The relationships between host factors, viral shedding, illness severity, and antibody response in respiratory syncytial virus (RSV)-induced bronchiolitis are poorly defined. These relationships were prospectively evaluated in 77 infants hospitalized with RSV bronchiolitis in multicenter, double-blind, placebo-controlled trials of RSV immunoglobulin therapy. Severity of illness was influenced by age and host risk factors but was not influenced by RSV neutralizing antibody titer or by the amount of virus in nasal secretions at enrollment. Virus recovery in nasal secretions was variable but was highest at enrollment. Viral shedding was not influenced by primary diagnosis, antibody titer, age, or duration of acute respiratory illness before enrollment. In intubated patients, the amounts of virus recovered in nasal secretions and endotracheal aspirates were highly correlated. A serum neutralizing antibody response was seen in 64% of subjects who received placebo. The response was not influenced by age, primary diagnosis, amount of virus recovered, or severity of illness but was suppressed by preexisting antibody.

Background. Mobile health and digital medicine technologies are becoming increasingly used by individuals with common, chronic diseases to monitor their health. Numerous devices, sensors, and apps are available to patients and consumers–some of which have been shown to lead to improved health management and health outcomes. However, no randomized controlled trials have been conducted which examine health care costs, and most have failed to provide study participants with a truly comprehensive monitoring system. Methods. We conducted a prospective randomized controlled trial of adults who had submitted a 2012 health insurance claim associated with hypertension, diabetes, and/or cardiac arrhythmia. The intervention involved receipt of one or more mobile devices that corresponded to their condition(s) (hypertension: Withings Blood Pressure Monitor; diabetes: Sanofi iBGStar Blood Glucose Meter; arrhythmia: AliveCor Mobile ECG) and an iPhone with linked tracking applications for a period of 6 months; the control group received a standard disease management program. Moreover, intervention study participants received access to an online health management system which provided participants detailed device tracking information over the course of the study. This was a monitoring system designed by leveraging collaborations with device manufacturers, a connected health leader, health care provider, and employee wellness program–making it both unique and inclusive. We hypothesized that health resource utilization with respect to health insurance claims may be influenced by the monitoring intervention. We also examined health-self management. Results & Conclusions. There was little evidence of differences in health care costs or utilization as a result of the intervention. Furthermore, we found evidence that the control and intervention groups were equivalent with respect to most health care utilization outcomes. This result suggests there are not large short-term increases or decreases in health care costs or utilization associated with monitoring chronic health conditions using mobile health or digital medicine technologies. Among secondary outcomes there was some evidence of improvement in health self-management which was characterized by a decrease in the propensity to view health status as due to chance factors in the intervention group.

Cardiac k-factors for all scanners and protocols are considerably higher than the k-factor currently used to estimate ED of cardiac CT studies, suggesting that radiation doses from cardiac CT have been significantly and systematically underestimated. Using cardiac-specific factors can more accurately inform the benefit-risk calculus of cardiac-imaging strategies.

No abstract

No abstract