Noninvasive Prenatal Testing for Trisomies 21, 18, and 13, Sex Chromosome Aneuploidies, and Microdeletions: A Health Technology Assessment.
Bibliographic record
Abstract
BACKGROUND: Pregnant people have a risk of carrying a fetus affected by a chromosomal anomaly. Prenatal screening is offered to pregnant people to assess their risk. Noninvasive prenatal testing (NIPT) has been introduced clinically, which uses the presence of circulating cell-free fetal DNA in the maternal blood to quantify the risk of a chromosomal anomaly. At the time of writing, NIPT is publicly funded in Ontario for pregnancies at high risk of a chromosomal anomaly. METHODS: We completed a health technology assessment, which included an evaluation of clinical benefits and harms, value for money, budget impact, and patient preferences related to NIPT. We performed a systematic literature search for studies on NIPT for trisomies 21, 18, and 13, sex chromosome aneuploidies, and microdeletions in the average-risk or general population. We evaluated the cost-effectiveness of traditional prenatal screening, NIPT as a second-tier test (performed after traditional prenatal screening), and NIPT as a first-tier test (performed instead of traditional prenatal screening). We also conducted a budget impact analysis to estimate the additional costs of funding first-tier NIPT. We interviewed people who had lived experience with NIPT and people living with the conditions NIPT screens for, or their families. RESULTS: The pooled clinical sensitivity of NIPT in the average-risk or general population was 99.5% (95% confidence interval [CI] 81.8%-99.9%) for trisomy 21, 93.1% (95% CI 75.9%-98.3%) for trisomy 18, and 92.7% (95% CI 81.6%-99.9%) for trisomy 13. The clinical specificity for any trisomy was 99.9% (95% CI 99.8%-99.9%). Compared with traditional prenatal screening, NIPT was more accurate in detecting trisomies 21, 18, and 13, and decreased the need for diagnostic testing. We found limited evidence on NIPT for sex chromosome aneuploidies or microdeletions in the average-risk or general population. Positive NIPT results should be confirmed by diagnostic testing.Compared with traditional prenatal screening, second-tier NIPT detected more affected fetuses, substantially reduced the number of diagnostic tests performed, and slightly reduced the total cost of prenatal screening. Compared with second-tier NIPT, first-tier NIPT detected more affected cases, but also led to more diagnostic tests and additional budget of $35 million per year for average-risk pregnant people in Ontario.People who had undergone NIPT were largely supportive of the test and the benefits of earlier, more accurate results. However, many discussed the need for improved pre- and post-test counselling and raised concerns about the quality of the information they received from health care providers about the conditions NIPT can screen for. CONCLUSIONS: NIPT is an effective and safe prenatal screening method for trisomies 21, 18, and 13 in the average-risk or general population. Compared with traditional prenatal screening, second-tier NIPT improved the overall performance of prenatal screening and slightly decreased costs. Compared with second-tier NIPT, first-tier NIPT detected more chromosomal anomalies, but resulted in a considerable increase in the total budget. Interviewees were generally positive about NIPT, but they raised concerns about the lack of good informed-choice conversations with primary care providers and the quality of the information they received from health care providers about chromosomal anomalies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.019 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".