Severe Insulin Resistance in the Setting of Therapeutic Hypothermia

Background: Hyperglycemia is common in critically ill patients and has been associated with poor outcomes. The effect of hypothermia, whether induced or spontaneous, on insulin resistance and hyperglycemia is not well understood and sometimes overlooked. Objectives: To review the literature on glucose metabolism and insulin resistance in patients treated with therapeutic Hypothermia (TH) after sudden cardiac arrest (SCA). And also to present knowledge about the possible effects of hypothermia on glycemic control and insulin sensitivity. Methods: we present an example of extreme insulin resistance during the period of therapeutic hypothermia in a patient who was admitted after a sudden cardiac Arrest. Literature review was conducted as a Medline/PubMed search. Studies were assessed regarding designs, primary and secondary efficacy parameters on TH after sudden cardiac arrest and effects on glucose metabolism. Results: A 68-year-old male who was admitted after SCA was noted to have extreme insulin resistance during TH which completely resolved during the rewarming phase of TH. No randomized, controlled trials were found in the Medline search. Two studies found that there was an increase in insulin resistance during TH especially during the cooling phase, whereas three studies found no direct association between TH and blood glucose levels. Conclusion: There is a possible association between TH and a reversible form of insulin resistance. More studies are needed. Patients who are treated with TH after SCA may present a challenge in glucose management

Severe Insulin Resistance in the Setting of Therapeutic Hypothermia

Background: Hyperglycemia is common in critically ill patients and has been associated with poor outcomes. The effect of hypothermia, whether induced or spontaneous, on insulin resistance and hyperglycemia is not well understood and sometimes overlooked. Objectives: To review the literature on glucose metabolism and insulin resistance in patients treated with therapeutic Hypothermia (TH) after sudden cardiac arrest (SCA). And also to present knowledge about the possible effects of hypothermia on glycemic control and insulin sensitivity. Methods: we present an example of extreme insulin resistance during the period of therapeutic hypothermia in a patient who was admitted after a sudden cardiac Arrest. Literature review was conducted as a Medline/PubMed search. Studies were assessed regarding designs, primary and secondary efficacy parameters on TH after sudden cardiac arrest and effects on glucose metabolism. Results: A 68-year-old male who was admitted after SCA was noted to have extreme insulin resistance during TH which completely resolved during the rewarming phase of TH. No randomized, controlled trials were found in the Medline search. Two studies found that there was an increase in insulin resistance during TH especially during the cooling phase, whereas three studies found no direct association between TH and blood glucose levels. Conclusion: There is a possible association between TH and a reversible form of insulin resistance. More studies are needed. Patients who are treated with TH after SCA may present a challenge in glucose management

Why Does Obesity Lead to Hypertension? Further Lessons from the Intersalt Study.

Objectives To analyze correlations between major determinants of blood pressure (BP), in efforts to generate and compare predictive models that explain for variance in systolic, diastolic, and mean BP amongst participants of the Intersalt study. Methods Data from the Intersalt study, consisting of nearly 10,000 subjects from 32 different countries, were reviewed and analyzed. Published mean values of 24 hour urinary electrolyte excretion (Na+, K+), 24 hour urine creatinine excretion, body mass index (BMI, kg/m^2), and blood pressure data were extracted and imported into Matlab™ for stepwise linear regression analysis. Results As shown earlier, strong correlations between urinary sodium excretion (UNaV) and systolic, diastolic and mean blood pressure were noted as well as between UNaV and the age dependent increase in systolic blood pressure. Of interest, BMI and urinary creatinine excretion rate (UCrV) also both correlated with systolic blood pressure, but the ratio of BMI/UCrV, constructed to be a measure of obesity, correlated negatively with systolic blood pressure. Conclusions Our results offer population-based evidence suggesting that increased size due to muscle mass rather than adiposity may correspond more to blood pressure. Additional data bases will need to be sampled and analyzed to further validate these observations

Role of Dietary Components in Modulating Hypertension

Hypertension is a major health issue, particularly in medically underserved populations that may suffer from poor health literacy, poverty, and limited access to healthcare resources. Management of the disease reduces the risk of adverse outcomes, such as cardiovascular or cerebrovascular events, vision impairment due to retinal damage, and renal failure. In addition to pharmacological therapy, lifestyle modifications such as diet and exercise are effective in managing hypertension. Current diet guidelines include the DASH diet, a low-fat and low-sodium diet that encourages high consumption of fruits and vegetables. While the diet is effective in controlling hypertension, adherence to the diet is poor and there are few applicable dietary alternatives, which is an issue that can arise from poor health literacy in at-risk populations. The purpose of this review is to outline the effect of specific dietary components, both positive and negative, when formulating a dietary approach to hypertension management that ultimately aims to improve patient adherence to the treatment, and achieve better control of hypertension

Predicting Adverse Outcomes in Chronic Kidney Disease Using Machine Learning Methods: Data from the Modification of Diet in Renal Disease

Background: Understanding factors which predict progression of renal failure is of great interest to clinicians. Objectives: We examined machine learning methods to predict the composite outcome of death, dialysis or doubling of serum creatinine using the modification of diet in renal disease (MDRD) data set. Methods: We specifically evaluated a generalized linear model, a support vector machine, a decision tree, a feed-forward neural network and a random forest evaluated within the context of 10 fold validation using the CARET package available within the open source architecture R program. Results: We found that using clinical parameters available at entry into the study, these computer learning methods trained on 70% of the MDRD population had prediction accuracies ranging from 66-77% on the remaining 30%. Although the support vector machine methodology appeared to have the highest accuracy, all models studied worked relatively well. Conclusions: These results illustrate the utility of employing machine learning methods within R to address the prediction of long term clinical outcomes using initial clinical measurements

Why Does Obesity Lead to Hypertension? Further Lessons from the Intersalt Study

Objectives To analyze correlations between major determinants of blood pressure (BP), in efforts to generate and compare predictive models that explain for variance in systolic, diastolic, and mean BP amongst participants of the Intersalt study. Methods Data from the Intersalt study, consisting of nearly 10,000 subjects from 32 different countries, were reviewed and analyzed. Published mean values of 24 hour urinary electrolyte excretion (Na+, K+), 24 hour urine creatinine excretion, body mass index (BMI, kg/m^2), and blood pressure data were extracted and imported into Matlab™ for stepwise linear regression analysis. Results As shown earlier, strong correlations between urinary sodium excretion (UNaV) and systolic, diastolic and mean blood pressure were noted as well as between UNaV and the age dependent increase in systolic blood pressure. Of interest, BMI and urinary creatinine excretion rate (UCrV) also both correlated with systolic blood pressure, but the ratio of BMI/UCrV, constructed to be a measure of obesity, correlated negatively with systolic blood pressure. Conclusions Our results offer population-based evidence suggesting that increased size due to muscle mass rather than adiposity may correspond more to blood pressure. Additional data bases will need to be sampled and analyzed to further validate these observations

Adiposity Predicts Pulse Pressure in Subjects with Chronic Kidney Disease: Data from the Modification of Diet in Renal Disease

Obesity is a known risk factor for hypertension, but the mechanisms by which this occurs are still unclear. As the body mass index (BMI) is frequently used to define obesity, but the BMI does not distinguish between adipose and other tissues, we sought to develop another index of obesity. We decided to look at the ratio of BMI to urinary creatinine excretion as the latter measurement is believed to be an index of muscle mass. We used data from the modification of diet in renal disease (MDRD) study as urinary creatinine collections and blood pressure measurements were readily available in this data set. The UCRV correlated well with lean body mass determined by anthropomorphic measurements available on this data set. We found that the BMI/UCRV ratio correlated with either percent body fat (PBF) or total body fat calculated as the product of PBF and weight. We also found that the BMI/UCRV ratio correlated significantly with systolic, diastolic and especially pulse pressure in this population. These data suggest that adipocyte mass has a relationship to blood pressure in subjects with renal disease. Should these data be confirmed in other populations, the BMI/UCRV ratio may prove to be a useful measurement in patients at risk for hypertension and other cardiovascular diseases

Predicting Adverse Outcomes in Chronic Kidney Disease Using Machine Learning Methods: Data from the Modification of Diet in Renal Disease

Background: Understanding factors which predict progression of renal failure is of great interest to clinicians. Objectives: We examined machine learning methods to predict the composite outcome of death, dialysis or doubling of serum creatinine using the modification of diet in renal disease (MDRD) data set. Methods: We specifically evaluated a generalized linear model, a support vector machine, a decision tree, a feed-forward neural network and a random forest evaluated within the context of 10 fold validation using the CARET package available within the open source architecture R program. Results: We found that using clinical parameters available at entry into the study, these computer learning methods trained on 70% of the MDRD population had prediction accuracies ranging from 66-77% on the remaining 30%. Although the support vector machine methodology appeared to have the highest accuracy, all models studied worked relatively well. Conclusions: These results illustrate the utility of employing machine learning methods within R to address the prediction of long term clinical outcomes using initial clinical measurements

Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction

With the rapidly increasing reliance on advances in IoT, we persist towards pushing technology to new heights. From ordering food online to gene editing-based personalized healthcare, disruptive technologies like ML and AI continue to grow beyond our wildest dreams. Early detection and treatment through AI-assisted diagnostic models have outperformed human intelligence. In many cases, these tools can act upon the structured data containing probable symptoms, offer medication schedules based on the appropriate code related to diagnosis conventions, and predict adverse drug effects, if any, in accordance with medications. Utilizing AI and IoT in healthcare has facilitated innumerable benefits like minimizing cost, reducing hospital-obtained infections, decreasing mortality and morbidity etc. DL algorithms have opened up several frontiers by contributing towards healthcare opportunities through their ability to understand and learn from different levels of demonstration and generalization, which is significant in data analysis and interpretation. In contrast to ML which relies more on structured, labeled data and domain expertise to facilitate feature extractions, DL employs human-like cognitive abilities to extract hidden relationships and patterns from uncategorized data. Through the efficient application of DL techniques on the medical dataset, precise prediction, and classification of infectious/rare diseases, avoiding surgeries that can be preventable, minimization of over-dosage of harmful contrast agents for scans and biopsies can be reduced to a greater extent in future. Our study is focused on deploying ensemble deep learning algorithms and IoT devices to design and develop a diagnostic model that can effectively analyze medical Big Data and diagnose diseases by identifying abnormalities in early stages through medical images provided as input. This AI-assisted diagnostic model based on Ensemble Deep learning aims to be a valuable tool for healthcare systems and patients through its ability to diagnose diseases in the initial stages and present valuable insights to facilitate personalized treatment by aggregating the prediction of each base model and generating a final prediction

Machine learning approach to predicting albuminuria in persons with type 2 diabetes: An analysis of the LOOK AHEAD Cohort

Abstract Albuminuria and estimated glomerular filtration rate (e‐GFR) are early markers of renal disease and cardiovascular outcomes in persons with diabetes. Although body composition has been shown to predict systolic blood pressure, its application in predicting albuminuria is unknown. In this study, we have used machine learning methods to assess the risk of albuminuria in persons with diabetes using body composition and other determinants of metabolic health. This study is a comparative analysis of the different methods to predict albuminuria in persons with diabetes mellitus who are older than 40 years of age, using the LOOK AHEAD study cohort‐baseline characteristics. Age, different metrics of body composition, duration of diabetes, hemoglobin A1c, serum creatinine, serum triglycerides, serum cholesterol, serum HDL, serum LDL, maximum exercise capacity, systolic blood pressure, diastolic blood pressure, and the ankle‐brachial index are used as predictors of albuminuria. We used Area under the curve (AUC) as a metric to compare the classification results of different algorithms, and we show that AUC for the different models are as follows: Random forest classifier‐0.65, gradient boost classifier‐0.61, logistic regression‐0.66, support vector classifier ‐0.61, multilayer perceptron ‐0.67, and stacking classifier‐0.62. We used the Random forest model to show that the duration of diabetes, A1C, serum triglycerides, SBP, Maximum exercise Capacity, serum creatinine, subtotal lean mass, DBP, and subtotal fat mass are important features for the classification of albuminuria. In summary, when applied to metabolic imaging (using DXA), machine learning techniques offer unique insights into the risk factors that determine the development of albuminuria in diabetes