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Record W4379600201

Placental Growth Factor (PlGF)- Based Biomarker Testing to Help Diagnose Pre-eclampsia in People With Suspected Pre-eclampsia: A Health Technology Assessment.

2023· review· en· W4379600201 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed · 2023
Typereview
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsnot available
Fundersnot available
KeywordsPlacental growth factorMedicineEclampsiaBiomarkerGrading (engineering)ObstetricsPregnancyInternal medicineVEGF receptorsVascular endothelial growth factor
DOInot available

Abstract

fetched live from OpenAlex

Background: Pre-eclampsia is a potentially serious condition affecting up to 5% of pregnancies, most frequently after 20 weeks' gestation. Placental growth factor (PlGF)-based tests measure either the blood level of PlGF or the ratio of soluble fms-like tyrosine kinase-1 (sFlt-1) to PlGF. They are intended to complement standard clinical assessment to help diagnose pre-eclampsia in people with suspected pre-eclampsia. We conducted a health technology assessment of PlGF-based biomarker testing as an adjunct to standard clinical assessment to help diagnose pre-eclampsia in pregnant people with suspected pre-eclampsia, which included an evaluation of diagnostic accuracy, clinical utility, cost-effectiveness, the budget impact of publicly funding PlGF-based biomarker testing, and an assessment of preferences and values. Methods: We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each included study using AMSTAR 2, Cochrane Risk of Bias tool, the Quality of Diagnostic Accuracy Studies 2 (QUADAS-2) tool, and the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We performed a systematic literature search of the economic evidence. We did not conduct a primary economic evaluation as the impact of the test on maternal and neonatal outcomes is uncertain. We also analyzed the budget impact of publicly funding PlGF-based biomarker testing in pregnant people with suspected pre-eclampsia in Ontario. To contextualize the potential value of PlGF-based biomarker testing, we spoke with people whose pregnancies had been impacted by pre-eclampsia as well as their family members. Results: We included one systematic review and one diagnostic accuracy study in the clinical evidence review. The Elecsys sFlt-1/PlGF ratio test using a test cut-off of less than 38 for ruling out pre-eclampsia within 1 week yielded a negative predictive value (NPV) of 99.2% and the DELFIA Xpress PlGF 1-2-3 test using a cut-off of 150 pg/mL or greater for ruling out pre-eclampsia within 1 week yielded a NPV of 94.8% (diagnostic GRADE: Moderate for both tests). All clinical utility outcomes were associated with uncertainties (GRADE: Low).We included 13 studies in the economic evidence review, most of which concluded that the use of PlGF-based biomarker testing resulted in cost savings. Seven studies were partially applicable to the Ontario health care setting but have some important limitations; the remaining 6 studies were not applicable. We estimated that publicly funding PlGF-based biomarker testing for people with suspected pre-eclampsia in Ontario would lead to an additional annual cost of $0.27 million in year 1 to $0.46 million in year 5, for a total additional cost of $1.83 million over 5 years.Direct engagement included 24 people who had been impacted by pre-eclampsia during their pregnancies as well as one family member. Participants described the emotional and physical impacts of having suspected pre-eclampsia and subsequent treatments. Those that we spoke with valued shared decision-making and identified potential gaps in patient education, specifically as it relates to symptom management for suspected pre-eclampsia. Overall, the participants viewed PlGF-based biomarker testing positively for its perceived medical benefits and minimal invasiveness. They felt that access to PlGF-based biomarker testing may also improve health outcomes through improved patient education, care coordination, and patient-centred care (e.g., prompting more frequent prenatal monitoring, when needed). In addition, PlGF-based biomarker testing was perceived to be equally beneficial for family members who may act as the health care proxy in an emergency. Lastly, participants emphasized that there should be equitable access to PlGF-based biomarker testing and support from a care provider should be offered when trying to interpret the results, particularly if the results are accessible through an online patient portal. Conclusions: Compared with standard clinical assessment alone in people with suspected pre-eclampsia (gestational age between 20 and 36 weeks + 6 days), PlGF-based biomarker testing as an adjunct to standard clinical assessment likely improves prediction of pre-eclampsia. It may also reduce time to pre-eclampsia diagnosis, severe adverse maternal outcomes, and length of stay in the neonatal intensive care unit, although the evidence is uncertain. PlGF-based biomarker testing may result in little to no difference in other clinical outcomes such as maternal admission to hospital and perinatal adverse outcomes.The economic literature review showed that PlGF-based biomarker testing was cost-effective for use in people with suspected pre-eclampsia, but with some uncertainties. A primary economic evaluation was not done for this health technology assessment because the impact of the test on maternal and neonatal outcomes is uncertain. Publicly funding PlGF-based biomarker testing for people with suspected pre-eclampsia would lead to an additional cost of $1.83 million over 5 years.Publicly funding PlGF-based biomarker testing was viewed favourably by people directly impacted by pre-eclampsia as well as their family members. Those with whom we spoke valued testing to help diagnose suspected pre-eclampsia and valued the potential medical benefits. Participants emphasized that patient education, and equitable access to PlGF-based biomarker testing should be requirements for implementation in Ontario.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.075
metaresearch head score (Gemma)0.249
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.075
Threshold uncertainty score0.394

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.249
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.012
Bibliometrics0.0210.011
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0020.003
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.082
GPT teacher head0.352
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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