Simon Lebech Cichosz

Moving prediction of exacerbation in chronic obstructive pulmonary disease for patients in telecare

We investigated whether physiological data can be used for predicting chronic obstructive pulmonary disease (COPD) exacerbations. Home measurements from 57 patients were analysed, during which 10 exacerbations occurred in nine patients. A total of 273 different features were evaluated for their discrimination abilities between periods with and without exacerbations. The analysis showed that if a sensitivity level of 70% is considered to be acceptable, then the specificity was 95% and the AUC was 0.73, i.e. it is possible to discriminate between periods of exacerbation and periods without. A system capable of predicting risk could provide support to COPD patients in their tele-rehabilitation.

Objective measurements of activity patterns in people with newly diagnosed Type 2 diabetes demonstrate a sedentary lifestyle

To evaluate physical activity in people with newly diagnosed Type 2 diabetes using objective measures.

A Novel Algorithm for Prediction and Detection of Hypoglycemia Based on Continuous Glucose Monitoring and Heart Rate Variability in Patients With Type 1 Diabetes

Background: Hypoglycemia is a common and serious side effect of insulin therapy in patients with diabetes. Early detection and prediction of hypoglycemia may improve treatment and avoidance of serious complications. Continuous glucose monitoring (CGM) has previously been used for detection of hypoglycemia, but with a modest accuracy. Therefore, our aim was to investigate whether a novel algorithm that adds information of the complex dynamic/pattern of heart rate variability (HRV) could improve the accuracy of hypoglycemia as detected by a CGM device. Methods: Data from 10 patients with type 1 diabetes studied during insulin-induced hypoglycemia were obtained. Blood glucose samples were used as reference. HRV patterns and CGM data were combined in a mathematical prediction algorithm. Detection of hypoglycemic periods, performed by the algorithm, was treated as a pattern recognition problem and features/patterns derived from HRV and CGM prior to each blood glucose sample were used to decide if that particular point in time was below the hypoglycemic threshold of 3.9 mmol/L. Results: A total of 903 samples were analyzed by the novel algorithm, which yielded a sensitivity of 79% and a specificity of 99%. The algorithm was able to detect 16/16 hypoglycemic events with no false positives and had a lead time of 22 minutes as compared to the CGM device. Conclusions: Detection accuracy and lead time were significantly improved by the novel algorithm compared to that of CGM alone.

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.

Combining information of autonomic modulation and CGM measurements enables prediction and improves detection of spontaneous hypoglycemic events

Background: We have previously tested, in a laboratory setting, a novel algorithm that enables prediction of hypoglycemia. The algorithm integrates information of autonomic modulation, based on heart rate variability (HRV), and data based on a continuous glucose monitoring (CGM) device. Now, we investigate whether the algorithm is suitable for prediction of hypoglycemia and for improvement of hypoglycemic detection during normal daily activities. Methods: Twenty-one adults (13 men) with T1D prone to hypoglycemia were recruited and monitored with CGM and a Holter device while they performed normal daily activities. We used our developed algorithm (a pattern classification method) to predict spontaneous hypoglycemia based on CGM and HRV. We compared 3 different models; (i) a model containing raw data from the CGM device; (ii) a CGM* model containing data derived from the CGM device signal; and (iii) a CGM+HRV model-combining model (ii) with HRV data. Results: A total of 12 hypoglycemic events (glucose levels < 3.9 mmol/L, 70 mg/dL) and 237 euglycemic measurements were included. For a 20-minute prediction, model (i) resulted in a ROC AUC of 0.69. If a high sensitivity of 100% was chosen, the corresponding specificity was 69%. (ii) The CGM* model yielded a ROC AUC of 0.92 with a corresponding sensitivity of 100% and specificity of 71%. (iii) The CGM+HRV model yielded a ROC AUC of 0.96 with a corresponding sensitivity of 100% and specificity of 91%. Conclusions: Data shows that adding information of autonomic modulation to CGM measurements enables prediction and improves the detection of hypoglycemia.

Hypoglycaemia and QT interval prolongation in type 1 diabetes – bridging the gap between clamp studies and spontaneous episodes

AIMS We propose a study design with controlled hypoglycaemia induced by subcutaneous injection of insulin and matched control episodes to bridge the gap between clamp studies and studies of spontaneous hypoglycaemia. The observed prolongation of the heart rate corrected QT interval (QTc) during hypoglycaemia varies greatly between studies. METHODS We studied ten adults with type 1 diabetes (age 41±15years) without cardiovascular disease or neuropathy. Single-blinded hypoglycaemia was induced by a subcutaneous insulin bolus followed by a control episode on two occasions separated by 4weeks. QT intervals were measured using the semi-automatic tangent approach, and QTc was derived by Bazetts (QTcB) and Fridericias (QTcF) formulas. RESULTS QTcB increased from baseline to hypoglycaemia (403±20 vs. 433±39ms, p<0.001). On the euglycaemia day, QTcB also increased (398±20 vs. 410±27ms, p<0.01), but the increase was less than during hypoglycaemia (p<0.001). The same pattern was seen for QTcF. Plasma adrenaline levels increased significantly during hypoglycaemia compared to euglycaemia (p<0.01). Serum potassium levels decreased similarly after insulin injection during both hypoglycaemia and euglycaemia. CONCLUSIONS Hypoglycaemia as experienced after a subcutaneous injection of insulin may cause QTc prolongation in type 1 diabetes. However, the magnitude of prolongation is less than typically reported during glucose clamp studies, possible because of the study design with focus on minimizing unwanted study effects.

Clinical Impact of Home Telemonitoring on Patients with Chronic Obstructive Pulmonary Disease

BACKGROUND Chronic obstructive pulmonary disease (COPD) affects millions of people worldwide. A complication of COPD is exacerbations that result in increased utilization of healthcare services, readmissions to the hospital, and a decline in health-related quality of life. Home telehealth has been shown both to improve health-related quality of life and to reduce admission rates. Using clinical data from a home telemonitoring group, this study sought to investigate the clinical impact of telemonitoring. SUBJECTS AND METHODS Fifty-seven subjects with COPD were included in a 4-month telemonitoring project. Differences between the clinical parameters during the first and last months of participation in the project were tested for significance, and the levels for the first month versus the difference were tested for correlation. RESULTS Significant declines were observed in prescriptions for antibiotics and steroids (p=0.03), clinical consultations (p=0.05), mean systolic blood pressure (p<0.001), standard deviation of systolic blood pressure (p=0.03), and mean diastolic blood pressure (p=0.02). No significant differences were observed for mean of oxygen saturation (p=0.77), standard deviation of oxygen saturation (p=0.36), mean of forced expiratory volume in 1 s (p=0.17), mean of forced vital capacity (p=0.29), mean of pulse rate (p=0.78), standard deviation of pulse rate (p=0.57), and standard deviation of diastolic blood pressure (p=0.27). CONCLUSIONS The results suggest that telemonitoring improves the condition of the patient by lowering the blood pressure, the number of prescribed antibiotics and steroids, and the number of clinical consultations.

Assessment of Postprandial Glucose Excursions Throughout the Day in Newly Diagnosed Type 2 Diabetes

BACKGROUND A growing body of evidence suggests that postprandial glucose (PPG) is independently linked to multiple complications and that testing of PPG should be added to hemoglobin A1c (HbA1c) and fasting glucose measurements in the evaluation of glycemic control of type 2 diabetes patients. An ongoing debate is questioning how to assess PPG. This observational study looks further into this question in a cohort of newly diagnosed type 2 diabetes patients. SUBJECTS AND METHODS PPG characteristics and intra-/intersubject variations post-breakfast, -lunch, and -dinner, obtained from continuous glucose monitoring (CGM), were retrospectively analyzed in 86 newly diagnosed non-insulin-treated type 2 diabetes patients. RESULTS In total, 462 recorded meals were analyzed. The area under the curve 1-4 h postmeal was significantly larger after breakfast compared with both lunch and dinner (P<0.001). Time to peak was approximately 90 min and did not differ significantly between meals. However, the distribution of the blood glucose peaks was only normally distributed among breakfasts, and time to peak had a day-to-day correlation coefficient of 0.60, compared with a nonsignificant result for lunch and dinner. Breakfast PPG peaks were highly correlated to HbA1c (P<0.05, r=0.64) and had a day-to-day correlation coefficient of 0.86 compared with 0.44 for lunch and 0.74 for dinner. CONCLUSIONS Self-monitoring of blood PPG should be evaluated with care. From our data, monitoring of PPG patterns in newly diagnosed type 2 diabetes patients should preferably be obtained following breakfast for a more consistent assessment, reducing day-to-day variations.

Glycemic Variability Is Associated With Reduced Cardiac Autonomic Modulation in Women With Type 2 Diabetes

OBJECTIVE To investigate the sex differences in cardiac autonomic modulation in patients with newly diagnosed type 2 diabetes and to determine whether cardiac autonomic modulation is associated with glycemic variability. RESEARCH DESIGN AND METHODS We investigated a cohort consisting of 48 men and 39 women with non-insulin-treated type 2 diabetes and a known duration of diabetes <5 years. All patients were equipped with a continuous glucose monitoring sensor for 3 days, and the mean amplitude of glycemic excursions (MAGE) was calculated to obtain individual glycemic variability. Cardiac autonomic modulation was quantified by analysis of heart rate variability (HRV) in time and frequency domains and during cardiovascular reflex tests (response to standing [RS], deep breathing [expiration–inspiration], and Valsalva maneuver). RESULTS Sex differences in age- and heart rate–adjusted HRV measures were observed in both active and passive tests. Low frequency (LF; P = 0.036), LF/high frequency (HF; P < 0.001), and RS (P = 0.006) were higher in men, whereas expiration–inspiration (P < 0.001), but not HF, was higher in women. In women, reduced cardiac autonomic modulation as assessed by the standard deviation of normal-to-normal intervals (P = 0.001), the root mean square of successive differences (P = 0.018), LF (P < 0.001), HF (P = 0.005), total power (P = 0.008), RS ratio (P = 0.027), and expiration-to-inspiration ratio (P = 0.006) was significantly associated with increased glycemic variability as assessed by MAGE. This was not the case in men. The association in women persisted in a multivariate regression analysis controlling for weight, mean heart rate, blood pressure (systolic), and triglycerides. CONCLUSIONS In patients with newly diagnosed and well-controlled type 2 diabetes, increased glycemic variability was associated with reduced cardiac autonomic modulation in women but not in men.

Low Physical Activity Is Associated With Increased Arterial Stiffness in Patients Recently Diagnosed With Type 2 Diabetes.

AIMS Several studies have indicated that low physical activity is associated with increased risk of cardiovascular disease (CVD) and all-cause mortality among patients with diabetes. The association between physical activity and subclinical cardiovascular changes preceding clinical events remains to be elucidated. We investigated the relationship between physical activity and arterial stiffness, an independent predictor of CVD, in patients with type 2 diabetes and controls. METHODS We included 100 patients with type 2 diabetes and 100 sex- and age-matched controls in a cross-sectional study. Arterial stiffness (carotid-femoral pulse wave velocity, cfPWV) was measured using the SphygmoCor device (AtCor Medical, Sydney, Australia). Physical activity was assessed by an accelerometer (counts per minute (cpm), Actiheart (CamNtech, Cambridge, UK)) worn by the participants for up to 6 days. High vs. low levels of physical activity was defined according to the median level of activity (cpm = 31). RESULTS Sixty-five patients and 65 controls were included in the final analysis (median age 59 years, 55% men, median diabetes duration 1.9 years). Participants with low physical activity had higher cfPWV compared to participants with high physical activity: (i) Patients and controls combined: 9.3±1.7 m/s vs. 7.8±1.5 m/s, P < 0.001; (ii) Patients with diabetes: 9.5±1.8 m/s vs. 8.3±1.6 m/s, P = 0.02 and C) Controls: 9.0±1.4 m/s vs. 7.7±1.4 m/s, P < 0.01). The difference remained significant after adjustment for other determinants of cfPWV including whole body fat percentage (P < 0.01). No significant interaction between diabetes and the effect of low activity was seen. CONCLUSIONS Low physical activity is associated with increased arterial stiffness in patients recently diagnosed with type 2 diabetes and in healthy controls. CLINICAL TRIALS REGISTRATION Trial Number NCT00674271.