Understanding critically ill sepsis patients with normal serum lactate levels: results from U.S. and European ICU cohorts.

While serum lactate level is a predictor of poor clinical outcomes among critically ill patients with sepsis, many have normal serum lactate. A better understanding of this discordance may help differentiate sepsis phenotypes and offer clues to sepsis pathophysiology. Three intensive care unit datasets were utilized. Adult sepsis patients in the highest quartile of illness severity scores were identified. Logistic regression, random forests, and partial least square models were built for each data set. Features differentiating patients with normal/high serum lactate on day 1 were reported. To exclude that differences between the groups were due to potential confounding by pre-resuscitation hyperlactatemia, the analyses were repeated for day 2. Of 4861 patients included, 47% had normal lactate levels. Patients with normal serum lactate levels had lower 28-day mortality rates than those with high lactate levels (17% versus 40%) despite comparable physiologic phenotypes. While performance varied between datasets, logistic regression consistently performed best (area under the receiver operator curve 87-99%). The variables most strongly associated with normal serum lactate were serum bicarbonate, chloride, and pulmonary disease, while serum sodium, AST and liver disease were associated with high serum lactate. Future studies should confirm these findings and establish the underlying pathophysiological mechanisms, thus disentangling association and causation.

Type D personality and global positioning system tracked social behavior in patients with cardiovascular disease.

OBJECTIVE: Social behavior (e.g., loneliness, isolation) has been indicated as an important risk factor for cardiovascular disease. Recent studies show that Type D personality might be an important predictor of social behavior. Hence, the current exploratory study aims to examine, using ecological assessment, whether Type D personality is associated with a lower likelihood to engage in social encounters in patients with cardiovascular disease. METHOD: Cardiac patients who participated in the Do CHANGE (Phase 2) trial were included in current analysis. As part of the Do CHANGE intervention, real-life data were collected in the intervention group using the MOVES app, which was installed on patients' mobile phones. For a period of 6 months, Global Positioning System (GPS) data from the participating patients were collected. From the GPS data, 3 target variables were computed: (a) general activity level, (b) social variety, and (c) social opportunity. RESULTS: A total of 70 patients were included in the analysis. Patients with a Type D personality had lower scores on the "social opportunity" variable compared to non-Type D patients (F = 6.72; p = .01). Type D personality was associated with lower social participation after adjusting for depression and anxiety. No association between Type D personality and general activity or behavioral variety was observed. CONCLUSIONS: This is the first study to use an ecological measure to assess social behavior of cardiac patients with a Type D personality. Results show that Type D personality might be associated with lower social engagement, which could, in turn, partly explain its association with adverse health outcomes. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Usefulness of a Lifestyle Intervention in Patients With Cardiovascular Disease.

The importance of modifying lifestyle factors in order to improve prognosis in cardiac patients is well-known. Current study aims to evaluate the effects of a lifestyle intervention on changes in lifestyle- and health data derived from wearable devices. Cardiac patients from Spain (n = 34) and The Netherlands (n = 36) were included in the current analysis. Data were collected for 210 days, using the Fitbit activity tracker, Beddit sleep tracker, Moves app (GPS tracker), and the Careportal home monitoring system. Locally Weighted Error Sum of Squares regression assessed trajectories of outcome variables. Linear Mixed Effects regression analysis was used to find relevant predictors of improvement deterioration of outcome measures. Analysis showed that Number of Steps and Activity Level significantly changed over time (F = 58.21, p < 0.001; F = 6.33, p = 0.01). No significant changes were observed on blood pressure, weight, and sleep efficiency. Secondary analysis revealed that being male was associated with higher activity levels (F = 12.53, p < 0.001) and higher number of steps (F = 8.44, p < 0.01). Secondary analysis revealed demographic (gender, nationality, marital status), clinical (co-morbidities, heart failure), and psychological (anxiety, depression) profiles that were associated with lifestyle measures. In conclusion results showed that physical activity increased over time and that certain subgroups of patients were more likely to have a better lifestyle behaviors based on their demographic, clinical, and psychological profile. This advocates a personalized approach in future studies in order to change lifestyle in cardiac patients.