Abstract
Background: Clinical trials increasingly aim to retard disease progression during presymptomatic phases of Mild Cognitive Impairment (MCI) and thus recruiting study participants at high risk for developing MCI is critical for cost-effective prevention trials. However, accurately identifying those who are destined to develop MCI is difficult. Collecting biomarkers is often expensive. Methods: We used only noninvasive clinical variables collected in the National Alzheimer's Coordinating Center (NACC) Uniform Data Sets version 2.0 and applied machine learning techniques to build a low-cost and accurate Mild Cognitive Impairment (MCI) conversion prediction calculator. Cross-validation and bootstrap were used to select as few variables as possible accurately predicting MCI conversion within 4 years. Results: A total of 31,872 unique subjects, 748 clinical variables, and additional 128 derived variables in NACC data sets were used. About 15 noninvasive clinical variables are identified for predicting MCI/aMCI/naMCI converters, respectively. Over 75% Receiver Operating Characteristic Area Under the Curves (ROC AUC) was achieved. By bootstrap we created a simple spreadsheet calculator which estimates the probability of developing MCI within 4 years with a 95% confidence interval. Conclusions: We achieved reasonably high prediction accuracy using only clinical variables. The approach used here could be useful for study enrichment in preclinical trials where enrolling participants at risk of cognitive decline is critical for proving study efficacy, and also for developing a shorter assessment battery.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 18-27 |
| Number of pages | 10 |
| Journal | Alzheimer Disease and Associated Disorders |
| Volume | 32 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2018 |
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Keywords
- bootstrap
- dementia
- incidence
- machine learning
- mild cognitive impairment
- National Alzheimer's Coordinating Center Uniform Data Set (NACC UDS)
- prediction
- ROC AUC
- study enrichment
ASJC Scopus subject areas
- Clinical Psychology
- Gerontology
- Geriatrics and Gerontology
- Psychiatry and Mental health
Cite this
Big Data Analytical Approaches to the NACC Dataset : Aiding Preclinical Trial Enrichment. / Lin, Ming; Gong, Pinghua; Yang, Tao; Ye, Jieping; Albin, Roger L.; Dodge, Hiroko.
In: Alzheimer Disease and Associated Disorders, Vol. 32, No. 1, 01.01.2018, p. 18-27.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Big Data Analytical Approaches to the NACC Dataset
T2 - Aiding Preclinical Trial Enrichment
AU - Lin, Ming
AU - Gong, Pinghua
AU - Yang, Tao
AU - Ye, Jieping
AU - Albin, Roger L.
AU - Dodge, Hiroko
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Background: Clinical trials increasingly aim to retard disease progression during presymptomatic phases of Mild Cognitive Impairment (MCI) and thus recruiting study participants at high risk for developing MCI is critical for cost-effective prevention trials. However, accurately identifying those who are destined to develop MCI is difficult. Collecting biomarkers is often expensive. Methods: We used only noninvasive clinical variables collected in the National Alzheimer's Coordinating Center (NACC) Uniform Data Sets version 2.0 and applied machine learning techniques to build a low-cost and accurate Mild Cognitive Impairment (MCI) conversion prediction calculator. Cross-validation and bootstrap were used to select as few variables as possible accurately predicting MCI conversion within 4 years. Results: A total of 31,872 unique subjects, 748 clinical variables, and additional 128 derived variables in NACC data sets were used. About 15 noninvasive clinical variables are identified for predicting MCI/aMCI/naMCI converters, respectively. Over 75% Receiver Operating Characteristic Area Under the Curves (ROC AUC) was achieved. By bootstrap we created a simple spreadsheet calculator which estimates the probability of developing MCI within 4 years with a 95% confidence interval. Conclusions: We achieved reasonably high prediction accuracy using only clinical variables. The approach used here could be useful for study enrichment in preclinical trials where enrolling participants at risk of cognitive decline is critical for proving study efficacy, and also for developing a shorter assessment battery.
AB - Background: Clinical trials increasingly aim to retard disease progression during presymptomatic phases of Mild Cognitive Impairment (MCI) and thus recruiting study participants at high risk for developing MCI is critical for cost-effective prevention trials. However, accurately identifying those who are destined to develop MCI is difficult. Collecting biomarkers is often expensive. Methods: We used only noninvasive clinical variables collected in the National Alzheimer's Coordinating Center (NACC) Uniform Data Sets version 2.0 and applied machine learning techniques to build a low-cost and accurate Mild Cognitive Impairment (MCI) conversion prediction calculator. Cross-validation and bootstrap were used to select as few variables as possible accurately predicting MCI conversion within 4 years. Results: A total of 31,872 unique subjects, 748 clinical variables, and additional 128 derived variables in NACC data sets were used. About 15 noninvasive clinical variables are identified for predicting MCI/aMCI/naMCI converters, respectively. Over 75% Receiver Operating Characteristic Area Under the Curves (ROC AUC) was achieved. By bootstrap we created a simple spreadsheet calculator which estimates the probability of developing MCI within 4 years with a 95% confidence interval. Conclusions: We achieved reasonably high prediction accuracy using only clinical variables. The approach used here could be useful for study enrichment in preclinical trials where enrolling participants at risk of cognitive decline is critical for proving study efficacy, and also for developing a shorter assessment battery.
KW - bootstrap
KW - dementia
KW - incidence
KW - machine learning
KW - mild cognitive impairment
KW - National Alzheimer's Coordinating Center Uniform Data Set (NACC UDS)
KW - prediction
KW - ROC AUC
KW - study enrichment
UR - http://www.scopus.com/inward/record.url?scp=85044323568&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044323568&partnerID=8YFLogxK
U2 - 10.1097/WAD.0000000000000228
DO - 10.1097/WAD.0000000000000228
M3 - Article
VL - 32
SP - 18
EP - 27
JO - Alzheimer Disease and Associated Disorders
JF - Alzheimer Disease and Associated Disorders
SN - 0893-0341
IS - 1
ER -