Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features.

BACKGROUND: The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes. While models like the Model-for-End-Stage-Liver-Disease (MELD) predict 90-day mortality on the waiting list, they do not predict post-LT survival accurately. There is a need for predictive models that can forecast post-LT survival beyond the immediate period after transplantation. METHOD: This study introduces new temporal variation features for predicting post-LT survival during the waiting list period. Cox Proportional-Hazards regression (CoxPH), Random Survival Forest (RSF), and Extreme Gradient Boosting (XGB) models are utilized, along with patient demographics and waiting list duration. Data from 716 LT patients from the University of Minnesota CTSI (2011-2021) are used to develop, evaluate, and compare post-LT survival prediction models. RESULTS: The temporal variation features, particularly when combined with the RSF model, proved most effective in predicting post-LT survival, with a C-index of 0.71 and an IBS of 0.151. This outperformed the predictive capability of the most recent MELD score, which had a C-index of <0.51 in the same cohort. CONCLUSIONS: Incorporating temporal variation features with the RSF model enhances long-term post-LT survival predictions. These insights can assist clinicians and patients in making more informed decisions about organ allocation and understanding the utility of LT, ultimately leading to improved patient outcomes.

Anxiety and depression among individuals with long COVID: Associations with social vulnerabilities.

BACKGROUND: We examined the association between symptoms of anxiety and depression among individuals with long COVID and five social vulnerabilities (expenses, employment, food insufficiency, housing, and insurance). METHODS: Data from the Census Bureau's Household Pulse Survey (HPS) detailing COVID incidence, duration, and symptoms between June 1st and November 14th, 2022 contained versions of the Generalized Anxiety Disorder (GAD-2) and the Patient Health Questionnaire (PHQ-2) questionnaires. Associations between anxiety, depression, and the five social vulnerabilities among respondents from different racial and ethnic groups experiencing long COVID were evaluated using generalized binomial logistic regression. Structural equation models tested whether social vulnerabilities mediated the pathway between race/ethnicity and anxiety/depression. RESULTS: Blacks, Asians/others, and Hispanics with long COVID were significantly more likely to report anxiety and depression and various social vulnerabilities than Whites. Anxiety among Blacks was significantly associated with difficulty with expenses [Odds Ratio (OR) = 1.743, 95 % Confidence Interval (CI) = 1.739, 1.747], employment (OR = 1.519, 95 % CI = 1.516, 1.523), and housing (OR = 1.192, 95 % CI = 1.19, 1.194). Anxiety among Hispanics was significantly associated with food insufficiency (OR = 1.048, 95 % CI = 1.044, 1.052). Depression among Blacks was significantly associated with trouble with expenses (OR = 1.201, 95 % CI = 1.198, 1.205) and employment (OR = 1.129, 95 % CI = 1.127, 1.132). Mediation analysis showed that the number of social vulnerabilities partially mediated the association between race and anxiety. LIMITATIONS: This retrospective study utilized secondary, observational, self-reported data from the HPS. Therefore, results may not be generalizable outside of the context in which they were collected. CONCLUSIONS: The development of tailored programs for population health should address the differential associations of anxiety and depression with social difficulties among racial and ethnic groups.

Symptom Descriptors in Individuals Living With Undiagnosed Lower Extremity Peripheral Artery Disease.

OBJECTIVE: Most individuals with lower extremity peripheral artery disease (PAD) experience symptoms other than claudication and live with undiagnosed PAD yet no tools exist to detect atypical PAD symptoms. The purpose of this study was to identify discriminating PAD symptom descriptors from a community-based sample of patients with no current diagnosis of PAD. METHODS: Symptoms descriptors were obtained in a sample of 22 participants with persistent lower extremity symptoms pre/post exercise. An ankle brachial index with exercise was used to classify participants as "PAD" or "No PAD." RESULTS: Thirteen (59%) participants had a positive ankle brachial index (<0.9, ≥20% drop postexercise, or 30 mmHg drop postexercise). Symptoms do not disappear while walking, trouble keeping up with friends/family, positive response to pain or discomfort while sitting, and pain outside of the calves and thighs were associated with a positive ankle brachial index. CONCLUSION: Atypical symptoms were common among study participants. Symptoms while sitting and symptoms outside of the calf and thigh were negatively associated with a positive ankle brachial index. More precise descriptions of symptom characteristics are needed to improve PAD symptom recognition.

An Information Model on Pain Management: Cultural Validation and Refinement.

This study aimed to validate and refine an information model on pain management in a Brazilian hospital, considering the institutional culture, using an expert consensus approach. The first stage took place through a computerized questionnaire and Content Validity Index calculation. Pain management attributes were considered validated with 75% consensus among 19 experts. The second stage validated and refined the information model by three experts via an online meeting. Results showed that out of 11 evaluated attributes, five were validated. In the second stage, the inclusion of new attributes was suggested to address institutional culture. The final information model resulted from 23 sets of revised attributes: 12 validated, seven suggested and four not validated. The resulting Brazilian model has the potential to support the implementation of interventions and propose improvements to the institution's electronic system, which can be reused in other institutions.

Validation of Educational Materials for a Mobile Application for Patients with Kidney Disease on Hemodialysis.

Worldwide, chronic kidney disease (CKD) is a public health problem due to its high morbidity and mortality rates. For CKD patients, mobile health applications have functioned as a strategy that promotes patient care through valid and reliable educational materials. This is a prospective and descriptive three-stage study using content experts. Results created three visual and three audiovisual materials with acceptable evaluations. The design and validation of educational materials are a valid and reliable method for patient health education through mobile health applications.

Data Literacy and Data Science Literacy for Nurses: State of the Art Literature Review.

In the rapidly evolving landscape of modern healthcare, nurses must proficiently navigate data utilization and grasp the principles of data science. Despite this urgency, nursing stakeholders currently do not fully understand the extent of data literacy or data science literacy they need to acquire. This paper aims to elucidate the distinctions between data literacy and data science literacy, offering insights into strategies for nurturing these competencies within nursing education, research, and practice. Through a state-of-the-art review of 22 articles and six healthcare industry resources, we identified a notable absence of comprehensive frameworks and assessment tools, highlighting key areas for future development.

The Relationship Between Patient Perceived Pain and the Numerical Rating System: A Prospective Validation Using Electronic Health Record Data.

We developed a method of using the Clinically Aligned Pain Assessment (CAPA) measures to reconstruct the Numeric Rating System (NRS). We used an observational retrospective cohort study design with prospective validation using de-identified adult patient data derived from a major health system. Data between 2011-2017 were used for development and 2018-2020 for validation. All included patients had at least one NRS and CAPA measurement at the same time. An ordinal regression model was built with CAPA components to predict NRS scores. We identified 6,414 and 3,543 simultaneous NRS-CAPA pairs in the development and validation dataset, respectively. All CAPA components were significantly related to NRS, with RMSE of 1.938 and Somers' D of 0.803 on the development dataset, and RMSE of 2.1 and Somers' D of 0.74 when prospectively validated. Our model was capable of accurately reconstructing NRS based on CAPA and was exact when the NRS was [0,7].

Future Needs in Nursing Informatics - Preliminary Findings from the Global Nursing Informatics Survey.

This study investigates the evolving landscape of nursing informatics by conducting a follow-up survey initiated by the International Medical Informatics Association (IMIA) Students and Emerging Professionals (SEP) Nursing Informatics (NI) group in 2015 and 2019. The participants were asked to describe what they thought should be done in their institutions and countries to advance nursing informatics in the next 5-10 years. For this paper, responses in English acquired by December 2023 were analysed using inductive content analysis. Identified needs covered a) recognition and roles, b) educational needs, c) technological needs, and d) research needs. The initial findings indicate that, despite significant progress in nursing informatics, the current needs closely mirror those identified in the 2015 survey.

Mobile Applications in Patients with Chronic Kidney Disease: A Systematic Review.

JUSTIFICATION: Worldwide, 850 million people suffer from chronic kidney disease (CKD), and in Mexico it is the tenth cause of mortality with 13,167 deaths per year. CKD patients undergoing hemodialysis present challenges in following the prescribed treatment and managing care; Therefore, different health strategies have been proposed to address those challenges, including mobile health applications. OBJECTIVE: Analyze the scientific evidence available worldwide on mobile health applications for patients with CKD on hemodialysis that have been validated, evaluated, implemented or in the process of development. METHODS: Systematic review of the literature following the PRISMA statement and search question with the PICOT-D format. Databases with keywords in 12 languages were consulted. RESULTS: Of 474 manuscripts, seven met the inclusion criteria. Mobile health applications were designed using different methodologies. Mobile health applications were found mainly aimed at self-monitoring and/or self-management, including health literacy, of patients with CKD.

Optimizing pain management in breast cancer care: Utilizing 'All of Us' data and deep learning to identify patients at elevated risk for chronic pain.

PURPOSE: The aim of the study was to develop a prediction model using deep learning approach to identify breast cancer patients at high risk for chronic pain. DESIGN: This study was a retrospective, observational study. METHODS: We used demographic, diagnosis, and social survey data from the NIH 'All of Us' program and used a deep learning approach, specifically a Transformer-based time-series classifier, to develop and evaluate our prediction model. RESULTS: The final dataset included 1131 patients. We evaluated the deep learning prediction model, which achieved an accuracy of 72.8% and an area under the receiver operating characteristic curve of 82.0%, demonstrating high performance. CONCLUSION: Our research represents a significant advancement in predicting chronic pain among breast cancer patients, leveraging deep learning model. Our unique approach integrates both time-series and static data for a more comprehensive understanding of patient outcomes. CLINICAL RELEVANCE: Our study could enhance early identification and personalized management of chronic pain in breast cancer patients using a deep learning-based prediction model, reducing pain burden and improving outcomes.

Technological Challenges and Solutions in Emergency Remote Teaching for Nursing: An International Cross-Sectional Survey.

OBJECTIVES: With the sudden global shift to online learning modalities, this study aimed to understand the unique challenges and experiences of emergency remote teaching (ERT) in nursing education. METHODS: We conducted a comprehensive online international cross-sectional survey to capture the current state and firsthand experiences of ERT in the nursing discipline. Our analytical methods included a combination of traditional statistical analysis, advanced natural language processing techniques, latent Dirichlet allocation using Python, and a thorough qualitative assessment of feedback from open-ended questions. RESULTS: We received responses from 328 nursing educators from 18 different countries. The data revealed generally positive satisfaction levels, strong technological self-efficacy, and significant support from their institutions. Notably, the characteristics of professors, such as age (p = 0.02) and position (p = 0.03), influenced satisfaction levels. The ERT experience varied significantly by country, as evidenced by satisfaction (p = 0.05), delivery (p = 0.001), teacher-student interaction (p = 0.04), and willingness to use ERT in the future (p = 0.04). However, concerns were raised about the depth of content, the transition to online delivery, teacher-student interaction, and the technology gap. CONCLUSIONS: Our findings can help advance nursing education. Nevertheless, collaborative efforts from all stakeholders are essential to address current challenges, achieve digital equity, and develop a standardized curriculum for nursing education.

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. [J Nurs Educ. 2024;63(X):XXX-XXX.].

Towards Automated Evaluation of Patient Centered Care-Assessing the Potential of Electronic Health Records.

Providing patient centered care is a crucial element of high quality care. It can be defined as a responsive way of caring for and empowering patients, embodying compassion, empathy, and responsiveness to the patient's needs. The aim of this study was to assess the potential of using EHRs as information source in the development of tools for assessing PCC. An annotation guide following the Person-centred Practice Framework proposed by McCance and McCormack was developed for the purpose of this study. Twenty patients' documents were manually annotated, resulting in 539 expressions. All dimensions of the framework were covered in the documents, with 61.3% of expressions describing the activity of engaging authentically with the patient. The results of this study indicate that electronic health records are one potential source of information in automated evaluation of patient centered care, however more information is still needed on how to interpret this information.

The ChatGPT effect and transforming nursing education with generative AI: Discussion paper.

AIM: The aim of this study is to present the possibilities of nurse education in the use of the Chat Generative Pre-training Transformer (ChatGPT) tool to support the documentation process. BACKGROUND: The success of the nursing process is based on the accuracy of nursing diagnoses, which also determine nursing interventions and nursing outcomes. Educating nurses in the use of artificial intelligence in the nursing process can significantly reduce the time nurses spend on documentation. DESIGN: Discussion paper. METHODS: We used a case study from Train4Health in the field of preventive care to demonstrate the potential of using Generative Pre-training Transformer (ChatGPT) to educate nurses in documenting the nursing process using generative artificial intelligence. Based on the case study, we entered a description of the patient's condition into Generative Pre-training Transformer (ChatGPT) and asked questions about nursing diagnoses, nursing interventions and nursing outcomes. We further synthesized these results. RESULTS: In the process of educating nurses about the nursing process and nursing diagnosis, Generative Pre-training Transformer (ChatGPT) can present potential patient problems to nurses and guide them through the process from taking a medical history, setting nursing diagnoses and planning goals and interventions. Generative Pre-training Transformer (ChatGPT) returned appropriate nursing diagnoses, but these were not in line with the North American Nursing Diagnosis Association - International (NANDA-I) classification as requested. Of all the nursing diagnoses provided, only one was consistent with the most recent version of the North American Nursing Diagnosis Association - International (NANDA-I). Generative Pre-training Transformer (ChatGPT) is still not specific enough for nursing diagnoses, resulting in incorrect answers in several cases. CONCLUSIONS: Using Generative Pre-training Transformer (ChatGPT) to educate nurses and support the documentation process is time-efficient, but it still requires a certain level of human critical-thinking and fact-checking.

Validación de la escala de autoevaluación de competencias de enfermeria en informática en español / Validation of the nursing informatics competence self-assessment scale in Spanish

RESUMEN Objetivo: validar la escala de autoevaluación de competencias de enfermería en informática (SANICS-MX) en profesionales de enfermería mexicanos. Método:diseño descriptivo prospectivo y polietápico. Las etapas fueon: traducción y validación lingüística, validación por jueces, prueba piloto y confiabilidad, prueba final en una muestra representativa y análisis de fiabilidad y factorial. Asi mismo, la muestra fue de 160 profesionales de enfermería de instituciones de salud. El periodo de la realizacion del estudio fue de enero 2022 a febrero 2023. Resultados:se evaluó el Índice de Validez por Ítem (3.0); Criterio de Validez (16.0%) e Índice de Validez de Contenido (8.33) y se obtuvo la segunda versión del instrumento. Se realizó una prueba piloto en 30 profesionales de enfermería para verificar la consistencia, persistencia y comprensión de los ítems, obteniendo una Alpha de Cronbach de 0.83, lo que indica que es un instrumento confiable. Se realizó una prueba final a 160 profesionales de enfermería. Se realizo el análisis factorial y el nivel de fiabilidad de la versión final de la escala SANICS-MX, en el cual se determinó un Alpha de Cronbach de 0.943. Conclusión:la escala SANICS-MX es un instrumento válido y confiable para medir las competencias en informática en enfermería.

ABSTRACT Objetive:to validate the Self-assessment Nursing Informatics Competence Scale (SANICS-MX) in mexican nurses. Method:descriptive prospective and multistage design. The steps were: translation and linguistic validation, validation by judges, pilot test and reliability, final test on a representative sample, and reliability and factorial analysis. Likewise, the sample consisted of 160 nursing professionals from health institutions. The period of the study was from January 2022 to February 2023. Results: the validity index per Item (3.0) was evaluated; validity criterion (16.0%) and content validity index (8.33) and the second version of the instrument was obtained. A pilot test was carried out on 30 nursing professionals to verify the consistency, persistence and comprehension of the items, obtaining a Cronbach's Alpha of 0.83, which indicates that it is a reliable scale. A final test was carried out on 160 nursing professionals. The factorial analysis and the level of reliability of the final version of the SANICS-MX scale was carried out, in which a Cronbach's Alpha of 0.943 was determined.Conclusion:SANICS-MX is valid and reliable instrument to measure computer skills and competencies in nurses.

Representation of Environmental Concepts Associated with Health Impacts in Computer Standardized Clinical Terminologies.

OBJECTIVE: To evaluate the representation of environmental concepts associated with health impacts in standardized clinical terminologies. METHODS: This study used a descriptive approach with methods informed by a procedural framework for standardized clinical terminology mapping. The United Nations Global Indicator Framework for the Sustainable Development Goals and Targets was used as the source document for concept extraction. The target terminologies were the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and the International Classification for Nursing Practice (ICNP). Manual and automated mapping methods were utilized. The lists of candidate matches were reviewed and iterated until a final mapping match list was achieved. RESULTS: A total of 119 concepts with 133 mapping matches were added to the final SNOMED CT list. Fifty-three (39.8%) were direct matches, 37 (27.8%) were narrower than matches, 35 (26.3%) were broader than matches, and 8 (6%) had no matches. A total of 26 concepts with 27 matches were added to the final ICNP list. Eight (29.6%) were direct matches, 4 (14.8%) were narrower than, 7 (25.9%) were broader than, and 8 (29.6%) were no matches. CONCLUSION: Following this evaluation, both strengths and gaps were identified. Gaps in terminology representation included concepts related to cost expenditures, affordability, community engagement, water, air and sanitation. The inclusion of these concepts is necessary to advance the clinical reporting of these environmental and sustainability indicators. As environmental concepts encoded in standardized terminologies expand, additional insights into data and health conditions, research, education, and policy-level decision-making will be identified.

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature.

OBJECTIVES: The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools. METHODS: We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021. We extracted data on model development, implementation-related strategies and measures, lessons learned, and challenges and stakeholder involvement. We also assessed whether reports of data science application implementations currently follow the guidelines of the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by AI (DECIDE-AI) framework. RESULTS: Of 4,943 articles found in PubMed (NLM) and CINAHL (EBSCOhost), 11 were included in the final review and data extraction. Systems leveraging data science were developed for adult patient populations and were primarily deployed in hospital settings. The clinical domains targeted included mortality/deterioration, utilization/resource allocation, and hospital-acquired infections/COVID-19. The composition of development teams and types of stakeholders involved varied. Research teams more frequently reported on implementation methods than implementation results. Most studies provided lessons learned that could help inform future implementations of data science systems in health care. CONCLUSION: In 2021, very few studies report on the implementation of data science-driven applications focused on structural- or outcome-related nurse-sensitive indicators. This gap in the sharing of implementation strategies needs to be addressed in order for these systems to be successfully adopted in health care settings.

Psychosocial problems and high amount of opioid administration are associated with opioid dependence and abuse after first exposure for chronic pain patients.

Controversy surrounding the use of opioids for the treatment and the unique characteristics of chronic pain heighten the risks for abuse and dependence; however, it's unclear if higher doses of opioids and first exposure are associated with dependence and abuse. This study aimed to identify patients who developed dependence or opioid abuse after exposed to opioids for the first time and what were the risks factors associated with the outcome. A retrospective observational cohort study analyzed 2,411 patients between 2011 and 2017 who had a diagnosis of chronic pain and received opioids for the first time. A logistic regression model was used to estimate the likelihood of opioid dependence/abuse after the first exposure based on their mental health conditions, prior substance abuse disorders, demographics, and the amount of MME per day patients received. From 2,411 patients, 5.5 % of the patients had a diagnosis of dependence or abuse after the first exposure. Patients who were depressed (OR = 2.09), previous non-opioid substance dependence or abuse (OR = 1.59) or received greater than 50 MME per day (OR = 1.03) showed statistically significant relationship with developing opioid dependence or abuse, while age (OR = -1.03) showed to be a protective factor. Further studies should stratify chronic pain patients into groups who is in higher risk in developing opioid dependence or abuse and develop alternative strategies for pain management and treatments beyond opioids. This study reinforces the psychosocial problems as determinants of opioid dependence or abuse and risk factors, and the need for safer opioid prescribing practices.