Using preventive home monitoring to reduce hospital admission rates and reduce costs: a case study of telehealth among chronic obstructive pulmonary disease patients

We studied whether preventive home monitoring of patients with chronic obstructive pulmonary disease (COPD) could reduce the frequency of hospital admissions and lower the cost of hospitalization. Patients were recruited from a health centre, general practitioner (GP) or the pulmonary hospital ward. They were randomized to usual care or tele-rehabilitation with a telehealth monitoring device installed in their home for four months. A total of 111 patients were suitable for inclusion and consented to be randomized: 60 patients were allocated to intervention and three were lost to follow-up. In the control group 51 patients were allocated to usual care and three patients were lost to follow-up. In the tele-rehabilitation group, the mean hospital admission rate was 0.49 per patient per 10 months compared to the control group rate of 1.17; this difference was significant (P = 0.041). The mean cost of admissions was €3461 per patient in the intervention group and €4576 in the control group; this difference was not significant. The Kaplan-Meier estimates for time to hospital admission were longer for the intervention group than the controls, but the difference was not significant. Future work requires large-scale studies of prolonged home monitoring with more extended follow-up.

Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications

Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies.

Cost-utility analysis of a telerehabilitation program: a case study of COPD patients

OBJECTIVE

The present study seeks to conduct cost-utility analysis (CUA) of the Danish TELEKAT (Telehomecare, Chronic Patients and the Integrated Healthcare System) project. The TELEKAT project seeks to test and develop a preventive home monitoring concept across sectors for chronic obstructive pulmonary disease (COPD) patients. The concept of the TELEKAT project is to reduce admissions by enabling the COPD patients to conduct self-monitoring and maintain rehabilitation activities in their own home. COPD patients with severe and very severe COPD were included in the study.

SUBJECTS AND METHODS

This economic evaluation follows international guidelines for the conduction of a CUA alongside a clinical randomized controlled trial. The analysis is based on a health sector perspective.

RESULTS

The mean incremental cost efficiency ratio, located in the southeast quadrant, shows that telerehabilitation is less costly and more effective than the rehabilitation given to the control group. The telerehabilitation program produces more value for money and generates savings on healthcare budgets.

CONCLUSIONS

The telerehabilitation program appears to be more cost-effective than the conventional rehabilitation program for COPD patients. Further studies of cost-effectiveness with a focus on large-scale studies are needed.

Sensor acceptance model - measuring patient acceptance of wearable sensors

OBJECTIVES

This project focuses on how patients respond to wearable biomedical sensors, since patient acceptance of this type of monitoring technology is essential for enhancing the quality of the data being measured. There is a lack of validated questionnaires measuring patient acceptance of telemedical solutions, and little information is known of how patients evaluate the use of wearable sensors.

METHODS

In information systems research, surveys are commonly used to evaluate the user satisfaction of software programs. Based on this tradition and adding measures of patient satisfaction and health-related quality of life (HRQoL), a Sensor Acceptance Model is developed. The model is made operational using two questionnaires developed for measuring the patients' perceived acceptance of wearable sensors.

RESULTS

The model is tested with 11 patients using a newly developed wearable ECG sensor, and with 25 patients in a reference group using a traditional "Holter Recorder". Construct validity is evaluated by confirmatory factor analysis, and internal consistency is calculated using the Cronbach's alpha coefficient. Sensor Acceptance Index (SAI) is calculated for each patient, showing reasonable dependencies and variance in scores.

CONCLUSIONS

This study attempts to identify patients' acceptance of wearable sensors, describing a user acceptance model. Understanding the patients' behavior and motivation represents a step forward in designing suitable technical solutions, and calculations of SAI can, hopefully, be used to compare different wearable sensor solutions. However, this instrument needs more extensive testing with a broader sample size, with different types of sensors and by explorative follow-up interviews.

Risk of Major Adverse Cardiovascular Events, Severe Hypoglycemia, and All-Cause Mortality for Widely Used Antihyperglycemic Dual and Triple Therapies for Type 2 Diabetes Management: A Cohort Study of All Danish Users

OBJECTIVE

The vast number of antihyperglycemic medications and growing amount of evidence make clinical decision making difficult. The aim of this study was to investigate the safety of antihyperglycemic dual and triple therapies for type 2 diabetes management with respect to major adverse cardiovascular events, severe hypoglycemia, and all-cause mortality in a real-life clinical setting.

RESEARCH DESIGN AND METHODS

Cox regression models were constructed to analyze 20 years of data from the Danish National Patient Registry with respect to effect of the antihyperglycemic therapies on the three end points.

RESULTS

A total of 66,807 people with type 2 diabetes were treated with metformin (MET) plus a combination of second- and third-line therapies. People on MET plus sulfonylurea (SU) had the highest risk of all end points, except for severe hypoglycemia, for which people on MET plus basal insulin (BASAL) had a higher risk. The lowest risk of major adverse cardiovascular events was seen for people on a regimen including a glucagon-like peptide 1 (GLP-1) receptor agonist. People treated with MET, GLP-1, and BASAL had a lower risk of all three end points than people treated with MET and BASAL, especially for severe hypoglycemia. The lowest risk of all three end points was, in general, seen for people treated with MET, sodium-glucose cotransporter 2 inhibitor, and GLP-1.

CONCLUSIONS

Findings from this study do not support SU as the second-line treatment choice for patients with type 2 diabetes. Moreover, the results indicate that adding a GLP-1 in people treated with MET and BASAL could be considered, especially if those people suffer from severe hypoglycemia.

How to Use Blockchain for Diabetes Health Care Data and Access Management: An Operational Concept

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.

A systematic review of the cost and cost-effectiveness of telehealth for patients suffering from chronic obstructive pulmonary disease

We conducted a systematic review of the evidence on the costs and cost-effectiveness of telehealth for patients with chronic obstructive pulmonary disease (COPD). A literature search identified six relevant economic evaluations that were assessed according to the Consensus Health Economic Criteria list (CHEC list). Three studies were from North America and three studies were from Europe. All studies reported the use of home monitoring devices that measured and transmitted different physical indicators to nurses who provided personalised feedback to patients during weekdays. The six studies involved a total of 559 COPD patients of whom 281 were randomised to telehealth. The review demonstrated a potential for cost savings. All six studies reported a lower average cost per patient with telehealth plus usual care compared with usual care alone. However, the quality of the economic evidence was poor. Five studies were evaluated as low quality and one study was evaluated as moderate quality, with CHEC list scores of 21-68%. Caution is advised for healthcare decision-makers seeking large-scale implementation of telehealth in routine clinical practice. The clinical effectiveness of such implementations with follow-up exceeding 12 months has not yet been demonstrated.

Cost-effectiveness of telehealthcare to patients with chronic obstructive pulmonary disease: results from the Danish 'TeleCare North' cluster-randomised trial

OBJECTIVES

To investigate the cost-effectiveness of a telehealthcare solution in addition to usual care compared with usual care.

DESIGN

A 12-month cost-utility analysis conducted alongside a cluster-randomised trial.

SETTING

Community-based setting in the geographical area of North Denmark Region in Denmark.

PARTICIPANTS

26 municipality districts define randomisation clusters with 13 districts in each arm. 1225 patients with chronic obstructive pulmonary disease were enrolled, of which 578 patients were randomised to telehealthcare and 647 to usual care.

INTERVENTIONS

In addition to usual care, patients in the intervention group received a set of telehealthcare equipment and were monitored by a municipality-based healthcare team. Patients in the control group received usual care.

MAIN OUTCOME MEASURE

Incremental costs per quality-adjusted life-years gained from baseline up to 12 months follow-up.

RESULTS

From a healthcare and social sector perspective, the adjusted mean difference in total costs between telehealthcare and usual care was €728 (95% CI -754 to 2211) and the adjusted mean difference in quality-adjusted life-years gained was 0.0132 (95% CI -0.0083 to 0.0346). The incremental cost-effectiveness ratio was €55 327 per quality-adjusted life-year gained. Decision-makers should be willing to pay more than €55 000 to achieve a probability of cost-effectiveness >50%. This conclusion is robust to changes in the definition of hospital contacts and reduced intervention costs. Only in the most optimistic scenario combining the effects of all sensitivity analyses, does the incremental cost-effectiveness ratio fall below the UK thresholds values (€21 068 per quality-adjusted life-year).

CONCLUSIONS

Telehealthcare is unlikely to be a cost-effective addition to usual care, if it is offered to all patients with chronic obstructive pulmonary disease and if the willingness-to-pay threshold values from the National Institute for Health and Care Excellence are applied.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT01984840, 14 November 2013.

Prevalence of taste and smell impairment in adults with diabetes: A cross-sectional analysis of data from the National Health and Nutrition Examination Survey (NHANES)

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.

Effectiveness and cost-effectiveness of telehealthcare for chronic obstructive pulmonary disease: study protocol for a cluster randomized controlled trial

BACKGROUND

Several feasibility studies show promising results of telehealthcare on health outcomes and health-related quality of life for patients suffering from chronic obstructive pulmonary disease, and some of these studies show that telehealthcare may even lower healthcare costs. However, the only large-scale trial we have so far - the Whole System Demonstrator Project in England - has raised doubts about these results since it conclude that telehealthcare as a supplement to usual care is not likely to be cost-effective compared with usual care alone.

METHODS/DESIGN

The present study is known as 'TeleCare North' in Denmark. It seeks to address these doubts by implementing a large-scale, pragmatic, cluster-randomized trial with nested economic evaluation. The purpose of the study is to assess the effectiveness and the cost-effectiveness of a telehealth solution for patients suffering from chronic obstructive pulmonary disease compared to usual practice. General practitioners will be responsible for recruiting eligible participants (1,200 participants are expected) for the trial in the geographical area of the North Denmark Region. Twenty-six municipality districts in the region define the randomization clusters. The primary outcomes are changes in health-related quality of life, and the incremental cost-effectiveness ratio measured from baseline to follow-up at 12 months. Secondary outcomes are changes in mortality and physiological indicators (diastolic and systolic blood pressure, pulse, oxygen saturation, and weight).

DISCUSSION

There has been a call for large-scale clinical trials with rigorous cost-effectiveness assessments in telehealthcare research. This study is meant to improve the international evidence base for the effectiveness and cost-effectiveness of telehealthcare to patients suffering from chronic obstructive pulmonary disease by implementing a large-scale pragmatic cluster-randomized clinical trial.

TRIAL REGISTRATION

Clinicaltrials.gov, http://NCT01984840, November 14, 2013.

Prediction of Nocturnal Hypoglycemia From Continuous Glucose Monitoring Data in People With Type 1 Diabetes: A Proof-of-Concept Study

BACKGROUND

Intensive insulin therapy has documented benefits but may also come at the expense of a higher risk of hypoglycemia. Hypoglycemia is associated with higher all-cause mortality and nocturnal hypoglycemia has been associated with the sudden dead-in-bed syndrome. This proof-of-concept study sought to investigate if nocturnal hypoglycemia can be predicted.

METHOD

Continuous glucose monitoring, meal, insulin, and demographics data from 463 people with type 1 diabetes were obtained from a clinical trial. A total of 4721 nights without or with hypoglycemia (429) were available including data from three consecutive days before the night. Thirty-two features were calculated based on these data. Data were split into 20% participants for evaluation and 80% for training. The optimal feature subset was found from forward selection of the 80% participants with linear discriminant analysis as basis for the classifier.

RESULTS

The forward selection resulted in a feature subset of four features. The evaluation resulted in an area under the receiver operating characteristics curve (ROC-AUC) of 0.79 leading to a sensitivity and a specificity of, e.g., 75% and 70%.

CONCLUSIONS

It was possible to predict nocturnal hypoglycemic episodes with a ROC-AUC of 0.79. A warning at bedtime about nocturnal hypoglycemia could be of great help for people with diabetes to enable preventive actions. Further development of the proposed algorithm is needed for implementation in everyday practice.

Comparison between one-point calibration and two-point calibration approaches in a continuous glucose monitoring algorithm

The purpose of this study was to investigate the effect of using a 1-point calibration approach instead of a 2-point calibration approach on the accuracy of a continuous glucose monitoring (CGM) algorithm. A previously published real-time CGM algorithm was compared with its updated version, which used a 1-point calibration instead of a 2-point calibration. In addition, the contribution of the corrective intercept (CI) to the calibration performance was assessed. Finally, the sensor background current was estimated real-time and retrospectively. The study was performed on 132 type 1 diabetes patients. Replacing the 2-point calibration with the 1-point calibration improved the CGM accuracy, with the greatest improvement achieved in hypoglycemia (18.4% median absolute relative differences [MARD] in hypoglycemia for the 2-point calibration, and 12.1% MARD in hypoglycemia for the 1-point calibration). Using 1-point calibration increased the percentage of sensor readings in zone A+B of the Clarke error grid analysis (EGA) in the full glycemic range, and also enhanced hypoglycemia sensitivity. Exclusion of CI from calibration reduced hypoglycemia accuracy, while slightly increased euglycemia accuracy. Both real-time and retrospective estimation of the sensor background current suggest that the background current can be considered zero in the calibration of the SCGM1 sensor. The sensor readings calibrated with the 1-point calibration approach indicated to have higher accuracy than those calibrated with the 2-point calibration approach.

The Effectiveness of Telemedicine Solutions for the Management of Type 2 Diabetes: A Systematic Review, Meta-Analysis, and Meta-Regression

BACKGROUND

Previous systematic reviews have aimed to clarify the effect of telemedicine on diabetes. However, such reviews often have a narrow focus, which calls for a more comprehensive systematic review within the field. Hence, the objective of the present systematic review, meta-analysis, and meta-regression is to evaluate the effectiveness of telemedicine solutions versus any comparator without the use of telemedicine on diabetes-related outcomes among adult patients with type 2 diabetes (T2D).

METHODS

This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We considered telemedicine randomized controlled trials (RCT) including adults (≥18 years) diagnosed with T2D. Change in glycated hemoglobin (HbA1c, %) was the primary outcome. PubMed, EMBASE, and the Cochrane Library Central Register of Controlled Trials (CENTRAL) were searched on October 14, 2020. An overall treatment effect was estimated using a meta-analysis performed on the pool of included studies based on the mean difference (MD). The revised Cochrane risk-of-bias tool was applied and the certainty of evidence was graded using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach.

RESULTS

The final sample of papers included a total of 246, of which 168 had sufficient information to calculate the effect of HbA1c%. The results favored telemedicine, with an MD of -0.415% (95% confidence interval [CI] = -0.482% to -0.348%). The heterogeneity was great (I2 = 93.05%). A monitoring component gave rise to the higher effects of telemedicine.

CONCLUSIONS

In conclusion, telemedicine may serve as a valuable supplement to usual care for patients with T2D. The inclusion of a telemonitoring component seems to increase the effect of telemedicine.

The effect of alcohol on blood glucose in Type 1 diabetes--metabolic modelling and integration in a decision support system

INTRODUCTION

We have recently shown, in studies with patients with Type 1 (insulin dependent) diabetes, that alcohol intake at 21:00 h significantly reduced blood glucose values after 10-12 h, compared with control studies with no alcohol.

HYPOTHESIS

We hypothesised that this was due to the following effects of alcohol: (1) alcohol metabolism increases NADH, leading to a reduction in hepatic gluconeogenesis; (2) increased glycogen phosphorylase activity depletes hepatic glycogen stores; (3) after the alcohol is metabolised, hepatic insulin sensitivity is increased, leading to the restoration of glycogen stores and reduction in blood glucose levels; and (4) consequently, after several hours, glycogen stores and insulin sensitivity return to normal.

RESULTS

A model describing these changes (DiasNet-Alcohol) was implemented into the DiasNet model of human glucose metabolism. Our study suggests that the DiasNet-Alcohol model gives a reasonable approximation of these effects of alcohol on blood glucose concentration observed in our study and supports our hypothesis for the mechanism behind these effects in Type 1 diabetes.

The impact of telehealth care on health-related quality of life of patients with heart failure: Results from the Danish TeleCare North heart failure trial

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.

Is predicted body-composition and relative fat mass an alternative to body-mass index and waist circumference for disease risk estimation?

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.

Association of severe hypoglycemia with mortality for people with diabetes mellitus during a 20-year follow-up in Denmark: a cohort study

AIMS

Severe hypoglycemia has a significant deteriorating effect on quality of life of the individual and has been associated with increased mortality. The aim of this study was to investigate the mortality among people with type 1 and type 2 diabetes suffering from severe hypoglycemia in Denmark in the last two decades.

METHODS

People diagnosed with type 1 (n = 44,033) and type 2 diabetes (n = 333,581) were extracted from the complete population of Denmark from 1996 to 2017 via ICD-10 diabetes codes and ATC diabetes medication codes. People suffering from severe hypoglycemia (type 1 diabetes n = 8808, type 2 diabetes n = 5605) as identified from ICD-10 codes were then matched 1:1 by year of birth, gender and year of diabetes diagnosis with those without severe hypoglycemia. Cox proportional hazards models were constructed to analyze the effect of severe hypoglycemia on mortality.

RESULTS

For both people with type 1 (HR 1.11, CI 95% 1.06 to 1.17) and type 2 diabetes (HR 1.77, CI 95% 1.67 to 1.87) suffering from hypoglycemia, an increased mortality risk was observed, compared to people without severe hypoglycemia. An investigation of the death causes did not indicate an association between the severe hypoglycemic episodes and death.

CONCLUSION

In this study, severe hypoglycemic episodes increased the mortality risk for people with type 1 and type 2 diabetes. The risk was higher among people with type 2 diabetes. Whether severe hypoglycemia is a symptom of other underlying illnesses increasing mortality risk or a risk factor itself needs further investigation.

Short-term prediction of future continuous glucose monitoring readings in type 1 diabetes: Development and validation of a neural network regression model

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.

Penalty weighted glucose prediction models could lead to better clinically usage

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.

Muscle grip strength is associated to reduced pulmonary capacity in patients with diabetes

AIM

This study investigates the role of muscle strength as a predictive factor for reduced pulmonary function in diabetes.

METHODS

Data from the NHANES 2011-2012 were analyzed. Lung function was assessed with spirometry (FEV1, FVC). Diabetes was assessed with an oral glucose tolerance test (OGTT), glycated hemoglobin (HbA1c), or fasting plasma glucose (FPG). Muscle strength was measured by a grip test using a handgrip dynamometer.

RESULTS

A total of 3521 people were suitable to be included for analysis in this study. Lung function was reduced in diabetes-after adjustment for covariates, the impact of diabetes on FVC was estimated as -331mL (SE 48) for known diabetes and -282mL (SE 41) for undiagnosed diabetes (P<0.001). Grip strength was also reduced in diabetes. After adjustment for covariates, the impact of diabetes was estimated as -5.9kg (SE 0.8) for known diabetes (P<0.001). An association between lung capacity and grip strength in people with known diabetes was observed (R=0.7, P<0.001). The adjusted impact on FVC from grip strength was estimated as 13.1mL (SE 3.4) per kg (P<0.001).

CONCLUSIONS

Muscle strength appears to be reduced in people with known diabetes, and this seems to affect the respiratory muscles as an independent factor.

Accuracy evaluation of a new real-time continuous glucose monitoring algorithm in hypoglycemia

BACKGROUND

The purpose of this study was to evaluate the performance of a new continuous glucose monitoring (CGM) calibration algorithm and to compare it with the Guardian(®) REAL-Time (RT) (Medtronic Diabetes, Northridge, CA) calibration algorithm in hypoglycemia.

SUBJECTS AND METHODS

CGM data were obtained from 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. Data were obtained in two separate sessions using the Guardian RT CGM device. Data from the same CGM sensor were calibrated by two different algorithms: the Guardian RT algorithm and a new calibration algorithm. The accuracy of the two algorithms was compared using four performance metrics.

RESULTS

The median (mean) of absolute relative deviation in the whole range of plasma glucose was 20.2% (32.1%) for the Guardian RT calibration and 17.4% (25.9%) for the new calibration algorithm. The mean (SD) sample-based sensitivity for the hypoglycemic threshold of 70 mg/dL was 31% (33%) for the Guardian RT algorithm and 70% (33%) for the new algorithm. The mean (SD) sample-based specificity at the same hypoglycemic threshold was 95% (8%) for the Guardian RT algorithm and 90% (16%) for the new calibration algorithm. The sensitivity of the event-based hypoglycemia detection for the hypoglycemic threshold of 70 mg/dL was 61% for the Guardian RT calibration and 89% for the new calibration algorithm. Application of the new calibration caused one false-positive instance for the event-based hypoglycemia detection, whereas the Guardian RT caused no false-positive instances. The overestimation of plasma glucose by CGM was corrected from 33.2 mg/dL in the Guardian RT algorithm to 21.9 mg/dL in the new calibration algorithm.

CONCLUSIONS

The results suggest that the new algorithm may reduce the inaccuracy of Guardian RT CGM system within the hypoglycemic range; however, data from a larger number of patients are required to compare the clinical reliability of the two algorithms.

The Effectiveness of Telemedicine Solutions in Type 1 Diabetes Management: A Systematic Review and Meta-analysis

BACKGROUND

Telemedicine holds a potential to strengthen self-management support outside health care settings in the everyday management of type 1 diabetes (T1D). However, existing effectiveness reviews are older or include a relatively narrow focus on specific definitions of telemedicine or included databases.

OBJECTIVE

To conduct a systematic review of the effectiveness of telemedicine solutions versus any comparator on diabetes-related outcomes among people with T1D.

METHODS

Studies including adults (≥18 years) with T1D published before October 14, 2020, were eligible. Primary outcome was glycated hemoglobin (HbA1c, %). The Cochrane Library, PubMed, EMBASE, and CINAHL were searched. Meta-analysis based on the mean difference in HbA1c% was used to pool effects. The Cochrane tool was applied to assess risk-of-bias, and the certainty of evidence was graded using the GRADE approach.

RESULTS

A total of 22 studies were included (with 1615 participants). Treatment effect for HbA1c% favored telemedicine (mean difference of -0.26% [95% confidence interval:-0.37% to -0.15%]) with moderate effect certainty. Heterogeneity was moderate (I² = 33.30%). Although not significant, secondary outcomes were all in favor of telemedicine except number of severe hypoglycemic events and diabetes knowledge, but the certainty of the evidence for those outcomes was all low or very low.

DISCUSSION

Reducing average HbA1c% levels are important to combat the risk of diabetic complications and premature death. However, the evidence mostly consist of small studies with a relative short duration and the estimated pooled effect is smaller than could be expected from quality improvement strategies in general for diabetes management.

PROSPERO NUMBER

CRD42020123565.

Cognitive impairment in elderly people with prediabetes or diabetes: A cross-sectional study of the NHANES population

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.

A wireless ECG system for continuous event recording and communication to a clinical alarm station

Development of new wearable biomedical sensors within a wireless infrastructure opens up possibilities for new telemedical applications leading to significant improvements in continuous monitoring, and thereby to better quality of patient care. In this paper we describe a new concept for a wireless electrocardiogram (ECG) system intended for continuous monitoring of ECG activity especially designed for arrhythmia diagnostic purposes. The patient is wearing an ECG sensor, "a smart electronic electrode", with wireless transmission of ECG signals to a dedicated hand held device (HHD). This device is monitoring the continuously recorded ECG signal, and can detect abnormal ECG activity using an automatic arrhythmia detector. Based on this, the device will transmit alarm conditions to a remote clinical alarm station (CAS). The system will act as a continuous event recorder, which can be used to follow up patients who have survived cardiac arrest, ventricular tachycardia or cardiac syncope but also for diagnostic purposes for patients with diffuse arrhythmia symptoms. This paper describes the principle design requirements for the new wireless ECG sensor and system design for the HHD in order to transfer detected alarms to the CAS.