Prediction of sarcopenia based on longitudinal physical examination data

YUE Yibing; YU Ying; SHEN Lei; WANG Yan; WANG Yingying; LYU Weibo; LIU Chuang

doi:10.16462/j.cnki.zhjbkz.2023.11.009

Volume 27 Issue 11

Nov. 2023

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2023 > 27(11): 1296-1299

YUE Yibing, YU Ying, SHEN Lei, WANG Yan, WANG Yingying, LYU Weibo, LIU Chuang. Prediction of sarcopenia based on longitudinal physical examination data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(11): 1296-1299. doi: 10.16462/j.cnki.zhjbkz.2023.11.009

Citation:

YUE Yibing, YU Ying, SHEN Lei, WANG Yan, WANG Yingying, LYU Weibo, LIU Chuang. Prediction of sarcopenia based on longitudinal physical examination data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(11): 1296-1299. doi: 10.16462/j.cnki.zhjbkz.2023.11.009

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Prediction of sarcopenia based on longitudinal physical examination data

doi: 10.16462/j.cnki.zhjbkz.2023.11.009

YUE Yibing¹,
YU Ying²,
SHEN Lei³,
WANG Yan³,
WANG Yingying⁴,
LYU Weibo^{2
,
,},
LIU Chuang^{1
,
,}

1.
Alibaba Business School, Hangzhou Normal University, Hangzhou 311121, China
2.
School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
3.
Tangzhen Community Health Care Center, Pudong New Area Shanghai, Shanghai 201210, China
4.
School of Public Health, Anhui Medical University, Hefei 230032, China

Funds:

National Natural Science Foundation of China 61873080

More Information

Corresponding author: LYU Weibo, E-mail: Lyu_weibo@shutcm.edu.cn; LIU Chuang, E-mail: liuchuang@hznu.edu.cn
Received Date: 2021-10-26
Rev Recd Date: 2022-03-22

Available Online: 2023-11-20

Publish Date: 2023-11-10

Abstract

Abstract

Objective In the elderly, sarcopenia is a common disease and efficient identification of sarcopenia is very important to keep health. Methods Based on the longitudinal physical examination data, a total of 2 544 subjects in a hospital from Shanghai during 2013-2019 were included. Considering the difference across annual indices, various machine learning models were constructed to predict the risk of sarcopenia in the elderly, and the decision curve analysis was applied to provide reference for clinical decision makers. Results The prediction models results showed that the prediction accuracy based on the Light Gradient Boosting Machine (LightGBM) model was relatively high (AUC=0.913 4). Decision curve analysis indicated that the net profit of the LightGBM model became larger when threshold probability (threshold for judging sarcopenia) ranged from 0.01 to 0.42 and from 0.84 to 0.92. And the net profit of the Random Forest model was larger when threshold probability ranged from 0.42 to 0.50 and from 0.60 to 0.67, while in the case of logistic regression model, the range was located in 0.50-0.60 and 0.67-0.84. Conclusions The prediction model established based on longitudinal physical examination data and machine learning methods can effectively predict the future risk of sarcopenia in the elderly, and is of great value for the early diagnosis and intervention of sarcopenia.
- Sarcopenia,
- Machine learning,
- Longitudinal physical examination data,
- Disease prediction

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References(9)

References

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