We present an evaluation of recent state-of-the-art electrocardiogram denoising methods and assess their impact on the performance of automatic diagnosis classifiers, with a focus on the risk prediction of torsade de pointes arrhythmia. Our findings indicate that the traditional approach of evaluating denoising methods independently of the application is insufficient. This is particularly the case for applications where the signals are used for phenotype prediction. We observed that when classifiers are fed denoised data instead of raw data, their performance significantly deteriorates, with a decline of up to 40 percentage points in accuracy and up to 27 percentage points in AUROC when a misclassification detection method is further applied, underscoring a notable reduction in model reliability. These findings highlight the importance of considering the downstream impact of denoising on automated classification tasks and it sheds light on the complexities of trustworthiness in the context of healthcare applications.