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The Importance of Interpolation in Computerized Growth Charting
James R Kiger1, Sarah N Taylor2
1Department of Pediatrics, Medical University of South Carolina, 165 Ashley Ave, MSC 917, Charleston, SC, 29425, USA. kiger@musc.edu.
Journal of Medical Systems
|November 18, 2015
Summary
Rounding to the nearest data point in computer growth charts can cause significant errors. Using an interpolation algorithm for LMS (least mean squares) data improves accuracy in calculating growth centiles and z-scores.
Area of Science:
- Pediatric growth assessment
- Biostatistics in healthcare
Background:
- Computerized growth charts are widely used in clinical and research settings.
- The LMS (least mean squares) method is standard for defining growth curves on these charts.
- Current methods often round LMS data points, potentially introducing errors.
Purpose of the Study:
- To evaluate if an interpolation algorithm can reduce errors in growth centile calculations compared to rounding.
- To quantify the accuracy improvements offered by interpolation for commonly used growth charts.
Main Methods:
- Developed a simple interpolation algorithm for LMS data.
- Compared growth centile predictions using interpolation versus standard rounding on published growth charts.
- Utilized a test case of a 50th percentile weight patient to assess z-score errors.
Main Results:
- Rounding led to maximal z-score errors of 2.02 (WHO), 1.07 (Fenton), 0.71 (Olsen), and 0.11 (CDC) standard deviations.
- Interpolation significantly reduced these errors across different growth charts.
- The magnitude of error varied considerably depending on the specific growth chart used.
Conclusions:
- Failure to implement interpolation algorithms in computerized growth charting can result in substantial inaccuracies.
- Interpolation is a crucial method for precise growth centile and z-score calculations.
- Accurate growth charting is essential for reliable clinical and research interpretations.
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