BACKGROUND: There has been great variation and uncertainty about how many and what CFTR mutations to include in cystic fibrosis (CF) newborn screening algorithms, and very little research on this topic using large populations of newborns. METHODS: We reviewed Wisconsin screening results for 1994-2008 to identify an ideal panel. RESULTS: Upon analyzing approximately 1 million screening results, we found it optimal to use a 23 CFTR mutation panel as a second tier when an immunoreactive trypsinogen (IRT)/DNA algorithm was applied for CF screening. This panel in association with a 96th percentile IRT cutoff gave a sensitivity of 97.3%, but restricting the DNA tier to F508del was associated with 90% (P<.0001). CONCLUSIONS: Although CFTR panel selection has been challenging, our data show that a 23 mutation method optimizes sensitivity and is advantageous. The IRT cutoff value, however, is actually more critical than DNA in determining CF newborn screening sensitivity.
BACKGROUND: There has been great variation and uncertainty about how many and what CFTR mutations to include in cystic fibrosis (CF) newborn screening algorithms, and very little research on this topic using large populations of newborns. METHODS: We reviewed Wisconsin screening results for 1994-2008 to identify an ideal panel. RESULTS: Upon analyzing approximately 1 million screening results, we found it optimal to use a 23 CFTR mutation panel as a second tier when an immunoreactive trypsinogen (IRT)/DNA algorithm was applied for CF screening. This panel in association with a 96th percentile IRT cutoff gave a sensitivity of 97.3%, but restricting the DNA tier to F508del was associated with 90% (P<.0001). CONCLUSIONS: Although CFTR panel selection has been challenging, our data show that a 23 mutation method optimizes sensitivity and is advantageous. The IRT cutoff value, however, is actually more critical than DNA in determining CF newborn screening sensitivity.
Authors: Andrew T Braun; Philip M Farrell; Claude Ferec; Marie Pierre Audrezet; Anita Laxova; Zhanhai Li; Michael R Kosorok; Marjorie A Rosenberg; William M Gershan Journal: J Cyst Fibros Date: 2005-11-04 Impact factor: 5.482
Authors: R G Gregg; A Simantel; P M Farrell; R Koscik; M R Kosorok; A Laxova; R Laessig; G Hoffman; D Hassemer; E H Mischler; M Splaingard Journal: Pediatrics Date: 1997-06 Impact factor: 7.124
Authors: Katelyn Parker-McGill; Melodee Nugent; Rachel Bersie; Gary Hoffman; Michael Rock; Mei Baker; Philip M Farrell; Pippa Simpson; Hara Levy Journal: Pediatr Pulmonol Date: 2015-08-10
Authors: H Levy; M Nugent; K Schneck; D Stachiw-Hietpas; A Laxova; O Lakser; M Rock; M K Dahmer; J Biller; S Z Nasr; M Baker; S A McColley; P Simpson; P M Farrell Journal: Clin Genet Date: 2016-01-20 Impact factor: 4.438
Authors: Veronika Krulišová; Miroslava Balaščaková; Veronika Skalická; Tereza Piskáčková; Andrea Holubová; Jana Paděrová; Petra Křenková; Lenka Dvořáková; Dana Zemková; Petr Kračmar; Blanka Chovancová; Věra Vávrová; Alexandra Stambergová; Felix Votava; Milan Macek Journal: Eur J Pediatr Date: 2012-05-12 Impact factor: 3.183
Authors: Mei W Baker; Anne E Atkins; Suzanne K Cordovado; Miyono Hendrix; Marie C Earley; Philip M Farrell Journal: Genet Med Date: 2015-02-12 Impact factor: 8.822
Authors: Ari J Silver; Jessica L Larson; Maxwell J Silver; Regine M Lim; Carlos Borroto; Brett Spurrier; Anne Morriss; Lee M Silver Journal: Genet Test Mol Biomarkers Date: 2016-04-22