Journal and News Scan

Researchers from the University of Nottingham studied 502,628 adults aged 40 to 69 years whose health information was logged in the UK Biobank between 2006 and 2010. Using demographic data and taking into account biometric, clinical, and lifestyle factors, the authors developed predictive mortality models using deep learning, random forest, and Cox regression.

Death occurred in 14,418 adults (2.9%) over a total follow-up time of 3,508,454 person-years, and mortality data was corroborated with national records. The age- and gender-based Cox model was the least predictive, with an area under the curve (AUC) of 0.689, followed by the multivariate Cox regression model, which improved discrimination by 6.2% for an AUC of 0.751. The application of machine-learning algorithms further improved discrimination by 3.2% using the random forest model (AUC = 0.783; 95% confidence interval (CI), 0.776 - 0.791) and 3.9% using deep learning model (AUC = 0.790; 95% CI, 0.783 - 0.797). The two machine-learning algorithms improved discrimination by 9.4% and 10.1% respectively from a simple age and gender Cox regression model.

This work suggest machine learning significantly improved accuracy of prediction of premature all-cause mortality in this middle-aged population, compared to standard methods. This study illustrates the value of machine learning for risk prediction within a traditional epidemiological study design.

In this state-of-the-art review review, the authors summarize current knowledge of anatomic, structural, metabolic, functional, and hemodynamic characteristics of the right ventricle (RV) in both health and disease. Highlights of this review are:

• Anatomically and functionally different from the left ventricle, the RV plays an increasingly recognized role in determining both symptoms and outcomes in multiple conditions.
• The normal RV is coupled to the low-pressure, high-compliance pulmonary circulation to ensure transfer of blood to the pulmonary arteries in an energy-efficient fashion. RV adaptation to disease is determined by the degree of pressure overload, volume overload, and alterations in intrinsic contractility. These 3 situations have distinct clinical course and therapeutic approach although they commonly coexist in various degrees.
• Advances promise to enhance our understanding of the mechanisms of RV adaptation or maladaptation to pathologic conditions. These include refinements in the evaluation of RV anatomy, myoarchitecture, ultrastructure, metabolism, perfusion, function, and degree of coupling to the pulmonary circulation. Such evaluation can be performed invasively but increasingly is accomplished through noninvasive imaging.

Cook Medical announced that Dr Joseph Lombardi in New Jersey treated the first patient in the US using the newly approved Zenith® Dissection Endovascular System as part of the device's commercial launch. The system consists of a proximal stent-graft component and a distal bare stent component. “The value in this dissection stent is that it’s pathology-specific, designed just for this disease," said Dr Lombardi, the Global Principal Investigator of the STABLE I and STABLE II clinical trials.

Sun and colleagues investigated the relationship between low skeletal muscle mass and the prognosis of early-stage non–small cell lung cancer (NSCLC). In more than 300 patients with early-stage NSCLC, the truncal muscle index at the first lumbar vertebral level correlated with lower rates of recurrence-free and overall survival five years after resection. The authors conclude that the truncal muscle index could be useful to include in the preoperative assessment of patients for whom curative resection of NSCLC is planned.