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Several cardiologists investigate the prognostic value of novel measurements obtained with cardiovascular imaging. These measurements are most often related to remodelling or cardiac function. For this purpose, they need to develop an extensive database. In addition, they have to provide a precise and reliable long-term follow-up. It is mandatory to have big numbers to reliably enter multiple variables in Cox proportional-hazards regression models or logistic regressions. This turns out to be a considerable venture. It takes many years before prognostic results become available. The size of the database and the quality of the observations both matter, to provide prognostic information, which is additive to what we have known. We fortunately have several such ventures on both sides of the Atlantic. For showing that a new measurement is useful for improving the prognostic prediction of a subject or cardiac patient, the investigator most often adds this measurement to clinical parameters, laboratory measurements, and baseline echocardiographic parameters (Table 1). The investigator has to demonstrate that he is capable of improving the explained variance, increasing the area under the curve (AUC), and net classification improvement (NRI) for nested models.

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