METHODS: We reviewed 22 previous studies that (1) empirically manipulated social support in a stressful situation, (2) measured CVR, and (3) tested a moderator of social support effects on CVR.
RESULTS: Although a majority of studies reported a CVR-mitigating effect of social support resulting in an overall significant combined p-value, we found that there were different effects of social support on CVR when we considered high- and low-engagement contexts. That is, compared to control conditions, social support lowered CVR in more engaging situations but had no significant effect on CVR in less engaging situations.
CONCLUSION: Our results suggest that a dual-effect model of social support effects on CVR may better capture the nature of social support, CVR, and health associations than the buffering hypothesis and emphasize a need to better understand the health implications of physiological reactivity in various contexts. Statement of contribution What is already known on this subject? According to the stress-buffering hypothesis (Cohen & McKay, ), one pathway social support benefits health is through mitigating the physiological arousal caused by stress. However, previous studies that examined the effects of social support on blood pressure and heart rate changes were not consistently supporting the hypothesis. Some studies reported that social support causes elevations in cardiovascular reactivity (CVR) to stress (Anthony & O'Brien, ; Hilmert, Christenfeld, & Kulik, ; Hilmert, Kulik, & Christenfeld, ) and others showed no effect of social support on CVR (Christian & Stoney, ; Craig & Deichert, ; Gallo, Smith, & Kircher, ). What does this study add? When participants were in more engaging conditions, social support decreased CVR relative to no support. When participants were in less engaging conditions, social support did not have a significant effect on CVR. Provide an alternative way to explain the ways social support affects cardiac health.
METHODS: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals.
RESULTS: Our review shows that all of these signals contain information for sleep stage scoring.
CONCLUSIONS: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.
METHODS: A cross-sectional study was conducted on T2DM patients at a tertiary hospital outpatient using the Malay and English version of the EQ-5D-5L questionnaire. Health utility values were derived using the Malaysian EQ-5D-5L value set. Ordinary least squares (OLS) multivariable regression model was used to estimate the health utility decrements associated with T2DM-related complications and clinical characteristics.
RESULTS: A total of 513 T2DM patients were recruited. Overall, pain was the most affected of all five EQ-5D-5L dimensions. Patients with foot ulcer, amputation, severe heart failure and frequent hypoglycemia reported more problems collectively in all EQ-5D-5L dimensions. Older age, lower education level, longer duration of T2DM, urine protein creatine index (UPCI) > 0.02 g/mmol, and injection therapy were significantly associated with lower EQ-5D-5L utility values (p heart failure 0.65 (interquartile range, IQR 0.50), frequent hypoglycemia 0.74 (0.22) and being amputated 0.78 (0.47). In the multivariable regression model after controlling for sociodemographic and clinical characteristics, the largest utility value decrement was observed for amputation (- 0.158, SE 0.087, p = 0.05), frequent hypoglycemia (- 0.101, SE 0.030, p = 0.001), myocardial infarction (-0.050, SE 0.022, p = 0.022) and obesity (-0.034, SE 0.016, p = 0.029).
CONCLUSION: Larger utility value decrements were found for severe stages of complications. These findings suggest the value of defining severity of complications in utility elicitation studies. The utility decrement quantified for different T2DM complication severity will be useful for economic evaluations within diabetic-related fields.
METHODS: Health-related quality of life was captured using the EuroQol-5 Dimension-3 Level (EQ-5D-3L), with data collected at baseline and throughout the trial. Multilevel mixed-effects linear regression with random effects estimated health-related quality of life over time, capturing variation between hospital sites and individuals, and a fixed-effects linear model estimated the impact of cardiovascular and gastrointestinal events.
RESULTS: Patients were followed for a median of 5 years (interquartile range 3.4-6.0). The average baseline EQ-5D score of 0.930 (SD 0.104) remained relatively unchanged over the trial period with no evidence of statistically significant differences in EQ-5D score between randomized treatment groups. The largest decrement in the year of an event was estimated for stroke (-0.107, P heart failure (-0.039, P = .022), MI (-0.021, P = .047), angina (-0.012, P = .047), and gastrointestinal events (-0.005, P = .430). MI and stroke reduced health-related quality of life beyond the year in which the event occurred (-0.031, P = .006, and -0.067, P heart failure, and angina reduce health-related quality of life around the time they occurred, but only MI and stroke impacted on longer-term health-related quality of life.
METHODS: Data from heart transplant recipients (n = 87) administered the oral immediate-release formulation of tacrolimus (Prograf®) were collected. Routine drug monitoring data, principally trough concentrations, were used for model building (n = 1099). A published tacrolimus model was used to inform the estimation of Ka , V2 /F, Q/F and V3 /F. The effect of concomitant azole antifungal use on tacrolimus CL/F was quantified. Fat-free mass was implemented as a covariate on CL/F, V2 /F, V3 /F and Q/F on an allometry scale. Subsequently, stepwise covariate modelling was performed. Significant covariates influencing tacrolimus CL/F were included in the final model. Robustness of the final model was confirmed using prediction-corrected visual predictive check (pcVPC). The final model was externally evaluated for prediction of tacrolimus concentrations of the fourth dosing occasion (n = 87) from one to three prior dosing occasions.
RESULTS: Concomitant azole antifungal therapy reduced tacrolimus CL/F by 80%. Haematocrit (∆OFV = -44, P heart transplant recipients, considering the tacrolimus-azole antifungal interaction was developed. Prospective evaluation is required to assess its clinical utility to improve patient outcomes.