Deep phenotyping to predict live birth outcomes in in vitro fertilization.

Prajna Banerjee, Banerjee P, Bokyung Choi, Choi B, Lora K. Shahine, Shahine LK, Jun SH, Sunny H. Jun, O'Leary K, Kathleen O’Leary, Ruth B. Lathi, Lynn M. Westphal, Westphal LM, Wing H. Wong, Wong WH, Mylene W. M. Yao, Yao MWM
Proceedings of the National Academy of Sciences of the United States of America · 2010 · doi:10.1073/pnas.1002296107 · PMID:20643955 · PMC2922227
OA: closed
📄 Open PDF View on PubMed View at publisher
⚙ AI-generated summary by qwen3.7-flash, 2026-08-27 ⓘ

This study developed the IVFBT model to predict live birth probabilities in subsequent IVF cycles using prior treatment data, demonstrating superior accuracy and discrimination compared to generalized age-based prognostic stratification.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

Abstract

Nearly 75% of in vitro fertilization (IVF) treatments do not result in live births and patients are largely guided by a generalized age-based prognostic stratification. We sought to provide personalized and validated prognosis by using available clinical and embryo data from prior, failed treatments to predict live birth probabilities in the subsequent treatment. We generated a boosted tree model, IVFBT, by training it with IVF outcomes data from 1,676 first cycles (C1s) from 2003-2006, followed by external validation with 634 cycles from 2007-2008, respectively. We tested whether this model could predict the probability of having a live birth in the subsequent treatment (C2). By using nondeterministic methods to identify prognostic factors and their relative nonredundant contribution, we generated a prediction model, IVF(BT), that was superior to the age-based control by providing over 1,000-fold improvement to fit new data (p<0.05), and increased discrimination by receiver-operative characteristic analysis (area-under-the-curve, 0.80 vs. 0.68 for C1, 0.68 vs. 0.58 for C2). IVFBT provided predictions that were more accurate for approximately 83% of C1 and approximately 60% of C2 cycles that were out of the range predicted by age. Over half of those patients were reclassified to have higher live birth probabilities. We showed that data from a prior cycle could be used effectively to provide personalized and validated live birth probabilities in a subsequent cycle. Our approach may be replicated and further validated in other IVF clinics.

My notes (saved in your browser only)

Citation neighborhood (sparse)

Too few in-corpus citations on either side for a chart; here are the lists.

Cites (3)

Cited by (2)

References (22)

Cited by (2)

Source provenance

crossref
last seen: 2026-09-02T07:26:39.429679+00:00
europepmc
last seen: 2026-09-06T09:34:12.023084+00:00
unpaywall
last seen: 2026-09-12T07:21:10.926195+00:00