Adaptive support ventilation as an effective treatment option for central sleep apnea in an older adult with heart failure with preserved ejection fraction: a case report

Background Adaptive support ventilation (ASV) is a proposed treatment option for central sleep apnea (CSA). Although the effectiveness of ASV remains unclear, some studies have reported promising results regarding the use of ASV in patients with heart failure with preserved ejection fraction (HfpEF). To illustrate the importance of suspecting and diagnosing sleep-disordered breathing (SDB) in older adults unable to recognize symptoms, we discuss a case in which ASV was effective in a patient with CSA and HfpEF, based on changes in the Holter electrocardiogram (ECG). Case presentation. An 82-year-old man presented to our hospital with vomiting on April 19, 2021. Approximately 10 years before admission, he was diagnosed with type 1 diabetes mellitus and recently required full support from his wife for daily activities due to cognitive dysfunction. Two days before admission, his wife was unable to administer insulin due to excessively high glucose levels, which were displayed as “high” on the patient’s glucose meter; therefore, we diagnosed the patient with diabetic ketoacidosis. After recovery, we initiated intensive insulin therapy for glycemic control. However, the patient exhibited excessive daytime sleepiness, and numerous premature ventricular contractions were observed on his ECG monitor despite the absence of hypoglycemia. As we suspected sleep-disordered breathing (SDB), we performed portable polysomnography (PSG), which revealed CSA. PSG revealed a central type of apnea and hypopnea due to an apnea–hypopnea index of 37.6, which was > 5. Moreover, the patient had daytime sleepiness; thus, we diagnosed him with CSA. We performed ASV and observed its effect using portable PSG and Holter ECG. His episodes of apnea and hypopnea were resolved, and an apparent improvement was confirmed through Holter ECG. Conclusion Medical staff should carefully monitor adult adults for signs of or risk factors for SDB to prevent serious complications. Future studies on ASV should focus on older patients with arrhythmia, as the prevalence of CSA may be underreported in this population and determine the effectiveness of ASV in patients with HfpEF, especially in older adults.

Minimum threshold determination method based on dataset characteristics in association rule mining

Association rule mining is a technique that is widely used in data mining. This technique is used to identify interesting relationships between sets of items in a dataset and predict associative behavior for new data. Before the rule is formed, it must be determined in advance which items will be involved or called the frequent itemset. In this step, a threshold is used to eliminate items excluded in the frequent itemset which is also known as the minimum support. Furthermore, the threshold provides an important role in determining the number of rules generated. However, setting the wrong threshold leads to the failure of the association rule mining to obtain rules. Currently, user determines the minimum support value randomly. This leads to a challenge that becomes worse for a user that is ignorant of the dataset characteristics. It causes a lot of memory and time consumption. This is because the rule formation process is repeated until it finds the desired number of rules. The value of minimum support in the adaptive support model is determined based on the average and total number of items in each transaction, as well as their support values. Furthermore, the proposed method also uses certain criteria as thresholds, therefore, the resulting rules are in accordance with user needs. The minimum support value in the proposed method is obtained from the average utility value divided by the total existing transactions. Experiments were carried out on 8 specific datasets to determine the association rules using different dataset characteristics. The trial of the proposed adaptive support method uses 2 basic algorithms in the association rule, namely Apriori and Fpgrowth. The test is carried out repeatedly to determine the highest and lowest minimum support values. The result showed that 6 out of 8 datasets produced minimum and maximum support values for the apriori and fpgrowth algorithms. This means that the value of the proposed adaptive support has the ability to generate a rule when viewed from the quality as adaptive support produces at a lift ratio value of > 1. The dataset characteristics obtained from the experimental results can be used as a factor to determine the minimum threshold value.

Guidelines on setting the target minute ventilation in Adaptive Support Ventilation

Adaptive Support Ventilation (ASV) is a fully closed loop ventilation where the operator input the desired PEEP, FiO2 and the target minute ventilation (MV) expressed as a percentage according to ideal body weight. The ventilator selects the target respiratory pattern (tidal volume, respiratory rate, and inspiratory time) based on the observed respiratory mechanics. However, there are no published guidelines on settings and adjusting the target MV in different disease states during ASV ventilation. INTELLiVENT-ASV, is the new generation modified algorithm of ASV, has made this issue much easier and simpler as the operator inputs a desired range of the end tidal exhaled carbon dioxide, and oxygen saturation and the algorithm will adjust the minute ventilation percentage as well as PEEP and FiO2 automatically to stay within that range. In this article we describe some evidence-based guidelines on how to set and adjust the target MV in various clinical conditions. Keywords: ASV, INTELLiVENT-ASV, Closed loop ventilation, End tidal CO2, ARDS, COPD, Respiratory failure

Situation Awareness Adaptive Alerting in an Aircraft Cockpit: A Simulator Study

The goal of this study was to develop an automated cockpit support system that is adaptive to the flight crew’s situation awareness (SA) estimated by online gaze analysis. Flight crew errors are often attributed to low SA. Online measurement of SA could be used to automatically guide the user’s attention for the sake of fewer errors and better performance.An eye-tracking based measure for SA was developed and used for the adaptive generation of alerts in a flight simulator. In an experiment, ten certified pilots conducted two trials with no and adaptive alerting. The experimental task involved tracking of flight parameters which were partially disturbed or changed at random times. Our online estimation of SA showed a strong correlation with observed pilot performance. With adaptive alerts, the average performance increased in those experimental tasks, where a situational change could not be predicted by participants. Also, adaptive alerts improved change detection and reduced the number of outliers, where a change was not noticed for an exceptionally long time. However, subjective rating was poor due to low transparency and false positives. SA-adaptive support can improve change detection performance in typical tasks on the flight deck. For a greater acceptance, pilots should be trained to understand the adaption policy.