Background The early recognition of paroxysmal atrial fibrillation (pAF) is a major clinical challenge for preventing thromboembolic events. In this prospective and multicentric study we evaluated prediction scores for the presence of pAF, calculated from non-invasive medical history and echocardiographic parameters, in patients with unknown AF status. Methods The 12-parameter score with parameters age, LA diameter, aortic root diameter, LV,ESD, TDI Aʹ, heart frequency, sleep apnea, hyperlipidemia, type II diabetes, smoker, ß-blocker, catheter ablation, and the 4-parameter score with parameters age, LA diameter, aortic root diameter and TDI A’ were tested. Presence of pAF was verified by continuous electrocardiogram (ECG) monitoring for up to 21 days in 305 patients. Results The 12-parameter score correctly predicted pAF in all 34 patients, in which pAF was newly detected by ECG monitoring. The 12- and 4-parameter scores showed sensitivities of 100% and 82% (95%-CI 65%, 93%), specificities of 75% (95%-CI 70%, 80%) and 67% (95%-CI 61%, 73%), and areas under the receiver operating characteristic (ROC) curves of 0.84 (95%-CI 0.80, 0.88) and 0.81 (95%-CI 0.74, 0.87). Furthermore, properties of AF episodes and durations of ECG monitoring necessary to detect pAF were analysed. Conclusions The prediction scores adequately detected pAF using variables readily available during routine cardiac assessment and echocardiography. The model scores, denoted as ECHO-AF scores, represent simple, highly sensitive and non-invasive tools for detecting pAF that can be easily implemented in the clinical practice and might serve as screening test to initiate further diagnostic investigations for validating the presence of pAF. Graphic abstract Prospective validation of a novel prediction model for paroxysmal atrial fibrillation based on echocardiography and medical history parameters by long-term Holter ECG
The HiGHmed consortium aims to create a shared information governance framework to integrate clinical routine data. One challenge is the replacement of unstructured reporting (e.g. doctoral letters) with structured reporting in clinical routine. The Heidelberg cardiology department evaluates dynamic PDF forms for structured data reporting of heart failure (HF) patients. In this use case, we aim to identify potential caveats or shortcomings in data processing at an early stage. We employed data mining strategies to detect patterns related to incomplete or false data, which we found to be present among all data types. We then discuss the characteristics of the baseline patient cohort in Heidelberg to find out about specific peculiarities and potential biases, which may be site-specific. Briefly, our patient population is predominantly male (67%), NYHA I & II are the most common severity classes, NYHA IV is missing entirely. Most patients have a dilated cardiomyopathy (DCM) or coronary heart disease (CHD) diagnosed as their cause of HF. Finally, we also analyzed how comorbidities and risk factors relate to specific disease entities of heart failure patients. Family anamnesis was more frequent among cardiomyopathy patients than among CHD patients, who show a more dominating presence of dyslipidemia instead. Generally, the most dominant risk factor was arterial hypertension, while at the other end of the scale alcoholism appears to be underreported.
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