Developing Indicators of Inpatient Adverse Drug Events Through Nonlinear Analysis Using Administrative Data
Male
Risk Management
Data Collection
Incidence
Delirium
Hemorrhage
Blood Coagulation Disorders
Middle Aged
Hospital Records
Psychoses, Substance-Induced
3. Good health
03 medical and health sciences
0302 clinical medicine
Nonlinear Dynamics
ROC Curve
International Classification of Diseases
Predictive Value of Tests
Utah
Adverse Drug Reaction Reporting Systems
Humans
Female
Aged
Retrospective Studies
DOI:
10.1097/mlr.0b013e3180616c2c
Publication Date:
2009-03-04T23:50:38Z
AUTHORS (11)
ABSTRACT
Background: Because of uniform availability, hospital administrative data are appealing for surveillance adverse drug events (ADEs). Expert-generated rules that rely on the presence International Classification Diseases, 9th Revision Clinical Modification (ICD-9-CM) codes have limited accuracy. Rules based nonlinear associations among all types available may be more accurate. Objectives: By applying hierarchically optimal classification tree analysis (HOCTA) to data, derive and validate bleeding/anticoagulation problems delirium/psychosis. Research Design: Retrospective cohort design. Subjects: A random sample 3987 admissions drawn from 41 Utah acute-care hospitals in 2001 2003. Measures: Professional nurse reviewers identified ADEs using implicit chart review. Pharmacists assigned Medical Dictionary Regulatory Activities ADE descriptions identification clinical groups events. Hospitals provided patient demographic, admission, ICD9-CM data. Results: Incidence proportions were 0.8% drug-induced 1.0% The model bleeding had very good discrimination sensitivity at 0.87 86% fair positive predictive value (PPV) 12%. delirium excellent 94%, 0.83, but low PPV 3%. Poisoning event designed targeted sensitivities and, when forced in, degraded Conclusions: Hierarchically is a promising method rapidly developing clinically meaningful resultant anticoagulation useful retrospective screening rate estimation.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (27)
CITATIONS (22)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....