Addressing Uncertainty: The Emergence of the CRMS/CFSPID Diagnostic Category Following Newborn Screening for Cystic Fibrosis

Abstract

This article uses cystic fibrosis as a case study to examine how physicians and scientists have navigated uncertainty following newborn screening. Despite the many benefits of newborn screening, including earlier diagnosis, therapeutic intervention, and a reduced diagnostic odyssey, this public health approach also comes with challenges. For example, physicians began to document infants with indeterminate diagnoses - those with a positive screen who did not clearly fit into the cystic fibrosis or “normal” categories - by the early twenty first century. As a means of addressing this uncertainty and to facilitate long-term follow up of such infants, the U.S. Cystic Fibrosis Foundation recommended in 2009 that a new diagnostic term, CFTR-related metabolic syndrome (CRMS), be used. However, the CRMS label was not favored in Europe and a different term, cystic fibrosis screen positive, inconclusive diagnosis (CFSPID), was adopted in 2015 instead. Efforts to address the uncertainty associated with indeterminate diagnoses have been complicated, as stakeholders have held differing views about whether the screening algorithms should aim to maximize or minimize CRMS/CFSPID cases. Many who favor the identification of babies with CRMS/CFSPID note that they are at increased risk of developing symptoms and signs consistent with a cystic fibrosis diagnosis. In contrast, those who support algorithms that reduce CRMS/CFSPID cases point to iatrogenic harms associated with the medicalization of children who may remain healthy into adulthood. Furthermore, investigators have grappled with how best to ensure equity in newborn screening among different racialized groups while concomitantly attempting to minimize false positive results. These issues are applicable beyond the context of cystic fibrosis, especially as programs contemplate the incorporation or expanded use of next generation sequencing in algorithms for a wide range of diseases, and this history highlights how efforts to reduce uncertainty in one setting can lead to new and persistent sources of uncertainty in other areas.