Citation

Bonnell LN, Littenberg B, Wshah SR, Rose GL. J. Am. Board Fam. Med. 2020; 33(3): 397-406.

Copyright

(Copyright © 2020, American Board of Family Medicine)

DOI

10.3122/jabfm.2020.03.190421

PMID

unavailable

Abstract

INTRODUCTION: Unhealthy drinking is prevalent in the United States, and yet it is underidentified and undertreated. Identifying unhealthy drinkers can be time-consuming and uncomfortable for primary care providers. An automated rule for identification would focus attention on patients most likely to need care and, therefore, increase efficiency and effectiveness. The objective of this study was to build a clinical prediction tool for unhealthy drinking based on routinely available demographic and laboratory data.
METHODS: We obtained 38 demographic and laboratory variables from the National Health and Nutrition Examination Survey (1999 to 2016) on 43,545 nationally representative adults who had information on alcohol use available as a reference standard. Logistic regression, support vector machines, k-nearest neighbor, neural networks, decision trees, and random forests were used to build clinical prediction models. The model with the largest area under the receiver operator curve was selected to build the prediction tool.
RESULTS: A random forest model with 15 variables produced the largest area under the receiver operator curve (0.78) in the test set. The most influential predictors were age, current smoker, hemoglobin, sex, and high-density lipoprotein. The optimum operating point had a sensitivity of 0.50, specificity of 0.86, positive predictive value of 0.55, and negative predictive value of 0.83. Application of the tool resulted in a much smaller target sample (75% reduced).
CONCLUSION: Using commonly available data, a decision tool can identify a subset of patients who seem to warrant clinical attention for unhealthy drinking, potentially increasing the efficiency and reach of screening.

Language: en

Keywords

Logistic Models; Alcohol Drinking; Decision Trees; Alcoholism; Area Under Curve; Clinical Decision Rules; Machine Learning; Neural Networks (Computer); Nutrition Surveys; Support Vector Machine