Abstract
Original language | English |
---|---|
Pages (from-to) | 639-647 |
Number of pages | 9 |
Journal | Aging Clinical and Experimental Research |
Volume | 35 |
Issue number | 3 |
Early online date | 2023 |
DOIs | |
Publication status | Published - Mar 2023 |
Keywords
- Artificial intelligence
- Deep learning
- Elderly patients
- Machine learning
- Postoperative infections
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In: Aging Clinical and Experimental Research, Vol. 35, No. 3, 03.2023, p. 639-647.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Prediction of postoperative infection in elderly using deep learning-based analysis
T2 - an observational cohort study
AU - Li, Pinhao
AU - Wang, Yan
AU - Li, Hui
AU - Cheng, Baoli
AU - Wu, Shuijing
AU - Ye, Hui
AU - Ma, Daqing
AU - Fang, Xiangming
AU - the International Surgical Outcomes Study (ISOS) group in China
AU - Cao, Ying
AU - Gao, Hong
AU - Hu, Tingju
AU - Lv, Jie
AU - Yang, Jian
AU - Yang, Yang
AU - Zhong, Yi
AU - Zhou, Jing
AU - Zou, Xiaohua
AU - He, Miao
AU - Li, Xiaoying
AU - Luo, Dihuan
AU - Wang, Haiying
AU - Yu, Tian
AU - Chen, Liyong
AU - Wang, Lijun
AU - Cai, Yunfei
AU - Cao, Zhongming
AU - Li, Yanling
AU - Lian, Jiaxin
AU - Sun, Haiyun
AU - Wang, Sheng
AU - Wang, Zhipeng
AU - Wang, Kenru
AU - Zhu, Yi
AU - du, Xindan
AU - Fan, Hao
AU - Fu, Yunbin
AU - Huang, Lixia
AU - Huang, Yanming
AU - Hwan, Haifang
AU - Luo, Hong
AU - Qu, Pi-Sheng
AU - Tao, Fan
AU - Wang, Zhen
AU - Wang, Guoxiang
AU - Wang, Shun
AU - Zhang, Yan
AU - Zhang, Xiaolin
AU - Chen, Chao
AU - Wang, Weixing
AU - Chen, Sijia
N1 - Funding Information: Acknowledgment to Liqi Shu, M.D., Department of Neurology, the Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States, who had revised the manuscript for important intellectual content. Membership of the International Surgical Outcomes Study (ISOS) group in China: National Co-ordinator: Xiangming Fang. Local investigators: *local co-ordinator. Affiliated Hospital of Guiyang Medical College: Ying Cao, Hong Gao*, Tingju Hu, Jie Lv, Jian Yang, Yang Yang, Yi Zhong, Jing Zhou, Xiaohua Zou. Affiliated Hospital of Zunyi Medical College: Miao He, Xiaoying Li, Dihuan Luo, Haiying Wang*, Tian Yu*. Daping Hospital & Research Institute of Surgery of the Third Military Medical University: Liyong Chen*, Lijun Wang. Guangdong Provincial People’s Hospital: Yunfei Cai, Zhongming Cao, Yanling Li, Jiaxin Lian, Haiyun Sun, Sheng Wang*, Zhipeng Wang, Kenru Wang, Yi Zhu. Hangzhou Red-cross Hospital: Xindan Du, Hao Fan, Yunbin Fu, Lixia Huang, Yanming Huang, Haifang Hwan, Hong Luo, Pi-Sheng Qu, Fan Tao*, Zhen Wang, Guoxiang Wang*, Shun Wang, Yan Zhang, Xiaolin Zhang. Huzhou Central Hospital: Chao Chen, Weixing Wang*. Lihuili Hospital, School of Medicine, Ningbo University: Zhengyuan Liu*. Lishui People’s Hospital of Zhejiang Province: Lihua Fan*, Nanfang Hospital of Southern Medical University: Jing Tang*. Ningbo Number 1 Hospital of Zhejiang Province: Yijun Chen, Yongjie Chen, Yangyang Han, Changshun Huang*, Guojin Liang, Jing Shen, Jun Wang, Qiuhong Yang, Jungang Zhen, Haidong Zhou. Ningbo Number 2 Hospital of Zhejiang Province: Junping Chen*, Zhang Chen, Xiaoyu Li, Bo Meng, Haiwang Ye, Xiaoyan Zhang. Qianfoshan Hospital Affiliated to Shandong University: Yanbing Bi, Jianqiao Cao, Fengying Guo, Hong Lin, Yang Liu, Meng Lv, Pengcai Shi, Xiumei Song, Chuanyu Sun, Yongtao Sun, Yuelan Wang*, Shenhui Wang, Min Zhang. Renmin Hospital of Wuhan University: Rong Chen, Jiabao Hou, Yan Leng, Qing-tao Meng, Li Qian, Zi-ying Shen, Zhong-yuan Xia*, Rui Xue, Yuan Zhang, Bo Zhao, Xian-jin Zhou. Shanxi Provincial People’s Hospital: Qiang Chen, Huinan Guo, Yongqing Guo, Yuehong Qi*, Zhi Wang, Jianfeng Wei, Weiwei Zhang, Lina Zheng. Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University: Qi Bao, Yaqiu Chen, Yijiao Chen, Yue Fei, Nianqiang Hu, Xuming Hu, Min Lei, Xiaoqin Li, Xiaocui Lv, Jie Lv, Fangfang Miao, Lingling Ouyang, Lu Qian, Conyu Shen, Yu Sun, Yuting Wang, Dong Wang, Chao Wu, Liyuan Xu, Jiaqi Yuan, Lina Zhang, Huan Zhang, Yapping Zhang, Jinning Zhao, Chong Zhao, Lei Zhao, Tianzhao Zheng, Dachun Zhou*, Haiyan Zhou*, Ce Zhou. Southwest Hospital of Third Military Medical University: Kaizhi Lu*, Ting Zhao. Affiliated Hospital of Hangzhou Normal University: Changlin He*. First Affiliated Hospital, School of Medicine, Zhejiang University: Hong Chen, Shasha Chen, Baoli Cheng, Jie He, Lin Jin, Caixia Li, Hui Li, Yuanming Pan, Yugang Shi, Xiao Hong Wen*, Shuijing Wu*, Guohao Xie, Kai Zhang, Bing Zhao. First Affiliated Hospital of Anhui Medical University: Xianfu Lu*. First Affiliated Hospital of Bengbu Medical College: Feifei Chen, Qisheng Liang*, Xuewu Lin, Yunzhi Ling, Gang Liu, Jing Tao, Lu Yang, Jialong Zhou. First Affiliated Hospital of Nanchang University: Fumei Chen, Zhonggui Cheng, Hanying Dai, Yunlin Feng, Benchao Hou, Haixia Gong, Chun hua Hu, Haijin Huang, Jian Huang, Zhangjie Jiang, Mengyuan Li, Jiamei Lin, Mei Liu, Weicheng Liu, Zhen Liu, Zhiyi Liu, Foquan Luo*, Longxian Ma, Jia Min, Xiaoyun Shi, Zhiping Song, Xianwen Wan, Yingfen Xiong, Lin Xu, Shuangjia Yang, Qin Zhang, Hongyan Zhang, Huaigen Zhang, Xuekang Zhang, Lili Zhao, Weihong Zhao, Weilu Zhao, Xiaoping Zhu. First Affiliated Hospital of Wenzhou Medical University: Yun Bai, Linbi Chen, Sijia Chen, Qinxue Dai, Wujun Geng, Kunyuan Han, Xin He, Luping Huang, Binbin Ji, Danyun Jia, Shenhui Jin, Qianjun Li, Dongdong Liang, Shan Luo, Lulu Lwang, Yunchang Mo, Yuanyuan Pan, Xinyu Qi, Meizi Qian, Jinling Qin, Yelong Ren, Yiyi Shi, Junlu Wang*, Junkai Wang, Leilei Wang, Junjie Xie, Yixiu Yan, Yurui Yao, Mingxiao Zhang, Jiashi Zhao, Xiuxiu Zhuang. First Affiliated Hospital of Zhengzhou University: Yanqiu Ai*, Fang Du, Long He, Ledan Huang, Zhisong Li, Huijuan Li, Yetong Li, Liwei Li, Su Meng, Yazhuo Yuan, Enman Zhang, Jie Zhang, Shuna Zhao. First University Hospital of China Medical University: Zhenrong Ji, Ling Pei*, Li Wang. General Hospital of Tianjin Medical University: Chen Chen, Beibei Dong, Jing Li, Ziqiang Miao, Hongying Mu, Chao Qin, Lin Su, Zhiting Wen, Keliang Xie*, Yonghao Yu*, Fang Yuan. The Second Affiliated Hospital of Anhui Medical University: Xianwen Hu, Ye Zhang*. The Second Affiliated Hospital of Jiaxing College: Wangpin Xiao*, Zhipeng Zhu. The Second Affiliated Hospital of Nanchang University: Qingqing Dai, Kaiwen Fu, Rong Hu, Xiaolan Hu, Song Huang, Yaqi Li, Yingping Liang, Shuchun Yu*. The Second Affiliated Hospital of Shanxi Medical University: Zheng Guo*, Yan Jing, Na Tang, Jie Wu, Dajiang Yuan*, Ruilin Zhang, Xiaoying Zhao. Shaoxing Hospital of Zhejiang University: Yuhong Li*. Third Hospital of Hebei Medical University: Hui-Ping Bai, Chun-Xiao Liu, Fei-Fei Liu, Wei Ren, Xiu-Li Wang*, Guan-Jie Xu. Third Xiangya Hospital of Central South University: Na Hu, Bo Li, Yangwen Ou*, Yongzhong Tang. Union Hospital, Tongji Medical College, Huazhong University of Science and Technology: Shanglong Yao*, Shihai Zhang. Xuan Wu Hospital, Capital Medical University: Cui-Cui Kong, Bei Liu, Tianlong Wang*, Wei Xiao. Zhejiang Hospital: Bo Lu, Yanfei Xia*, Jiali Zhou. Zhejiang Provincial People’s Hospital: Fang Cai, Pushan Chen, Shuangfei Hu*, Hongfa Wang, Jie Wu, Qiong Xu. Zhongda Hospital, Southeast University: Liu Hu, Liang Jing, Jing Li, Bin Li, Qiang Liu, Yuejiang Liu, Xinjian Lu, Zhen Dan Peng, Xiaodong Qiu, Quan Ren, Youliang Tong, Zhen Wang, Jin Wang, Yazhou Wen, Qiong Wu, Jiangyan Xia, Jue Xie, Xiapei Xiong, Shixia Xu, Tianqin Yang, Hui Ye, Ning Yin*, Jing Yuan, Qiuting Zeng, Baoling Zhang, Kang Zheng. Zhongshan Hospital Fudan University: Jing Cang, Shiyu Chen, Fang Du, Yu Fan, Shuying Fu, Xiaodong Ge, Baolei Guo, Wenhui Huang, Linghui Jiang, Xinmei Jiang, Lin Jin, Yi Liu, Yan Pan, Yun Ren, Qi Shan, Jiaxing Wang, Fei Wang, Chi Wu, Xiaoguang Zhang*. Publisher Copyright: © 2023, The Author(s).
PY - 2023/3
Y1 - 2023/3
N2 - Elderly patients are susceptible to postoperative infections with increased mortality. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. 1510 patients were randomly assigned to be training dataset for establishing deep learning-based models, and 504 patients were used to validate the effectiveness of these models. The conventional model predicted postoperative infections was 0.728 (95% CI 0.688–0.768) with the sensitivity of 66.2% (95% CI 58.2–73.6) and specificity of 66.8% (95% CI 64.6–68.9). The deep learning model including risk factors relevant to baseline clinical characteristics predicted postoperative infections was 0.641 (95% CI 0.545–0.737), and sensitivity and specificity were 34.2% (95% CI 19.6–51.4) and 88.8% (95% CI 85.6–91.6), respectively. Including risk factors relevant to baseline variables and surgery, the deep learning model predicted postoperative infections was 0.763 (95% CI 0.681–0.844) with the sensitivity of 63.2% (95% CI 46–78.2) and specificity of 80.5% (95% CI 76.6–84). Our feasibility study indicated that a deep learning model including risk factors for the prediction of postoperative infections can be achieved in elderly. Further study is needed to assess whether this model can be used to guide clinical practice to improve surgical outcomes in elderly.
AB - Elderly patients are susceptible to postoperative infections with increased mortality. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. 1510 patients were randomly assigned to be training dataset for establishing deep learning-based models, and 504 patients were used to validate the effectiveness of these models. The conventional model predicted postoperative infections was 0.728 (95% CI 0.688–0.768) with the sensitivity of 66.2% (95% CI 58.2–73.6) and specificity of 66.8% (95% CI 64.6–68.9). The deep learning model including risk factors relevant to baseline clinical characteristics predicted postoperative infections was 0.641 (95% CI 0.545–0.737), and sensitivity and specificity were 34.2% (95% CI 19.6–51.4) and 88.8% (95% CI 85.6–91.6), respectively. Including risk factors relevant to baseline variables and surgery, the deep learning model predicted postoperative infections was 0.763 (95% CI 0.681–0.844) with the sensitivity of 63.2% (95% CI 46–78.2) and specificity of 80.5% (95% CI 76.6–84). Our feasibility study indicated that a deep learning model including risk factors for the prediction of postoperative infections can be achieved in elderly. Further study is needed to assess whether this model can be used to guide clinical practice to improve surgical outcomes in elderly.
KW - Artificial intelligence
KW - Deep learning
KW - Elderly patients
KW - Machine learning
KW - Postoperative infections
UR - http://www.scopus.com/inward/record.url?scp=85146038831&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/s40520-022-02325-3
DO - https://doi.org/10.1007/s40520-022-02325-3
M3 - Article
C2 - 36598653
SN - 1594-0667
VL - 35
SP - 639
EP - 647
JO - Aging Clinical and Experimental Research
JF - Aging Clinical and Experimental Research
IS - 3
ER -