Variation Among Immunoreactive Trypsinogen

Concentrations, Michigan Newborn Screening, 10/2007-

4/2008

Steven J. Korzeniewski, MA, MSc,Maternal & Child Health Epidemiology

Section Manager

Grigorescu, V., Young, W., Hawkins, H., Cavanaugh, K., Nasr, S.Z., Langbo, C.

Outline

•Background•Research question •Methods •Results•Discussion•Public Health Implications

Background • CF Screening in MI commenced Oct. 2007

• IRT is used to identify infants at increased risk of CF for DNA testing.

• Mutation analysis, using a panel of 40 CF mutations among > 96th percentile.

• In the absence of a mutation sweat testing is recommended only among infants having IRT concentrations > 99.8th percentile.

Research Question•Anecdotal evidence suggested a high rate

of false positives among NICU infants

•This study explores variations in IRT concentrations in hopes of developing a strategy to reduce false positives.

•R1: Do IRT concentrations vary among the general population by sex, race, birth weight, gestational age, and fetal growth ratio?

Methods• Data: Newborn screening IRT concentrations and infant

demographic data collected from Oct 2007-April 2008 were used for this study.

• Analysis: Crude and adjusted generalized linear models (GLM) of the association between demographic variables and IRT concentrations▫ Least squares means and p-values are reported

▫ LS-means are within-group adjusted means, they estimate the marginal means for a balanced population (as opposed to the unbalanced design). Also called estimated population marginal means by Searle, Speed, and Milliken (1980).

▫ We also calculated means and percentiles (96th, 99.8th) by race and gestational age strata

ResultsGeneralized Linear Models of the Crude & Adjusted Associations between Initial Screening IRT Values & Subject

Demographics, Michigan Newborn Screening, Oct. 2007- April 2008

DemographicsCrude Adjusted*

IRT (LS Mean) P-value IRT (LS Mean) P-value

Race (n=59150)

American Indian 26.4

<.0001

27.7

<.0001

Arab Descent 26.4 27.7

Asian/Pacific Islander 22.9 24.1

Black 33.2 34.3

Multi-Racial 27.1 28.4

White 25.9 27.1

Gestational Age (n=67643)

LT 28 wks 27.1

<.0001

28.3

<.000128-37 wks 29.0 28.5

>=37 wks 27.1 27.9

Birth Weight (n=67643)

<1800 grams 29.3

<.0001

29.6

<.00011800g-2500g 28.7 27.9

>=2500g 27.0 27.1

Sex (n=64803)Female 26.9

<.000127.9

<.0001Male 27.7 28.5

FGR (n=48039)

< 25th % 29.1

<.0001

30.9

<.0001>= 25th% & < 75th% 27.5 29.6

>=75th% 26.6 28.9

Race*GA 0.0005

*Adjusted for other demographic covariates included in the table (Race, GA, BW, Sex) and age at specimen collection. (N=58,789)

ResultsIRT Means & Percentiles (96th & 99.8th) by Race & Gestational Age, Michigan Newborn Screening, Oct/2007-April/2008, N=59,150

Race

GA < 28 Weeks GA 28-37 Weeks GA > 37 Weeks

N MeanPercentile

N MeanPercentile

N MeanPercentile

96 99.8 96 99.8 96 99.8

White 3151 26.12 58 167 3623 26.82 53 161 34346 25.80 53 106

Black 1344 32.14 78 163 1402 35.36 78 224 9323 33.05 72 133

American Indian 26 20.27 35 50 13 25.54 43 49 248 27.10 54 150

Asian/Pacific Islander 112 21.52 42 62 102 24.83 44 79 1245 22.90 48 92

Arab Descent127 26.69 60 139 104 28.42 59 81 1273 26.24 54 99

Multi-Racial231 25.23 66 98 233 27.91 60 116 2059 27.24 55 104

Total* 8200 27.13 62 163 5925 28.99 62 166 53563 27.12 57 115

*Total N exceeds the sum of racial categories because it includes those records missing race information.

Updated Results•At one year (Oct 2007-Oct 2008)

▫Effect modification of race by gestational age absolved

▫Racial variation remained significant in both crude & adjusted models

Conclusion•Failure to account for racial variation results

in:▫Over sampling of black infants

those at lower risk of CF▫Under sampling of white infants

those at the greater risk of CF

•False positive and false negative rates could be inflated ▫However, no false negatives have been

detected thus far

Public Health Implications• Calculation of IRT % cutoffs stratified by race would:

▫ Reduce the FPR & Improve PPV

▫ Require further research to discern appropriate cutoffs, particularly for racial minorities or those with missing data

▫ Require significant change in laboratory operating procedures Sorting of cards Verification of Race information Development of strategy to calculate cutoffs over time

Acknowledgements

•Co-Investigators: Grigorescu, V., Young, W., Hawkins, H., Cavanaugh, K., Nasr, S.Z., Langbo, C.

•NBS Follow-up Staff

•CF Advisory Committee

Contact

KorzeniewskiS@Michigan.gov

Thank You