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Background: Pragmatic and easy-to-use alternatives to estimating body composition, such as lean body mass and fat mass, could be valuable tools for assessing the risk of diabetes or other metabolic diseases. Previous work has shown how demographic and anthropometric data could be used in a neural network to estimate body composition with high precision. However, there is still a need for a publicly available and user-friendly format before these results can have clinical impact. Methods: We used data from 18 430 NHANES participants and stepwise linear regression with inclusion of linear, interactions, and quadratic terms to model lean body and fat mass. HTML and Javascript was used to develop a webapp as a frontend of the model. Results: The models had a correlation cofficent R = 0.99-0.98 (P < .001) withstandard error of estimate [SEE] = 2.07-2.05. Conclusions: The results indicate that it is possible to develop a “white-box” model with high precision. The proof of concept webapp is available as open source under the MIT license.

This report describes how a Conditional Generative Adversarial Network (CGAN) was used to synthesize realistic continuous glucose monitoring systems (CGM) from healthy individuals and individuals with type 1 diabetes over a range of different HbA1c levels. The results showed that even though the CGAN generated data, did not perfectly reflect real world CGM, many of the important features were captured and reflected in the synthetic signals. It is briefly discussed how heterogenous data sources constitutes a challenge for comparison of predictive CGM models. Therefore 40,000 CGM days were generated by the trained CGAN, equivalent to 940,000 hours of synthetic CGM measurements. These data have been made available in a public database, which can be used as a reference in future studies.

Background and Objective: Delayed gastric emptying is a substantial challenge for people with diabetes, affecting quality of life and blood glucose regulation. The complication is underdiagnosed, and current diagnostic tests are expensive or time consuming or have modest accuracy. The assessment of glycemic variations has potential use in gastroparesis screening. The aim of this study was to investigate the differences in glycemic variability between type 1 diabetes patients with gastroparesis and without a diagnosis of gastroparesis and the potential for using a classification model to differentiate between groups. Methods: Continuous glucose monitoring (CGM) from 425 patients with diabetes was included in the analytic cohort, including 16 patients with a diagnosis of gastroparesis and 409 without a known gastroparesis diagnosis. Sixteen features (9 daytime features and 7 nighttime features) describing glucose dynamics were extracted to assess differences between patients with and without a diagnosis of gastroparesis. A logistic regression model was trained using forward selection and cross-validation. Results: In total, 3 features were included in the model utilizing forward selection of features and cross-validation: mean absolute glucose (MAG), span, and standard deviation during the night. The Receiver operating characteristic (ROC) AUC for the classification model was 0.76. Conclusions: Gastroparesis seems to have an impact on glucose variability, especially during the night. Moreover, CGM could possibly be used as a part of the screening process for delayed gastric emptying, but more studies are needed to determine a realistic accuracy.

Background: Evidence-based learning systems built on prediction models can support wound care community nurses (WCCNs) during diabetic foot ulcer care sessions. Several prediction models in the area of diabetic foot ulcer healing have been developed, most built on cardiovascular measurement data. Two other data types are patient information (i.e. sex and hemoglobin A1c) and wound characteristics (i.e. wound area and wound duration); these data relate to the status of the diabetic foot ulcer and are easily accessible for WCCNs. The aim of the study was to assess simple bedside wound characteristics for a prediction model for diabetic foot ulcer outcomes. Method: Twenty predictor variables were tested. A pattern prediction model was used to forecast whether a given diabetic foot ulcer would (i) increase in size (or not) or (ii) decrease in size. Sensitivity, specificity, and area under the curve (AUC) in a receiver-operating characteristics curve were calculated. Results: A total of 162 diabetic foot ulcers were included. In combination, the predictor variables necrosis, wound size, granulation, fibrin, dry skin, and age were most informative, in total an AUC of 0.77. Conclusions: Wound characteristics have potential to predict wound outcome. Future research should investigate implementation of the prediction model in an evidence-based learning system.

Background: Estimating body composition is relevant in diabetes disease management, such as drug administration and risk assessment of morbidity/mortality. It is unclear how machine learning algorithms could improve easily obtainable body muscle and fat estimates. The objective was to develop and validate machine learning algorithms (neural networks) for precise prediction of body composition based on anthropometric and demographic data. Methods: Cross-sectional cohort study of 18 430 adults and children from the US population. Participants were examined with whole-body dual X-ray absorptiometry (DXA) scans, anthropometric assessment, and answered a demographic questionnaire. The primary outcomes were predicted total lean body mass (predLBM), total body fat mass (predFM), and trunk fat mass (predTFM) compared with reference values from DXA scans. Results: Participants were randomly partitioned into 70% training (12 901) data and 30% validation (5529) data. The prediction model for predLBM compared with lean body mass measured by DXA (DXALBM) had a Pearson’s correlation coefficient of R = 0.99 with a standard error of estimate (SEE) = 1.88 kg ( P predFM compared with fat mass measured by DXA (DXAFM) had a Pearson’s coefficient of R = 0.98 with a SEE = 1.91 kg ( P predTFM compared with DXA measured trunk fat mass (DXAFM) had a Pearson’s coefficient of R = 0.98 with a SEE = 1.13 kg ( P Conclusions: In this study, neural network models based on anthropometric and demographic data could precisely predict body muscle and fat composition. Precise body estimations are relevant in a broad range of clinical diabetes applications, prevention, and epidemiological research.

Background and objective Numerous attempts to predict glucose value from continuous glucose monitors (CGM) have been published. However, there is a lack of proper analysis and modeling of penalty for errors in different glycemic ranges. The aim of this study was to investigate the potential for developing glucose prediction models with focus on the clinical aspects. Methods We developed and compared six different models to test which approach were best suited for predicting glucose levels at different lead times between 10 and 60 min. The models were: last observation carried forward, linear extrapolation, ensemble methods using LSBoost and bagging, neural networks, one without error-weights and one with error-weights. The modeling and test were based on 225 type 1 diabetes patients with 315,000 h of CGM data. Results Results show that it is possible to predict glucose levels based on CGM with a reasonable accuracy and precision with a 30-min prediction lead time. A comparison of different methods shows that there are improvements on performance gained from using more advanced machine learning algorithms (MARD 10.26–10.79 @ 30-min lead time) compared to a simple modeling (MARD 10.75–12.97 @ 30-min lead time). Moreover, the proposed use of error weights could lead to better clinical performance of these models, which is an important factor for real usage. E.g., the percentages in the C-zone of the consensus error grid without error-weights (0.57-0.68%) vs including error-weights (0.28%). Conclusions The results point toward that using error weighting in the training of the models could lead to better clinical performance.

Background: The prevalence of smoking and diabetes is increasing in many developing countries. The aim of this study was to investigate the association of smoking with inadequate glycemic control and glycemic variability with continuous glucose monitoring (CGM) data in people with type 1 diabetes. Methods: Forty-nine smokers and 320 nonsmokers were obtained from the Novo Nordisk Onset 5 trial. After 16 weeks of treatment with continuous subcutaneous insulin infusion, risk of not achieving glycemic target and glycemic variability from six CGM measures was investigated. Analyzes were carried out with logistic regression models (glycemic target) and general linear models (glycemic variability). Finally, CGM median profiles were examined for the identification of daily glucose excursions. Results: A 4.7-fold (95% confidence interval: 1.5-15.4) increased risk of not achieving glycemic target was observed for smokers compared with nonsmokers. Increased time in hyperglycemia, decreased time in range, increased time in hypoglycemia (very low interstitial glucose), and increased fluctuation were observed for smokers compared with nonsmokers from CGM measures. CGM measures of coefficient of variation and time in hypoglycemia were not statistically significantly different. Examination of CGM median profiles revealed that risk of morning hypoglycemia is increased for smokers. Conclusions: In conclusion, smoking is associated with inadequate glycemic control and increased glycemic variability for people with type 1 diabetes with especially risk of morning hypoglycemia. It is important for clinicians to know that if the patient has type 1 diabetes and is smoking, a preemptive action to treat high glycated hemoglobin levels should not necessarily be treatment intensification due to the risk of hypoglycemia.

Aim: Our study investigated the relationship between both spontaneous and iatrogenic hypoglycaemia and mortality. Methods: A total of 200,859 patients from the eICU Collaborative Research Database were grouped by whether they had registered episodes of hypoglycaemia or not. Patients with hypoglycaemia were then further divided into subgroups according to type of hypoglycaemia—whether spontaneous or iatrogenic. Spontaneous hypoglycaemia during an ICU stay was defined as one or more registered blood glucose measurements < 70 mg/dL (3.9 mmol/L) with no evidence of insulin therapy. Results: Evidence of at least one hypoglycaemic episode during ICU hospitalization was associated with a significant increase in mortality: the observed mortality rate in such patients was 15.6% compared with 8% in patients without hypoglycaemia (P < 0.001). Multivariate logistic regression analysis was performed with APACHE Scores, hypoglycaemia and baseline data (age, gender, and ethnicity). Spontaneous hypoglycaemia remained a statistically significant predictor of mortality with an adjusted odds ratio (OR) of 1.61 (95% CI: 1.38–1.88; P < 0.001), whereas iatrogenic hypoglycaemia was not a significant predictor with an adjusted OR of 0.97 (95% CI: 0.82–1.14; P = 0.71). Conclusion: Spontaneous hypoglycaemia observed in ICU patients was associated with increased mortality and increased length of ICU stay. Although the present study, given its observational design, cannot provide a definitive answer, the clear difference between spontaneous and iatrogenic hypoglycaemia does not support a causal relationship between (short-lasting) hypoglycaemia and adverse outcomes, but instead indicates that (short-lasting) hypoglycaemia may be a marker of illness severity.

Background and aims: New methods to estimate body-composition have recently been proposed, but their relation to diseases, such as diabetes and coronary heart disease, needs further investigation. The purpose of this study was to investigate the association between proposed prediction of body-composition (PBC); Relative Fat Mass (RFM), Body Mass Index (BMI), Waist Circumference (WC) and disease. Methods: In a cross-sectional cohort (NHANES) the association between the four body measures and diabetes, high blood pressure, coronary heart disease, cancer, arthritis, and hospitalization were assessed. A total of 13,348 people was included in this study. Receiver operating characteristic (ROC), Area Under Curve (AUC) and statistical testing were used to evaluate the differences. Results: PBC/RFM had significant higher AUC than BMI or WC for diabetes, high blood pressure, hospitalization, and arthritis. PBC had a significant higher AUC than RFM, BMI, WC for Cancer and coronary heart disease. Conclusions: RFM and PBC could be a better indicator to distinguish amongst people with a risk of diseases compared to traditional measures such as BMI and WC. However, future studies need to investigate the longitudinal association between RFM, PBC and the risk of disease development to assess if these measures are better suited for risk-stratification.

Aim Obstructive sleep apnoea (OSA) is frequent in type 2 diabetes (T2D). The aim was to investigate the effect of a 12‐week treatment with continuous positive airway pressure (CPAP) on glycaemic control assessed by continuous glucose monitoring (CGM), HbA1c and fasting blood glucose in patients with T2D and newly detected OSA. Methods In a randomized controlled multicentre study, 72 participants with T2D and moderate to severe OSA (78% male, age 62 ± 7, AHI 35 ± 15) were recruited from outpatient clinics in three Danish hospitals and were randomized to CPAP intervention or control. The main outcome was glycaemic control assessed by 6 days CGM at baseline and after 12‐week therapy, as well as by HbA1c and fasting blood glucose. Results No significant changes were found in average glucose levels, time in glucose range, time with hypoglycaemia, time with hyperglycaemia or coefficient of variability. HbA1c decreased 0.7 mmol/mol (0.07%; P = .8) in the CPAP group and increased 0.8 mmol/mol (0.08%; P = .6) in the control group (intergroup difference, P = .6). Fasting blood glucose increased by 0.2 mmol/L (P = .02) in the CPAP group and by 0.4 mmol/L (P = .01) in the control group (intergroup difference, P = .7). In a prespecified subgroup analysis comparing participants with high adherence (minimum usage of four hours/night for 70% of all nights) to CPAP to the control group, no significant changes were observed either, although these participants had a tendency towards better glycaemic indices. Conclusions CPAP treatment for 12 weeks does not significantly change glycaemic control in patients with type 2 diabetes and OSA., Former studies show a high prevalence of obstructive sleep apnoea (OSA) in people with type 2 diabetes (T2D). Previous data are divergent on whether continuous positive airway pressure (CPAP) therapy improves glycaemic control in people with T2D and OSA. In this randomized controlled study, no effect was found of 12‐week CPAP treatment on glycaemic control measured by continuous glucose monitoring, HbA1c and fasting blood glucose. This study does not support implementation of routine CPAP to people with T2D and OSA with the purpose of improving glycaemic control.

Background: Currently, evidence-based learning systems to increase knowledge and evidence level of wound care are unavailable to wound care nurses in Denmark, which means that they need to learn about diabetic foot ulcers from experience and peer-to-peer training, or by asking experienced colleagues. Interactive evidence-based learning systems built on case-based reasoning (CBR) have the potential to increase wound care nurses’ diabetic foot ulcer knowledge and evidence levels. Method: A prototype of a CBR-interactive, evidence-based algorithm-operated learning system calculates a dissimilarity score (DS) that gives a quantitative measure of similarity between a new case and cases stored in a case base in relation to six variables: necrosis, wound size, granulation, fibrin, dry skin, and age. Based on the DS, cases are selected by matching the six variables with the best predictive power and by weighing the impact of each variable according to its contribution to the prediction. The cases are ranked, and the six cases with the lowest DS are visualized in the system. Results: Conventional education, that is, evidence-based learning material such as books and lectures, may be less motivating and pedagogical than peer-to-peer training, which is, however, often less evidence-based. The CBR interactive learning systems presented in this study may bridge the two approaches. Showing wound care nurses how individual variables affect outcomes may help them achieve greater insights into pathophysiological processes. Conclusion: A prototype of a CBR-interactive, evidence-based learning system that is centered on diabetic foot ulcers and related treatments bridges the gap between traditional evidence-based learning and more motivating and interactive learning approaches.

Background and objective CGM systems are still subject to a time-delay, which especially during rapid changes causes clinically significant difference between the CGM and the actual BG level. This study had the aim of exploring the potential of developing and validating a model for prediction of future CGM measurements in order to overcome the time-delay. Methods An artificial neural network regression (NN) approach were used to predict CGM values with a lead-time of 15 min. The NN were trained and internally validated on 23 million minutes of CGM and externally validated on 2 million minutes of CGM. The validation included data from 278 type 1 diabetes patients using three different CGM sensors. The NN performance were compared with three alternative methods, linear extrapolation, spline extrapolation and last observation carried forward. Results The internal validation yielded a RMSE of 9.1 mg/dL, a MARD of 4.2 % and 99.9 % of predictions were in the A + B zone of the consensus error grid. The external validation yielded a RMSE of 5.9–11.3 mg/dL, a MARD of 3.2–5.4 % and 99.9–100 % of predictions were in the A + B zone of the consensus error grid. The NN performed better on all parameters compared to the two alternative methods. Conclusions We proposed and validated a NN glucose prediction model that is potential simple to use and implement. The model only needs input from a CGM system in order to facilitate glucose prediction with a lead time of 15 min. The approach yielded good results for both internal and external validation.

Aim: To investigate the cognitive function in people without diabetes, with prediabetes and with diabetes. Methods/Design: The study design used was a cross-sectional analysis of data in people above 60 years registered in NHANES from 2011 to 2014.Three assessments were used to characterize cognitive function: (a) CERAD Word Learning subtest assessing immediate and delayed learning ability, (b) The Animal Fluency test assesing categorical verbal fluency, and (c) The Digit Symbol Substitution test assessing processing speed, sustained attention, and working memory. Results: (A) Memory recall (−0.19, [−0.34; −0.039], p = 0.014) and Delayed memory recall decline was associated with diabetes (−0.285, [−0.503; −0.067], p = 0.01), but not in an adjusted analysis. (B) Animal Fluency score decline was associated with diabetes (−1.185, [−1.688; −0.682], p < 0.001). (C) Digit Symbol score decline was associated with diabetes (−6.897, [−8.491; −5.302], p < 0.001). Prediabetes was not associated with cognitive function. Conclusions: This study demonstrates an association between cognitive dysfunction and diabetes. Results may also indicate that cognitive decline is not yet present in people with mild impairments of glucose homeostasis.

Aims: The aim of this study was to investigate glucose profiles assessed by oral glucose tolerance tests (OGTT), fasting glucose, and lipid profiles among smokers, ex-smokers and never-smokers. Materials and methods: The study design used was a cross-sectional analysis of data from several years of the NHANES (National Health and Nutrition Examination Survey) from 2005 to 2014. A total of 12,460 participants with measures of OGTT, triglycerides, LDL-cholesterol and HDL-cholesterol were included for the data analysis. Outcomes were all assessed in an unadjusted and in an adjusted gender analysis. A GLM model was used to assess 2-hour OGTT, fasting plasma glucose, difference between fasting plasma glucose and OGTT, HbA1c, HDL-cholesterol, LDL-cholesterol, and triglyceride in relation to current smoking, ex-smoking and never smoking. The effects were adjusted with covariates: gender, BMI, age, alcohol usage, educational level and ethnicity. Results: The OGTT results was lower for the group smoking (−10.1 [−13.2; −7.1], p < 0.001), and no effect was observed from ex-smoking (−2.7 [−5.7; 0.8], p = 0.08). Fasting glucose was not different for smokers (−0.2 [−1.6; 1.2], p = 0.80) or ex-smokers (0.1 [−1.3; 1.5], p = 0.90). For smokers', triglycerides (1.2 [1.1; 1.3], p < 0.001), LDL-cholesterol (7.7 [6.0; 9.3], p < 0.001) were increased and HDL-cholesterol was decreased (−2.1 [−2.8; −1.5], p < 0.001). Conclusions: Although this study is cross-sectional and cannot, by the same nature of the design, prove a cause-effect relationship, the present results indicate that cigarette smoking may be associated with factors that are adversely related to the metabolic syndrome. But the evidence from our results are not unanimous pointing in the same direction as 2-hour OGTT measurements are considerably lower in participants smoking.

Aim The aim of this study was to assess the impact on health-related quality of life (HRQoL) of a telehealth care solution compared with usual practice of patients with heart failure (HF). Methods A randomized controlled trial with a telehealth care solution (Telekit) as the intervention (with a focus on self-empowerment achieved by engaging patients in their own illness through self-monitoring) combined with usual care and usual care as the control. The primary outcome was a change in HRQoL as measured by the Short Form-36 (SF-36) questionnaire Physical Component Summary (PCS) score. Secondary outcomes were changes in HRQoL as measured by the SF-36 questionnaire Mental Component Summary (MSC) score and the HF disease-specific questionnaire Kansas City Cardiomyopathy Questionnaire 12 (KCCQ12) score, all of which were assessed from baseline to approximately 12 months’ follow-up between the two groups. Outcomes were assessed via unadjusted and adjusted analyses. Results At baseline, 299 (145 interventions, 154 controls) patients were enrolled. In the primary analysis ( n = 299), the adjusted intervention effects were PCS –0.81 (95% CI −2.7–1.1), MCS 4.66 (95% CI 1.8–7.5) and KCCQ12 3.67 (95% CI −0.7–8.1). Only the change in MCS was statistically significant. An unadjusted analysis replicated the primary analysis. Complete case analyses ( n = 193) generally resulted in a lower intervention effect on the PCS score, but the difference remained statistically insignificant. Conclusions Only the MCS score was significantly higher in the telehealth care group compared to the control group. ClinicalTrials.gov (NCT02860013), July 28, 2016

Automation of organizing and analyzing NHANES data can provide easier access to data and potentially reducing risk of introducing bias. This study investigates the potential for developing a software for this purpose. MATLAB R2016b was used for transforming and analyzing data from the NHANES. The software was tested successful by analyzing the association between smoking and glucose metabolism in the general population.

Resting heart rate (rHR) and heart rate variability (HRV) are non-invasive measurements that predict the risk of sudden cardiac death (SCD). Marine n-3 polyunsaturated fatty acid (PUFA) supplementation may decrease rHR, increase HRV, and reduce the risk of SCD. To date, no studies have investigated the effect of marine n-3 PUFA on HRV in renal transplant recipients. In a randomized controlled trial, 132 renal transplant recipients were randomized to receive either three 1 g capsules of marine n-3 PUFA, each containing 460 mg/g EPA and 380 mg/g DHA, or control (olive oil) for 44 weeks. HRV was calculated in the time and frequency domains during a conventional cardiovascular reflex test (response to standing, deep breathing, and Valsalva maneuver) and during 2 min of resting in the supine position. There was no significant effect of marine n-3 PUFA supplementation on time-domain HRV compared with controls. rHR decreased 3.1 bpm (&plusmn, 13.1) for patients receiving marine n-3 PUFA compared to 0.8 (&plusmn, 11.0) in controls (p = 0.28). In the frequency domain HRV analyses, there was a significant change in response to standing in both high and low frequency measures, 2.9 (p = 0.04, 95% CI (1.1, 8)) and 2.7 (p = 0.04, 95% CI (1.1, 6.5)), respectively. In conclusion, 44 weeks of supplemental marine n-3 PUFAs in renal transplant recipients significantly improved the cardiac autonomic function, assessed by measuring HRV during conventional cardiovascular reflex tests.

Resting heart rate (rHR) and heart rate variability (HRV) are non-invasive measurements that predict the risk of sudden cardiac death (SCD). Marine n-3 polyunsaturated fatty acid (PUFA) supplementation may decrease rHR, increase HRV, and reduce the risk of SCD. To date, no studies have investigated the effect of marine n-3 PUFA on HRV in renal transplant recipients. In a randomized controlled trial, 132 renal transplant recipients were randomized to receive either three 1 g capsules of marine n-3 PUFA, each containing 460 mg/g EPA and 380 mg/g DHA, or control (olive oil) for 44 weeks. HRV was calculated in the time and frequency domains during a conventional cardiovascular reflex test (response to standing, deep breathing, and Valsalva maneuver) and during 2 min of resting in the supine position. There was no significant effect of marine n-3 PUFA supplementation on time-domain HRV compared with controls. rHR decreased 3.1 bpm (± 13.1) for patients receiving marine n-3 PUFA compared to 0.8 (± 11.0) in controls (p = 0.28). In the frequency domain HRV analyses, there was a significant change in response to standing in both high and low frequency measures, 2.9 (p = 0.04, 95% CI (1.1;8)) and 2.7 (p = 0.04, 95% CI (1.1;6.5)), respectively. In conclusion, 44 weeks of supplemental marine n-3 PUFAs in renal transplant recipients significantly improved the cardiac autonomic function, assessed by measuring HRV during conventional cardiovascular reflex tests.

Introduction: Patients with diabetes often generate large amounts of data specifically related to the disease and to their general health. Cross-institutional sharing of patient health care data is complex, and as a consequence, data are not always available to the health care provider treating the patient. Accommodating this challenge could lead to better clinical effectiveness and improve clinical research. This work aims to present an approach for a blockchain-based platform for sharing health care data. The approach considers privacy concerns, data sharing, and patients as the center for governing their own data. Methods: The concept of this blockchain-based platform consists of using the NEM multi-signature blockchain contracts for access control of data management and the sharing and encryption of data to allow privacy and control of health care data. The architecture is built around cryptography, tokens, and multi-signature contracts. The multi-signature contract enables several entities to administrate the activity of an account and control the assets of one account. Multi-signature generates a contract that assigns the rights and powers of a certain account to other accounts; this contract can be edited to allow or remove entities. Discussion: Using blockchain could lead to improvements in diabetes data management. In the coming years, this technology should be implemented in existing small-scale diabetes health care system to explore its real-world benefits and challenges. Conclusion: This new approach could potentially lead to more efficient sharing of data between institutions and utilization of new types of data and research possibilities.

Objective: The development of in-hospital acquired pressure ulcers (PUs) is of great concern for both patients and professionals in health care. Based on the hypothesis that identification of patients who are prone to develop PU will enhance preventive measures in this group of patients, we investigated a new tool, Qscale, for in-hospital prediction of PU. Approach: A total of 383 patients were recruited from three departments. The investigations were performed in two steps: 252 patients were used to train the algorithm, and 131 patients were used in the validation. The new scale combines observational and on-site available information regarding patient mobility. Results: The validation data yielded an area under the curve (AUC) of 0.82. The Qscale had a significantly higher AUC compared with that of the Braden Scale with an AUC of 0.76 (p < 0.05). When comparing the performance at specific thresholds, a sensitivity of 47% and a specificity of 94% were observed. This was significantly (p < 0.05) better than the Braden score with a sensitivity of 20% and a specificity of 94%. Innovation: Our study showed promising results on both the training and validation data of the Qscale in comparison with the Braden Scale. Conclusion: The new scale has a potential benefit in the prevention of PU in a hospital setting.

BACKGROUND: COPD is a leading cause of morbidity and mortality worldwide. Spirometry is the most common lung function test for diagnosing COPD with a post-bronchodilator FEV 1/FVC < 0.70 confirming the presence of the disease. However, diagnosticians appear reluctant to use broncho-dilators, which have been linked to the misdiagnosis of COPD. Therefore, we investigated whether the threshold for diagnosing COPD should be modified when diagnosing COPD based on pre-bronchodilator spirometry. METHODS: Data from 23,433 participants were merged from the National Health and Nutrition Examination Survey (NHANES) 2007–2012. Participants were excluded for having no post-bronchodilator spirometry, for being below the age of 40, and for having asthma or lung cancer. These exclusion criteria resulted in 680 participants being included in the present study. Participants with COPD were defined as those with a post-bronchodilator FEV 1/FVC < 0.70. The threshold for diagnosing COPD based on pre-bronchodilator FEV 1/FVC was varied while calculating accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: Diagnosing COPD based on pre-bronchodilator FEV 1/FVC < 0.70 contributes to the misdiagnosis of COPD through poor classification rates. By adjusting the threshold and diagnosing COPD based on a pre-bronchodilator FEV 1/FVC < 0.66, the overall classification rates increase noticeably, resulting in a 15% increase in accuracy. CONCLUSION: We suggest a slight adjustment using pre-bronchodilator FEV 1/FVC < 0.66 when diagnosing COPD based on pre-bronchodilator spirometry. This approach could prevent cases of misdiagnosed COPD, which can lead to potentially harmful treatment of people who do not have COPD and foregone treatment in patients with COPD.

Conventional one-layer models have yet to achieve clinically relevant classification rates in predicting exacerbations for patients with COPD. The present study investigates whether a two-layer probabilistic model can increase classification rates compared to a one-layer model. Continuous measurements of oxygen saturation, pulse rate, and blood pressure from nine patients with COPD were structured into 17 prodromal exacerbation periods and 398 control periods. A one-layer model was compared to a two-layer model based on prior probabilities using double cross-validation. The two models were compared by the area under the receiver operating characteristics curve and sensitivity at an arbitrarily set specificity of 0.95. This comparison was carried out across nine different classification algorithms. The area under the receiver operating characteristics curve was increased across all nine classification algorithms and by a mean value of 0.11. Sensitivity at an arbitrarily set specificity of 0.95 was also increased by a mean value of 0.13. In conclusion, a two-layer probabilistic model for predicting COPD exacerbations can increase classification rates compared to a one-layer model, and to a level of clinical relevance, for patients in telehealth.

The objective of this study was to develop an algorithm for prediction of exacerbation onset in Chronic Obstructive Pulmonary Disease (COPD) patients based on continuous self-monitoring of physiological parameters from telehome-care monitoring. 151 physiological parameters of COPD patients were monitored on a daily/weekly basis for up to 2 years. Data were segmented in 30-day periods leading up to an exacerbation (exacerbation episode) and starting from a 14-day recovery period post-exacerbation (control episode) and tested in 6 intervals to predict exacerbation onset using k-nearest neighbour (k = 1, 3, 5). A classifier with sensitivity of 73%, specificity of 74%, positive predictive value of 69%, negative predictive value of 78% and an accuracy of 74% was achieved using data intervals consisting of 5 days. Intelligent processing of physiological recordings have potential for predicting exacerbation onset.

COPD is a leading cause of morbidity and mortality worldwide. Spirometry is the most common lung function test for diagnosing COPD with a post-bronchodilator FEVData from 23,433 participants were merged from the National Health and Nutrition Examination Survey (NHANES) 2007-2012. Participants were excluded for having no post-bronchodilator spirometry, for being below the age of 40, and for having asthma or lung cancer. These exclusion criteria resulted in 680 participants being included in the present study. Participants with COPD were defined as those with a post-bronchodilator FEVDiagnosing COPD based on pre-bronchodilator FEVWe suggest a slight adjustment using pre-bronchodilator FEV

Aim: This study investigates the prevalence of smell and taste impairment in adults with diabetes and potential risk factors for sense deterioration and its influence of daily food intake. Methods: Data from the NHANES 2013-2014 were analyzed. Smell impairment was defined as failing to identify ≥3 of 8 odors in NHANES Pocket Smell Test. Taste impairment was defined as being unable to identify quinine or NaCl in NHANES Tongue Tip and Whole-mouth Test. Results: A total of 3204 people (428 patients with diabetes, 2776 controls) were suitable to be included. The prevalence of smell impairment in patients with diabetes was higher compared to the controls: 22% versus 15% (p < 0.001). The difference prevailed after adjustment for age, BMI, alcohol misuse and smoking status. Taste was not impaired in patients with diabetes (p = 0.29). Patients with diabetes and smell impairment had a lower daily calorie intake compared to patients with diabetes and normal smell function. The duration of diabetes, diabetic complications and other potential risk factors were not associated with smell dysfunction. Conclusions: Smell dysfunction appears with a higher prevalence in patients with diabetes, and this seems to negatively affect daily food intake.