An empirically based proposal to identify a short battery to detect neuropsychological impairment in a general adult practice

Abstract

Objectives: To demonstrate that 1) models based on small numbers of tests can be statistically developed to identify neuropsychological impairment in a general adult neuropsychology clinic and 2) those models show strong predictive validity on replication in a slightly different sample. Method: Latent Class Analyses (LCA) were used to determine neuropsychological classification in 231 patients referred to general adult neuropsychology services. A clinical rating scale was also used to approximate clinical decision-making. Regression models were constructed in a training sample (n = 127) drawn from an adult neuropsychology clinic using test scores from seven different a priori test battery combinations to predict group membership or clinical rating. The utility of the seven models was assessed in a testing sample (n = 104) from another independent adult neuropsychology clinic. Results: The LCA yielded a two class solution characterized by impaired versus non-impaired performance on neuropsychological tests. A seven test battery provided the best balance of accuracy and length in predicting LCA group with a sensitivity of 84.4% and a specificity of 90%. Sensitivity and specificity were slightly attenuated using the clinical rating scale as the criterion, but the seven test battery still provided good accuracy (AUC=.906). Conclusions: Test protocols based on only five to eight test scores can accurately identify most patients with clinical impairment in a diverse adult neuropsychology clinic. Development of short protocols with adequate sensitivity and specificity will become increasingly important to address long waiting lists in light of the COVID pandemic against the general backdrop of increasing demand for neuropsychological services.

Keywords:

• Neuropsychological assessment
• ethics
• short protocol

Acknowledgements

The authors would like to acknowledge the assistance of Danny Bermudez, Ted Hogan, Fred Holley, Courtney Kissel, and Jeff Zoslov with this study.