Conectar
by Siting Chen, Corey L. Nagel, Ruotong Liu, Anda Botoseneanu, Heather G. Allore, Jason T. Newsom, Stephen Thielke, Jeffrey Kaye, Ana R. Quiñones
IntroductionMultimorbidity may confer higher risk for cognitive decline than any single constituent disease. This study aims to identify distinct trajectories of cognitive impairment probability among middle-aged and older adults, and to assess the effect of changes in mental-somatic multimorbidity on these distinct trajectories.
MethodsData from the Health and Retirement Study (1998–2016) were employed to estimate group-based trajectory models identifying distinct trajectories of cognitive impairment probability. Four time-varying mental-somatic multimorbidity combinations (somatic, stroke, depressive, stroke and depressive) were examined for their association with observed trajectories of cognitive impairment probability with age. Multinomial logistic regression analysis was conducted to quantify the association of sociodemographic and health-related factors with trajectory group membership.
ResultsRespondents (N = 20,070) had a mean age of 61.0 years (SD = 8.7) at baseline. Three distinct cognitive trajectories were identified using group-based trajectory modelling: (1) Low risk with late-life increase (62.6%), (2) Low initial risk with rapid increase (25.7%), and (3) High risk (11.7%). For adults following along Low risk with late-life increase, the odds of cognitive impairment for stroke and depressive multimorbidity (OR:3.92, 95%CI:2.91,5.28) were nearly two times higher than either stroke multimorbidity (OR:2.06, 95%CI:1.75,2.43) or depressive multimorbidity (OR:2.03, 95%CI:1.71,2.41). The odds of cognitive impairment for stroke and depressive multimorbidity in Low initial risk with rapid increase or High risk (OR:4.31, 95%CI:3.50,5.31; OR:3.43, 95%CI:2.07,5.66, respectively) were moderately higher than stroke multimorbidity (OR:2.71, 95%CI:2.35, 3.13; OR: 3.23, 95%CI:2.16, 4.81, respectively). In the multinomial logistic regression model, non-Hispanic Black and Hispanic respondents had higher odds of being in Low initial risk with rapid increase and High risk relative to non-Hispanic White adults.
ConclusionsThese findings show that depressive and stroke multimorbidity combinations have the greatest association with rapid cognitive declines and their prevention may postpone these declines, especially in socially disadvantaged and minoritized groups.
The QCovid 2 and 3 algorithms are risk prediction tools developed during the second wave of the COVID-19 pandemic that can be used to predict the risk of COVID-19 hospitalisation and mortality, taking vaccination status into account. In this study, we assess their performance in Scotland.
We used the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 national data platform consisting of individual-level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR virology testing, hospitalisation and mortality data. We assessed the discrimination and calibration of the QCovid 2 and 3 algorithms in predicting COVID-19 hospitalisations and deaths between 8 December 2020 and 15 June 2021.
Our validation dataset comprised 465 058 individuals, aged 19–100. We found the following performance metrics (95% CIs) for QCovid 2 and 3: Harrell’s C 0.84 (0.82 to 0.86) for hospitalisation, and 0.92 (0.90 to 0.94) for death, observed-expected ratio of 0.24 for hospitalisation and 0.26 for death (ie, both the number of hospitalisations and the number of deaths were overestimated), and a Brier score of 0.0009 (0.00084 to 0.00096) for hospitalisation and 0.00036 (0.00032 to 0.0004) for death.
We found good discrimination of the QCovid 2 and 3 algorithms in Scotland, although performance was worse in higher age groups. Both the number of hospitalisations and the number of deaths were overestimated.
by John Efromson, Giuliano Ferrero, Aurélien Bègue, Thomas Jedidiah Jenks Doman, Clay Dugo, Andi Barker, Veton Saliu, Paul Reamey, Kanghyun Kim, Mark Harfouche, Jeffrey A. Yoder
Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish (Danio rerio), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM™), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo. Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM™ we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species.Nurses' lack of clinical judgment often leads to adverse patient outcomes due to failure to recognize clinical deterioration, intervene, and manage complications. Teaching clinical judgment through a nursing process can help nursing students provide safe and competent patient care with improved health outcomes and to pass the National Council Licensure Examination for Registered Nurses (NCLEX-RN).
The aim of this study was to examine the effect of tutoring on clinical judgment of undergraduate nursing students utilizing Lasater's Clinical Judgment Rubric (LCJR). This study also compared the clinical judgment of male and female nursing students and students from different semester levels.
This quasi-experimental study utilized a single group pretest, posttest design. A convenience sample of n = 40 undergraduate nursing students from the Los Angeles County College of Nursing and Allied Health participated in the study. The participants underwent a pretest simulation, four sessions of the Clinical Judgment Model (CJM)-based tutoring, and a posttest simulation.
The posttest clinical judgment scores (35.70 ± 3.6) were significantly different from the pretest scores (25.78 ± 5.20). The tutoring had a significant effect on the clinical judgment of nursing students t(39) = −11.64, n = 40, p < .001, at 95% CI of the mean difference.
Enhancing nursing students' clinical judgment is crucial to provide high-quality, safe patient care with improved health outcomes. The CJM-based tutoring is an effective strategy for developing clinical judgment in nursing students. This new teaching approach can train students to critically think, develop clinical judgment, and prepare for the complex healthcare environment. Therefore, nurse educators should focus on integrating clinical judgment into the prelicensure nursing program curriculum as a priority.
Background
Engagement with self-monitoring of blood pressure (BP) declines, on average, over time but may vary substantially by individual.
Objectives
We aimed to describe different 1-year patterns (groups) of self-monitoring of BP behaviors, identify predictors of those groups, and examine the association of self-monitoring of BP groups with BP levels over time.
Methods
We analyzed device-recorded BP measurements collected by the Health eHeart Study—an ongoing prospective eCohort study—from participants with a wireless consumer-purchased device that transmitted date- and time-stamped BP data to the study through a full 12 months of observation starting from the first day they used the device. Participants received no instruction on device use. We applied clustering analysis to identify 1-year self-monitoring, of BP patterns.
Results
Participants had a mean age of 52 years and were male and White. Using clustering algorithms, we found that a model with three groups fit the data well: persistent daily use (9.1% of participants), persistent weekly use (21.2%), and sporadic use only (69.7%). Persistent daily use was more common among older participants who had higher Week 1 self-monitoring of BP frequency and was associated with lower BP levels than the persistent weekly use or sporadic use groups throughout the year.
Conclusion
We identified three distinct self-monitoring of BP groups, with nearly 10% sustaining a daily use pattern associated with lower BP levels. 
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