Browsing by Subject "Predictive Analytics"

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    Atrial Fibrillation Readmissions: Temporal Trends, Risk Factors and Data Driven Modeling
    (2021-12) Salsabili, Mahsa
    This dissertation provides a background with overview of the clinical perspective, policy perspective and application of data driven modeling for atrial fibrillation (AF) and hospital readmissions. Additionally, three aims focused on temporal trends in AF hospitalization and readmission, predictors of AF readmission, and application of machine learning models in AF readmission are presented. The overall purpose of this dissertation is to develop stronger understanding of the temporal trends in AF hospitalizations and readmission and identify factors that increase the likelihood of readmission among the AF population. The value of application of machine learning algorithms to predict readmissions were assessed and compared to traditional methods. Atrial fibrillation is the most common clinically significant cardiac arrhythmia in the United States. Poorly controlled atrial fibrillation patients are likely to be hospitalized and potentially readmitted to the hospital within 30 days. The Nationwide Readmission Database (NRD) was analyzed using the International Classification of Diseases, Ninth Revision (ICD‐9) and tenth revision (ICD-10) codes to identify adult patients with a primary diagnosis of atrial fibrillation at discharge. Among those admitted with atrial fibrillation on average 57,883 individuals were readmitted per year for all-cause readmission within 30 days from 2010 to 2017. The AF index hospitalization rate increased from 10.4 per 1000 adults in 2010 to 11.1 in 2013 and dropped back to 10.4 in 2014 and increased to 10.9 in 2017. This nationally representative study of primary atrial fibrillation admissions and readmissions found that over the 2010 to 2017 time frame, crude atrial fibrillation index admissions increased, except for 2014 wherethere was a decline. Thirty- day all-cause readmission rates remained relatively stable for atrial fibrillation index patients across the study years. There is limited data regarding 30‐day readmission rates and predictors after discharge for atrial fibrillation. The 2017 NRD was assessed using ICD‐10 codes to identify the AF population. Predictors of readmissions, and performance of the predictive model were analyzed. A hierarchical mixed linear model was used on the best performing model to identify the predictors of readmission based on index admission. Presence of comorbidities such as metastatic cancer, lymphoma and severe renal failure present in index atrial fibrillation during index hospitalization predicted higher likelihood 30‐day readmissions. About 1 in 6 patients had an all-cause 30-day readmission. The patient comorbidities contributed significantly to readmission with oncology comorbidities being the top predictor. There is a lack of studies attempting to predict readmissions among AF population using various machine learning techniques. Using the 2017 NRD, we explored the performance of four common and widely used classification approaches (random forest, decision tree, gradient boosting and Naïve Bayes) in 30-day all-cause readmission for AF patients. To have a less biased and more generalizable model 10-fold cross validation was performed to train and test the data, with five variations of feature presentation. We compared and reported common key performance indicators for binary classification techniques (e.g. Area-Under Curve (AUC), accuracy, sensitivity, specificity, and F1 score) among the various classifiers. Our results reveal that Gradient Boosting has the greatest performance with an AUC of 0.667, which was followed by Naïve Bayes and Random Forest with AUCs of 0.641 and 0.640 respectively. The feature variations with comorbidities present have better performance for these three classifiers. Using Gradient Boosting, Random Forest, and Naïve Bayes we get acceptable performance when assessing AF all cause 30-day readmission. Overall, the results of the dissertation show that the prevalence of AF hospitalizations and readmission is increasing over time. Presence of comorbidities among patients increased the likelihood of readmissions. The performance of linear based model and majority of the machine learning based models improved with the presence of variables representing comorbidities. The overall performance of the best performing machine learning models was similar to the linear model in predicting readmissions among the AF population.