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Prediction of permanent pacemaker implantation after transcatheter aortic valve replacement:The role of machine learning

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World Journal of Cardiology 2023年第3期15卷 95-105页

作者： Pradyumna Agasthi Hasan Ashraf Sai Harika Pujari Marlene Girardo Andrew Tseng Farouk Mookadam Nithin Venepally Matthew R Buras Bishoy Abraham banveet k khetarpal Mohamed Allam Siva k Mulpuru MD Mackram F Eleid kevin L Greason Nirat Beohar John Sweeney David Fortuin David R Jr Holmes Reza Arsanjani Department of Cardiology Mayo ClinicPhoenixAZ 85054United States Department of Internal Medicine The Brooklyn Hospital CenterBrooklynNY 11201United States Department of Biostatistics Mayo ClinicPhoenixAZ 85054United States Department of Cardiovascular Diseases Mayo ClinicRochesterMN 55905United States Department of Statistics Mayo ClinicPhoenixAZ 85054United States Department of Cardiovascular Surgery Mayo ClinicRochesterMN 55905United States Mount Sinai Medical Center Columbia UniversityMiami BeachFL 33138United States

BACkGROUND Atrioventricular block requiring permanent pacemaker(PPM)implantation is an important complication of transcatheter aortic valve replacement(TAVR).Application of machine learning could potentially be used to predict preprocedural risk for *** To apply machine learning to be used to predict pre-procedural risk for *** A retrospective study of 1200 patients who underwent TAVR(January 2014-December 2017)was performed.964 patients without prior PPM were included for a 30-d analysis and 657 patients without PPM requirement through 30 d were included for a 1-year *** the exclusion of variables with near-zero variance or≥50%missing data,167 variables were included in the random forest gradient boosting algorithm(GBM)optimized using 5-fold cross-validations repeated 10 *** receiver operator curve(ROC)for the GBM model and PPM risk score models were calculated to predict the risk of PPM at 30 d and 1 *** Of 964 patients included in the 30-d analysis without prior PPM,19.6%required PPM *** mean age of patients was 80.9±8.7 years.42.1%were *** 657 patients included in the 1-year analysis,the mean age of the patients was 80.7±*** those,42.6%of patients were female and 26.7%required PPM at 1-year *** area under ROC to predict 30-d and 1-year risk of PPM for the GBM model(0.66 and 0.72)was superior to that of the PPM risk score(0.55 and 0.54)with a P valuek of PPM post-TAVR.

关键词： Transcatheter aortic valve replacement Permanent pacemaker implantation Machine learning

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Apert’s syndrome:Study by whole exome sequencing

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Genes & Diseases 2018年第2期5卷 119-122页

作者： Anjana Munshi Preeti khetarpal Satrupa Das Venkateshwar Rao Monica Valecha Manita Bansal Roshan kumar Centre for Human Genetics and Molecular Medicine School of Health SciencesCentral University of PunjabBathindaPunjabIndia Institute of Genetics and Hospital for Genetic Diseases Osmania UniversityBegumpetHyderabadIndia Dr.NTR University of Health Sciences VijayawadaAndhra PradeshIndia Positive Bioscience Ltd. MumbaiIndia Dept.of Paediatrics Civil HospitalBathindaPunjabIndia

In the present study we attempted a parente-child trio,whole exome sequencing(WES)approach to study Apert’s *** characteristics of the child were noted down and WES was carried out using Ion Torrent System that revealed the presence of previously reported P253R mutation in FGFR2 *** of two SNPs rs1047057 and rs554851880 in FGFR2 gene with an allelic frequency of 0.5113 and 0.001176 respectively and 161 complete damaging mutations were *** study is the first reported case of exome sequencing approach on an Apert’s syndrome patient aimed at providing better genetic counselling in a non-consanguineous relationship.

关键词： Apert syndrome Craniosynostosis Exome sequencing FGFR2 gene Parente-child trio study

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