Analyzing Risk Factors for Healthcare-Associated Infections Using Multiple Methodological Approaches

Healthcare-associated infections (HAIs) are among the most common and significant patient safety issues posing great threats to public health. One in every 25 inpatients in the United States experiences a HAI. Because they have continuously been a major reason for increased morbidity and mortality in healthcare facilities, increased attention to understanding the spread of HAIs is an urgently needed. Therefore, the purpose of this dissertation, was to examine the risk factors for two of the most common HAIs (surgical site infection [SSI] and Clostridioides difficile infection [CDI]), using multiple methodological approaches. Chapter 1 provides an overview of HAIs, the risk factors identified from the previous literature, and the necessity of different methodological approaches to identify the risk of HAIs. Chapter 2 is an integrative review synthesizing the findings from seven published studies examining the association between the development of pocket hematoma and the risk of wound infection in individuals with cardiovascular implantable electronic devices. Chapter 3 is a summary of a retrospective cohort study using machine learning techniques—logistic regression, decision tree, and support vector machine approaches—to build predictive models of SSI among individuals with permanent pacemakers, followed by a comparison of the predictive abilities of the three algorithms. Chapter 4 describes a retrospective matched case-control study to examine (1) temporal changes in the incidence of community or hospital-acquired CDI, (2) the risk factors for hospital-acquired CDI including individual-host factors and pharmacological-related factors, and (3) temporal changes in the risk factors for hospital-acquired CDI. Lastly, Chapter 5 summarizes and synthesizes the findings of the studies included in this dissertation, the strengths and limitations of the studies, implications for public health and clinical practice, advanced studies on methodology, and future research. In conclusion, this dissertation adds comprehensive knowledge regarding the associations between risk factors and HAIs by identifying reliable risk factors measured in various ways and applying various methodological approaches

Do caregivers’ involvement in Type 2 diabetes education affect patients’ health outcomes?: A systematic review and meta-analysis

Introduction: The prevalence of Type 2 diabetes mellitus (T2DM) is rising worldwide. Patients frequently struggle with controlling their diabetes and need the assistance of caregivers for effective self-management because managing diabetes requires a variety of strategies, including diet, glucose monitoring, and exercise. This study aimed to examine the effect of caregiver involvement in T2DM education within a community on patients’ diabetes care outcomes. Methods: Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic review of all published studies from the earliest record to May 2022 that reported adult caregivers of T2DM patients who participated in educational interventions concerning diabetes management and that reported one or more outcomes of the interventions were conducted. Four databases were used, including PubMed, Cochrane Library, EMBASE, and CINAHL. The meta-analysis focused on glycated hemoglobin (HbA1c) levels among randomized controlled trials (RCTs), with additional attention to lipid levels. Review Manager 5.4 was used to perform this meta-analysis. Results: A total of 17 out of 683 studies were synthesized. Involvement of caregivers in T2DM education is shown to reduce body mass index and HbA1c. This involvement also improves patients’ knowledge, physical activity, and self-efficacy, but the effect on medication adherence varies. A meta-analysis of six RCT studies shows that caregiver involvement in T2DM education reduced pooled HbA1c levels by 0.83 (95% Confidence interval: −1.27–−0.38) compared to involvement (p = 0.0003). Meta-analysis of three types of lipids (low-density lipoprotein, total cholesterol, and high-density lipoprotein) showed no strong evidence that caregiver participation in diabetes education improved lipid levels. Conclusions: Caregivers play key roles in diabetes management and can contribute to improving patient HbA1c levels. Future research should focus on enhancing caregiver participation in T2DM education

Antileishmanial High-Throughput Drug Screening Reveals Drug Candidates with New Scaffolds

Drugs currently available for leishmaniasis treatment often show parasite resistance, highly toxic side effects and prohibitive costs commonly incompatible with patients from the tropical endemic countries. In this sense, there is an urgent need for new drugs as a treatment solution for this neglected disease. Here we show the development and implementation of an automated high-throughput viability screening assay for the discovery of new drugs against Leishmania. Assay validation was done with Leishmania promastigote forms, including the screening of 4,000 compounds with known pharmacological properties. In an attempt to find new compounds with leishmanicidal properties, 26,500 structurally diverse chemical compounds were screened. A cut-off of 70% growth inhibition in the primary screening led to the identification of 567 active compounds. Cellular toxicity and selectivity were responsible for the exclusion of 78% of the pre-selected compounds. The activity of the remaining 124 compounds was confirmed against the intramacrophagic amastigote form of the parasite. In vitro microsomal stability and cytochrome P450 (CYP) inhibition of the two most active compounds from this screening effort were assessed to obtain preliminary information on their metabolism in the host. The HTS approach employed here resulted in the discovery of two new antileishmanial compounds, bringing promising candidates to the leishmaniasis drug discovery pipeline

Home Health Care Clinicians’ Use of Judgment Language for Black and Hispanic Patients: Natural Language Processing Study

BackgroundA clinician’s biased behavior toward patients can affect the quality of care. Recent literature reviews report on widespread implicit biases among clinicians. Although emerging studies in hospital settings show racial biases in the language used in clinical documentation within electronic health records, no studies have yet investigated the extent of judgment language in home health care. ObjectiveWe aimed to examine racial differences in judgment language use and the relationship between judgment language use and the amount of time clinicians spent on home visits as a reflection of care quality in home health care. MethodsThis study is a retrospective observational cohort study. Study data were extracted from a large urban home health care organization in the Northeastern United States. Study data set included patients (N=45,384) who received home health care services between January 1 and December 31, 2019. The study applied a natural language processing algorithm to automatically detect the language of judgment in clinical notes. ResultsThe use of judgment language was observed in 38% (n=17,141) of the patients. The highest use of judgment language was found in Hispanic (7,167/66,282, 10.8% of all clinical notes), followed by Black (7,010/65,628, 10.7%), White (10,206/107,626, 9.5%), and Asian (1,756/22,548, 7.8%) patients. Black and Hispanic patients were 14% more likely to have notes with judgment language than White patients. The length of a home health care visit was reduced by 21 minutes when judgment language was used. ConclusionsRacial differences were identified in judgment language use. When judgment language is used, clinicians spend less time at patients’ homes. Because the language clinicians use in documentation is associated with the time spent providing care, further research is needed to study the impact of using judgment language on quality of home health care. Policy, education, and clinical practice improvements are needed to address the biases behind judgment language

Structural, electrical and electrochemical characteristics of La0.1Sr0.9Co1-xNbxO3-delta as a cathode material for intermediate temperature solid oxide fuel cells

The perovskite-oxides, such as (La, Sr)CoO3, have received a large amount of attention in recent years as cathode materials for intermediate temperature-solid oxide fuel cells (IT-SOFCs). In this study, we have investigated the structural, electrical, and electrochemical properties of La0.1Sr0.9Co1-xNbxO 3-?? (x = 0, 0.1, 0.15, and 0.2) cathodes under IT-SOFC operating conditions. Nb doping significantly improves the structural stability and electrochemical performance of La0.1Sr0.9Co 1-xNbxO3-?? (LSCNbx) oxides compared to undoped La0.1Sr0.9CoO3-?? (LSC). At a given temperature, the electrical conductivity decreases with further increases of the Nb doping content. The electrochemical performance of LSCNbx-GDC cathodes is measured using an LSCNbx-GDC/GDC/ Ni-GDC anode supported cell. For LSCNbx (x = 0.1), the maximum power density of a single cell is 1.478 W cm-2 at 600 ??C. The Nb doped LSCNbx (x = 0.1) perovskite is recommended, considering its high power density and structural stability as an IT-SOFC cathode material.close0

Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers.

The prevalence of patients who are Incapacitated with No Evident Advance Directives or Surrogates (INEADS) remains unknown because such data are not routinely captured in structured electronic health records. This study sought to develop and validate a natural language processing (NLP) algorithm to identify information related to being INEADS from clinical notes. We used a publicly available dataset of critical care patients from 2001 through 2012 at a United States academic medical center, which contained 418,393 relevant clinical notes for 23,904 adult admissions. We developed 17 subcategories indicating reduced or elevated potential for being INEADS, and created a vocabulary of terms and expressions within each. We used an NLP application to create a language model and expand these vocabularies. The NLP algorithm was validated against gold standard manual review of 300 notes and showed good performance overall (F-score = 0.83). More than 80% of admissions had notes containing information in at least one subcategory. Thirty percent (n = 7,134) contained at least one of five social subcategories indicating elevated potential for being INEADS, and <1% (n = 81) contained at least four, which we classified as high likelihood of being INEADS. Among these, n = 8 admissions had no subcategory indicating reduced likelihood of being INEADS, and appeared to meet the definition of INEADS following manual review. Among the remaining n = 73 who had at least one subcategory indicating reduced likelihood of being INEADS, manual review of a 10% sample showed that most did not appear to be INEADS. Compared with the full cohort, the high likelihood group was significantly more likely to die during hospitalization and within four years, to have Medicaid, to have an emergency admission, and to be male. This investigation demonstrates potential for NLP to identify INEADS patients, and may inform interventions to enhance advance care planning for patients who lack social support

The ChatGPT Effect:Nursing Education and Generative Artificial Intelligence

This article examines the potential of generative artificial intelligence (AI), such as ChatGPT (Chat Generative Pre-trained Transformer), in nursing education and the associated challenges and recommendations for their use. Generative AI offers potential benefits such as aiding students with assignments, providing realistic patient scenarios for practice, and enabling personalized, interactive learning experiences. However, integrating generative AI in nursing education also presents challenges, including academic integrity issues, the potential for plagiarism and copyright infringements, ethical implications, and the risk of producing misinformation. Clear institutional guidelines, comprehensive student education on generative AI, and tools to detect AI-generated content are recommended to navigate these challenges. The article concludes by urging nurse educators to harness generative AI’s potential responsibly, highlighting the rewards of enhanced learning and increased efficiency. The careful navigation of these challenges and strategic implementation of AI is key to realizing the promise of AI in nursing education