Anxiety and depression in patients with chronic obstructive pulmonary disease and obstructive sleep apnea: the overlap syndrome.

PURPOSE: Psychological symptoms are increasingly being noted in patients with chronic diseases. Currently, little evidence is available on the mental health of patients with overlap syndrome (OVS, chronic obstructive pulmonary disease plus obstructive sleep apnea). This study aimed to describe the prevalence and identify influencing factors of anxiety and depression in patients with OVS. METHODS: We recruited patients admitted for chronic obstructive pulmonary disease (COPD) from July 2018 to July 2019 who also underwent polysomnography tests to assess obstructive sleep apnea (OSA). COPD patients who had an apnea-hypopnea index (AHI) ≥ 5/h were defined as OVS. COPD patients who had an AHI < 5/h were identified as pure COPD. Questionnaires were administered to evaluate depression and anxiety in all subjects. We compared the differences in scores between patients with OVS and pure COPD. RESULTS: Two hundred and fifty-two patients were included, 180 (71%) patients had OVS, while only 72 patients had pure COPD. In the OVS group, 54% of the patients had depression, and 77% of the patients had anxiety. We found that patients with OVS had higher anxiety (8.00 (4.00, 10.00) vs. 6.00 (3.00, 9.00), p = 0.018) and depression (8.00 (4.00, 10.00) vs. 5.50 (2.25, 10.00), p = 0.022) scores than patients with pure COPD. A higher proportion of patients with hypertension (41% vs. 21%) and coronary heart disease (14% vs. 4%) were found in the OVS group. Chest pain, COPD Assessment Test (CAT) score, and OVS were independent risk factors for depression (P<0.05). A positive correlation was shown between anxiety and depression (r=0.638, p < 0.001). CONCLUSIONS: Anxiety and depression were more severe in patients with OVS than in patients with pure COPD. More attention should be paid to the mental health of OVS patients. TRIAL REGISTRATION: ClinicalTrials.gov; URL: www. CLINICALTRIALS: gov . NO.: NCT03182309. Registered on June 9, 2017; https://clinicaltrials.gov/ct2/show/NCT03182309?term=NCT+03182309&draw=2&rank=1.

Numerical Analysis of Consensus Measures within Groups.

Measuring the consensus for a group of ordinal-type responses is of practical importance in decision making. Many consensus measures appear in the literature, but they sometimes provide inconsistent results. Therefore, it is crucial to compare these consensus measures, and analyze their relationships. In this study, we targeted five consensus measures: Φ e (from entropy), Φ 1 (from absolute deviation), Φ 2 (from variance), Φ 3 (from skewness), and Φ m v (from conditional probability). We generated 316,251 probability distributions, and analyzed the relationships among their consensus values. Our results showed that Φ 1 ,   Φ e ,   Φ 2 , and Φ 3 tended to provide consistent results, and the ordering Φ 1 ≤ Φ e ≤ Φ 2 ≤ Φ 3 held at a high probability. Although Φ m v had a positive correlation with Φ 1 ,   Φ e ,   Φ 2 , and Φ 3 , it had a much lower tolerance for even a small proportion of extreme opposite opinions than Φ 1 ,   Φ e ,   Φ 2 , and Φ 3 did.

Using n-gram analysis to cluster heartbeat signals.

BACKGROUND: Biological signals may carry specific characteristics that reflect basic dynamics of the body. In particular, heart beat signals carry specific signatures that are related to human physiologic mechanisms. In recent years, many researchers have shown that representations which used non-linear symbolic sequences can often reveal much hidden dynamic information. This kind of symbolization proved to be useful for predicting life-threatening cardiac diseases. METHODS: This paper presents an improved method called the "Adaptive Interbeat Interval Analysis (AIIA) method". The AIIA method uses the Simple K-Means algorithm for symbolization, which offers a new way to represent subtle variations between two interbeat intervals without human intervention. After symbolization, it uses the n-gram algorithm to generate different kinds of symbolic sequences. Each symbolic sequence stands for a variation phase. Finally, the symbolic sequences are categorized by classic classifiers. RESULTS: In the experiments presented in this paper, AIIA method achieved 91% (3-gram, 26 clusters) accuracy in successfully classifying between the patients with Atrial Fibrillation (AF), Congestive Heart Failure (CHF) and healthy people. It also achieved 87% (3-gram, 26 clusters) accuracy in classifying the patients with apnea. CONCLUSIONS: The two experiments presented in this paper demonstrate that AIIA method can categorize different heart diseases. Both experiments acquired the best category results when using the Bayesian Network. For future work, the concept of the AIIA method can be extended to the categorization of other physiological signals. More features can be added to improve the accuracy.