Sarah W.E. Baalman

A novel tool to evaluate the implant position and predict defibrillation success of the subcutaneous implantable cardioverter-defibrillator: The PRAETORIAN score

Anne‐Floor B.E. Quast, Sarah W.E. Baalman, Tom F. Brouwer et al. · Heart Rhythm · 2018 · 139 citations

Electrophysiological Evaluation, Predict Defibrillation Success, Cardiovascular Disease +8

Rationale and design of the PRAETORIAN-DFT trial: A prospective randomized CompArative trial of SubcutanEous ImplanTable CardiOverter-DefibrillatoR ImplANtation with and without DeFibrillation testing

Anne‐Floor B.E. Quast, Sarah W.E. Baalman, Timothy R. Betts et al. · American Heart Journal · 2019 · 58 citations

Heart Failure, Praetorian-dft Trial, Device Therapy +7

Computer versus cardiologist: Is a machine learning algorithm able to outperform an expert in diagnosing a phospholamban p.Arg14del mutation on the electrocardiogram?

Hidde Bleijendaal, Lucas A. Ramos, Ricardo R. Lopes et al. · Heart Rhythm · 2020 · 55 citations · Full text

Extracellular matrix remodeling precedes atrial fibrillation: Results of the PREDICT-AF trial

Nicoline W.E. van den Berg, Jolien Neefs, Makiri Kawasaki et al. · Heart Rhythm · 2021 · 49 citations · Full text

A morphology based deep learning model for atrial fibrillation detection using single cycle electrocardiographic samples

Sarah W.E. Baalman, Florian E. Schroevers, Abel J. Oakley et al. · International Journal of Cardiology · 2020 · 37 citations · Full text