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With growing numbers of patients with congenital heart disease (CHD) surviving to adulthood, prepregnancy counselling is increasingly required for women with this heterogeneous condition.1 This represents a major challenge for clinicians, especially in female patients with medium or high-complexity CHD.
While individual risk assessment should be advocated based on the patient's underlying condition, previous operations/interventions, history of complications and the outcome of previous pregnancies, it is desirable to have standardised tools for estimating the risk of serious complications during pregnancy. These tools have the advantage of providing a quantitative (numeric) risk estimate, thus potentially offering a better, more accurate basis for prepregnancy counselling to allow the woman an informed decision on whether or not she wants to embark on pregnancy.
Additionally, such prediction models may eliminate some of the subjective elements of risk assessment, thus making recommendations between clinicians more reproducible. These potential advantages are achieved at the cost of some informational loss (ie, generalisation), ignoring specific aspects of the patient's condition that are not implemented in the risk score employed. While the desire to quantify matters is understandable and is, indeed, deep-seated in our scientific culture as exemplified by Galileo Galilei's demand to ‘[m]easure what is measurable, and make measurable what is not so’, one should not underestimate the problems introduced by risk prediction models in the setting of CHD. This is compounded by the fact that humans (including experts) tend to overestimate the accuracy of their predictions,2 providing a false sense of correctness to themselves and patients. Therefore, such scoring tools do not obviate the need for expert assessment, a thorough understanding of the …
Contributors both authors contributed equally to the manuscript, wrote and critically reviewed the final product.
Competing interests None.
Provenance and peer review Commissioned; internally peer reviewed.
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