Layer-specific and whole wall global longitudinal strain predict major adverse cardiovascular events in patients with stable angina pectoris

The International Journal of Cardiovascular Imaging - Global longitudinal strain (GLS) has proven to be a powerful prognostic marker in various patient populations, but the prognostic value of...     

Glycaemic control for patients with severe acute brain injury: Protocol for a systematic review

Background

Hyperglycaemia is common in patients with acute brain injury admitted to an intensive care unit (ICU). Many studies have found associations between development of hyperglycaemia and increased mortality in hospitalised patients. However, the optimal target for blood glucose control is unknown. We want to conduct a systematic review with meta-analysis and trial sequential analysis to explore the beneficial and harmful effects of restrictive versus liberal glucose control on patient outcomes in adults with severe acute brain injury.

Methods

We will systematically search medical databases including CENTRAL, Embase, MEDLINE and trial registries. We will search the following websites for ongoing or unpublished trials: http://www.controlled-trials.com/ , http://www.clinicaltrials.gov/ , www.eudraCT.com , http://centerwatch.com/ , The Cochrane Library's CENTRAL, PubMed, EMBASE, Science Citation Index Expanded and CINAHL. Two authors will independently review and select trials and extract data. We will include randomised trials comparing levels of glucose control in our analyses and observational studies will be included to address potential harms. The primary outcomes are defined as all-cause mortality, functional outcome and health-related quality of life. Secondary outcomes include serious adverse events including hypoglycaemia, length of ICU stay and duration of mechanical ventilation, and explorative outcomes including intracranial pressure and infection. Trial Sequential Analysis will be used to investigate the risk of type I error due to repetitive testing and to further explore imprecision. Quality of trials will be evaluated using the Cochrane Risk of Bias tool, and quality of evidence will be assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach.

Discussion

The results of the systematic review will be disseminated through peer-reviewed publication. With the review, we hope to inform future randomised clinical trials and improve clinical practice.     

Risk as a pattern over time: Delineation of time-dependent risk factors in biological,psychological, and social variables in cancer patients

Background

Survival in cancer patients is associated with a multitude of biological, social, and psychological factors. Although it is well established that all these factors add to overall mortality, it is not well understood how the predictive power of these parameters changes in a comprehensive model and over time.

Methods

Patients who attended the authors’ outpatient clinic were invited to participate. The authors followed 5180 mixed cancer patients (51.1% female; mean age, 59.1 years [SD = 13.8]) for up to 16 years and analyzed biological (age, sex, cancer site, anemia), psychological (anxiety, depression), and social variables (marital status, education, employment status) potentially predicting overall survival in a Cox proportional hazards model.

Results

The median survival time for the entire sample was 4.3 years (95% confidence interval, 4.0–4.7). The overall survival probabilities for 1 and 10 years were 76.8% and 38.0%, respectively. Following an empirical approach, the authors split the time interval into five periods: acute, subacute, short-term, medium-term, and long-term. A complex pattern of variables predicted overall survival differently in the five periods. Biological parameters were important throughout most of the time, social parameters were either time-independent predictors or tended to be more important in the longer term. Of the psychological parameters, only depression was a significant predictor and lost its predictive power in the long-term.

Conclusions

The findings of this study allow the development of comprehensive patient-specific models of risk and resilience factors addressing biopsychosocial needs of cancer patients, paving the way for a personalized treatment plan that goes beyond biomedical cancer care.     

Contribution of cost to treatment nonadherence in the US breast cancer survivors: a population-based analysis

Breast Cancer Research and Treatment - Breast cancer survivors are often prescribed medications for at least 5 years to reduce recurrence risk, yet some forego this treatment due to cost....