BME Seminar: Mehran Asghari
Monday, March 13th, 2023 - 12:00 p.m.
Mehran Asghari
Ph.D. Candidate of Biomedical Engineering
"An Upper Extremity Approach to Predict Adverse Outcome in Chronic Obstructive Pulmonary Disease"
Keating 103
Live Zoom link | Passcode: BearDown
Hosts: Dr. Beth Hutchinson and Dr. Shang Song
(Instructor permission required for enrolled students to attend via Zoom)
Persons with a disability may request a reasonable accommodation by contacting the Disability Resource Center at 621-3268 (V/TTY).
Abstract: Chronic obstructive pulmonary disease (COPD) is a common progressive disease, which is the third leading cause of death among adults in the United States. Over 80% of patients with COPD have at least one comorbid chronic condition, and 25% are frail. Risk prediction using function reflective of total burden of COPD patients is important because it identify vulnerable patients and helps to enhance bronchodilator therapies and specialized COPD treatment strategies including pharmacotherapy, rehabilitation, exercise programs, and measures to consolidate care in this complex population. COPD diagnosis, prognosis, and pharmacologic and non-pharmacologic treatment are assessed using spirometry, with low forced expiratory volume in one second (FEV1) predicting worse health outcomes. While FEV1 is related to mortality, it is not sensitive in “risk prediction” to identify vulnerable patients. Assessment of functional capacity (reflective of the geriatric syndromes of dynapenia and clinical frailty), in combination with pulmonary function testing (PFT) can help to predict COPD health outcomes. The 6-minute walk distance (6MWD) test is commonly used to assess functional capacity in COPD patients and has been shown to predict mortality better than PFTs. While the 6MWD has good reliability and validity, it is time-consuming, burdensome in clinical settings, or simply not feasible for some patients, especially elders with mobility impairments. For these reasons, an alternative objective, quick and simple approach for assessing functional capacity in COPD would be beneficial to risk stratify, predict outcomes, and direct appropriate care.
We developed an upper extremity function (UEF) for frailty assessment, and we developed a frailty index based on the kinematic information that we obtain from the arm motions. Our previous investigations showed a correlation between this index and COPD adverse outcomes. However, the frailty score lacks direct assessment of muscle dysfunction. Studies showed that Dynapenia (age-related loss of muscle performance) is related to limb muscle dysfunction, and is associated with reduced exercise tolerance, quality of life, and mortality in COPD. We extended the UEF approach by developing a 2 degree-of-freedom subject-specific computational muscle model to simulate elbow motions and predict muscle forces during the UEF test. We used optimization approach and recruited entropy assisted cost function to predict muscle forces that simulate the patients’ elbow motions. We collected EMG signals of biceps and triceps to verify the model. Muscle parameters were extracted from muscle forces to better study muscle dysfunction in COPD patients. Our model is able to predict muscle co-contraction and muscle force ratio during arm flexion extension motions without using EMG data. Along with kinematic parameters, we extracted muscle model parameters to more accurately study muscle dysfunction in COPD patients.
In order to train and test the adverse outcome prediction based on UEF results, we recruited 200 newly admitted COPD patients and they performed the UEF test while they were in hospital. In-hospital and longitudinal outcomes were recorded by following up the patients for 90 days. We used elastic net method to obtain optimum feature set to predict COPD adverse outcomes. several machine learning (ML) algorithm such as SVM, KNN, and Logistic regression were used for adverse outcomes prediction. Our results showed that using UEF approach and studying kinematic and muscle dysfunction based on the muscle model we can predict adverse outcomes in COPD patients. This approach is quick, objective, and more feasible compared to 6MWD test to predict adverse outcomes in patients with COPD.