Browsing by Author "Park, Chanhyun"
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Item A Monoallelic DNM1L Mutation presenting with Epilepsia Partialis Continua: A Case Report(2022) Park, ChanhyunBackground: Variants in DNM1L are reported as a rare cause of refractory epilepsy and status epilepticus. We report a patient with epilepsia partialis continua (EPC) secondary to a monoallelic DNM1L mutation. Case Information: An 11-year-old boy with prior history of speech delay and well-controlled absence epilepsy on valproate presented in clinic with status epilepticus and posterior frontal diffusion restriction on MRI. Seizures were characterized by hemifacial clonus consistent with EPC. Extensive workup including EEG, MRI, cytokine, and encephalitis panels were unrevealing for etiology. Genetic peroxisomal panel revealed a monoallelic missense mutation (R403C) in the DNM1L locus as cause for his EPC. GDF15 was also elevated, reaffirming the presence of mitochondrial disease. This DNM1L mutation was determined to be the underlying etiology for his presentation. Lacosamide, clobazam and phenobarbital, among other interventions, were ultimately used to control the patient's epilepsy; he was sent home after extensive stays in the PICU and inpatient rehabilitation unit. Conclusions: DNM1L mutations can cause cerebral dysmyelinations, abnormal gyral patterns, microcephaly, and death within the first year of life. Yet several recent cases, including ours, have linked DNM1L variants with other neurological phenotypes, including a late onset of symptoms such as intractable epilepsy, myoclonus, and developmental delay. This case is strikingly like that of a previous report but with additional clinical features such as aphasia and EPC. The presentation of EPC in our patient, as well as the difficulty finding its etiology, exemplifies the unclear clinical pattern that remains with DNM1L mutations. The clinical ambiguity of this mutation complicates diagnosis and demonstrates the importance of prompt genetic testing.Item Evaluating Equity in XGBoost Predictions of High Healthcare Expenditures for Older Women with Osteoarthritis in the United States(2024-03-21) Elchehabi, Sahar; Dehghan, Arshama; Pathak, Mona; Sambamoorthi, Nethra; Park, Chanhyun; Shen, Chan; Sambamoorthi, UshaPurpose: Osteoarthritis (OA) is a highly prevalent and debilitating condition among older adults. Studies suggest that women are more prone to develop symptomatic disease than men. OA is associated with high direct healthcare costs, attributable to its complex disease management. Furthermore, a small segment of this population may incur very high costs. Identifying these high-cost users is important for allocation of resources, cost containment, quality improvement, and population health management. However, current research in the prediction of high-cost users in OA using machine learning (ML) models is limited. Furthermore, ML model predictions of high-cost users must be equitable across sensitive attributes such as race and ethnicity and socio-economic status. This study investigated the leading predictors of high-cost users among older women with OA utilizing ML methods and the fairness of the ML algorithm in its predictions across subgroups of race and ethnicity, poverty, and education. Methods: A cross-sectional study was conducted using data from older women (age>65 years) with OA from the 2021 Medical Expenditure Panel Survey, a nationally representative survey of the non-institutionalized civilian households in the US. High-cost users were identified as having higher than the 90th percentile (>$39,388) in total healthcare expenditures. Key predictors were identified using interpretable ML model eXtreme Gradient Boosting (XGBoost) Classification and SHapley Additive exPlanations (SHAP). Overall model fit was evaluated with AUC, recall, and precision. Fairness was measured with demographic parity (equalization of odds, disparate impact, and equal opportunity) across racial and ethnic groups (Non-Hispanic White (NHW), Non-Hispanic Black (NHB), Hispanic ethnicity), education (no college and college) and poverty status (low income and high income). Counterfactual fairness was evaluated to ensure consistency in high-cost predictions between actual scenarios and counterfactual situations where individuals belong to different groups. Results: A higher percentage of Hispanic (12.2%) and NHB (14.4%) were high-cost users compared to NHW (9.0%). A higher percentage of older women without college education (10.7%) and with low income (11.2%) compared to those with college education (2.5%) and high income (5.2%) were high-cost users. The overall model fit was acceptable with AUC 0.81, recall 0.62, and precision 0.91. Multimorbidity, high school education level, and anxiety were the top 3 predictors of high-cost users. Prediction was lower among older women without college education (AUC = 0.80) and low income compared (AUC = 0.77) compared to overall prediction (AUC = 0.81). Demographic parity revealed little to no differences across racial and ethnic, education, and income groups. Conclusion: The fairness metrics indicated no bias in the predictions, likely attributable to the nationally representative nature of the survey sample and its large size. These findings need to be confirmed with other data that contain diverse populations. Leading predictors indicated that effective management of multimorbidity may reduce the risk of high-cost use in older women with OA.Item Leading Predictors of Economic Burden Among Postmenopausal Women with Heart Failure: An Application of Machine Learning with XGBoost and SHapley Additive exPlanations(2023) Dehghan, Arshama; Park, Chanhyun; Sambamoorthi, Nethra; Shen, Chan; Shara, Nawar; Sambamoorthi, UshaObjective: Heart Failure is associated with high direct healthcare costs, including out-of-pocket spending by the patients. However, there are knowledge gaps in HF research among postmenopausal women. Therefore, this study uses machine learning methods to identify leading predictors and their associations with economic burden among postmenopausal women (age > 50 years) with heart failure. Methods: This cross-sectional study used data from postmenopausal women with heart failure from the 2020 Medical Expenditure Panel Survey (MEPS: weighted N= 600,742). The economic burden was measured with total healthcare expenditures by the payors (third-party expenditures) and out-of-pocket expenditures by the patients and their families. We employed eXtreme Gradient Boosting (XGBoost) regression to determine key predictors. Global and local interpretations of associations were performed using SHapley Additive exPlanations (SHAP). Our predictive model used 21 features such as age, health status including comorbidities (anxiety, arthritis, asthma, cancer, COPD, depression, diabetes, high cholesterol, hypertension, and thyroid disease), perceived physical and mental health status, and polypharmacy. Social determinants of health (SDoH) consisted of marital status, health insurance coverage, prescription drug coverage, education, poverty status, and region. The model building included 70% training and 30% testing split of the data, 10-fold cross-validations, and up to six rounds of optimization using Python 3.9.12. Model performance metrics included absolute mean squared errors, root mean squared error and coefficient of determination; these were evaluated using the test dataset. Results: The model offered excellent accuracy as evidenced by its low mean absolute errors (0.442,0.310), root mean square errors (0.452,0.342), and high coefficients of determination (0.935,0.987) for third-party and out-of-pocket expenditures, respectively. The top 10 leading predictors of third-party expenditures included polypharmacy, age, resident of the Midwest region, asthma, perceived physical and mental health, anxiety, hypertension, white race, and low income. The SHAP plots from the third-party expenditures revealed complex relationships of age, physical, and mental health with the target variable. Polypharmacy, low income, anxiety, and asthma were associated with higher third-party expenditures. Non-Hispanic white Women and those with hypertension had lower third-party expenditures. The top 10 leading predictors of out-of-pocket expenditures included age, Latinx ethnicity, asthma, cancer, being poor, having middle income and high income, prescription drug coverage, private insurance, and polypharmacy. Out-of-pocket expenditure plots only highlighted age as the key complex factor. Being poor, having middle income, and reporting Latinx ethnicity were associated with lower out-of-pocket expenditures. High income, prescription drug coverage, private insurance, polypharmacy, and the presence of asthma and cancer were associated with higher out-of-pocket expenditures. Conclusion: The leading predictors differed by payor source. SDoH were associated with economic burden, suggesting that addressing SDoH may reduce healthcare costs. Cost-containment policies, programs, and interventions at the payor and patient levels need to include effective comorbidity management strategies. The limitations of this study include cross-sectional study design, self-reported data that may be subject to recall bias, and severity of comorbidities that may affect the economic burden. However, the study also has several strengths, such as nationally representative data, the inclusion of SDoH, validated information on expenditures, and robust interpretable machine learning methods.Item Relationship Between Pain and Total Healthcare Expenditure in Elderly Osteoarthritis Patients: An Interpretable Machine Learning (ML) Investigation with eXtreme Gradient Boosting(2024-03-21) Middleton, Claire; Dehghan, Arshama; Park, Chanhyun; Shen, Chan; Sambamoorthi, UshaObjective: Osteoarthritis (OA), a painful degenerative joint condition, affects over 32.5 million adults in the United States and one-fourth experience severe joint pain. In 2019, adults with OA had $45.4 billion more annual expenditures relative to those without OA. Although statistical methods have been utilized in studying the additional costs associated with pain among adults with OA, there is still a notable gap in understanding the relationship through the lens of ML methods. The objective of this study is to determine pain as a leading predictor of economic burden among older adults (age > 65 years) with OA using ML methods. Methods: We used data on older adults (age > 65 years) with OA (N = 1,640) from the 2021 Medical Expenditure Panel Survey (MEPS), a nationally representative survey of households in the US. Log-transformed total healthcare expenditures, which included payments by the insurers and the patients, represented the economic burden. We employed eXtreme Gradient Boosting (XGBoost) regression to determine key predictors. Global and local interpretations of associations were performed using a SHapley Additive exPlanation (SHAP), including a Partial Dependence Plot (PDP) for pain. Our predictive model utilized 24 features including biological (sex, age), race and ethnicity, clinical (pain, polypharmacy, physical and mental health status, and chronic conditions), and Social Determinants of Health (SDOH) such as marital status, education, poverty status, census region, insurance coverage, and prescription drug coverage. Chronic conditions included anxiety, depression, thyroid disease, diabetes, hypertension, coronary artery disease, cancer, hyperlipidemia, asthma, and chronic obstructive pulmonary disease. Pain interfering with regular work over the past four weeks was assessed using the Veterans Rand 12-item Health survey (VR-12), employing a Likert scale ranging from 0 (none) to 4 (extreme) to represent pain level. Missing values for pain level were imputed using K-Nearest Neighbors (KNN) Imputation. The model building included 70% training and 30% testing split of the data and 3-fold cross-validations using Python 3.10.12. Model performance was evaluated with R-square, mean absolute error, and Root Mean Square Error (RMSE) using the test dataset. Results: Approximately, one in 4 adults with OA reported moderate to extreme pain. The top 3 predictors of healthcare expenditures were: polypharmacy, physical health, and pain level. Higher pain levels and polypharmacy were associated with higher total expenditures. Excellent physical health was associated with lower total healthcare expenditures. Additionally, the SHAP PDP suggested a linear relationship between pain levels and total expenditures. Model performance was modest with a mean absolute error (1.086), RMSE (1.736), and R-square (0.452) for total expenditures. Conclusion: Higher pain levels predicted higher economic burden in older adults with OA. Effective management of pain may be a pathway to reduce the economic burden of OA. As polypharmacy was a leading predictor of healthcare expenditures, this model underscores the importance of reducing polypharmacy use in older adults with medication utilization review and management.