Trail Making Test: Normative Data for Patients with Severe Alcohol Use Disorder.
Franz MoggiNatalie OssolaYolanda GraserLeila Maria SoraviaPublished in: Substance use & misuse (2020)
Background: Patients with alcohol use disorders (AUD) often show cognitive impairment, particularly in executive functions that has been linked to poor treatment outcomes. The Trail Making Test (TMT) is the most widely used neuropsychological test to investigate executive functions with available normative data. However, no such norms exist for patients with AUD, although there is extensive evidence that TMT performance is altered in AUD patients. Purpose: To provide normative data for patients with AUD and compare the performance of AUD patients with already existing normative data from healthy subjects. Methods: Data of 494 recently detoxified patients with AUD who entered an abstinence-oriented residential treatment program were analyzed. Patients completed a standardized diagnostic procedure and the TMT Parts A and B at treatment admission. Results: AUD patients' performance on the TMT was impaired compared to the normative data of healthy individuals and decreased with increasing age and lower levels of education, with stronger effects in Part B assessing more complex executive functioning. Alcohol-related variables showed no direct associations with TMT performance. Conclusions: The results replicate the association of age and education with TMT performance, suggesting that AUD may be associated with impaired cognitive functioning earlier in life in abstinent patients shortly after withdrawal from alcohol compared to healthy individuals. The presented normative data for patients with AUD particularly improve the examination of executive deficits, and may enable clinicians to evaluate patients' cognitive functioning in treatment more precisely.
Keyphrases
- end stage renal disease
- alcohol use disorder
- ejection fraction
- chronic kidney disease
- newly diagnosed
- prognostic factors
- healthcare
- peritoneal dialysis
- electronic health record
- emergency department
- working memory
- machine learning
- early onset
- artificial intelligence
- quality improvement
- patient reported
- data analysis