Using Machine Learning to Predict Likelihood and Cause of Readmission After Hospitalization for Chronic Obstructive Pulmonary Disease Exacerbation.

Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of hospital readmissions. Few existing tools use electronic health record (EHR) data to forecast patients' readmission risk during index hospitalizations. Objective: We used machine learning and in-hospital data to model 90-day risk for and cause of readmission among inpatients with acute exacerbations of COPD (AE-COPD). Design: Retrospective cohort study. Participants: Adult patients admitted for AE-COPD at the University of Chicago Medicine between November 7, 2008 and December 31, 2018 meeting International Classification of Diseases (ICD)-9 or -10 criteria consistent with AE-COPD were included. Methods: Random forest models were fit to predict readmission risk and respiratory-related readmission cause. Predictor variables included demographics, comorbidities, and EHR data from patients' index hospital stays. Models were derived on 70% of observations and validated on a 30% holdout set. Performance of the readmission risk model was compared to that of the HOSPITAL score. Results: Among 3238 patients admitted for AE-COPD, 1103 patients were readmitted within 90 days. Of the readmission causes, 61% (n = 672) were respiratory-related and COPD (n = 452) was the most common. Our readmission risk model had a significantly higher area under the receiver operating characteristic curve (AUROC) (0.69 [0.66, 0.73]) compared to the HOSPITAL score (0.63 [0.59, 0.67]; p = 0.002). The respiratory-related readmission cause model had an AUROC of 0.73 [0.68, 0.79]. Conclusion: Our models improve on current tools by predicting 90-day readmission risk and cause at the time of discharge from index admissions for AE-COPD. These models could be used to identify patients at higher risk of readmission and direct tailored post-discharge transition of care interventions that lower readmission risk.

Large non-functioning substernal parathyroid cyst: A case report and review of the literature.

OBJECTIVES: Parathyroid cysts are rare benign lesions of the head and neck that account for less than 1% of cystic neck masses. We present a rare case of a large 6 cm substernal parathyroid cyst. PRESENTATION OF CASE: An otherwise healthy 65 year-old female presented to the otolaryngology clinic for evaluation of an anterior, midline neck mass. On physical exam, she was noted to have a fullness in the anterior neck extending to the sternal notch. CT demonstrated an enlarged thyroid with a cyst extending to the aortic arch. Initial evaluation suggested a bilateral goiter with substernal extension. The cyst was managed with drainage and observation. After two years of continued growth, the patient underwent a left thyroid lobectomy and mediastinal mass resection via the cervical approach. Final pathology was consistent with a parathyroid cyst. CONCLUSIONS: Parathyroid cysts are a rare cause of neck mass in an adult, and a 6 cm substernal parathyroid cyst represents an unusual site and size for these cysts. Parathyroid cysts are not often considered on the differential of neck and mediastinal cystic lesions. However, appropriate steps should be taken to ensure a proper diagnosis for any cystic lesion in the neck.