Understanding the Course of Critical Illness Through a Lifeworld Approach

An increasing number of individuals receive and survive intensive care treatment; however, several individuals experience problems afterward, which may threaten recovery. Grounded in a lifeworld approach, the aim of this study was to explore and describe what intensive care patients experience as limiting and strengthening throughout their illness trajectories. Ten former intensive care patients were interviewed three to eight months after hospital discharge. Using Giorgi’s phenomenological analysis, a general structure of gaining strength through a caring interaction with others was revealed. The structure consisted of three constituents: feeling safe through a caring presence, being seen and met as a unique person, and being supported to restore capacity. Being met with a humanistic approach and individualized care appeared to be important, and the findings are discussed within the framework of lifeworld-led care. To facilitate improved aftercare of the critically ill, more tailored support throughout the illness trajectory is needed.

A Randomized Controlled Trial Examining the Effect of Aromatherapy Using the Damask Rose Essential Oil on Pre-operative Anxiety Levels

Background: Non-pharmacological care interventions like aromatherapy can be cost-effective and efficient ways to reduce anxiety and adverse results before surgery. Objectives: In this study, the efficacy of aromatherapy on pre-operative anxiety in patients undergoing Percutaneous Nephrolithotomy (PCNL) referring to Peymaniyeh Hospital in Jahrom-Iran was the main goal. Methods: This controlled-randomized trial was conducted on 38 patients that were randomly assigned to two groups: Control and Aromatherapy (Rosa damascena). The anxiety levels were recorded for all two groups the night before the surgery. On the day of surgery and after re-communication, patients were approached in a pre-operative holding area, and the intervention was performed. Data were collected over 11 months from June 2015 to May 2016. Results: The statistically significant difference after the intervention between the control and intervention groups indicated that Aromatherapy with Rosa damascene reduced the patient’s anxiety. Conclusions: This survey prepares evidence for the use of Damask rose as an anti-anxiety intervention. Using Damask rose as a nursing intervention helps nurses provide individualized care and helps patients control their anxiety.

The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas

Pituitary adenomas (PAs) are a group of tumors with complex and heterogeneous clinical manifestations. Early accurate diagnosis, individualized management, and precise prediction of the treatment response and prognosis of patients with PA are urgently needed. Artificial intelligence (AI) and machine learning (ML) have garnered increasing attention to quantitatively analyze complex medical data to improve individualized care for patients with PAs. Therefore, we critically examined the current use of AI and ML in the management of patients with PAs, and we propose improvements for future uses of AI and ML in patients with PAs. AI and ML can automatically extract many quantitative features based on massive medical data; moreover, related diagnosis and prediction models can be developed through quantitative analysis. Previous studies have suggested that AI and ML have wide applications in early accurate diagnosis; individualized treatment; predicting the response to treatments, including surgery, medications, and radiotherapy; and predicting the outcomes of patients with PAs. In addition, facial imaging-based AI and ML, pathological picture-based AI and ML, and surgical microscopic video-based AI and ML have also been reported to be useful in assisting the management of patients with PAs. In conclusion, the current use of AI and ML models has the potential to assist doctors and patients in making crucial surgical decisions by providing an accurate diagnosis, response to treatment, and prognosis of PAs. These AI and ML models can improve the quality and safety of medical services for patients with PAs and reduce the complication rates of neurosurgery. Further work is needed to obtain more reliable algorithms with high accuracy, sensitivity, and specificity for the management of PA patients.

Physician Experiences and Perceived Barriers to Providing Quality Infertility Care in the Greater Accra Region of Ghana

High-quality, patient-centered care is essential to achieving equity and dignity for individuals with infertility, yet few studies have explored quality of infertility care in sub-Saharan Africa. We interviewed 13 non-specialist physicians and 2 medical school faculty to explore experiences in and perceptions of providing infertility care in Greater Accra, Ghana. We used a patient-centered infertility care model to inform our analysis and results. Individualized care and taking time to counsel and emotionally support patients were perceived as the most important things a physician can do to provide quality infertility care. Financial costs and lack of infertility services within a single facility were the most common barriers reported to providing quality infertility care. To the best of our knowledge, our study is the first to explore quality of infertility care provided by physicians in public sector facilities in Ghana, shedding light on existing barriers and identifying strategies for improvement.

Deep learning-driven risk-based subtyping of cognitively impaired individuals

Quantifying heterogeneity in Alzheimers disease (AD) risk is critical for individualized care and management. Recent attempts to assess AD heterogeneity have used structural (magnetic resonance imaging (MRI)-based) or functional (Ab-42; or tau) imaging, which focused on generating quartets of atrophy patterns and protein spreading, respectively. Here we present a computational framework that facilitated the identification of subtypes based on their risk of progression to AD. We used cerebrospinal fluid (CSF) measures of Ab-42; from the Alzheimers Disease Neuroimaging Initiative (ADNI) (n=544, discovery cohort) as well as the National Alzheimer's Coordinating Center (NACC) (n=508, validation cohort), and risk-stratified individuals with mild cognitive impairment (MCI) into quartiles (high-risk (H), intermediate-high risk (IH), intermediate-low risk (IL), and low-risk (L)). Patients were divided into subgroups utilizing patterns of brain atrophy found in each of these risk-stratified quartiles. We found H subjects to have a greater risk of AD progression compared to the other subtypes at 2- and 4-years in both the discovery and validation cohorts (ADNI: H subtype versus all others, p < 0.05 at 2 and 4 years; NACC: H vs. IL and LR at 2 years, p < 0.05, and a trend toward higher risk vs. IH, and p < 0.05 vs. IH, and L risk groups at 48 months with a trend toward lower survival vs. IL). Using MRI-based neural models that fused various deep neural networks with survival analysis, we then predicted MCI to AD conversion. We used these models to identify subtype-specific regions that demonstrate the largest levels of atrophy-related importance, which had minimal overlap (Average pairwise Jaccard Similarity in regions between the top 5 subtypes, 0.25+/-0.05 (+/- std)). Neuropathologic changes characteristic of AD were present across all subtypes in comparable proportions (Chi-square test, p>0.05 for differences in ADNC, n=31). Our risk-based approach to subtyping individuals provides an objective means to intervene and tailor care management strategies at early stages of cognitive decline.

Virtual Interdisciplinary Collaboration in Statewide Implementation of the MIND at Home Dementia Care Program

MIND at Home, a well-researched holistic, family-centered dementia care coordination program, provides collaborative support to community-dwelling persons living with dementia (PLWD) and their informal care partners (CP). Through comprehensive home-based assessment of 13 memory-care domains covering PLWD and CPs, individualized care plans are created, implemented, monitored, and revised over the course of the illness. Non-clinical Memory Care Coordinators (MCCs) working with an interdisciplinary team provide education and coaching to PLWD and their identified CP, and serve as a critical liaison and resource and between families, medical professional, and formal and informal community resources. This paper will describe a statewide pilot implementation of the program within a health plan across diverse sites in Texas and will present qualitative and quantitative descriptions of a key component of the program's effective translation to practice, the virtual collaborative case-based learning sessions. Health plan teams completed online interactive training modules and an intensive in-person case-based training with the Johns Hopkins team prior to program launch, and then engaged in weekly, hour-long virtual collaborative sessions that included health plan teams (site-based field teams, health plan clinical supervisory and specialty personnel [RNs, pharmacists, a geriatric psychiatrist, behavioral health specialists] and Johns Hopkins MIND program experts and geriatric psychiatrists. To date, the program has enrolled 350 health plan members, conducted 65 virtual collaborative sessions, and provided 423 CME/CEU units to team members. We will provide an overview of virtual collaborative session structure, participant contributions and discussion topics, case complexity, as well as didactic learning topics covered.

Çocuk Hastalara Uygulanan Fekal Mikrobiyota Transplantasyonuna Yönelik Hemşirelik Yaklaşımı

Fecal Microbiota Transplantation (FMT) is the process of taking stool from a healthy donor and placing it in the gastrointestinal tract of the sick individual. Today, it has been seen that FMT is mostly used for the treatment of clostridium difficile infection. Depending on the child’s condition, physician preferences, and/or protocol requirements, the route of administration in the FMT procedure can be oral capsule, upper or lower gastrointestinal route. Detailed information about FMT, including all aspects of the process, needs to be provided in writing to children, families and donors. Pediatric nurses should plan the individualized care process with a holistic approach based on child-centered care, family-centered care, atraumatic care models in the management of the FMT process. It is the nurse’s responsibility to increase the child’s comfort, protect his privacy, and prevent complications that may arise in the child. Because the FMT procedure is a specific application, nurses need to have detailed information about a qualified nursing process. In this review, the topics of microbiota, FMT and the nursing process before, during and after the procedure are discussed. Keywords: Child, fecal microbiota transplantation, nursing, pediatrics

Machine Learning-Based Predictive Models of Behavioral and Psychological Symptoms of Dementia

Models predicting the occurrence of specific types of behavioral and psychological symptoms of dementia (BPSD) can be highly beneficial for its early intervention and individualized care planning. Using a machine learning approach, this study developed and validated predictive models of the occurrence of BPSD, categorized into seven subsyndromes, among community-dwelling older adults with dementia in South Korea. BPSD dairy was used to measure BPSD and the state of unmet needs daily. We measured sleep and activity levels using actigraphy, and stress and fatigue using a portable heart rate variability analyzer. We developed predictive models and conducted cross-validation using training data that consisted of the first two wave dataset, and then validated the models using wave 3 test data. To deal with imbalanced datasets, we used Synthetic Minority Oversampling Technique (SMOTE), an over-sampling method. Categorical variables were pre-processed using target encoding. We then compared the machine-learning models with logistic regression. The area under the receiver operating characteristic curve (AUC) scores of the support vector machine (SVM) models for the wave 3 test data showed a similar or greater value than logistic regression models across all BPSD subsyndromes. The SVM model (AUC = 0.899) had an AUC value greater than that of the logistic regression model (AUC = 0.717), particularly for hyperactivity symptoms. Machine learning algorithms, especially SVM models, can be used to develop BPSD prediction models to help identify at-risk individuals and implement symptom-targeted individualized interventions.