Machine Learning Model in Predicting Sarcopenia in Crohn’s Disease Based on Simple Clinical and Anthropometric Measures
2022
Yujen Tseng | Shaocong Mo | Yanwei Zeng | Wanwei Zheng | Huan Song | Bing Zhong | Feifei Luo | Lan Rong | Jie Liu | Zhongguang Luo
Sarcopenia is associated with increased morbidity and mortality in Crohn&rsquo:s disease. The present study is aimed at investigating the different diagnostic performance of different machine learning models in identifying sarcopenia in Crohn&rsquo:s disease. Patients diagnosed with Crohn&rsquo:s disease at our center provided clinical, anthropometric, and radiological data. The cross-sectional CT slice at L3 was used for segmentation and the calculation of body composition. The prevalence of sarcopenia was calculated, and the clinical parameters were compared. A total of 167 patients were included in the present study, of which 127 (76.0%) were male and 40 (24.0%) were female, with an average age of 36.1 ±: 14.3 years old. Based on the previously defined cut-off value of sarcopenia, 118 (70.7%) patients had sarcopenia. Seven machine learning models were trained with the randomly allocated training cohort (80%) then evaluated on the validation cohort (20%). A comprehensive comparison showed that LightGBM was the most ideal diagnostic model, with an AUC of 0.933, AUCPR of 0.970, sensitivity of 72.7%, and specificity of 87.0%. The LightGBM model may facilitate a population management strategy with early identification of sarcopenia in Crohn&rsquo:s disease, while providing guidance for nutritional support and an alternative surveillance modality for long-term patient follow-up.
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