1.Strategies and Recommendations for the Development of Clinical Machine Learning Predictive Models
Zhengyao HOU ; Jinqi LI ; Yong YANG ; Mengting LI ; Hao SHEN ; Huan CHANG ; Xinyu LIU ; Bo DENG ; Guangjie GAO ; Yalin WEN ; Shiyue LIANG ; Yanqiu YU ; Shundong LEI ; Xingwei WU
Herald of Medicine 2024;43(12):2048-2056
Objective To propose strategies for developing clinical predictive models,aiming to assist researchers in conducting standardized clinical prediction model studies.Methods Literature review was conducted to summarize the operational steps and content for developing clinical predictive models.Then,a methodological framework was summarized and refined through expert consultation.Results The 11-step methodological framework for developing clinical predictive models was obtained by synthesizing the experience of 456 clinical predictive modeling studies and expert consultation,and the details were analyzed and elaborated.Conclusions This study presents methodological strategies and recommendations for the development of clinical predictive models,intended to serve as a guide for researchers.
2.Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients (version 2024)
Yao LU ; Yang LI ; Leiying ZHANG ; Hao TANG ; Huidan JING ; Yaoli WANG ; Xiangzhi JIA ; Li BA ; Maohong BIAN ; Dan CAI ; Hui CAI ; Xiaohong CAI ; Zhanshan ZHA ; Bingyu CHEN ; Daqing CHEN ; Feng CHEN ; Guoan CHEN ; Haiming CHEN ; Jing CHEN ; Min CHEN ; Qing CHEN ; Shu CHEN ; Xi CHEN ; Jinfeng CHENG ; Xiaoling CHU ; Hongwang CUI ; Xin CUI ; Zhen DA ; Ying DAI ; Surong DENG ; Weiqun DONG ; Weimin FAN ; Ke FENG ; Danhui FU ; Yongshui FU ; Qi FU ; Xuemei FU ; Jia GAN ; Xinyu GAN ; Wei GAO ; Huaizheng GONG ; Rong GUI ; Geng GUO ; Ning HAN ; Yiwen HAO ; Wubing HE ; Qiang HONG ; Ruiqin HOU ; Wei HOU ; Jie HU ; Peiyang HU ; Xi HU ; Xiaoyu HU ; Guangbin HUANG ; Jie HUANG ; Xiangyan HUANG ; Yuanshuai HUANG ; Shouyong HUN ; Xuebing JIANG ; Ping JIN ; Dong LAI ; Aiping LE ; Hongmei LI ; Bijuan LI ; Cuiying LI ; Daihong LI ; Haihong LI ; He LI ; Hui LI ; Jianping LI ; Ning LI ; Xiying LI ; Xiangmin LI ; Xiaofei LI ; Xiaojuan LI ; Zhiqiang LI ; Zhongjun LI ; Zunyan LI ; Huaqin LIANG ; Xiaohua LIANG ; Dongfa LIAO ; Qun LIAO ; Yan LIAO ; Jiajin LIN ; Chunxia LIU ; Fenghua LIU ; Peixian LIU ; Tiemei LIU ; Xiaoxin LIU ; Zhiwei LIU ; Zhongdi LIU ; Hua LU ; Jianfeng LUAN ; Jianjun LUO ; Qun LUO ; Dingfeng LYU ; Qi LYU ; Xianping LYU ; Aijun MA ; Liqiang MA ; Shuxuan MA ; Xainjun MA ; Xiaogang MA ; Xiaoli MA ; Guoqing MAO ; Shijie MU ; Shaolin NIE ; Shujuan OUYANG ; Xilin OUYANG ; Chunqiu PAN ; Jian PAN ; Xiaohua PAN ; Lei PENG ; Tao PENG ; Baohua QIAN ; Shu QIAO ; Li QIN ; Ying REN ; Zhaoqi REN ; Ruiming RONG ; Changshan SU ; Mingwei SUN ; Wenwu SUN ; Zhenwei SUN ; Haiping TANG ; Xiaofeng TANG ; Changjiu TANG ; Cuihua TAO ; Zhibin TIAN ; Juan WANG ; Baoyan WANG ; Chunyan WANG ; Gefei WANG ; Haiyan WANG ; Hongjie WANG ; Peng WANG ; Pengli WANG ; Qiushi WANG ; Xiaoning WANG ; Xinhua WANG ; Xuefeng WANG ; Yong WANG ; Yongjun WANG ; Yuanjie WANG ; Zhihua WANG ; Shaojun WEI ; Yaming WEI ; Jianbo WEN ; Jun WEN ; Jiang WU ; Jufeng WU ; Aijun XIA ; Fei XIA ; Rong XIA ; Jue XIE ; Yanchao XING ; Yan XIONG ; Feng XU ; Yongzhu XU ; Yongan XU ; Yonghe YAN ; Beizhan YAN ; Jiang YANG ; Jiangcun YANG ; Jun YANG ; Xinwen YANG ; Yongyi YANG ; Chunyan YAO ; Mingliang YE ; Changlin YIN ; Ming YIN ; Wen YIN ; Lianling YU ; Shuhong YU ; Zebo YU ; Yigang YU ; Anyong YU ; Hong YUAN ; Yi YUAN ; Chan ZHANG ; Jinjun ZHANG ; Jun ZHANG ; Kai ZHANG ; Leibing ZHANG ; Quan ZHANG ; Rongjiang ZHANG ; Sanming ZHANG ; Shengji ZHANG ; Shuo ZHANG ; Wei ZHANG ; Weidong ZHANG ; Xi ZHANG ; Xingwen ZHANG ; Guixi ZHANG ; Xiaojun ZHANG ; Guoqing ZHAO ; Jianpeng ZHAO ; Shuming ZHAO ; Beibei ZHENG ; Shangen ZHENG ; Huayou ZHOU ; Jicheng ZHOU ; Lihong ZHOU ; Mou ZHOU ; Xiaoyu ZHOU ; Xuelian ZHOU ; Yuan ZHOU ; Zheng ZHOU ; Zuhuang ZHOU ; Haiyan ZHU ; Peiyuan ZHU ; Changju ZHU ; Lili ZHU ; Zhengguo WANG ; Jianxin JIANG ; Deqing WANG ; Jiongcai LAN ; Quanli WANG ; Yang YU ; Lianyang ZHANG ; Aiqing WEN
Chinese Journal of Trauma 2024;40(10):865-881
Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.
3.Development and validation of a CT-based radiomics model for differentiating pneumonia-like primary pulmonary lymphoma from infectious pneumonia: A multicenter study.
Xinxin YU ; Bing KANG ; Pei NIE ; Yan DENG ; Zixin LIU ; Ning MAO ; Yahui AN ; Jingxu XU ; Chencui HUANG ; Yong HUANG ; Yonggao ZHANG ; Yang HOU ; Longjiang ZHANG ; Zhanguo SUN ; Baosen ZHU ; Rongchao SHI ; Shuai ZHANG ; Cong SUN ; Ximing WANG
Chinese Medical Journal 2023;136(10):1188-1197
BACKGROUND:
Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia.
METHODS:
In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared.
RESULTS:
A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94-0.99), 0.93 (95% CI: 0.85-0.98), and 0.94 (95% CI: 0.87-0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63-0.83), 0.72 (95% CI: 0.62-0.82), and 0.73 (95% CI: 0.62-0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model ( P <0.05).
CONCLUSIONS
The CT-based radiomics model may provide an effective and non-invasive tool to differentiate pneumonia-like PPL from infectious pneumonia, which might provide assistance for clinicians in tailoring precise therapy.
Humans
;
Retrospective Studies
;
Pneumonia/diagnostic imaging*
;
Analysis of Variance
;
Tomography, X-Ray Computed
;
Lymphoma/diagnostic imaging*
4.Machine and deep learning-based clinical characteristics and laboratory markers for the prediction of sarcopenia.
He ZHANG ; Mengting YIN ; Qianhui LIU ; Fei DING ; Lisha HOU ; Yiping DENG ; Tao CUI ; Yixian HAN ; Weiguang PANG ; Wenbin YE ; Jirong YUE ; Yong HE
Chinese Medical Journal 2023;136(8):967-973
BACKGROUND:
Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts.
METHODS:
We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models.
RESULTS:
The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749).
CONCLUSIONS:
The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population.
TRIAL REGISTRATION
Chictr.org, ChiCTR 1800018895.
Humans
;
Aged
;
Sarcopenia/diagnosis*
;
Deep Learning
;
Aging
;
Algorithms
;
Biomarkers
5.Exploring the Effects and Mechanisms of Liver Failing to Control Dispersion Caused by Long-Term Negative Emotion Accumulation on Working Memory in Normal People Based on ERPs Technique
Linpei XU ; Lile ZHOU ; Yong LIU ; Junlin HOU ; Ziwei ZHAO ; Jinchai DENG ; Zhongpeng QIN ; Anqi GAO ; Gege WANG ; Xianghong ZHAN
World Science and Technology-Modernization of Traditional Chinese Medicine 2023;25(8):2668-2676
Objective Using event-related potentials(ERPs),to study the effect and mechanism of negative emotion accumulation hepatic insufficiency on working memory in normal people.Methods Fifty subjects in each of the emotionally stable group and emotionally unstable group were given two load tasks(0-back and 1-back)in the N-back paradigm,the reaction time and correct rate were recorded,and the ERPs components N200 and P300 were detected.The latency and amplitude of P300 were analyzed statistically.Results ①Compared with the emotionally stable group,the emotionally unstable group had a longer reaction time(P<0.05).②Compared with the emotionally stable group,the subjects in the emotionally unstable group had prolonged N200 latency,decreased P300 amplitude significantly(P<0.05),and P300 latency had a tendency to extend(P<0.1).Conclusion Long-term accumulation of negative emotions and liver failure in normal people have the performance of decreased working memory,which may be related to the reduction of attention resource allocation and the impairment of cognitive processing function.
6.Aleukemic mast cell leukemia: report of a case.
Min Ying DENG ; Qi SONG ; Yun Shan TAN ; Lei REN ; Rong Kui LUO ; Lin SUN ; Ying Yong HOU
Chinese Journal of Pathology 2023;52(6):627-629
7.Research progress on influencing factors of depressive symptoms in adolescents
Chinese Journal of School Health 2023;44(5):786-790
Abstract
In recent years, mental health problems such as anxiety and depression among adolescents in China have attracted attention from all walks of life. Given that adolescence is a transitional and critical period for individual development, mental health affect the developmental opportunities. Therefore, in the review, the effects of environment, psychosocial factors and behavioral patterns on depressive symptoms are analyzed by combining with the characteristics of physical and mental development among adolescents. It is found that early adolescence and even childhood should be the key period for the prevention and intervention of depression. In order to formulate effective interventions and prevention strategies, it is proposed that future research should combine real situation in China with active exploration of protective factors and early predictors of depression.
8. The protective effect of naringin on cardiac injury in diabetic rats via activating Maxi K
Wen-Jing XIAO ; Kai-Wen DENG ; Yong-He HU ; Jim HOU ; Ting-Ting WANG
Chinese Pharmacological Bulletin 2022;38(1):38-42
Aim To investigate the protective effect of naringin ( NA) on diabetic cardiomyopathy by activating the large conduction Ca2+ activated K4 channels (Maxi K ).Methods SD rats were fed with high-fat diet combined with intraperitoneal injection of strepto- zotocin (STZ) to establish a diabetic rat model.Then the rats were randomly divided into model group ( DCM) , naringin group ( NA) and naringin + Maxi K-specific inhibitor group ( NA + PAX) , with 8 rats in each group.Hats in treatment group received administration for 12 weeks and blood glucose was monitored regularly during experiments.The changes of cardiac function, morphology and fibrosis were detected after the treatment.The changes of cx and (3 subunits of Maxi K in heart were detected.Results Cardiac ultrasound results showed that NA could partially restore the cardiac function of rats.However, the cardiac protec tive function of NA was significantly reduced in diabetic rats after Maxi K was specifically blocked.Fibrosis analysis showed that the expression of collagen and fi- bronectin in rats could be decreased after NA treatment, which could be partially reversed by PAX.Western blot results showed that the expression of Maxi K a and p-subunit decreased in DCM group, but there was no significant change after NA treatment.Conclusions NA has a cardioprotective effect on diabetic rats by promoting the opening of the Maxi K channel on the membrane surface rather than increasing its expression.
10.Chinese consensus on surgical treatment of traumatic rib fractures (2021)
Lingwen KONG ; Guangbin HUANG ; Yunfeng YI ; Dingyuan DU ; Baoguo JIANG ; Jinmou GAO ; Lianyang ZHANG ; Jianxin JIANG ; Xiangjun BAI ; Tianbing WANG ; Xingji ZHAO ; Xingbo DANG ; Zhanfei LI ; Feng XU ; Zhongmin LIU ; Ruwen WANG ; Yingbin XIAO ; Qingchen WU ; Chun WU ; Liming CHENG ; Bin YU ; Shusen CUI ; Jinglan WU ; Gongliang DU ; Jin DENG ; Ping HU ; Jun YANG ; Xiaofeng YANG ; Jun ZENG ; Haidong WANG ; Jigang DAI ; Yong FU ; Lijun HOU ; Guiyou LIANG ; Yidan LIN ; Qunyou TAN ; Yan SHEN ; Peiyang HU ; Ning TAO ; Cheng WANG ; Dali WANG ; Xu WU ; Yongfu ZHONG ; Anyong YU ; Dongbo ZHU ; Renju XIAO ; Biao SHAO
Chinese Journal of Trauma 2021;37(10):865-875
Traumatic rib fractures are the most common injury in thoracic trauma. Previously,the patients with traumatic rib fractures were mostly treated non-surgically,of which 50%,especially those combined with flail chest presented chronic pain or chest wall deformities and over 30% had long-term disabilities,being unable to retain a full-time job. In the past two decades,thanks to the development of internal fixation material technology,the surgical treatment of rib fractures has achieved good outcomes. However,there are still some problems in clinical treatment,including inconsistency in surgical treatment and quality control in medical services. The current consensuses on the management of regional traumatic rib fractures published at home and abroad mainly focus on the guidance of the overall treatment decisions and plans,and relevant clinical guidelines abroad lacks progress in surgical treatment of rib fractures in recent years. Therefore,the Chinese Society of Traumatology affiliated to Chinese Medical Association and Chinese College of Trauma Surgeons affiliated to Chinese Medical Doctor Association,in conjunction with national multidisciplinary experts,formulate the Chinese Consensus for Surgical Treatment of Traumatic Rib Fractures(2021)following the principle of evidence-based medicine,scientific nature and practicality. This expert consensus puts forward some clear,applicable,and graded recommendations from aspects of preoperative imaging evaluation,surgical indications,timing of surgery,surgical methods,rib fracture sites for surgical fixation,internal fixation methods and material selections,treatment of combined injuries in rib fractures,in order to provide references for surgical treatment of traumatic rib fractures.


Result Analysis
Print
Save
E-mail