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Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
Published on: February 19, 2021
An Automated Malnutrition Screening Tool Using Routinely Collected Data for Older Adults in Long-Term Care:
Jonathan Foo1, Melanie Roberts2, Lauren T Williams2
1Department of Physiotherapy, Monash University, Melbourne, VIC, Australia.
Objective:
To develop and internally validate a malnutrition screening tool based on routinely collected data in the long-term care setting.
Design:
Diagnostic prediction model development and internal validation study.
Setting And Participants:
Residents (n = 539) from 10 long-term care facilities in Australia.
Methods:
Candidate variables identified through expert consultation were collected from routinely collected data in a convenience sample of long-term care facilities. Logistic regression using the Subjective Global Assessment as the reference standard was conducted on 500 samples derived using bootstrapping from the original sample. Candidate variables were selected if included in more than 95% of samples using backwards stepwise elimination. The final model was developed using logistic regression of selected variables. Internal validation was conducted using bootstrapping to calculate the optimism-adjusted performance. Overall discrimination was evaluated via receiver operator characteristic curve and calculation of the area under the curve. Youden's Index was used to identify the optimal threshold value for classifying malnutrition. Sensitivity and specificity were calculated.
Results:
Body mass index and weight change % over 6 months were included in the automated malnutrition screening model (AutoMal), identified in 100% of bootstrapped samples. AutoMal demonstrated excellent discrimination of malnutrition, with area under the curve of 0.8378 (95% CI, 0.80-0.87). Youden's Index value was 0.37, resulting in sensitivity of 78% (95% CI, 71%-83%) and specificity of 77% (72%-81%). Optimism-corrected area under the curve was 0.8354.
Conclusions And Implications:
The AutoMal demonstrates excellent ability to differentiate malnutrition status. It makes automated identification of malnutrition possible by using 2 variables commonly found in electronic health records.
