Predictions of pregnancy are based on information from the first IVF cycle



IVF procedures are frequently performed in Jaipur, a city in Rajasthan. One of the greatest IVF clinics in Jaipur if you’re trying to start the family of your dreams is Mishka IVF. In a pleasant and comfortable environment, you will discover a wide choice of services and the most recent technologies.


This study aims to identify the most useful pre- and post-cycle variables that can be used to predict success in an autologous oocyte IVF cycle. This retrospective study was done using 22,413 IVF cycles involving autologous oocytes from 2001 to 2018. The models were used to predict pregnancy after an IVF cycle that included a fresh embryo transfer. Each variable’s importance was calculated by the coefficient of a logistic regression model. The prediction accuracy based on different variables was also reported. For prediction accuracy, the AUC (area under the receiver operating characteristic curve) was used.

predicting IVF success

The predictive model was able to predict first-cycle IVF pregnancy using all variables and an AUC of 68% +/- 0.1%. Parsimonious predictive models that take into account age (38-40, 41-40, 42-42, and above 42), number of embryos transferred and number of cryopreserved embryos have an AUC of 65% +/- 0.011%.  models accurately predict the outcome of an IVF cycle and identify key predictive variables. .

Control and Prevention’s (CDC) report,

The U.S. Centers for Disease Control and Prevention’s (CDC) report, from the National Survey of Family Growth 2015-2017 shows that 13.1% of U.S. women have impaired fecundity and 12.7% of U.S. women have used infertility services 

  1. Infertility management and treatment can be costly, time-consuming, and emotionally draining. The Society for Assisted Reproductive Technology has compiled IVF success rates for each clinic. 
  2.  These are the Univfy PreIVF report and the SART Patient Predictor. The SART Patient Predictor is a free tool that uses demographic information about a patient’s weight, height, fertility diagnosis, and past pregnancy outcomes to predict the likelihood of live birth after one to three IVF cycles.
  3.  It uses the individual’s age and body mass index (BMI), results from ovarian reserve tests, clinical diagnosis, and reproductive history to determine their pre-IVF success.
  4. These tools are useful to anyone considering IVF. These data may improve the accuracy of prediction for embryo transfer patients. This could be useful psychologically to help them prepare psychologically for a positive pregnancy outcome or a negative one within their first treatment cycle.

Data acquisition

 Multiple IVF centers from California, New York, and Massachusetts were included in the list of participating practices. Donor oocytes, embryo transfers, and subsequent IVF cycles were exclusion criteria.   The BUMC IRB waived the requirement to obtain informed consent because our study was approved for non-human subject research.


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