1.Value of ST segment elevation of aVR lead in patients with acute ST segment elevation myocardial infarction
Guoyong PEI ; Wenzhi PAN ; Lei GE ; Feng ZHANG ; Juying QIAN ; Junbo GE
Chinese Journal of Emergency Medicine 2008;17(10):1085-1087
Objective To asses the value of ST segment elevation of aVR lead (aVRSTE) in patients with acute ST segment elevation myocardial infarction (STEMI). Method Myocardial enzymes detection, electrocar-dingraphy, emergency eornary artery angiography, echoeardiography [taken(10±2) days after emergency cornary artery angiography] were obtained and analyzed in 140 consecutive patients with STEMI enrolled in this study. The value of aVRSTE (≥0.05 mV) was assessed for detecting left main stem lesions(defined as ≥50% stenosis of or acute embolism of left main stem)or its equivalent (defined as total or subtotal acute occlusion of left anterior de-scending artery), and predicting the left ventricular systolic function after myocardial infarction. Results The sensitivity, specificity, positive predictive value and negative predictive value of aVRSIE in detection of left main stem lesions were 72.73 % (8/11), 83.72 % (108/129),27.59 % (8/29) and 97.30% (108/111), respectively; in detection of left main stem lesions or its equivalent, they were 41.86 % (18/43), 88.66% (86/97), 62.07 % (18/29), 77.48% (86/111); aVRSYE were combined with STaVR-STv1>0 to detect left main stem lesions, the semi-tivity, specificity, positive predictive value and negative predictive value were 63.64% (7/11),98.45%(127/129),77.78%(7/9),96.95% (127/131). Patients were divided into two groups: groups A with aVRSIE and group B without aVRSYE. KIIJJP class,and left ventricular ejection fraction (LVEF) in group A was higher than those in group B (P<0.05). Conclusions For patients with STEMI: (1) aVRSTE indicated left main stem le-sions or its equivalent; if combined with STaVR-STv1>0, it indicated left main stem lesions more strongly; (2)aVRSTE predicted poorer left ventricular systohc function short time after STEMI.
2.Establishment of an auxiliary diagnosis system of newborn screening for inherited metabolic diseases based on artificial intelligence technology and a clinical trial
Rulai YANG ; Yanling YANG ; Ting WANG ; Weize XU ; Gang YU ; Jianbin YANG ; Qiaoling SUN ; Maosheng GU ; Haibo LI ; Dehua ZHAO ; Juying PEI ; Tao JIANG ; Jun HE ; Hui ZOU ; Xinmei MAO ; Guoxing GENG ; Rong QIANG ; Guoli TIAN ; Yan WANG ; Hongwei WEI ; Xiaogang ZHANG ; Hua WANG ; Yaping TIAN ; Lin ZOU ; Yuanyuan KONG ; Yuxia ZHOU ; Mingcai OU ; Zerong YAO ; Yulin ZHOU ; Wenbin ZHU ; Yonglan HUANG ; Yuhong WANG ; Cidan HUANG ; Ying TAN ; Long LI ; Qing SHANG ; Hong ZHENG ; Shaolei LYU ; Wenjun WANG ; Yan YAO ; Jing LE ; Qiang SHU
Chinese Journal of Pediatrics 2021;59(4):286-293
Objective:To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology.Methods:This was a retrospectively study. Newborn screening data ( n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data ( n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns ' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results:A total of 3 665 697 newborns ' screening data were collected including 3 019 cases ' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment ( n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion:An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.