Automated Detection of Incomplete Exhalation as an Indirect Detection of Auto-PEEP on Mechanically Ventilated Adults

Auto-PEEP is auto positive end-expiratory pressure due to excessive amounts of alveolar gas produced by sustained recurrent incomplete exhalation. Incomplete exhalation occurs when the exhaled breath never reaches a flow rate of 0 L/min. The objective of this dissertation is to develop an automated detection system of auto-PEEP through incomplete exhalation as revealed by ventilator graphics for mechanically ventilated adults. Auto-PEEP can cause adverse effects if allowed to linger and if not quickly identified. An automated detection system will be instrumental in helping to quickly identify auto-PEEP. A computerized algorithm was developed to detect incomplete exhalation based on the following three parameters:1) Foi, was used to represent the value of the flow at the onset of inhalation, 2) ∆T, was used to represent the value of time difference between onset inhalation to the 0 L/min mark, and 3) slope threshold, a value set for the slope of change of flow over ∆T. Optimum parameters of the algorithm were achieved for Foi = -3 L/min, ∆T = 0.2 s, and slope threshold = 90 L-s/min. A novel data set was introduced to validate the algorithm, yielding no significant difference in true positive rates (t = 1.5, df = 12.402, p-value = 0.1408) and false positive rates (t = 1.9, df = 16.765, p-value = 0.0725) as outcomes for two-tailed t-tests comparing the novel and old data set. To determine the relationship between auto-PEEP and detection of sustained incomplete exhalation, a correlation of a linear model of the novel data set between auto-PEEP and the percentage of incomplete exhalation detection out of the existing breaths (index) was investigated. A linear model should interpret the index value that corresponds to significant auto-PEEP presence; unfortunately, no significant linear model was found between incomplete exhalation index and auto-PEEP (F1,62 = 1.67, p-value = 0.2010). However, there was a relationship between the intrinsic PEEP values and the incomplete exhalation index as functions of time. The automated detection algorithm produced by this work provides a non-invasive method of automatically detecting auto-PEEP

Establishing Techniques to Increase Physical Health Exam Compliance in Adults with Developmental Disabilities

Adults with developmental disabilities are significantly more likely to be in fair or poor general health status when compared to the general population (Sullivan et al., 2011). One of the main reasons is the lack of preventive health services within the developmental disabilities population (Yen et al., 2014). Many studies that have used behavioral treatment package to increase medical compliance involved children (Allen et al., 1992; Cuvo et al., 2010), but very few were with adults. The first purpose of the present study was to investigate the use of a behavioral treatment package that included modeling, choice-making, chaining and shaping with percentile schedules to increase physical health exam compliance in adults with developmental disabilities. The second purpose was to investigate whether or not carry-over effects were present across physical health exam components when training two physical health exam components simultaneously. Last, it evaluated whether generalization of physical health exam compliance occurred across time and settings. The present study found that the behavioral treatment package was successful in increasing physical health exam compliance in adults with developmental disabilities