Faculty Bibliography

Purpose: The criteria for the diagnosis of narcolepsy changed when the 2 nd edition of the International Classification of Sleep Disorders supplanted the first edition (ICSD-1). The impact of using the updated criteria is unknown. We thus compared the characteristics of a population of patients diagnosed using ICSD- 2 with those that only met ICSD-1 criteria. Minimum criteria for narcolepsy in ICSD-1 are sleepiness with cataplexy, sleep paralysis, or hypnogogic hallucinations plus sleep latency <10 minutes or REM latency <20 min on polysomnography. ICSD-2 requires sleepiness with cataplexy or a positive MSLT (MSLT-SL ≤ 8 min and SOREMPs >= 2). Methods: The records of 148 subjects with suspected narcolepsy were reviewed. Seventy subjects were excluded because of other diagnoses or use of REM suppressants. 59 patients were diagnosed on the basis of cataplexy (54%) or positive MSLT (55%). The remaining 19 patients without cataplexy or positive MSLT fulfilled the minimal criteria using ICSD-1. Results: The anthropometrics, narcolepsy symptoms including refreshing naps, Epworth sleepiness scores and sleep bioparameters were comparable between patient with and without cataplexy. Sleep paralysis was more prevalent in those with cataplexy. MSLT was negative in 38% of patients with cataplexy compared with 53% in those without cataplexy. However, 53% of the latter group had SOREMPs >= 2. Conclusions: These findings show potential for misclassification of patients with ramifications on heath care coverage, employment, and research. The absence of typical cataplexy or negative MSLT should be interpreted in the contextual presentation of the patient (chronic pervasive daytime sleepiness, refreshing naps, sleep paralysis, hypnogogic hallucinations in the absence of medical and psychiatric co-morbidities with exclusion of circadian sleep disorders and sleep curtailment) and should not necessarily exclude the diagnosis

The purpose of this systematic review was to review the cost-effectiveness of first-line non-nicotine therapies (varenicline and bupropion SR) for smoking cessation, identify differences in the models used and their conclusions of cost-effectiveness, and to determine which variables, if any, impact conclusions of cost-effectiveness. A systematic literature search was conducted in MEDLINE, PsychINFO, the National Health Service Economic Evaluation Database, the Health Technology Database and the Tufts Cost-effectiveness Analysis Registry from the earliest possible date through May 2011. To be included, studies had to compare cost-effectiveness of varenicline to bupropion using either a Markov model or discrete event simulation and be published as a full text manuscript in English or Spanish. Study selection and data extraction were done in duplicate with disagreement resolved through discussion. Data regarding the model characteristics, results and conclusions were extracted as were details to assess the quality of the study. Model characteristics and cost-effectiveness results were compared across studies and summarised qualitatively. Ten unique studies were included, all of which were Markov models. Eight studies used the Benefits of Smoking Cessation on Outcomes (BENESCO) model and all found varenicline to dominate bupropion. The two non-BENESCO models found varenicline to be cost-effective. Conclusions regarding the cost-effectives were changed upon sensitivity analysis with the following variables: time horizon, cost of bupropion, efficacy of either drug, age and the incidence of smoking related disease. Varenicline dominated bupropion in most cost-effectiveness models. However, applicability of models to clinical practice and variables which changed conclusion of cost-effectiveness should be considered in the interpretation of results.

OBJECTIVE: Using the Continuous Glucose Monitoring System (CGMS; Medtronic Minimed) for a group of pregnant women with and without glucose intolerance, we attempted to answer the following questions: (1) when does the physiological peak of postprandial glucose occur?; (2) do non-diabetic pregnant women and pregnant women with diabetes have different postprandial glucose profiles?; and (3) what is the optimal time for postprandial glucose measurement rated according to clinical outcome? METHODS: We included 53 pregnant women in our study. Based on the criteria of the German Diabetes Association (fasting, 5.0 mmol/L; 1-h, 10.0 mmol/L; 2-h, 8.6 mmol/L) we included 13 women with gestational diabetes, four with type 1 diabetes and 36 non-diabetic pregnant (NDP) women. Gestational and type 1 diabetics were classed as one group: pregnancy complicated by diabetes (PCD). Patients with carbohydrate intolerance underwent dietary counseling in accordance with the recommendations of the American Diabetes Association. Patients received a CGMS for use over 72 h. This was calibrated seven times a day with an Accu-Check. The pre- and postprandial glucose levels were documented at 15-min intervals for 3 h from the beginning of each meal. The postprandial data from the three meals were added. The group was divided according to three clinical outcome parameters: mode of delivery, birth weight percentile, and diabetes-associated complications. RESULTS: Statistically significant differences between groups were found for body mass index, fetal birth weight and oral glucose tolerance test. No significant differences were found for age, parity and gestational age, mode of delivery, and diabetes-associated complications. The sensor provided similar numbers of measurements in both groups (278+/-43 vs. 298+/-73, P = 0.507). The postprandial glucose peak was reached after 82+/-18 min in the non-diabetics vs. 74+/-23 min in the PCD group (not significant). Postprandial glucose values were normally slightly higher in PCD (not significant). We added the postprandial glucose values at each time interval for the three meals for each day. For the sum, there was a significant difference between the measurements at 120 min and at 135 min postprandial (P < 0.05). Dividing the group by clinical outcome showed a significant difference between the postprandial time intervals of 75 min and 105 min (P < 0.05). In addition, the time interval was different from 60 min to 135 min for the mode of delivery and birth weight percentile (P < 0.05). CONCLUSION: The 120-min interval is too long and has a lower correlation to clinical outcome parameters than earlier measurements. Our findings show that the optimal time for testing is between 45 and 120 min postprandial. Based on our practical experience and dietary recommendations, we would prefer a 60-min interval, because patients can calculate this more easily and can have more freedom to eat the recommended number of snacks.

OBJECTIVE: To assess the detection rate of hyperglycemia with a continuous glucose monitoring system compared to a self-monitoring blood glucose profile in non-pregnant, non-diabetic pregnant women, and patients with impaired glucose tolerance or gestational diabetes.. METHODS: Eight non-pregnant (NP) and 56 pregnant women (17 dietary-treated gestational diabetics (GDM), 15 women with impaired glucose tolerance (IGT), and 24 non-diabetic pregnant women (NDP)) underwent a 72-hour measurement with the CGMS (Medtronic Minimed, Northridge, CA, USA). Self-monitored blood glucose measurements, performed 30 minutes before and 120 minutes after each meal, were compared to the duration of hyperglycemia monitored by the continuous glucose monitoring system. RESULTS: No clinically observable infection was found at the subcutaneous tissue where the electrode was placed. A statistically significant difference was found between the groups in body mass index, HbA1c, and in gestational age, but not in age or parity. Using the self-monitored blood glucose (SMBG), 88 % (7/8) of the NP and 54 % (13/24) of the NDP had no measurement above 6.7 mmol/l. However, 17 % (4/24) of the NDP and 40 % (6/15) of the IGT showed more than two measurements above 6.7 mmol/l compared to 24 % (4/17) of the dietary-treated GDM. The differences between these groups were not significant (p = 0.21). The mean durations (+/- SD) of hyperglycemia above 6.7 mmol/l/24 h were: NP 111 +/- 120 min, NDP 138 +/- 120 min, IGT 381.8 +/- 295 min, and GDM 190 +/- 155 min, p = 0.017; above 7.8 mmol/l/24 h NP 24 +/- 49 min, NDP 38 +/- 47 min, IGT 170.7 +/- 190 min, and GDM 64 +/- 88 min, p = 0.016; and above 8.9 mmol/l/24 h NP 9.3 +/- 25 min, NDP 7.5 +/- 14 min, IGT 59 +/- 77 min, and GDM 14 +/- 21 min, p = 0.026. There was no significant difference in the fetal outcome or rate of birth percentiles using the sensor data. CONCLUSIONS: The use of the sensor in pregnant women is unproblematic. a) The CGMS detected more frequent and longer durations of hyperglycemia in GDM compared to non-diabetic pregnant women than the SMBG. b) Women with an IGT exhibited higher glucose levels than patients with gestational diabetes. c) The clinical importance of these hyperglycemic intervals, e.g. with respect to the risk for macrosomia, must be assessed in larger trials.