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Machine Learning-Based Prediction of Atrial Fibrillation Risk Using Electronic Medical Records in Older Aged Patients
Atrial fibrillation (AF) is an independent risk factor that increases the risk of stroke 5-fold. The purpose of our study was to develop a 1-year new-onset AF predictive model by machine learning based on 3-year medical information without electrocardiograms in our database to identify AF risk in older aged patients. We developed the predictive model according to the Taipei Medical University clinical research database electronic medical records, including diagnostic codes, medications, and laboratory data.
Source: The American Journal of Cardiology - May 18, 2023 Category: Cardiology Authors: Yung-Ta Kao, Chun-Yao Huang, Yu-Ann Fang, Ju-Chi Liu, Tzu-Hao Chang Source Type: research

Relation of Hemoglobin A1C Levels to Risk of Ischemic Stroke and Mortality in Patients With Diabetes Mellitus and Atrial Fibrillation
This study aimed to assess the incidence and risks of ischemic stroke and mortality according to baseline HbA1c levels in patients with DM and AF. We conducted a cohort study using Clalit Health Services electronic medical records. The study population included all Clalit Health Services members aged ≥25 years, with the first diagnosis of AF between January 1, 2010, and December 31, 2016, who had a diagnosis of DM.
Source: The American Journal of Cardiology - March 29, 2022 Category: Cardiology Authors: Louise Kezerle, Moti Haim, Amichay Akriv, Adi Berliner Senderey, Asaf Bachrach, Maya Leventer-Roberts, Meytal Avgil Tsadok Source Type: research

Comparison of Stroke Risk Stratification Scores for Atrial Fibrillation
Several stroke risk stratification scores have been developed to guide clinical decision making in patients with non valvular atrial fibrillation (AF). The aim of this study was to compare the performance of the CHADS2, CHA2DS2-VASc and R2CHADS2 risk scores to predict stroke.This retrospective cohort study was based on electronic medical records from Clalit Health Services (CHS), the largest payer provider healthcare organization in Israel. Data of CHS members with AF diagnosis between 2004- 2015 were extracted.
Source: The American Journal of Cardiology - March 12, 2019 Category: Cardiology Authors: Meytal Avgil Tsadok, Adi Berliner Senderey, Orna Reges, Morton Leibowitz, Maya Leventer-Roberts, Moshe Hoshen, Moti Haim Source Type: research

A Simple and Portable Algorithm for Identifying Atrial Fibrillation in the Electronic Medical Record
Atrial fibrillation (AF) is common and increases stroke risk and mortality. Many knowledge gaps remain with respect to practice patterns and outcomes. Electronic medical records (EMR) may serve as powerful research tools if AF status can be properly ascertained. We sought to develop an algorithm for identifying individuals with and without AF in the EMR and compare it to previous methods. Using a hospital network EMR (n=5,737,846), we randomly selected 8,200 individuals seen at a large academic medical center in January 2014 to derive and validate seven AF classification schemas (4 case and 3 control) in order to construct...
Source: The American Journal of Cardiology - November 5, 2015 Category: Cardiology Authors: Shaan Khurshid, John Keaney, Patrick T. Ellinor, Steven A. Lubitz Source Type: research