Publications by authors named "Tanvi Mansukhani"

2 Publications

  • Page 1 of 1

Competing risks model for prediction of small-for-gestational-age neonates from biophysical markers at 19 to 24 weeks' gestation.

Am J Obstet Gynecol 2021 Apr 24. Epub 2021 Apr 24.

Fetal Medicine Research Institute, King's College Hospital, London, United Kingdom. Electronic address:

Background: Antenatal identification of women at high risk to deliver small-for-gestational-age neonates may improve the management of the condition. The traditional but ineffective methods for small-for-gestational-age screening are the use of risk scoring systems based on maternal demographic characteristics and medical history and the measurement of the symphysial-fundal height. Another approach is to use logistic regression models that have higher performance and provide patient-specific risks for different prespecified cutoffs of birthweight percentile and gestational age at delivery. However, such models have led to an arbitrary dichotomization of the condition; different models for different small-for-gestational-age definitions are required and adding new biomarkers or examining other cutoffs requires refitting of the whole model. An alternative approach for the prediction of small-for-gestational-age neonates is to consider small for gestational age as a spectrum disorder whose severity is continuously reflected in both the gestational age at delivery and z score in birthweight for gestational age.

Objective: This study aimed to develop a new competing risks model for the prediction of small-for-gestational-age neonates based on a combination of maternal demographic characteristics and medical history with sonographic estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure at 19 to 24 weeks' gestation.

Study Design: This was a prospective observational study of 96,678 women with singleton pregnancies undergoing routine ultrasound examination at 19 to 24 weeks' gestation, which included recording of estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure. The competing risks model for small for gestational age is based on a previous joint distribution of gestational age at delivery and birthweight z score, according to maternal demographic characteristics and medical history. The likelihoods of the estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure were fitted conditionally to both gestational age at delivery and birthweight z score and modified the previous distribution, according to the Bayes theorem, to obtain an individualized posterior distribution for gestational age at delivery and birthweight z score and therefore patient-specific risks for any desired cutoffs for birthweight z score and gestational age at delivery. The model was internally validated by randomly dividing the data into a training data set, to obtain the parameters of the model, and a test data set, to evaluate the model. The discrimination and calibration of the model were also examined.

Results: The estimated fetal weight was described using a regression model with an interaction term between gestational age at delivery and birthweight z score. Folded plane regression models were fitted for uterine artery pulsatility index and mean arterial pressure. The prediction of small for gestational age by maternal factors was improved by adding biomarkers for increasing degree of prematurity, higher severity of smallness, and coexistence of preeclampsia. Screening by maternal factors with estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure, predicted 41%, 56%, and 70% of small-for-gestational-age neonates with birthweights of <10th percentile delivered at ≥37, <37, and <32 weeks' gestation, at a 10% false-positive rate. The respective rates for a birthweight of <3rd percentile were 47%, 65%, and 77%. The rates in the presence of preeclampsia were 41%, 72%, and 91% for small-for-gestational-age neonates with birthweights of <10th percentile and 50%, 75%, and 92% for small-for-gestational-age neonates with birthweights of <3rd percentile. Overall, the model was well calibrated. The detection rates and calibration indices were similar in the training and test data sets, demonstrating the internal validity of the model.

Conclusion: The performance of screening for small-for-gestational-age neonates by a competing risks model that combines maternal factors with estimated fetal weight, uterine artery pulsatility index, and mean arterial pressure was superior to that of screening by maternal characteristics and medical history alone.
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http://dx.doi.org/10.1016/j.ajog.2021.04.247DOI Listing
April 2021

Robot-assisted laparoscopic myomectomy for deep intramural myomas.

Int J Med Robot 2017 Jun 16;13(2). Epub 2016 Mar 16.

Seoul St. Mary's fibroid center, Department of Obstetrics and Gynecology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

Background: To evaluate the efficacy of robot-assisted laparoscopic myomectomy for deep intramural myomas.

Methods: We have conducted a retrospective study for 170 patients who underwent robot-assisted laparoscopic myomectomy by a single operator of tertiary university hospital.

Results: There were 100 cases of robot-assisted laparoscopic myomectomy for deep intramural myomas. The patients had 3.8±3.5 myomas on average, and the mean size of the largest myoma of each patient was 7.5±2.1 centimeters in diameter. Mean operative time was 276.4±97.1 minutes, and mean console time was 146.0±62.7 minutes. Thirty two patients had surgeries for other gynecologic conditions such as pelvic endometriosis or endometrial polyps along with myomectomy at the same time. All the patients recovered without any major complication. After the surgery, nine(75.0 %) of the 12 women pursuing a pregnancy became pregnant.

Conclusion: Robot-assisted laparoscopic myomectomy for deep intramural myomas could be a minimal invasive surgical option for women who wish preserve fertility. Copyright © 2016 John Wiley & Sons, Ltd.
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http://dx.doi.org/10.1002/rcs.1742DOI Listing
June 2017