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Retinal vascular features enhance short-term prediction of delivery in hypertensive disorders of pregnancy
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DOI：10.1097/HJH.0000000000004369.png)
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Abstract
En 中文
Hypertensive disorders of pregnancy (HDP) are a significant global health concern requiring effective strategies for risk stratification. We hypothesized that integrating quantitative retinal vascular features with the soluble fms-like tyrosine kinase-1 (sFlt-1)/placental growth factor (PlGF) ratio and clinical data would significantly improve the prediction of delivery within 2 weeks due to HDP progression.
Keywords:
deep learning
hypertensive disorders of pregnancy
multimodal prediction model
preeclampsia
retinal vascular features
AI
artificial intelligence
ALT
alanine transaminase
AUC
area under the curve
AVR
artery-to-vein ratio
CRAE
central retinal artery equivalent
CRVE
central retinal vein equivalent
CV
cross-validation
DXM
dexamethasone
FMF
Fetal Medicine Foundation
GA
gestational age
HDP
hypertensive disorders of pregnancy
IQR
interquartile range
IVF
in-vitro fertilization
LASSO
least absolute shrinkage and selection operator
MAP
mean arterial pressure
ML
machine-learning
NPV
negative predictive value
PE
preeclampsia
PlGF
placental growth factor
PPV
positive predictive value
SGA
small for gestational age
SLE
systemic lupus erythematosus
sFlt-1
soluble fms-like tyrosine kinase-1
VSD
vessel skeleton density
Journal
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4.1
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Citations:
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