Neuroradiological perspectives of severe preeclampsia and eclampsia spectrum – Correlation from posterior reversible encephalopathy syndrome

Basavarajappa, Darshan Hosapatna; Saha, Pradip Kumar; Bagga, Rashmi; Khandelwal, Niranjan; Modi, Manish

doi:10.1016/j.preghy.2020.04.003

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…Host defence peptide (HDP) can cause hemodynamic changes in pregnant women and is one of the main causes of maternal and infant death. HDP can cause abnormal blood perfusion in important organs, eventually leading to damage or ischemia failure of vital organs such as the heart, brain, kidney, and even involving intrauterine fetuses in severe cases, resulting in perinatal death [10,16]. Severe preeclampsia is due to insufficient trophoblast infiltration outside the villi of patients, which leads to abnormal development of placental blood vessels in pregnant women and causes placental ischemia and hypoxia.…”

Section: Discussionmentioning

confidence: 99%

“…Pregnant women with early severe preeclampsia usually have spasm of arterioles and vessel walls throughout the body, lumen stenosis, villous retardation, reduced blood flow to the umbilical artery, and increased blood flow resistance. Color Doppler ultrasound can detect that the umbilical artery-systolic/diastolic (UA-S/D) values increased, which affects the normal development of the fetus [9][10][11]. During clinical diagnosis, the ultrasonic imaging effect is poor because of the inherent patch noise and texture characteristics of ultrasonic instrument.…”

Section: Introductionmentioning

confidence: 99%

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Value of Ultrasonic Image Features in Diagnosis of Perinatal Outcomes of Severe Preeclampsia on account of Deep Learning Algorithm

Wang

Liu

2022

Computational and Mathematical Methods in Medicine

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This study is aimed at discussing the value of ultrasonic image features in diagnosis of perinatal outcomes of severe preeclampsia on account of deep learning algorithm. 140 pregnant women singleton with severe preeclampsia were selected as the observation group. At the same time, 140 normal singleton pregnant women were selected as the control group. The hemodynamic indexes were detected by color Doppler ultrasound. The CNN algorithm was used to classify ultrasound images of two groups of pregnant women. The differential scanning calorimetry (DSC), mean pixel accuracy (MPA), and mean intersection of union (MIOU) values of CNN algorithm were 0.9410, 0.9228, and 0.8968, respectively. Accuracy, precision, recall, and F 1 -score were 93.44%, 95.13%, 95.09%, and 94.87%, respectively. The differences were statistically significant ( P < 0.05 ). Compared with the normal control group, the umbilical artery (UA), uterine artery-systolic/diastolic (UTA-S/D), uterine artery (UTA), and digital video (DV) of pregnant women in the observation group were remarkably increased; the minimum alveolar effective concentration (MCA) of the observation group was obviously lower than the MCA of the control group, and the differences between groups were statistically valid ( P < 0.05 ). Logistic regression analysis showed that UA-S/D, UA-resistance index (UA-RI), UTA-S/D, UTA-pulsatility index (UTA-PI), DV-peak velocity index for veins (DV-PVIV), and MCA-S/D were independent risk factors for the outcome of perinatal children with severe preeclampsia. In the perinatal management of severe epilepsy, the combination of the above blood flow indexes to select the appropriate delivery time had positive significance to improve the pregnancy outcome and reduce the perinatal mortality.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Value of Ultrasonic Image Features in Diagnosis of Perinatal Outcomes of Severe Preeclampsia on account of Deep Learning Algorithm

Wang

Liu

2022

Computational and Mathematical Methods in Medicine

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“…Of the 29 included studies, 6 were prospective studies, 5 – 10 the remainder were retrospective reviews. 11 – 33 A summary of the characteristics of the included studies presenting data from 1519 women is presented in Table 1 .…”

Section: Resultsmentioning

confidence: 99%

“…The overall prevalence of PRES with eclampsia in the included studies was 66.7% (569 PRES-diagnosed women in 852 women with eclampsia). Restricting to the six studies [5][6][7][8][9][10] which reported neuroimaging on presumed consecutive or "all" women, the frequency was 51.4% (176/342 women).…”

Section: Frequency Of Presmentioning

confidence: 99%

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A systematic review of posterior reversible encephalopathy syndrome in pregnant women with severe preeclampsia and eclampsia

Tawati

Chan

2023

Obstet Med

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Background The association of posterior reversible encephalopathy syndrome (PRES) and severe preeclampsia/eclampsia has been established but the frequency is uncertain. Objectives To determine the frequency of PRES in severe preeclampsia or eclampsia. Methods We searched published articles in PubMed, Cochrane library, Embase, and CINAHL from 1990 to 2020. We included articles that reported on six or more cases of PRES with eclampsia or severe preeclampsia who underwent neuroimaging during pregnancy or up to 6 weeks postpartum. Results We identified 29 studies presenting data on 1519 women with eclampsia or severe preeclampsia. Among 342 women with eclampsia who had neuroimaging, 176 (51.4%) were diagnosed with PRES. Of 121 women with severe preeclampsia, 24 (19.8%) had PRES. The pooled maternal death rate was 5.3% (21/395). Conclusion PRES is commonly reported on neuroimaging of women with eclampsia/ severe preeclampsia. The role of neuroimaging in eclampsia and especially in women with severe preeclampsia requires re-evaluation as further management is often dictated by this finding.

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Risk factors for posterior reversible encephalopathy syndrome in hypertensive pregnant women presenting with seizures

et al. 2022

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Neuroradiological perspectives of severe preeclampsia and eclampsia spectrum – Correlation from posterior reversible encephalopathy syndrome

Cited by 7 publications

References 13 publications

Value of Ultrasonic Image Features in Diagnosis of Perinatal Outcomes of Severe Preeclampsia on account of Deep Learning Algorithm

Value of Ultrasonic Image Features in Diagnosis of Perinatal Outcomes of Severe Preeclampsia on account of Deep Learning Algorithm

A systematic review of posterior reversible encephalopathy syndrome in pregnant women with severe preeclampsia and eclampsia

Risk factors for posterior reversible encephalopathy syndrome in hypertensive pregnant women presenting with seizures

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