The Hospital Readmissions Reduction Program (HRRP) permits Centers for Medicare and Medicaid Services (CMS) to reduce reimbursement to hospitals with excess 30-day unplanned readmissions. Diabetes disproportionately impacts the hospitalized patient population, affecting 25-30% of admissions and increases the risk for unplanned readmission. We hypothesized that a readmission risk prediction model for hospitalized patients with type 2 diabetes using machine learning and a diagnosis-specific 30-day readmission outcome will outperform traditional prediction models. We demonstrate that L1 penalized logistic regression and random forest show improved discriminatory performance over LACE, a commonly used logistic regression-based model predicting all-cause readmission. L1 penalized logistic regression is also well-calibrated, efficient and produces interpretable results through feature selection. Random forest was less well-calibrated consistent with its use in other areas of the biomedical literature. In the setting of class imbalance, all of our models suffered from low precision at low thresholds near the outcome prevalence. Random forest precision improved when evaluated at higher thresholds enabling application in a clinical setting. Using an approach that includes a diagnosis-specific outcome enables actionable models for use by disease-specific service lines. Prospective evaluation is needed to assess the validity of this approach and to evaluate for overfitting in the setting of class imbalance.
| Identifer | oai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-03202018-104702 |
| Date | 23 March 2018 |
| Creators | Mize, Dara Lee Eckerle |
| Contributors | Shubhada Jagasia, Mia Levy, Colin Walsh |
| Publisher | VANDERBILT |
| Source Sets | Vanderbilt University Theses |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.vanderbilt.edu/available/etd-03202018-104702/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Vanderbilt University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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