Cardiac myxomas (CM) are the most common type of primary cardiac tumours in adults, which have an approximate incidence of up to 0.2% in some autopsy series. The purpose of this review is to summarise the literature on CM, including clinical presentation, differential diagnosis, work-up including imaging modalities and histopathology, management, and prognosis. CM are benign neoplasms developed from multipotent mesenchyme and usually present as an undifferentiated atrial mass. They are typically pedunculated and attached at the fossa ovalis, on the left side of the atrial septum. Potentially life-threatening, the presence of CM calls for prompt diagnosis and surgical resection. Infrequently asymptomatic, patients with CM exhibit various manifestations, ranging from influenza-like symptoms, heart failure and stroke, to sudden death. Although non-specific, a classic triad for CM involves constitutional, embolic, and obstructive or cardiac symptoms. CM may be purposefully characterised or incidentally diagnosed on an echocardiogram, CT scan or cardiac MRI, all of which can help to differentiate CM from other differentials. Echocardiogram is the first-line imaging technique; however, it is fallible, potentially resulting in uncommonly situated CM being overlooked. The diagnosis of CM can often be established based on clinical, imaging and histopathology features. Definitive diagnosis requires macroscopic and histopathological assessment, including positivity for endothelial cell markers such as CD31 and CD34. Their prognosis is excellent when treated with prompt surgical resection, with postsurgical survival rates analogous to overall survival in the age-matched general population.

The translation of promising preclinical research into successful trials often fails. One contributing factor is the “Princess and the Pea” problem, which refers to how an initially significant effect size dissipates as research transitions to more complex systems. This work aimed to quantify the effects of spreading variability on sample size requirements. Sample size estimates were performed by Monte Carlo simulation. To simulate the process of progressing from preclinical to clinical studies, nested sigmoidal dose–response transformations with modifiable input parameter variability were used. The results demonstrated that adding variabilty to the dose–response parameters substantially increases sample size requirements compared to standared calculations. Increasing the number of consecutive studies further increases the sample size. These results quantitatively demonstrate how the spread of variability in translational research, which is not typically accounted for, can result in drastic increases in the sample size required to maintain a desired study power.