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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Algorithms for Operationalizing Mild Cognitive Impairment Using the Spanish-Language NIH Toolbox Cognition Battery.

Justin E Karr1, Monica Rivera Mindt2,3, Grant L Iverson4,5,6

  • 1University of Kentucky, Lexington, KY, USA.

Archives of Clinical Neuropsychology : the Official Journal of the National Academy of Neuropsychologists
|June 23, 2022
PubMed
Summary

New algorithms help neuropsychologists identify cognitive impairment in Spanish speakers. These flexible methods account for education and cultural factors, improving diagnostic accuracy for diverse populations.

Keywords:
AssessmentCross-cultural/minorityNorms/normative studies

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Area of Science:

  • Neuropsychology
  • Cognitive Science
  • Psychometrics

Background:

  • Accurate assessment of cognitive impairment in Spanish-speaking individuals is crucial for effective neuropsychological practice.
  • Existing methods may not adequately account for variations in intelligence, education, and sociocultural factors, potentially leading to misdiagnosis.
  • Flexible operationalization of psychometric cognitive impairment is needed for diverse populations.

Purpose of the Study:

  • To develop and validate algorithms for identifying psychometric cognitive impairment in Spanish-speaking examinees using the NIH Toolbox Cognition Battery (NIHTB-CB).
  • To create flexible scoring methods that consider varying levels of crystallized ability, education, and sociocultural characteristics.
  • To establish score combinations (base rates) indicative of cognitive impairment within specific demographic strata.

Main Methods:

  • Utilized a normative sample of 250 healthy Latinx adults for the Spanish-language NIHTB-CB, including crystallized and fluid cognitive tests.
  • Converted test performances into age- and demographically adjusted normed scores (adjusting for age, gender, and education).
  • Developed algorithms combining low and high fluid cognitive test scores at base rates approximating 1 SD (~16%) or 1.5 SDs (~7%) below the mean.

Main Results:

  • Provided algorithms for both age-adjusted and demographically adjusted scores, stratified by crystallized ability, education, and sociocultural factors.
  • Demonstrated that specific score combinations approximate a 1 SD below the mean base rate in subgroups such as those born or educated abroad, monolingual Spanish speakers, and individuals from low-income households.
  • Reported base rates for these subgroups, e.g., 16.2% for those born abroad, 18.6% for those educated abroad, 16.4% for monolingual Spanish speakers, and 13.6% for low-income households.

Conclusions:

  • The developed algorithms offer a flexible framework for operationalizing psychometric cognitive impairment in Spanish-speaking populations.
  • These tools allow for the application of different diagnostic criteria based on individual examinee characteristics.
  • Enhances the precision and cultural sensitivity of cognitive impairment assessments in neuropsychology.