Inborn Errors of Metabolism Collaborative: large-scale collection of data on long-term follow-up for newborn-screened conditions

Genet Med. 2016 Dec;18(12):1276-1281. doi: 10.1038/gim.2016.57. Epub 2016 May 19.

Abstract

Purpose: The Inborn Errors of Metabolism Information System (IBEM-IS) collects data on the clinical history of inborn errors of metabolism (IBEMs). The IBEM-IS is accessible to metabolic clinics nationwide and seeks to (i) influence clinical management of affected individuals and (ii) provide information to support public health decision making.

Methods: Thirty centers in 21 states are enrolling persons with newborn-screened conditions, collecting information on diagnosis and treatment at the time of enrollment and all subsequent visits. Prospective data are collected using electronic capture forms allowing aggregation of information regarding outcomes for individuals affected with IBEMs.

Results: A total of 1,893 subjects have been enrolled in the IBEM-IS, and more than 540,000 individual data points have been collected. Data collection has been initiated for subjects with 41 of 46 conditions on the recommended uniform screening panel; 4 conditions have more than 100 subjects enrolled. Median follow-up time for subjects with more than one visit (n = 898) is 1.5 years (interquartile range = 2.2 years). Subjects with critical conditions are more likely to have emergency letters and sick-day plans. Mortality was exclusive to children with critical conditions.

Conclusion: Large-scale prospective data can be collected for individuals with rare conditions, permitting enhanced decision making for clinical management and supporting decision making in public health newborn screening programs.Genet Med 18 12, 1276-1281.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Data Collection
  • Follow-Up Studies
  • Genetic Testing*
  • Humans
  • Infant, Newborn
  • Metabolism, Inborn Errors / diagnosis
  • Metabolism, Inborn Errors / genetics*
  • Neonatal Screening*
  • Public Health
  • Rare Diseases / diagnosis
  • Rare Diseases / genetics*