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Updated: Apr 11, 2026
FISH for Pre-implantation Genetic Diagnosis
Published on: February 23, 2011
Integrated genomic and clinical indicators for predicting foetal chromosomal abnormalities: development and
Yingjie Zhou1, Peng Liu2, Xiaolin Zhang1
1School of Public Health, Hebei Medical University, Shijiazhuang, China.
Background:
Foetal chromosomal abnormalities are identified as one of the leading causes of adverse pregnancy outcome in pregnancies that are referred on suspicion of structural abnormalities in the foetus. While various types of genomic analyses can be carried out in order to determine prenatal risk factors, no single nomogram in existence combines these factors to provide the probability of chromosomal abnormalities in the foetus.
Methods:
This was a retrospective cohort analysis among pregnant women referred for possible foetal structural anomalies who received invasive prenatal diagnostic tests at two tertiary referral centres within Hebei Province. The study participants received uniform prenatal evaluation and genomic analysis that included non-invasive prenatal testing as well as copy number variation analysis. Foetal anomalies identified by ultrasound were re-evaluated by expert reviewers, with confirmed structural anomalies being candidate predictors. Predictors were assessed with multivariable logistic regression analysis. Performance of the models was assessed by the discrimination method, calibration with bootstrap resampling, decision curve analysis, and internal validation with repeated cross-validation.
Results:
Abnormal results from Down syndrome screening, non-invasive prenatal testing, copy number variation sequencing, the presence of foetal structural abnormalities confirmed by ultrasound, and high gravidity were found to be independently associated with foetal chromosomal abnormalities. The nomogram showed high discriminative power, as evidenced by the area under the curve in the receiver operating characteristic curve graph, which exceeded 0.90. Calibration plots demonstrated good agreement between predicted and observed risks. Results from decision curve analysis suggested significant clinical benefit over a broad range of threshold probabilities. Validation results confirmed the stability of the nomogram.
Conclusions:
The proposed nomogram incorporates genomic technologies into clinical and ultrasound indicators for the precise estimation of risk on an individual basis for foetal chromosomal abnormalities. This tool may support prenatal counselling and assist clinicians in optimising diagnostic decision-making in pregnancies with suspected foetal structural abnormalities.
