Sarcopenia Risk Prediction Model Derived from Health Checkup Data

A retrospective study of 3,277 participants from a health checkup population, used to develop a predictive nomogram for sarcopenia. The model, created by Hailin Yang and last updated in April 2026, incorporates eight predictors from routine physical exams. It was validated using a 70/30 training/testing split and is available as a dynamic online tool.

Use Cases

• Developing a clinical risk score for sarcopenia based on predictors like calf circumference and BMI.
• Validating a predictive model using ROC and calibration curves as described in the study.
• Creating an accessible web-based risk assessment tool based on the published nomogram.
• Analyzing the relationship between routine lab values (e.g., bilirubin, cholesterol) and muscle health outcomes.

Strengths

• Model validation on a cohort of 3,277 participants, providing a substantial sample size.
• Strong discriminative performance with an AUC of 0.891 in the testing set.
• Includes a publicly accessible, dynamic online nomogram for practical application.
• Predictors are derived from standard, routinely collected health examination indicators.

Limitations

• Row count for the underlying dataset is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect geographic or demographic bias inherent to the specific health checkup population studied.

Provenance

Source

Hailin Yang

Collection Method

Retrospective analysis of data derived from standard physical examination indicators.

Time Range

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Freshness

Last updated 2026-04-28 04:24:55; freshness should be verified.

Geography

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