Multicenter Study to Validate a Hospitalization Risk Assessment Tool in Hemodialysis Patients.

Introduction Protein-energy wasting is a prevalent condition in patients with chronic kidney disease. Our goal was to validate the risk assessment tool (Hashmi's tool) in multiple centers, developed in 2018, as it was easily applicable and cost-effective. Methods The following variables were scored as 0, 1, 2, or 3 as per severity: body mass index, HD vintage in years, functional capacity, serum albumin, serum ferritin, and the number of co-morbid conditions (diabetes mellitus, hypertension, ischemic heart disease, and cerebrovascular disease). This scoring system was applied to maintenance hemodialysis patients in six different centers. The patient's record was evaluated for two years. Patients were divided into low-risk (score <6) and high-risk (score ≥6). We compared the two groups using the chi-square test for the difference in hospitalization and mortality. Results A total of 868 patients' records were analyzed, and the maximum score was 13 with the application of Hashmi's tool. Four hundred twenty-nine patients were in the low-risk group, and 439 patients fell into the high-risk group. Four hundred sixty-seven patients were male, and 401 were females; 84% had hypertension, and 54% had diabetes mellitus. In the high-risk group, we identified more females. Patients' likelihood of being in the high-risk group was higher if they had diabetes mellitus, hypertension, or ischemic heart disease. Hospitalization due to vascular or non-vascular etiologies was more common in the high-risk group (p=0.036 and p<0.001, respectively). A total of 123 patients died during the study period, 92 from the high-risk group as compared to 31 from the low-risk group. This was three times higher and statistically significant (p<0.001). Conclusion Using a simple and cost-effective tool, we have identified malnourished patients who are at risk of hospitalization and mortality. This study has validated the previous work at a single center, which has now been reflected in six dialysis units across Saudi Arabia.

A quasi-experimental study of the effect of a comprehensive blended health educational program on self-management practices among patients with chronic obstructive pulmonary disease.

BACKGROUND: The prevalence and illness burden of chronic obstructive pulmonary disease (COPD) are both high. Currently, limited guidance is available to support the establishment of effective health programs to increase self-management practices in patients with COPD. OBJECTIVES: To explore the effect of a comprehensive blended health education program on self-management practices in patients with mild-to-moderate COPD in Jeddah City, Saudi Arabia. METHODS: A quasi-experimental research study was carried out with a convenience sample of 60 discharged or stable patients with COPD following treatment. Participants were divided into an intervention group (n = 30) that received usual hospital care and blended health education program, and a control group (n = 30) that obtained the usual hospital care without involvement in the health education program from May 2021- to August 2021. Data were collected before and three months after the intervention using the COPD Self-Management Scale and patient socio-demographic and clinical information surveys. RESULTS: Statistically significant differences were found between the control and intervention groups after three months of the intervention based on total COPD Self-Management Scale scores. There were no statistically significant relationships between the participants' mean COPD Self-Management Scale scores in both groups with their socio-demographic and clinical characteristics before and after the intervention. CONCLUSIONS: A nurse-led, comprehensive blended health education program was found to be an effective method for improving COPD patients' self-management practices. COPD nurses and nurse researchers must collaborate to identify the most common interventions with the best cost/benefit ratios and greater positive effects on early COPD patients' self-management practices and general well-being.

Predictive models for personalized asthma attacks based on patient's biosignals and environmental factors: a systematic review.

BACKGROUND: Asthma is a chronic disease that exacerbates due to various risk factors, including the patient's biosignals and environmental conditions. It is affecting on average 7% of the world population. Preventing an asthma attack is the main challenge for asthma patients, which requires keeping track of any risk factor that can cause a seizure. Many researchers developed asthma attacks prediction models that used various asthma biosignals and environmental factors. These predictive models can help asthmatic patients predict asthma attacks in advance, and thus preventive measures can be taken. This paper introduces a review of these models to evaluate the used methods, model's performance, and determine the need to improve research in this field. METHOD: A systematic review was conducted for the research articles introducing asthma attack prediction models for children and adults. We searched the PubMed, ScienceDirect, Springer, and IEEE databases from January 2000 to December 2020. The search includes the prediction models that used biosignal, environmental, and both risk factors. The research article's quality was assessed and scored based on two checklists, the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and the Critical Appraisal Skills Programme clinical prediction rule checklist (CASP). The highest scored articles were selected to review. RESULT: From 1068 research articles we reviewed, we found that most of the studies used asthma biosignal factors only for prediction, few of the studies used environmental factors, and limited studies used both of these factors. Fifteen different asthma attack predictive models were selected for this review. we found that most of the studies used traditional prediction methods, like Support Vector Machine and regression. We have identified the pros and cons of the reviewed asthma attack prediction models and propose solutions to advance the studies in this field. CONCLUSION: Asthma attack predictive models become more significant when using both patient's biosignal and environmental factors. There is a lack of utilizing advanced machine learning methods, like deep learning techniques. Besides, there is a need to build smart healthcare systems that provide patients with decision-making systems to identify risk and visualize high-risk regions.