Consensus on communication curriculum content in Danish undergraduate medical education: A Delphi study.

BACKGROUND: The acquisition of skills in patient-centered communication is a critical aspect of medical education which demands both resource-intensive instruction and longitudinal opportunities for learning. Significant variation currently exists in the content and timing of communication education. The aim of this study was to establish consensus regarding communication curriculum content for undergraduate medical education (UME) within the country of Denmark. METHODS: This study employed a Delphi process which is a widely accepted method for establishing consensus among experts and can be utilized to guide planning and decision-making in education. For this study, consensus was based on greater than 60% agreement between participants. Diverse stakeholders, representing all four universities with medical schools in Denmark, participated in an iterative three-round Delphi process which involved: (1) identifying key curricular elements for medical student education, (2) rating the importance of each item, and (3) prioritizing items relative to one another and rating each item based on the level of mastery that was expected for each skill (i.e. knowledge, performance with supervision, or performance independently). RESULTS: A national sample of 149 stakeholders participated with a 70% response rate for round 1, 81% for round 2, and 86% for round 3. The completed Delphi process yielded 56 content items which were prioritized in rank order lists within five categories: (1) establishing rapport, engaging patient perspectives and responding to needs; (2) basic communication skills and techniques; (3) phases and structure of the encounter; (4) personal characteristics and skills of the student; (5) specific challenging patient groups and context-dependent situations. DISCUSSION: Using a Delphi process, it was possible to achieve consensus regarding communication curriculum content for UME. These findings provide an important foundation for ensuring greater uniformity in UME, as well as supporting the important longitudinal goals of communication skill development across medical training.

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.

Effect of Continuous Glucose Monitoring Accuracy on Clinicians' Retrospective Decision Making in Diabetes: A Pilot Study.

BACKGROUND: The use of continuous glucose monitoring (CGM) in clinical decision making in diabetes could be limited by the inaccuracy of CGM data when compared to plasma glucose measurements. The aim of the present study is to investigate the impact of CGM numerical accuracy on the precision of diabetes treatment adjustments. METHOD: CGM profiles with maximum 5-day duration from 12 patients with type 1 diabetes treated with a basal-bolus insulin regimen were processed by 2 CGM algorithms, with the accuracy of algorithm 2 being higher than the accuracy of algorithm 1, using the median absolute relative difference (MARD) as the measure of accuracy. During 2 separate and similar occasions over a 1-month interval, 3 clinicians reviewed the processed CGM profiles, and adjusted the dose level of basal and prandial insulin. The precision of the dosage adjustments were defined in terms of the interclinician agreement and the intraclinician reproducibility of the decisions. The Cohen's kappa coefficient was used to assess the precision of the decisions. The study was based on retrospective and blind CGM data. RESULTS: For the interclinician agreement, in the first occasion, the kappa of algorithm 1 was .32, and that of algorithm 2 was .36. For the interclinician agreement, in the second occasion, the kappas of algorithms 1 and 2 were .17 and .22, respectively. For the intraclinician reproducibility of the decisions, the kappas of algorithm 1 were .35, .22, and .80 and the kappas of algorithm 2 were .44, .52, and .32, for the 3 clinicians, respectively. For the interclinician agreement, the relative kappa change from algorithm 1 to algorithm 2 was 86.06%, and for the intraclinician reproducibility, the relative kappa change from algorithm 1 to algorithm 2 was 53.99%. CONCLUSIONS: Results indicated that the accuracy of CGM algorithms might potentially affect the precision of the CGM-based insulin adjustments for type 1 diabetes patients. However, a larger study with several clinical centers, with higher number of clinicians and patients is required to validate the impact of CGM accuracy on decisions precision.

A classification model for predicting eye disease in newly diagnosed people with type 2 diabetes.

Diabetic retinopathy may be present at the time type 2 diabetes is diagnosed, and initial screening encompassing an eye examination performed by an ophthalmologist or optometrist is therefore recommended. However, proper screening for retinopathy may be challenging in many parts of the world. We hypothesized that simple, commonly available patient characteristics can be used to identify patients at high risk for having retinopathy. We investigated data from multiple years extracted from the National Health and Nutrition Examination Survey which holds information about blood glucose and eye examinations. Individuals with hitherto undiagnosed diabetes were classified according to the presence or absence of retinopathy. Linear classification was used to predict which patients had retinopathy at the time of diagnosis. A total of 266 individuals with undiagnosed diabetes were identified from the cohorts. Of these, 222 individuals had no sign of retinopathy, whereas 44 had mild or moderate non-proliferative retinopathy. Using information regarding HbA1c, BMI, waist circumference, age, systolic blood pressure, urinary albumin, and urinary creatinine, we were able to construct a model that predicts the presence of retinopathy with a positive predictive value of 22% and a negative predictive value of 99%. Only one true positive (1/44) with mild non-proliferative retinopathy was falsely classified. A classification model using readily available patient information and routine biochemical measures can be used to identify patients at high risk of having retinopathy at the time their diabetes is diagnosed. The model may be used to identify high-risk patients for retinopathy screening.

Evaluation of an Algorithm for Retrospective Hypoglycemia Detection Using Professional Continuous Glucose Monitoring Data.

BACKGROUND: People with type 1 diabetes (T1D) are unable to produce insulin and thus rely on exogenous supply to lower their blood glucose. Studies have shown that intensive insulin therapy reduces the risk of late-diabetic complications by lowering average blood glucose. However, the therapy leads to increased incidence of hypoglycemia. Although inaccurate, professional continuous glucose monitoring (PCGM) can be used to identify hypoglycemic events, which can be useful for adjusting glucose-regulating factors. New pattern classification approaches based on identifying hypoglycemic events through retrospective analysis of PCGM data have shown promising results. The aim of this study was to evaluate a new pattern classification approach by comparing the performance with a newly developed PCGM calibration algorithm. METHODS: Ten male subjects with T1D were recruited and monitored with PCGM and self-monitoring blood glucose during insulin-induced hypoglycemia. A total of 19 hypoglycemic events occurred during the sessions. RESULTS: The pattern classification algorithm detected 19/19 hypoglycemic events with 1 false positive, while the PCGM with the new calibration algorithm detected 17/19 events with 2 false positives. CONCLUSIONS: We can conclude that even after the introduction of new calibration algorithms, the pattern classification approach is still a valuable addition for improving retrospective hypoglycemia detection using PCGM.

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.

A novel model enhances HbA1c-based diabetes screening using simple anthropometric, anamnestic, and demographic information.

BACKGROUND: The sensitivity of HbA1c is not optimal for the screening of patients with latent diabetes. We hypothesize that simple healthcare information could improve accuracy. METHODS: We retrospectively analyzed data, including HbA1c, from multiple years from the National Health and Nutrition Examination Survey (NHANES) database (2005-2010). The data were used to create a logistic regression classification model for screening purposes. RESULTS: The study evaluated data for 5381 participants, including 404 with undiagnosed diabetes. The HbA1c screening data were supplemented with information about age, waist circumference, and physical activity in the HbA1c+ model. Alone, HbA1c alone had a receiver operating characteristics (ROC) curve for the area under the curve (AUC) of 0.808 (95% confidence interval [CI] 0.792-0.834). The HbA1c+ model had an ROC AUC of 0.851 (95% CI 0.843-0.872). There was a significant difference in the AUC between our model and using HbA1c without supplementary information (P < 0.05). CONCLUSIONS: We have developed a novel screening model that could help improve screening for type 2 diabetes with HbA1c. It seems beneficial to systematically add additional patient healthcare information in the process of screening with HbA1c.

Improved diabetes screening using an extended predictive feature search.

BACKGROUND: Screening entire populations for diabetes is not cost-effective. Hence, an efficient screening process must select those people who are at high risk for diabetes. In this study, we investigated whether screening procedures could be improved using an extended predictive feature search. MATERIALS AND METHODS: In order to develop our model and identify persons with diabetes (prevalence) we used data from years of the National Health and Nutrition Examination Survey (2005-2010), which has not been explored for this purpose before. We calculated all combinations of predictors in order to identify the optimal subset, and we used a linear logistic classification model to predict diabetes. V-fold cross-validation was used for the process of including variables and for validating the final models. This new model was compared with two established models. RESULTS: In total, 5,398 participants were included in this study. Among these, 478 participants had unidentified diabetes. The established models had a receiver operating characteristics curve for the area under the curve (AUC) of 0.74 and 0.71 compared with an AUC of 0.78 for the new model, showing a significant difference (P<0.05). A proposed cutoff point for the established models yielded respective sensitivities/specificities of 63%/72% and 40%/72% compared with the new model, which had a sensitivity/specificity of 70%/72%. CONCLUSIONS: Our data indicate that simple healthcare and economic information such as ratio of family income to poverty can add value in deciding who is at risk of unknown diabetes by using extended investigations of predictor combinations.

An information and communication technology system to detect hypoglycemia in people with type 1 diabetes.

Continuous glucose monitoring (CGM) is a new technology with the potential to detect hypoglycemia in people with Type 1 diabetes. However, the inaccuracy of the device in the hypoglycemic range is unfortunately too large. The aim of this study was to develop an information and communication technology system for improving hypoglycemia detection in CGM. The system was developed as an Android application with a build-in pattern classification algorithm. The algorithm processes features from CGM and typed in data from the patient, then warns the patient about incoming hypoglycemia. The system improved the detection of hypoglycemic events by 29%, with only one 1 false alert compared to CGM alone. Furthermore, the algorithm increased the average lead-time by 14 minutes. These findings indicate that it is possible to improve the hypoglycemia detection with an information and communication technology system, but that the system must be validated on a larger dataset.

Professional continuous glucose monitoring in subjects with type 1 diabetes: retrospective hypoglycemia detection.

BACKGROUND: An important task in diabetes management is detection of hypoglycemia. Professional continuous glucose monitoring (CGM), which produces a glucose reading every 5 min, is a powerful tool for retrospective identification of unrecognized hypoglycemia. Unfortunately, CGM devices tend to be inaccurate, especially in the hypoglycemic range, which limits their applicability for hypoglycemia detection. The objective of this study was to develop an automated pattern recognition algorithm to detect hypoglycemic events in retrospective, professional CGM. METHOD: Continuous glucose monitoring and plasma glucose (PG) readings were obtained from 17 data sets of 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. The CGM readings were automatically classified into a hypoglycemic group and a nonhypoglycemic group on the basis of different features from CGM readings and insulin injection. The classification was evaluated by comparing the automated classification with PG using sample-based and event-based sensitivity and specificity measures. RESULTS: With an event-based sensitivity of 100%, the algorithm produced only one false hypoglycemia detection. The sample-based sensitivity and specificity levels were 78% and 96%, respectively. CONCLUSIONS: The automated pattern recognition algorithm provides a new approach for detecting unrecognized hypoglycemic events in professional CGM data. The tool may assist physicians and diabetologists in conducting a more thorough evaluation of the diabetes patient's glycemic control and in initiating necessary measures for improving glycemic control.

Patient support ICT tool for hypertension monitoring.

Detection of hypertension is traditionally a matter for the general practitioner, but an alternative detection scheme is home blood pressure measurement by patients, on patients' or doctors' decision. We designed and implemented a prototype software tool to provide information about hypertension, video instructions on correct home blood pressure measurement technique and a measurements diary. The system was developed using standard, software development methods and techniques. The program was developed for Danish-speaking patients. Usability (navigability, level and outcome of instructions, logical arrangement, level and focus of information, and program accessibility) was evaluated in a think-aloud test with test users performing specific, realistic tasks. The prototype provides written information about hypertension, written and video instructions on correct blood pressure measurement technique, and measurements diary functionality. All test users performed all tasks and rated navigability, level and outcome of instructions, logical arrangement, level and focus of information, and program accessibility high, and had positive attitudes towards the system. The components in the patient support tool can be used separately or in combination. The effects of video for home blood pressure measurement technique instruction remain unexplored.

Interindividual and intraindividual variations in postprandial glycemia peak time complicate precise recommendations for self-monitoring of glucose in persons with type 1 diabetes mellitus.

BACKGROUND: In glycemic control, postprandial glycemia may be important to monitor and optimize as it reveals glycemic control quality, and postprandial hyperglycemia partly predicts late diabetic complications. Self-monitoring of blood glucose (SMBG) may be an appropriate technology to use, but recommendations on measurement time are crucial. METHOD: We retrospectively analyzed interindividual and intraindividual variations in postprandial glycemic peak time. Continuous glucose monitoring (CGM) and carbohydrate intake were collected in 22 patients with type 1 diabetes mellitus. Meals were identified from carbohydrate intake data. For each meal, peak time was identified as time from meal to CGM zenith within 40-150 min after meal start. Interindividual (one-way Anova) and intraindividual (intraclass correlation coefficient) variation was calculated. RESULTS: Nineteen patients were included with sufficient meal data quality. Mean peak time was 87 ± 29 min. Mean peak time differed significantly between patients (p = 0.02). Intraclass correlation coefficient was 0.29. CONCLUSIONS: Significant interindividual and intraindividual variations exist in postprandial glycemia peak time, thus hindering simple and general advice regarding postprandial SMBG for detection of maximum values.

Hypoglycemia impairs quality of blood glucose simulation in a clinical decision support system.

BACKGROUND: Clinical decision support systems allow for decisions based on blood glucose simulations. The DiasNet simulation tool is based on accepted principles of physiology and simulates blood glucose concentrations accurately in type 1 diabetes mellitus (T1DM) patients during periods without hypoglycemia, but deviations appear after hypoglycemia, possibly because of the long-term glucose counter-regulation to hypoglycemia. The purpose of this study was to evaluate the impact of hypoglycemia on blood glucose simulations. METHOD: Continuous glucose monitoring (CGM) data and diary data (meals, insulin, self-monitored blood glucose) were collected for 2 to 5 days from 17 T1DM patients with poor glycemic control. Hypoglycemic episodes [CGM glucose <63 mg/dl (3.5 mmol/liter) for ≥20 min] were identified in valid (well-calibrated) CGM data. For 24 hours after each hypoglycemic episode, a simulated (DiasNet) glucose profile was compared to the CGM glucose. RESULTS: A total of 52 episodes of hypoglycemia were identified in valid data. All subjects had at least one hypoglycemic episode. Ten episodes of hypoglycemia from nine subjects were eligible for analysis. The CGM glucose was significantly (p < .05) higher than simulated blood glucose for a period of 13 h, beginning 8 h after hypoglycemia onset. CONCLUSIONS: The present data show that hypoglycemia introduces substantial and systematic simulation errors for up to 24 h after hypoglycemia. This underlines the need for further evaluation of mechanisms behind this putative long-term glucose counter-regulation to hypoglycemia. When using blood glucose simulations in decision support systems, the results indicate that simulations for several hours following a hypoglycemic event may underestimate glucose levels by 100 mg/dl (5.6 mmol/liter) or more.

A pilot assessment of why patients choose not to participate in self-monitoring oral anticoagulant therapy.

Patients suffering from heart diseases often face lifelong oral anticoagulant therapy. Traditionally, the patient's general practitioner takes care of the treatment. An alternative management scheme is a self-monitoring setup where the patient monitors and manages the oral treatment himself. Despite international evidence of reduced thrombosis risk and death rate among patients enrolled in self-monitoring, a majority of eligible patients deselect this opportunity. Little is about the causes if this. This study is a pilot assessment of why patients, located in the North Denmark Region, choose not to participate. The study is based on qualitative interviews with two nurses working in a medical practice and two patients participating in conventional anticoagulant therapy. The results of this study seem to suggest that at least some patients feel a lack of information to base their decision regarding self-monitoring or conventional management on and that the knowledge among the health personnel at the medical clinics should be increased.

Modeling the effect of blood glucose and physical exercise on plasma adrenaline in people with type 1 diabetes.

BACKGROUND: Adrenaline is often studied in people with type 1 diabetes during hypoglycemic episodes. Adrenaline is difficult and costly to measure, and therefore a pharmacokinetic model of adrenaline can be a supportive tool that adds information and saves measurements resources. METHODS: We have developed a compartment model of adrenaline secretion and elimination. It is based on input on physical exercise, blood glucose level, and optional infused adrenaline. The model parameters are identified using least square regression on published data of adrenaline kinetics measured in a number of different clinical studies. RESULTS: Simulation of published adrenaline measurements shows agreement with data of adrenaline infusion (R(2) = 0.9), exercise (R(2) = 0.97), and hypoglycemic episodes (R(2) = 0.93-0.97). The identified function describing adrenaline secretion during hypoglycemia shows an exponential increase for a blood glucose decreasing below 3.5 mmol/L and an approaching maximum around 1 mmol/L. Exercise intensity increasing above 50% of maximal oxygen uptake maximum causes approximately exponential increase in adrenaline secretion. CONCLUSION: The model is a simple tool that can be used to simulate and predict adrenaline concentrations in situations of hypoglycemia, physical exercise, and adrenaline infusion. In conclusion, the developed model, although simple, seems to be useful for simulating adrenaline dynamics in situations with hypoglycemic episodes, physical exercise, or infusion.