains where massive training data come in structured or semi-structured form. Further, one of the least explored and critical components of clinical text summarization is factual accuracy of clinical summaries. This is specifically crucial in the healthcare domain, cardiology in particular, where an accurate summary generation that preserves the facts in the source notes is critical to the well-being of a patient. In this study, we propose a framework for improving the factual accuracy of abstractive summarization of clinical text using knowledge-guided multi-objective optimization. We propose to jointly optimize three cost functions in our proposed architecture during training: generative loss, entity loss and knowledge loss and evaluate the proposed architecture on 1) clinical notes of patients with heart failure (HF), which we collect for this study; and 2) two benchmark datasets, Indiana University Chest X-ray collection (IU X-Ray), and MIMIC-CXR, that are publicly available. We experiment with three transformer encoder-decoder architectures and demonstrate that optimizing different loss functions leads to improved performance in terms of entity-level factual accuracy.Comment: Accepted to EMBC 202

Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure

Heart failure is a syndrome which occurs when the heart is not able to pump blood and oxygen to support other organs in the body. Identifying the underlying themes in the diagnostic codes and procedure reports of patients admitted for heart failure could reveal the clinical phenotypes associated with heart failure and to group patients based on their similar characteristics which could also help in predicting patient outcomes like length of stay. These clinical phenotypes usually have a probabilistic latent structure and hence, as there has been no previous work on identifying phenotypes in clinical notes of heart failure patients using a probabilistic framework and to predict length of stay of these patients using data-driven artificial intelligence-based methods, we apply natural language processing technique, topic modeling, to identify the themes present in diagnostic codes and in procedure reports of 1,200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health). Topic modeling identified twelve themes each in diagnostic codes and procedure reports which revealed information about different phenotypes related to various perspectives about heart failure, to study patients\u27 profiles and to discover new relationships among medical concepts. Each theme had a set of keywords and each clinical note was labeled with two themes - one corresponding to its diagnostic code and the other corresponding to its procedure reports along with their percentage contribution. We used these themes and their percentage contribution to predict length of stay. We found that the themes discovered in diagnostic codes and procedure reports using topic modeling together were able to predict length of stay of the patients with an accuracy of 61.1% and an Area under the Receiver Operating Characteristic Curve (ROC AUC) value of 0.828

Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure

Heart failure occurs when the heart is not able to pump blood and oxygen to support other organs in the body as it should. Treatments include medications and sometimes hospitalization. Patients with heart failure can have both cardiovascular as well as non-cardiovascular comorbidities. Clinical notes of patients with heart failure can be analyzed to gain insight into the topics discussed in these notes and the major comorbidities in these patients. In this regard, we apply machine learning techniques, such as topic modeling, to identify the major themes found in the clinical notes specific to the procedures performed on 1,200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health). Topic modeling revealed five hidden themes in these clinical notes, including one related to heart disease comorbidities

Participant retention practices in longitudinal clinical research studies with high retention rates

Abstract Background There is a need for improving cohort retention in longitudinal studies. Our objective was to identify cohort retention strategies and implementation approaches used in studies with high retention rates. Methods Longitudinal studies with ≥200 participants, ≥80% retention rates over ≥1 year of follow-up were queried from an Institutional Review Board database at a large research-intensive U.S. university; additional studies were identified through networking. Nineteen (86%) of 22 eligible studies agreed to participate. Through in-depth semi-structured interviews, participants provided retention strategies based on themes identified from previous literature reviews. Synthesis of data was completed by a multidisciplinary team. Results The most commonly used retention strategies were: study reminders, study visit characteristics, emphasizing study benefits, and contact/scheduling strategies. The research teams were well-functioning, organized, and persistent. Additionally, teams tailored their strategies to their participants, often adapting and innovating their approaches. Conclusions These studies included specialized and persistent teams and utilized tailored strategies specific to their cohort and individual participants. Studies’ written protocols and published manuscripts often did not reflect the varied strategies employed and adapted through the duration of study. Appropriate retention strategy use requires cultural sensitivity and more research is needed to identify how strategy use varies globally

Nightly Variation in Sleep Influences Self-efficacy for Adhering to a Healthy Lifestyle: A Prospective Study

Background: Self-efficacy, or the perceived capability to engage in a behavior, has been shown to play an important role in adhering to weight loss treatment. Given that adherence is extremely important for successful weight loss outcomes and that sleep and self-efficacy are modifiable factors in this relationship, we examined the association between sleep and self-efficacy for adhering to the daily plan. Investigators examined whether various dimensions of sleep were associated with self-efficacy for adhering to the daily recommended lifestyle plan among participants (N = 150) in a 12-month weight loss study. Method: This study was a secondary analysis of data from a 12-month prospective observational study that included a standard behavioral weight loss intervention. Daily assessments at the beginning of day (BOD) of self-efficacy and the previous night’s sleep were collected in real-time using ecological momentary assessment. Results: The analysis included 44,613 BOD assessments. On average, participants reported sleeping for 6.93 ± 1.28 h, reported 1.56 ± 3.54 awakenings, and gave low ratings for trouble sleeping (3.11 ± 2.58; 0: no trouble; 10: a lot of trouble) and mid-high ratings for sleep quality (6.45 ± 2.09; 0: poor; 10: excellent). Participants woke up feeling tired 41.7% of the time. Using linear mixed effects modeling, a better rating in each sleep dimension was associated with higher self-efficacy the following day (all p values <.001). Conclusion: Our findings supported the hypothesis that better sleep would be associated with higher levels of reported self-efficacy for adhering to the healthy lifestyle plan

Health literacy and health outcomes in very old patients with heart failure

INTRODUCTION AND OBJECTIVES: Health literacy (HL) has been associated with lower mortality in heart failure (HF). However, the results of previous studies may not be generalizable because the research was conducted in relatively young and highly-educated patients in United States settings. This study assessed the association of HL with disease knowledge, self-care, and all-cause mortality among very old patients, with a very low educational level. METHODS: This prospective study was performed in 556 patients (mean age, 85 years), with high comorbidity, admitted for HF to the geriatric acute-care unit of 6 hospitals in Spain. About 74% of patients had less than primary education and 71% had preserved systolic function. Health literacy was assessed with the Short Assessment of Health Literacy for Spanish-speaking Adults questionnaire, knowledge of HF with the DeWalt questionnaire, and HF self-care with the European Heart Failure Self-Care Behaviour Scale. RESULTS: Disease knowledge progressively increased with HL; compared with being in the lowest (worse) tertile of HL, the multivariable beta coefficient (95%CI) of the HF knowledge score was 0.60 (0.01-1.19) in the second tertile and 0.87 (0.24-1.50) in the highest tertile, P-trend = .008. However, no association was found between HL and HF self-care. During the 12 months of follow-up, there were 189 deaths. Compared with being in the lowest tertile of HL, the multivariable HR (95%CI) of mortality was 0.84 (0.56-1.27) in the second tertile and 0.99 (0.65-1.51) in the highest tertile, P-trend = .969. CONCLUSIONS: No association was found between HL and 12-month mortality. This could be partly due to the lack of a link between HL and self-care

Trust in the Transplant Team Associated With the Level of Chronic Illness Management—A Secondary Data Analysis of the International BRIGHT Study

A trustful relationship between transplant patients and their transplant team (interpersonal trust) is essential in order to achieve positive health outcomes and behaviors. We aimed to 1) explore variability of trust in transplant teams; 2) explore the association between the level of chronic illness management and trust; 3) investigate the relationship of trust on behavioral outcomes. A secondary data analysis of the BRIGHT study (ID: NCT01608477; https://clinicaltrials.gov/ct2/show/NCT01608477?id=NCT01608477&amp;rank=1) was conducted, including multicenter data from 36 heart transplant centers from 11 countries across four different continents. A total of 1,397 heart transplant recipients and 100 clinicians were enrolled. Trust significantly varied among the transplant centers. Higher levels of chronic illness management were significantly associated with greater trust in the transplant team (patients: AOR= 1.85, 95% CI = 1.47–2.33, p &lt; 0.001; clinicians: AOR = 1.35, 95% CI = 1.07–1.71, p = 0.012). Consultation time significantly moderated the relationship between chronic illness management levels and trust only when clinicians spent ≥30 min with patients. Trust was significantly associated with better diet adherence (OR = 1.34, 95%CI = 1.01–1.77, p = 0.040). Findings indicate the relevance of trust and chronic illness management in the transplant ecosystem to achieve improved transplant outcomes. Thus, further investment in re-engineering of transplant follow-up toward chronic illness management, and sufficient time for consultations is required

mHealth Use in Older People with Heart Failure

Abstract Background: mHealth, or the use of mobile technology to support the achievement of health objectives, has the potential to revolutionize heart failure (HF) self-management. However, the potential benefits from mHealth can only be realized through its adoption. Hence, a better understanding of the factors that influence mHealth adoption could guide the development and implementation of mHealth-based HF interventions. The purpose of this dissertation was to examine facilitators and barriers that influence intention to adopt mHealth among older people with HF. Methods: To systematically investigate the factors that influence mHealth adoption, a cross-sectional, explanatory sequential mixed-methods design was used guided by the Technology Acceptance Model (TAM). Convenience sampling was used to recruit participants from Johns Hopkins Hospital and online (Qualtrics) for the quantitative phase of the study. Participants responded to a 52-item self-report questionnaire, which included a modified TAM scale and the eHealth Literacy scale (eHEALS). In-person participants were then purposively sampled for the qualitative phase of the study. Semi-structured interviews were conducted using interview guides tailored to the participant’s response to the questionnaire. Results: A total of 129 older adults with HF participated in the study. And a subsample of 10 participants was interviewed for the qualitative phase. Social influence (=0.17, P=0.010), perceived ease of use (=0.16, P<0.001), and perceived usefulness (=0.33, P<0.001) were significantly associated with intention to use mHealth even after controlling for potential confounders (age, gender, race, education, income, and smartphone use). The following themes emerged from the content analysis of the interview transcripts: (facilitators) previous experience with mobile technology, willingness to learn mHealth, ease of use, presence of useful features, adequate training, free equipment, and doctor’s recommendation; (barriers) lack of knowledge regarding how to use mHealth, decreased sensory perception, lack of need for technology, poorly-designed interface, cost of technology, and limited/fixed income. Conclusion: Overall, the findings suggest that older adults are willing to use mHealth albeit not without reservations. Future researchers looking to implement mHealth-based interventions should address the person-related, technology-related, and contextual barriers, at the same time capitalize on the influence of potential facilitators