Wearable sensors in patient acuity assessment in critical care.

Acuity assessments are vital for timely interventions and fair resource allocation in critical care settings. Conventional acuity scoring systems heavily depend on subjective patient assessments, leaving room for implicit bias and errors. These assessments are often manual, time-consuming, intermittent, and challenging to interpret accurately, especially for healthcare providers. This risk of bias and error is likely most pronounced in time-constrained and high-stakes environments, such as critical care settings. Furthermore, such scores do not incorporate other information, such as patients' mobility level, which can indicate recovery or deterioration in the intensive care unit (ICU), especially at a granular level. We hypothesized that wearable sensor data could assist in assessing patient acuity granularly, especially in conjunction with clinical data from electronic health records (EHR). In this prospective study, we evaluated the impact of integrating mobility data collected from wrist-worn accelerometers with clinical data obtained from EHR for estimating acuity. Accelerometry data were collected from 87 patients wearing accelerometers on their wrists in an academic hospital setting. The data was evaluated using five deep neural network models: VGG, ResNet, MobileNet, SqueezeNet, and a custom Transformer network. These models outperformed a rule-based clinical score (Sequential Organ Failure Assessment, SOFA) used as a baseline when predicting acuity state (for ground truth we labeled as unstable patients if they needed life-supporting therapies, and as stable otherwise), particularly regarding the precision, sensitivity, and F1 score. The results demonstrate that integrating accelerometer data with demographics and clinical variables improves predictive performance compared to traditional scoring systems in healthcare. Deep learning models consistently outperformed the SOFA score baseline across various scenarios, showing notable enhancements in metrics such as the area under the receiver operating characteristic (ROC) Curve (AUC), precision, sensitivity, specificity, and F1 score. The most comprehensive scenario, leveraging accelerometer, demographics, and clinical data, achieved the highest AUC of 0.73, compared to 0.53 when using SOFA score as the baseline, with significant improvements in precision (0.80 vs. 0.23), specificity (0.79 vs. 0.73), and F1 score (0.77 vs. 0.66). This study demonstrates a novel approach beyond the simplistic differentiation between stable and unstable conditions. By incorporating mobility and comprehensive patient information, we distinguish between these states in critically ill patients and capture essential nuances in physiology and functional status. Unlike rudimentary definitions, such as equating low blood pressure with instability, our methodology delves deeper, offering a more holistic understanding and potentially valuable insights for acuity assessment.

A Systematic Review on Participant Diversity in Clinical Trials-Have We Made Progress for the Management of Obesity and Its Metabolic Sequelae in Diet, Drug, and Surgical Trials.

OBJECTIVE: Individuals from Black and Hispanic backgrounds represent a minority of the overall US population, yet are the populations most affected by the disease of obesity and its comorbid conditions. Black and Hispanic individuals remain underrepresented among participants in obesity clinical trials, despite the mandate by the National Institutes of Health (NIH) Revitalization Act of 1993. This systematic review evaluates the racial, ethnic, and gender diversity of clinical trials focused on obesity at a national level. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review of clinicaltrials.gov, PubMed, Cochrane Central, and Web of Science was undertaken to locate phase 3 and phase 4 clinical trials on the topic of obesity that met associated inclusion/exclusion criteria. Ultimately, 18 studies were included for review. RESULTS: White non-Hispanic individuals represented the majority of clinical trial participants, as did females. No study classified participants by gender identity. Reporting of race/ethnicity was not uniform, with noted variability among racial/ethnic subgroups. CONCLUSIONS: Our findings suggest that disparities remain in the diverse racial, ethnic, and gender representation of participants engaged in clinical trials on obesity relative to the prevalence of obesity in underrepresented populations. Commitment to inclusive and intentional recruiting practices is needed to increase the representation of underrepresented groups, thus increasing the generalizability of future research.