Hierarchically Optimal Classification Tree Analysis of Adverse Drug Reactions Secondary to Warfarin Therapy

Robert C. Soltysik & Paul R. Yarnold

Optimal Data Analysis, LLC

Identification and detection of adverse drug reactions (ADRs) is critical to patient safety improvement, and warfarin is a medication known to be associated with a high rate of ADRs. Hierarchically optimal CTA was used to predict ADRs attributable to medications taken in addition to warfarin, for a sample of 2,289 hospital in-patients. A model involving five medications was identified, with relatively high ADR rates identified for patients, already on warfarin, who also received zolpidem tartrate, tamsulosin HCL, famotidine, nitroglycerin, and rofecoxib. The CTA model achieved moderate classification accuracy (ESS=38.0), correctly classifying 1,323 of 2,246 patients (58.9%) without an ADR, and 34 of 43 patients (79.1%) experiencing an ADR.

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Emergency Severity Index (Version 3) Score Predicts Hospital Admission

Paul R. Yarnold & Robert C. Soltysik

Optimal Data Analysis, LLC

Hospital admission status, Emergency Severity Index (ESI) Version 3 triage score, and binary indicators of whether lab work or radiological examinations were completed in the Emergency Department (ED), were available for 160,471 patients seen over a three-year period in the ED of a leading community teaching hospital in Toronto. Hierarchical CTA conducted for these data yielded ESS=63.8, a relatively strong effect. The model correctly classified 80.2% of the admitted patients, and 83.6% of the patients who weren’t admitted. The model was correct 60.1% of the time that it predicted that a patient would be admitted, and correct 93.2% of the time that it predicted that a patient wouldn’t be admitted.

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