Performance of a closed-loop glucose control system, comprising a continuous glucose monitoring system and an AI-based controller in swine during severe hypo- and hyperglycemic provocations

Impact of dysglycemia during the ebb and flow phases of critically ill burn patients: An observational study

BACKGROUND

Critically ill burn patients face severe metabolic stress, divided into early ebb and late flow phases, causing dysglycemia. While detrimental effects of hyper- and hypoglycemia in burn patients have been reported over the entire stay, its impact during the ebb and flow phases remains unexplored. This study is the first to investigate phase-separated dysglycemia for outcome prediction.

METHODS

This retrospective, single-center observational study examined burn ICU patients between 2009 and 2022. Non-severe (ABSI<7) and severe (ABSI≥7) burn patients were investigated separately. Furthermore, the effect of low (<50 %) versus high (≥50 %) dysglycemic rates (<70 or >140 mg/dL) was evaluated within the ebb and flow phases. Dysglycemia was calculated using the time-unified rate, an innovative method representing blood glucose over time. The primary outcome of this study was mortality.

RESULTS

This study included 67 non-severe and 101 severe burn patients. During the flow compared to the ebb phase, non-severe burn patients showed increased hyperglycemic rates (>140 mg/dL, p = 0.027) and mean blood glucose levels (p = 0.003), while severe burn patients showed increased glycemic variability (p < 0.001) and hypoglycemic rates (<70 mg/dL, p = 0.003). Non-severe burn patients with high dysglycemic rates showed increased length of ICU stay (ebb: p = 0.029, flow: p = 0.040) and pneumonia incidence (ebb: p = 0.005, flow: p = 0.002) compared to patients with low dysglycemic rates. High dysglycemic rate was associated with higher mortality in severe burn patients (ebb: p = 0.027, flow: p = 0.008). Multivariate logistic regression revealed that hyper- (OR: 1.034, 95 %-CI: [1.001-1.068], p = 0.045) and hypoglycemic rates (OR: 1.744, 95 %-CI: [1.180-2.577], p = 0.005) during the flow, but not the ebb phase, predicted mortality in severe burn patients.

CONCLUSIONS

This study suggests that increased dysglycemic rate plays a relevant role in both non-severe and severe burn patients, with a varying impact. Over time, the flow phase was characterized by higher glycemic variability as well as hyper- and hypoglycemic rates, with the latter two predicting mortality in severe burn patients. While larger cohorts are needed to confirm these findings, the data indicate that reducing the dysglycemic rate, particularly during the flow phase, could improve outcomes in critically ill burn patients.

Beyond the Pain Management Clinic: The Role of AI-Integrated Remote Patient Monitoring in Chronic Disease Management - A Narrative Review

Remote Patient Monitoring (RPM) stands as a pivotal advancement in patient-centered care, offering substantial improvements in the diagnosis, management, and outcomes of chronic conditions. Through the utilization of advanced digital technologies, RPM facilitates the real-time collection and transmission of critical health data, enabling clinicians to make prompt, informed decisions that enhance patient safety and care, particularly within home environments. This narrative review synthesizes evidence from peer-reviewed studies to evaluate the transformative role of RPM, particularly its integration with Artificial Intelligence (AI), in managing chronic conditions such as heart failure, diabetes, and chronic pain. By highlighting advancements in disease-specific RPM applications, the review underscores RPM's versatility and its ability to empower patients through education, shared decision-making, and adherence to therapeutic regimens. The COVID-19 pandemic further emphasized the importance of RPM in ensuring healthcare continuity during systemic disruptions. The integration of AI with RPM has refined these capabilities, enabling personalized, real-time data collection and analysis. While chronic pain management serves as a focal area, the review also examines AI-enhanced RPM applications in cardiology and diabetes. AI-driven systems, such as the NXTSTIM EcoAI™, are highlighted for their potential to revolutionize treatment approaches through continuous monitoring, timely interventions, and improved patient outcomes. This progression from basic wearable devices to sophisticated, AI-driven systems underscores RPM's ability to redefine healthcare delivery, reduce system burdens, and enhance quality of life across multiple chronic conditions. Looking forward, AI-integrated RPM is expected to further refine disease management strategies by offering more personalized and effective treatments. The broader implications, including its applicability to cardiology, diabetes, and pain management, showcase RPM's capacity to deliver automated, data-driven care, thereby reducing healthcare burdens while enhancing patient outcomes and quality of life.

Value of Glycemic Indices for Delayed Cerebral Ischemia after Aneurysmal Subarachnoid Hemorrhage: A Retrospective Single-Center Study

Delayed cerebral ischemia (DCI) is a severe complication following aneurysmal subarachnoid hemorrhage (aSAH), linked to poor functional outcomes and prolonged intensive care unit (ICU) stays. Timely DCI diagnosis is crucial but remains challenging. Dysregulated blood glucose, commonly observed after aSAH, may impair the constant glucose supply that is vital for brain function, potentially contributing to DCI. This study aimed to assess whether glucose indices could help identify at-risk patients and improve DCI detection. This retrospective, single-center observational study examined 151 aSAH patients between 2016 and 2019. Additionally, 70 of these (46.4%) developed DCI and 81 did not (no-DCI). To determine the value of glycemic indices for DCI, they were analyzed separately in patients in the period before (pre-DCI) and after DCI (post-DCI). The time-weighted average glucose (TWAG, p = 0.024), mean blood glucose (p = 0.033), and novel time-unified dysglycemic rate (TUDR140, calculated as the ratio of dysglycemic to total periods within a glucose target range of 70-140 mg/dL, p = 0.042), showed significantly higher values in the pre-DCI period of the DCI group than in the no-DCI group. In the time-series analysis, significant increases in TWAG and TUDR140 were observed at the DCI onset. In conclusion, DCI patients showed elevated blood glucose levels before and a further increase at the DCI onset. Prospective studies are needed to confirm these findings, as this retrospective, single-center study cannot completely exclude confounders and limitations. In the future blood glucose indices might become valuable parameters in multiparametric models to identify patients at risk and detect DCI onset earlier.

Flexible Graphene Paper Modified Using Pt&Pd Alloy Nanoparticles Decorated Nanoporous Gold Support for the Electrochemical Sensing of Small Molecular Biomarkers

The exploration into nanomaterial-based nonenzymatic biosensors with superb performance in terms of good sensitivity and anti-interference ability in disease marker monitoring has always attained undoubted priority in sensing systems. In this work, we report the design and synthesis of a highly active nanocatalyst, i.e., palladium and platinum nanoparticles (Pt&Pd-NPs) decorated ultrathin nanoporous gold (NPG) film, which is modified on a homemade graphene paper (GP) to develop a high-performance freestanding and flexible nanohybrid electrode. Owing to the structural characteristics the robust GP electrode substrate, and high electrochemically catalytic activities and durability of the permeable NPG support and ultrafine and high-density Pt&Pd-NPs on it, the resultant Pt&Pd-NPs-NPG/GP electrode exhibits excellent sensing performance of low detection limitation, high sensitivity and anti-interference capability, good reproducibility and long-term stability for the detection of small molecular biomarkers hydrogen peroxide (H2O2) and glucose (Glu), and has been applied to the monitoring of H2O2 in different types of live cells and Glu in body fluids such as urine and fingertip blood, which is of great significance for the clinical diagnosis and prognosis in point-of-care testing.

Microdialysis techniques and microdialysis-based patient-near diagnostics

This article will debate the usefulness of POCT measurements and the contribution microdialysis can make to generating valuable information. A particular theme will be the rarely considered difference between ex vivo sampling, which typically generates only a static measure of concentration, and in vivo measurements that are subject to dynamic changes due to mass transfer. Those dynamic changes provide information about the patients' physiological state.