Development of a neural network model for predicting glucose levels in a surgical critical care setting

Development of neural network models for the prediction of glucose levels in critically ill patients through the application of continuous glucose monitoring may provide enhanced patient outcomes. Here we demonstrate the utilization of a predictive model in real-time bedside monitoring. Such modeling may provide intelligent/directed therapy recommendations, guidance, and ultimately automation, in the near future as a means of providing optimal patient safety and care in the provision of insulin drips to prevent hyperglycemia and hypoglycemia

Implementation of the "FASTHUG" concept decreases the incidence of ventilator-associated pneumonia in a surgical intensive care unit

<p>Abstract</p> <p>Background</p> <p>Ventilator-associated pneumonia (VAP) is a leading cause of morbidity and mortality in critically ill patients. The Institute for Healthcare Improvement 100,000 Lives Campaign made VAP a target of prevention and performance improvement. Additionally, the Joint Commission on Accreditation of Health Organizations' 2007 Disease Specific National Patient Safety Goals included the reduction of healthcare-associated infections. We report implementation of a performance improvement project that dramatically reduced our VAP rate that had exceeded the 90^thpercentile nationally.</p> <p>Methods</p> <p>From 1 January 2004 to 31 December 2005 a performance improvement project was undertaken to decrease our critical care unit VAP rate. In year one (2004) procedural interventions were highlighted: aggressive oral care, early extubation, management of soiled or malfunctioning respiratory equipment, hand washing surveillance, and maximal sterile barrier precautions. In year two (2005) an evaluative concept called FASTHUG (daily evaluation of patients' feeding, analgesia, sedation, thromboembolic prophylaxis, elevation of the head of the bed, ulcer prophylaxis, and glucose control) was implemented. To determine the long-term effectiveness of such an intervention a historical control period (2003) and the procedural intervention period of 2004, i.e., the pre-FASTHUG period (months 1–24) were compared with an extended post-FASTHUG period (months 25–54).</p> <p>Results</p> <p>The 2003 surgical intensive care VAP rate of 19.3/1000 ventilator-days served as a historical control. Procedural interventions in 2004 were not effective in reducing VAP, p = 0.62. However, implementation of FASTHUG in 2005, directed by a critical care team, resulted in a rate of 7.3/1000 ventilator-days, p ≤ .01. The median pneumonia rate was lower after implementation of FASTHUG when compared to the historical control year (p = .028) and the first year after the procedural interventions (p = .041) using follow-up pairwise comparisons. The pre-FASTHUG period (2003–2004, months 1–24) when compared with an extended post-FASTHUG period (2005–2007, 25–54 months) also demonstrated a significant decrease in the VAP rate, p = .0004. This reduction in the post-FASTHUG period occurred despite a rising Severity of Illness index in critically ill patients, p = .001.</p> <p>Conclusion</p> <p>Implementation of the FASTHUG concept, in the daily evaluation of mechanically ventilated patients, significantly decreased our surgical intensive care unit VAP rate.</p

Intensivist supervision of resident-placed central venous catheters decreases the incidence of catheter-related blood stream infections

Catheter-related blood stream infections (CRBSI) cause significant morbidity and mortality. A retrospective study of a performance improvement project in our teaching hospital's surgical intensive care unit (SICU) showed that intensivist supervision was important in reinforcing maximal sterile barriers (MSB) use during the placement of a central venous catheter (CVC) in the prevention of CRBSI. A historical control period, 1 January 2001–31 December 2003, was established for comparison. From 1 January 2003–31 December 2007, MSB use for central venous line placement was mandated for all operators. However, in 2003 there was no intensivist supervision of CVC placements in the SICU. The use of MSB alone did not cause a significant change in the CRBSI rate in the first year of the project, but close supervision by an intensivist in years 2004–2007, in conjunction with MSB use, demonstrated a significant drop in the CRBSI rate when compared to the years before intensivist supervision (2001–2003), p < .0001. A time series analysis comparing monthly rates of CRBSI (2001–2007) also revealed a significant downward trend, p = .028. Additionally, in the first year of the mandated MSB use (2003), 85 independently observed resident-placed CVCs demonstrated that breaks in sterile technique (34/85), as compared those placements that had no breaks in technique (51/85), had more CRBSI, 6/34 (17.6%) vs. 1/51 (1.9%), p < .01. Interventions to reduce CRBSI in our SICU needed emphasis on adequate supervision of trainees in CVC placement, in addition to use of MSB, to effect lower CRBSI rates

1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function

HapMap imputed genome-wide association studies (GWAS) have revealed &gt;50 loci at which common variants with minor allele frequency &gt;5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-analysis of kidney function based on the estimated glomerular filtration rate (eGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value &lt; 5 × 10(-8) previously missed by HapMap-based GWAS. Six of these loci (HOXD8, ARL15, PIK3R1, EYA4, ASTN2, and EPB41L3) are tagged by common SNPs unique to the 1000 Genomes reference panel. Using pathway analysis, we identified 39 significant (FDR &lt; 0.05) genes and 127 significantly (FDR &lt; 0.05) enriched gene sets, which were missed by our previous analyses. Among those, the 10 identified novel genes are part of pathways of kidney development, carbohydrate metabolism, cardiac septum development and glucose metabolism. These results highlight the utility of re-imputing from denser reference panels, until whole-genome sequencing becomes feasible in large samples

1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function

HapMap imputed genome-wide association studies (GWAS) have revealed > 50 loci at which common variants with minor allele frequency > 5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-analysis of kidney function based on the estimated glomerular filtration rate (eGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value < 5 x 10(-8) previously missed by HapMap-based GWAS. Six of these loci (HOXD8, ARL15, PIK3R1, EYA4, ASTN2, and EPB41L3) are tagged by common SNPs unique to the 1000 Genomes reference panel. Using pathway analysis, we identified 39 significant (FDR < 0.05) genes and 127 significantly (FDR < 0.05) enriched gene sets, which were missed by our previous analyses. Among those, the 10 identified novel genes are part of pathways of kidney development, carbohydrate metabolism, cardiac septum development and glucose metabolism. These results highlight the utility of re-imputing from denser reference panels, until wholegenome sequencing becomes feasible in large samples

1000 Genomes-based metaanalysis identifies 10 novel loci for kidney function

HapMap imputed genome-wide association studies (GWAS) have revealed >50 loci at which common variants with minor allele frequency >5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-Analysis of kidney function based on the estimated glomerular filtration rate (EGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value < 5 × 10-8 previously missed by HapMap-based GWAS. Six of these loci (HOXD8, ARL15, PIK3R1, EYA4, ASTN2, and EPB41L3) are tagged by common SNPs unique to the 1000 Genomes reference panel. Using pathway analysis, we identified 39 significant (FDR < 0.05) genes and 127 significantly (FDR < 0.05) enriched gene sets, wh

Hydrodynamic Assembly of Astrocyte Cells in Conductive Hollow Microfibers

The manufacturing of 3D cell scaffoldings provides advantages for modeling diseases and injuries as it enables the creation of physiologically relevant platforms. A triple-flow microfluidic device is developed to rapidly fabricate alginate/graphene hollow microfibers based on the gelation of alginate induced with CaCl2. This five-channel microdevice actualizes continuous mild fabrication of hollow fibers under an optimized flow rate ratio of 300:200:100 µL min−1. The polymer solution is 2.5% alginate in 0.1% graphene and a 30% polyethylene glycol solution is used as the sheath and core solutions. The biocompatibility of these conductive microfibers by encapsulating mouse astrocyte cells (C8D1A) within the scaffolds is investigated. The cells can successfully survive both the manufacturing process and prolonged encapsulation for up to 8 days, where there is between 18–53% of live cells on both the alginate microfibers and alginate/graphene microfibers. These unique 3D hollow scaffolds can significantly enhance the available surface area for nutrient transport to the cells. In addition, these conductive hollow scaffolds illustrate unique advantages such as 0.728 cm3 gr−1 porosity and two times more electrical conductivity in comparison to alginate scaffolds. The results confirm the potential of these scaffolds as a microenvironment that supports cell growth.This article is published as Ouedraogo, Lionel J., Mychal J. Trznadel, McKayla Kling, Vahid Nasirian, Alexandra G. Borst, Mehran Abbasi Shirsavar, Andrew Makowski, Marilyn C. McNamara, Reza Montazami, and Nicole N. Hashemi. "Hydrodynamic Assembly of Astrocyte Cells in Conductive Hollow Microfibers." Advanced biology: e2300455. doi: https://doi.org/10.1002/adbi.202300455. © 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial, and no modifications or adaptations are made

Evaluation of a model for glycemic prediction in critically ill surgical patients.

We evaluated a neural network model for prediction of glucose in critically ill trauma and post-operative cardiothoracic surgical patients. A prospective, feasibility trial evaluating a continuous glucose-monitoring device was performed. After institutional review board approval, clinical data from all consenting surgical intensive care unit patients were converted to an electronic format using novel software. This data was utilized to develop and train a neural network model for real-time prediction of serum glucose concentration implementing a prediction horizon of 75 minutes. Glycemic data from 19 patients were used to "train" the neural network model. Subsequent real-time simulated testing was performed in 5 patients to whom the neural network model was naive. Performance of the model was evaluated by calculating the mean absolute difference percent (MAD%), Clarke Error Grid Analysis, and calculation of the percent of hypoglycemic (≤70 mg/dL), normoglycemic (>70 and <150 mg/dL), and hyperglycemic (≥150 mg/dL) values accurately predicted by the model; 9,405 data points were analyzed. The models successfully predicted trends in glucose in the 5 test patients. Clark Error Grid Analysis indicated that 100.0% of predictions were clinically acceptable with 87.3% and 12.7% of predicted values falling within regions A and B of the error grid respectively. Overall model error (MAD%) was 9.0% with respect to actual continuous glucose modeling data. Our model successfully predicted 96.7% and 53.6% of the normo- and hyperglycemic values respectively. No hypoglycemic events occurred in these patients. Use of neural network models for real-time prediction of glucose in the surgical intensive care unit setting offers healthcare providers potentially useful information which could facilitate optimization of glycemic control, patient safety, and improved care. Similar models can be implemented across a wider scale of biomedical variables to offer real-time optimization, training, and adaptation that increase predictive accuracy and performance of therapies