1.A clinical interpretation of ESC guidelines on diagnosis and treatment of heart failure 2008.
Chinese Journal of Practical Internal Medicine 2002;0(08):-
European Society of Cardiology guidelines on diagnosis and treatment of heart failure are among the mostly authorized international directing documents,applied widely in clinical practice,epidemiological researches and randomized clinical trials.The guidelines published in 2008 combined chronic and acute hear failure and made significant modifications in the definition,classification,diagnosis,pharmacological and non-pharmacological treatment of heart failure.Simplification of the recommendations and enhancement of the guidelines implication are also point of focusing.This article represents a brief review of the new guidelines,together with comparison with the previous edition and analysis of recommendations based on local clinical practice,to enhance the standardized management of heart failure in China. Abstract:Summ ary:European Soc iety of Card iology gu idelines on d iagnosis and treatm ent of heart failure are among the mostly au-thorized international d irecting docum ents,app lied w idely in c lin ical practice,ep idem iological researches and random ized c lin ical trials.The gu idelines pub lished in 2008 comb ined chron ic and acute hear failure and m ade sign ificant mod ifica-tions in the defin ition,c lassification,d iagnosis,pharm acological and non-pharm acological treatm ent of heart failure.S imp li-fication of the recomm endations and enhancem ent of the gu idelines imp lication are also point of focusing.Th is artic le re-presents a brief review of the new gu idelines,togetherw ith comparison w ith the previous ed ition and analysis of recomm en-dations based on local c lin ical practice,to enhance the standard ized m anagem ent of heart failure in Ch ina.
2.The national rare diseases registry system of China and the related cohorts studies:vision and roadmap
Shi FENG ; Mengchun GONG ; Shuyang ZHANG
Chinese Journal of Endocrinology and Metabolism 2016;32(12):977-982
[Summary] Rare diseases are one of the major challenges we face today in the era of precision medicine, because of the low incidence and prevalence, difficulty in diagnosis, lack of sufficient therapeutic methods, as well as their significant impacts on affected individuals, families and the society. Integration of clinical phenotypic and biological omics data and the further analysis are providing a way to illustrate the mechanisms of rare diseases, discovering novel diagnostic and prognostic biomarkers, developing orphan drugs and other therapeutics, and improving clinical outcomes and quality of life for the patients. A nation-wide registry system and the cohorts studies based on the registry are vital to the research of rare diseases. National Rare Diseases Registry System ( NRDRS) of China will provide this essential platform to promote the rare diseases research in China. With the collaboration of 20 leading medical institutes and innovation in medical informatics technologies, this system will, for the first time in China, collect the epidemiological, clinical, socio-economical, genomics and metabolomics data of more than 50 rare diseases and not less than 50 000 cases. As a national strategy for enhancing the development of medical sciences and the improvement of population health in China, NRDRS and its cohort studies will provide the pivotal support to policy making, clinical care, novel drug discovery, patient advocacy, and finally scientific progress in the field of rare diseases.
3.Impacts of different creatinine detection methods on the efficacy of different GFR estimation equations
Ling QIU ; Xiuzhi GUO ; Yan ZHU ; Weiling SHOU ; Mengchun GONG ; Lin ZHANG ; Huijuan HAN ; Guoqiang QUAN ; Tao XU ; Hang LI ; Xuewang LI
Chinese Journal of Laboratory Medicine 2011;34(12):1062-1068
ObjectiveTo investigate the impacts of different serum creatinine detection methods,including Jaffe and enzymatic methods,on the efficacy of different GFR estimation equations in CKD patients in China.MethodsrGFR of 176 patients with CKD were determined by dual plasma sample method 99mTc-diethylenetriamine pentaacetic acid (99mTc-DTPA) plasma clearance rate.Serum creatinine was detected with four kinds of creatinine reagents from different manufacturers.Cockcroft-Gault Equation corrected for body surface area (CG/BSA),simplified Modification of Diet in Renal Disease (MDRD) Study equation,IDMS-traceable MDRD equation,CKD epidemiology collaborative research (CKD-EPI) equation and two Chinese simplified MDRD equation (project group equation 1,2) were applied to calculate estimated GFR (eGFR)respectively.eGFRwerecomparedwithrGFRforthecorrelation, deviation, precisionand30% accuracy.ResultsThe mean rGFR of 176 patients with CKD,was [ 40.70 ( 19.41 -84.35 ) ] ml · min- 1 ·( 1.73 m2 ) -1.For all GFR estimation equations,there were significant differences in eGFR results between enzymatic method and Jaffe method,when analyzed by the Wilcoxon signed-rank test.eGFR results assessed by two enzymatic creatinine detection systems showed no significant difference,while eGFR results analyzed by two Jaffe detection system were significantly different.The intraclass correlation coefficient (ICC) of eGFR and rGFR ranged from 0.879 to 0.923 by Jaffe method,while from 0.925 to 0.946 by enzymatic creatinine method.ICC and Pearson correlation analysis revealed a significant correlation between eGFR and rGFR,and the correlation was better when using enzymatic method.Bland-Altman plots indicated that large deviation occurred in the high value area of GFR using various equations.However,deviation with the enzymatic creatinine method was smaller than that with the Jaffe method. When rGFR ≥ 60 ml · min- 1 ·(1.73 m2) -1,the 30% accuracy of eGFR using enzymatic creatinine method for all six equations was between 68.3% and 90.0%,while it was between 41% and 75% when using Jaffe method. The 30% accuracy of eGFR using enzymatic creatinine method was significantly higher than that using picric acid method for these equations except for the project group equation 1.When rGFR <60 ml · min -1 · ( 1.73 m2 ) -1,the 30%accuracy of eGFR using both methods was between 39.7% -49.1%,40.5% -52.6%respectively,and the difference of data showed no statistical significance.For the same equation,there was a significant differernce in 30% accuracy of eGFR between two enzymatic creatinine detection systems,while there was no significant differernce between two Jaffe creatinine detection systems.ConclusionsA significant difference was demonstrated in the same GFR evaluation equation using two different creatinine detection methods (Jaffe method and enzymatic method).The correlation between rGFR and eGFR,the degree of deviation,and accuracy of eGFR results assessed by enzymatic creatinine method were better than those by Jaffe method.The eGFR results assessed by different enzymatic detection systems revealed no significant difference.
4.Artificial Intelligence Supports Research Progress in the Diagnosis and Treatment of Rare Diseases
Mengchun GONG ; Yuanshi JIAO ; Wuren MA ; Peng LIU ; Ye JIN ; Jifa HU ; Ling NIU ; Wenzhao SHI ; Shuyang ZHANG
JOURNAL OF RARE DISEASES 2022;1(2):101-109
It is noteworthy that only 5% of more than 7000 described rare diseases are treated. In the era of big data, there is ever-increasing data for understanding biomedicine. The need for efficient and rapid data collection, analyses and characterization methods is pressing. Rare diseases can particularly benefit from artificial intelligence (AI) application. AI, with an emphasis on machine learning, creates a path for such efforts and is being applied to diagnosis and treatment. AI has demonstrated its potential to learn and analyze data from different sources with results in prediction。Presently, there are AI-driven technologies applied for rare diseases and this review aims to summarize these advances. Moreover, this review scrutinizes the limitation and identifies the pitfalls of AI applications in the diagnosis and treatment of rare diseases.
5.High-risk phenotypes of genetic disease in a Neonatal Intensive Care Unit population.
Tiantian XIAO ; Qi NI ; Huiyao CHEN ; Huijun WANG ; Lin YANG ; Bingbing WU ; Yun CAO ; Guoqiang CHENG ; Laishuan WANG ; Liyuan HU ; Hongfang MEI ; Yulan LU ; Mengchun GONG ; Xinran DONG ; Wenhao ZHOU
Chinese Medical Journal 2022;135(5):625-627