Browsing by Author "Broyles, Andrea"

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    Hyperparathyroidism and parathyroidectomy in X-linked hypophosphatemia patients
    (Elsevier, 2019-10-01) DeLacey, Sean; Liu, Ziyue; Broyles, Andrea; El-Azab, Sarah A.; Guandique, Cristian F.; James, Benjamin C.; Imel, Erik A.; Medicine, School of Medicine
    Background X-linked hypophosphatemia (XLH) causes rickets, osteomalacia, skeletal deformities and growth impairment, due to elevated fibroblast growth factor 23 and hypophosphatemia. Conventional therapy requires high doses of phosphate salts combined with active vitamin D analogues. Risks of this regimen include nephrocalcinosis and secondary hyperparathyroidism or progression to tertiary (hypercalcemic) hyperparathyroidism. Methods The primary goals were to estimate the prevalence of hyperparathyroidism and to characterize parathyroidectomy outcomes regarding hypercalcemia among XLH patients. XLH patients attending our center from 1/2000 to 12/2017 were included in a retrospective chart review. Prevalence of nephrocalcinosis and eGFR<60 mL/min/1.732 was also assessed. Results Of 104 patients with XLH, 84 had concurrent measurements of calcium and PTH (40 adults and 44 children). Of these, 70/84 (83.3%), had secondary or tertiary hyperparathyroidism at any time point. Secondary hyperparathyroidism was persistent in 62.2% of those with data at multiple timepoints. Tertiary hyperparathyroidism had an overall prevalence of 14/84 (16.7%) patients. Parathyroidectomy was performed in 8/84 (9.5%) of the total population. After parathyroidectomy, persistent or recurrent tertiary hyperparathyroidism was detected in 6/8 (75%) patients at a median of 6 years (from 0 to 29 years). One patient had chronic postoperative hypoparathyroidism and one patient remained normocalcemic 4 years after surgery. Nephrocalcinosis was more prevalent in patients with tertiary hyperparathyroidism than those without (60.0% vs 18.6%). Chronic kidney disease (eGFR<60 mL/min/1.732) was also more prevalent in patients with tertiary hyperparathyroidism than those without (35.7% vs 1.5%). Conclusion The majority of patients with XLH develop secondary hyperparathyroidism during treatment with phosphate and active vitamin D. A significant proportion develops tertiary hyperparathyroidism and most have recurrence or persistence of hypercalcemia after surgery.
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    Influence of Breast Cancer and Metastases on Incidence of Diabete
    (Research Square, 2021) Ballinger, Tarah; Liu, Ziyue; El-Azab, Sarah A.; Broyles, Andrea; Guise, Theresa; Imel, Erik A.; Medicine, School of Medicine
    Purpose: Diabetes increases the risk of subsequent breast cancer. However, the inverse relationship of breast cancer to incident diabetes development is unclear. In preclinical models increased bone turnover due to bone metastases or endocrine therapies impacts insulin secretion. This analysis was conducted to estimate the incidence of diabetes after breast cancer and the influence of metastases and therapeutic agents. Methods: This retrospective case-control study combined data from a large electronic health data exchange and the Indiana State Cancer Registry on breast cancer patients and controls between 2007 and 2017. Primary exposure was presence of breast cancer and bone or non-bone metastases. The primary outcome was frequency of incident diabetes detected by ICD codes, medication use, or laboratory results, compared between breast cancer cases and controls using conditional or ordinary logistic regressions. Results: 36,083 cases and 36,083 matched controls were detected. Incident diabetes was higher in early stage breast cancer (OR 1.17, 95%CI 1.11-1.23, p<0.0001) and metastatic breast cancer (OR 1.62, 95% CI 1.25-2.09, p=0.0002), compared to controls. Bone metastases conferred higher odds of both pre-existing (OR 1.20, 95% CI 1.03-1.63, p=0.0272) and incident diabetes (OR 1.64, 95% CI 1.19-2.25, p=0.0021). Endocrine therapy was associated with reduced diabetes (OR 0.86, 95% CI 0.79-0.83, p=0.002). Anti-resorptives reduced incident diabetes in those with bone metastases (OR 0.44, 95% CI 0.25-0.78, p=0.005). Conclusion: Breast cancer, especially with metastases, increases subsequent risk of diabetes. As patients with breast cancer live longer, identifying and managing diabetes may impact treatment delivery, cost, survival, and quality of life.
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    Using machine learning to detect sarcopenia from electronic health records
    (Sage, 2023-08-29) Luo, Xiao; Ding, Haoran; Broyles, Andrea; Warden, Stuart J.; Moorthi, Ranjani N.; Imel, Erik A.; Physical Therapy, School of Health and Human Sciences
    Introduction: Sarcopenia (low muscle mass and strength) causes dysmobility and loss of independence. Sarcopenia is often not directly coded or described in electronic health records (EHR). The objective was to improve sarcopenia detection using structured data from EHR. Methods: Adults undergoing musculoskeletal testing (December 2017-March 2020) were classified as meeting sarcopenia thresholds for 0 (controls), ≥1 (Sarcopenia-1), or ≥2 (Sarcopenia-2) tests. Electronic health record diagnoses, medications, and laboratory testing were extracted from the Indiana Network for Patient Care. Five machine learning models were applied to EHR data for predicting sarcopenia. Results: Of 1304 participants, 1055 were controls, 249 met Sarcopenia-1 and 76 met Sarcopenia-2. Sarcopenic participants were older, with higher fat mass, Charlson Comorbidity Index, and more chronic diseases. All models performed better for Sarcopenia-2 than Sarcopenia-1. The top performing models for Sarcopenia-1 were Logistic Regression [area under the curve (AUC) 71.59 (95% confidence interval [CI], 71.51-71.66)] and Multi-Layer Perceptron [AUC 71.48 (95%CI, 71.00-71.97)]. The top performing models for Sarcopenia-2 were Logistic Regression [AUC 91.44 (95%CI, 91.28-91.60)] and Support Vector Machine [AUC 90.81 (95%CI, 88.41-93.20)]. For the best Logistic Regression Model, important sarcopenia predictors included diabetes mellitus, digestive system complaints, signs and symptoms involving the nervous, musculoskeletal and respiratory systems, metabolic disorders, and kidney or urinary tract disorders. Opioids, corticosteroids, and antihyperlipidemic drugs were also more common among sarcopenic participants. Conclusions: Applying machine learning models, sarcopenia can be predicted from structured data in EHR, which may be developed through future studies to facilitate large-scale early detection and intervention in clinical populations.