Multivariable machine learning models for clinical prediction of subsequent hip fractures in older people using the Chinese population database

Author:

Huang Wenbo1ORCID,Wang Jie23,Xu Jilai4,Guo Guinan5,Chen Zhenlei67,Xue Haolei4

Affiliation:

1. Department of Medicine, Beijing Municipal Welfare Medical Research Institute Ltd , Beijing 102400 , China

2. Department of data analytics , School of Information Studies (iSchool), , NY 13244 , USA

3. Syracuse University , School of Information Studies (iSchool), , NY 13244 , USA

4. Department of Rehabilitation Medicine, Graduate School of Medicine, Juntendo University , Bunkyo, Tokyo 113-8421 , Japan

5. Aerospace Information Research Institute, Chinese Academy of Sciences , Guangzhou, Guangdong 100864 , China

6. Department of Physical Education , School of Physical Education, , Wuhan, Hubei 430000 , China

7. Hubei University of Education , School of Physical Education, , Wuhan, Hubei 430000 , China

Abstract

Abstract Purpose This study aimed to develop and validate clinical prediction models using machine learning (ML) algorithms for reliable prediction of subsequent hip fractures in older individuals, who had previously sustained a first hip fracture, and facilitate early prevention and diagnosis, therefore effectively managing rapidly rising healthcare costs in China. Methods Data were obtained from Grade A Tertiary hospitals for older patients (age ≥ 60 years) diagnosed with hip fractures in southwest China between 1 January 2009 and 1 April 2020. The database was built by collecting clinical and administrative data from outpatients and inpatients nationwide. Data were randomly split into training (80%) and testing datasets (20%), followed by six ML-based prediction models using 19 variables for hip fracture patients within 2 years of the first fracture. Results A total of 40,237 patients with a median age of 66.0 years, who were admitted to acute-care hospitals for hip fractures, were randomly split into a training dataset (32,189 patients) and a testing dataset (8,048 patients). Our results indicated that three of our ML-based models delivered an excellent prediction of subsequent hip fracture outcomes (the area under the receiver operating characteristics curve: 0.92 (0.91–0.92), 0.92 (0·92–0·93), 0.92 (0·92–0·93)), outperforming previous prediction models based on claims and cohort data. Conclusions Our prediction models identify Chinese older people at high risk of subsequent hip fractures with specific baseline clinical and demographic variables such as length of hospital stay. These models might guide future targeted preventative treatments.

Publisher

Oxford University Press (OUP)

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