Risk Factors for Adverse Drug Events: A 10-Year Analysis

Author:

Evans R Scott1,Lloyd James F2,Stoddard Gregory J3,Nebeker Jonathan R4,Samore Matthew H5

Affiliation:

1. R Scott Evans MS PhD, Senior Medical Informaticist, Department of Medical Informatics, LDS Hospital & Intermountain Health Care; Professor, Department of Medical Informatics, University of Utah School of Medicine, Salt Lake City, UT

2. James F Lloyd BS, Systems Analyst, Department of Medical Informatics, LDS Hospital & Intermountain Health Care

3. Gregory J Stoddard MPH, Head, Section of Biostatistics, Division of Clinical Epidemiology, University of Utah School of Medicine

4. Jonathan R Nebeker MS MD, Associate Director, Informatics Decision Enhancement and Surveillance Center, Veterans Affairs Medical Center; Assistant Professor of Medicine, Division of Geriatrics, University of Utah School of Medicine

5. Matthew H Samore MD, Chief, Division of Clinical Epidemiology; Professor of Medicine, University of Utah School of Medicine

Abstract

BACKGROUND Many adverse drug events (ADEs) are the result of known pharmacologic properties, and some result from medication errors. However, some are the result of patient-specific risk factors. OBJECTIVE To identify inpatient risk factors for ADEs. METHODS Conditional logistic regression was used to analyze all pharmacist-verified ADEs by therapeutic class of drugs and severity during a 10-year study period. All inpatients ≥18 years of age from a 520-bed tertiary teaching hospital were included. Each case patient was matched with up to 16 control patients. Odds ratios for patient factors associated with ADEs were calculated from different therapeutic classes of drugs. RESULTS Odds ratios for numerous risk factors were identified for 4376 ADEs and were found to vary depending on therapeutic classification. The risk factors for the different classifications were grouped by (1) patient characteristics—female (OR 1.5–1.7), age (0.7–0.9), weight (1.2–1.4), creatinine clearance (0.8–4.7), and number of comorbidities (1.1–12.6); (2) drug administration—dosage (1.2–3.7), administration route (1.4–149.9), and number of concomitant drugs (1.2–2.4); and (3) patient type—service (1.2–4.9), nursing division (1.5–3.8), and diagnosis-related group (1.5–5.7). CONCLUSIONS Some risk factors are consistent for all ADEs and across multiple therapeutic classes of drugs, while others are class specific. High-risk agents should be closely monitored based on patient characteristics (gender, age, weight, creatinine clearance, number of comorbidities) and drug administration (dosage, administration route, number of concomitant drugs).

Publisher

SAGE Publications

Subject

Pharmacology (medical)

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