Wednesday, February 5, 2025
2/5/2025
Project Summary/Abstract Newborn screening (NBS) is a successful public health initiative; newborns in every state are screened for multiple genetic disorders. There are over 30 genetic disorders recommended for NBS, many states screen for fewer due to resource restrictions. Cystic fibrosis (CF), an autosomal recessive disorder with hundreds of known CF-causing mutations, is one of the more prevalent disorders in NBS, and is included in every state’s NBS initiative. The gold standard Sweat Chloride (SC) diagnostic test for CF is expensive and impractical as a screening test. Therefore, all states use multi-tiered screening algorithms (i.e., processes) for CF NBS, consisting of relatively inexpensive screening tests that are used on dried blood spots routinely obtained from the newborns. Most CF screening algorithms start with the low-cost, low-efficacy immunoreactive trypsinogen (IRT) test, followed by a genetic test, where the latter searches for a subset of known CF-causing mutations, and end with an SC test for confirmation. However, CF NBS algorithms, namely the combination of specific tests and the decision rules used (e.g., IRT thresholds, number of testing tiers, mutations to search for, when to send a newborn for an SC test), vary widely among the states, leading to quite different rates of false screen negatives and false screen positives, and testing costs. The primary goal of this proposal is to develop a holistic framework for designing optimal NBS algorithms that are accurate and equitable, using CF as a model disorder. This framework will utilize prescriptive analytics, including optimization, risk prediction, and data analytics, comprised of novel, data-driven models and methodologies, and will consider novel testing methods, such as pooled genetic testing. To accomplish our research goals, and validate and demonstrate our results, we will: (1) develop models to optimize current screening algorithms; (2) develop novel screening algorithms for CF utilizing pooled and multiplex genetic testing combining disorders; and (3) validate the modeling framework, and demonstrate its effectiveness by designing an optimal CF NBS algorithm based on data from the state of New York and validating the results via predictive analytics (via sophisticated simulations and sensitivity analyses), and descriptive analytics. The models and ideas developed in this research can be extended to other disorders. This research is in collaboration between The University of Alabama and New York State Department of Health, Wadsworth Center, and has the potential to change screening practices nationwide.
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