Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD

Abstract

Chronic obstructive pulmonary disease (COPD) is one of the commonest causes of death in the world and poses a substantial burden on healthcare systems and patients’ quality of life. The largest component of the related healthcare costs is attributable to admissions due to acute exacerbation (AECOPD). The evidence that might support the effectiveness of the telemonitoring interventions in COPD is limited partially due to the lack of useful predictors for the early detection of AECOPD. Electronic stethoscopes and computerised analyses of respiratory sounds (CARS) techniques provide an opportunity for substantial improvement in the management of respiratory diseases. This exploratory study aimed to evaluate the feasibility of using: (a) a respiratory sensor embedded in a self-tailored housing for ageing users; (b) a telehealth framework; (c) CARS and (d) machine learning techniques for the remote early detection of the AECOPD. In a 6-month pilot study, 16 patients with COPD were equipped with a home base-station and a sensor to daily record their respiratory sounds. Principal component analysis (PCA) and a support vector machine (SVM) classifier was designed to predict AECOPD. 75.8% exacerbations were early detected with an average of 5 ± 1.9 days in advance at medical attention. The proposed method could provide support to patients, physicians and healthcare systems.

Recruiting Rural Healthcare Providers Today: a Systematic Review of Training Program Success and Determinants of Geographic Choices

“…of studiesGRADEEffect and directionResults and supporting statement Provider aspects Rural background22 studiesGRADE: High*Positive association• Rural hometown was a predictor in a multivariate analysis of West Virginia medical student graduates ( N  = 1517; OR 4.02; CI 2.17–7.74)24 • Significant association with being raised in rural area in multivariate model of Oklahoma State University graduates ( N  = 190, p  < 0.05) and graduates of the University of Minnesota ( N  = 3365; OR 2.82; CI 2.1–3.79)25 , 26 • Rural origin was a significant predictor in a multivariate analysis of Michigan State University College of Human Medicine graduates ( N  = 2382; OR 2.80; CI 2.09–3.74)27 • Significant association with rural high school in multivariate analysis of West Virginia physician assistants ( N  = 168; p  < 0.01)28 • Being raised in a rural area was associated with practicing in a less populated county in a multivariate analysis ( N  = 683; p  < 0.05)29 • Significant correlation with non-urban high school or college30 • Respondents who graduated from a rural high school were significantly more likely to practice in rural settings31 • Significant association with population of hometown32 • Qualitative analysis suggested rural exposure via upbringing33 • Significant difference due to rural childhood34 , 35 • 70% of rural providers had a rural background36 • 60% of rural providers had lived in a rural community44 • Birthplace in rural county increased odds23 • A combination of growing up in a rural area, plans to practice in rural area, and plans for family medicine showed a positive association37 • Higher proportion attending rural high school in rural vs. urban providers38 • Significant association with having a rural upbringing39, 47 • Significant relationship with rural background40 No association• Majority of rural providers did not grow up in small town41 , 42 Family12 studiesGRADE: Very low †‡ Association• Family ties reported as major reason43 • Family/spouse reported to be a very important factor34 • Significant association with location partner grew up in30 • Proximity to family listed as motivation36 • Significant association with having a child during or before medical school30 • Conclusion that support of and for significant other was most important factor31 • Many interviewees had sought out life partners who were willing to live in a rural community44 No association• Having children was not associated with practice location30 • Family obligation did not influence decision36 ...…”

Monitoring of Physiological Parameters to Predict Exacerbations of Chronic Obstructive Pulmonary Disease (COPD): A Systematic Review

“…Five studies out of 16 [13,15,19,20,22] did not provide sufficient statistical data to draw conclusions consistent with the results of other studies, despite reporting changes in physiological variables (no p -value). The methodological quality of the studies was variable but generally low with 12 cohort studies ranked as moderate or low quality, and four trials ranked as having a high risk of bias.…”

“…In some studies, a mobile/tablet app was used to communicate with the participant [19,20], and transfer data. Some studies set up a monitoring station for each individual with different devices [7,13,14,15,17,18,19,21,22,23,24,25], where the data were transmitted automatically through an Internet modem. If a red flag was raised or threshold breached, a notification alert was sent to the system operator in real time.…”

“…Five studies (two at high risk of bias and three at moderate quality) out of 16 [13,15,19,20,22] did not provide sufficient statistical analysis of changes in vital signs despite reporting these variables. For example, Pedone [19] evaluated the capability of a tele-monitoring system for lower hospitalisation rates, and to identify COPD exacerbation onset.…”

“…In 2015 Fernandez-Granero at moderate quality [22] reported a study demonstrating that 25 out of 33 COPD exacerbations could be detected via monitoring patient’s respiratory sounds at home. Each participant was asked to record his/her respiratory sounds daily by placing a microphone on the suprasternal notch.…”