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S-11-1: ARTERIAL STIFFNESS AND HEMODYNAMIC PARAMETERS FOR THE EARLY PREDICTION OF PREECLAMPSIA: A PROSPECTIVE COHORT STUDY

2023· article· en· W4315702995 on OpenAlexaff
Stella S. Daskalopoulou, Kim Phan, Ian Schiller, Nandini Dendukuri, Yessica Gomez, Jessica Gorgui, Amira El Messidi, Haim A. Abenhaim, Elham Rahme, Robert Gagnon

Bibliographic record

VenueJournal of Hypertension · 2023
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicinePreeclampsiaArterial stiffnessCardiologyPulse wave velocityBlood pressureInternal medicineProspective cohort studyHemodynamicsMean arterial pressurePregnancyHeart rate

Abstract

fetched live from OpenAlex

Objective: Preeclampsia is a leading complication of pregnancy, yet there remains no reliable clinical tool for its early prediction. Assessment of vascular dysfunction, a central feature of preeclampsia, by arterial stiffness and hemodynamic measurements could be a promising tool for preeclampsia prediction. This study aimed to evaluate 1) arterial stiffness and hemodynamic parameters as an early predictive tool for preeclampsia, and 2) longitudinal changes in these parameters and identify changepoints prior to preeclampsia onset. Design and method: In this prospective longitudinal cohort study of women with singleton high-risk pregnancies (n = 236), arterial stiffness (carotid-femoral pulse wave velocity, cfPWV) and wave reflection (augmentation index [AIx], and time to wave reflection [T1R]) were assessed using applanation tonometry (SphygmoCor, AtCor) at 10–13 weeks and repeated every 4 weeks throughout pregnancy. Circulating angiogenic biomarkers (soluble fms-like tyrosine kinase, and placental growth factor) were measured (Quantikine, R&D Systems) at each trimester, and a bilateral uterine artery Doppler (UAD) was performed in the second trimester. The predictive ability of arterial stiffness was compared to that of peripheral blood pressure, UAD indices, as well as angiogenic biomarkers. Furthermore, changepoints in cfPWV, AIx, and T1R were compared between women who did and did not subsequently develop preeclampsia. Results: A first-trimester 1 m/s increase in cfPWV was associated with 64% increased odds (p < 0.05), while a 1 ms increase in T1R with 11% decreased odds for preeclampsia (p < 0.01). The area under the curve of arterial stiffness, blood pressure, ultrasound indices, and angiogenic biomarkers was 0.84, 0.68, 0.66, and 0.64, respectively (Figure). A changepoint in cfPWV was detected at 14–17 weeks. cfPWV then increased in women who subsequently developed preeclampsia but decreased in women who did not; a 1.2 m/s difference in cfPWV between the groups was observed at 22–25 weeks. An increase in AIx was noted at 18–21 weeks while at 30–33 weeks in women who did and did not develop preeclampsia, respectively. Conclusions: Arterial stiffness and wave reflection were higher in the first trimester and throughout pregnancy in women destined to develop preeclampsia. These indices predicted preeclampsia earlier (in the first trimester) and with greater ability than blood pressure, UAD, and/or angiogenic biomarkers. Furthermore, altered vascular adaptations in the early second trimester were observed in women who subsequently developed preeclampsia. These findings demonstrate the potential clinical utility of arterial stiffness and hemodynamic parameters as an early screening tool for preeclampsia, which can be used to inform clinical management of high-risk pregnancies.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.261
Teacher spread0.226 · 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 designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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