四個合併因素可以預測試管嬰兒胚胎植入的成功率


  July 2, 2008 — 根據7月2日線上發表於PLOS One的研究,四個因素(胚胎總數、8細胞胚胎數量、胚胎停止分裂百分比、母體的濾泡刺激素[FSH],可以合併用來準確預測70%試管嬰兒胚胎植入(IVF)懷孕的成功率。
  
  加州史丹佛大學醫學院的Mylene W. M. Yao醫師在新聞稿中指出,如果你與IVF病患及醫師討論,他們對於循環中所有胚胎— 而不是只有植入的那一個 — 的質量很重要並不會感到驚訝;但是,為了推廣此一領域,在直覺之外,更重要的是有科學證據。
  
  這個研究的假設是胚胎世代–特別是兄弟姊妹的胚胎變項可以預測發展能力,就像反應IVF週期的懷孕結果一樣。
  
  研究者分析了2005年間在史丹佛大學醫學中心進行的1,117件IVF週期的所有資料,以及其他665件新鮮IVF的臨床資料,非捐贈者週期以及相關的4,144個配胎;使用回歸樹模式進行無偏見分析,關於病患特徵、臨床診斷、治療計畫、胚胎特徵、血清乙型-人類絨毛膜促性腺激素(beta-hCG)驗孕陽性等30個變項。
  
  使用四個胚胎世代特定變項,可以有將近70%準確預測IVF週期的結果;令人驚訝的是,這些變項對於個別的、轉換的胚胎變項更有預測力;Yao醫師表示,人們是根據機率做決定,此時,準確預測就相當重要。
  
  這四個最有預測力的世代因素是,胚胎總數、8細胞胚胎數量、胚胎停止分裂百分比、母體第三天的FSH值;這四個變項之中,只有停止分裂百分比獨立於任何已知變項。
  
  研究作者寫道,我們的發現支持,藉由世代中特定兄弟姊妹胚胎的固定預後變項,確認人類胚胎顯型;世代特定決定因素的觀念,代表觀念從選擇最佳胚胎的研究努力,改為確認可以改善整個世代品質的方法。
  
  這項研究的限制包括缺乏兄弟姊妹胚胎的冷凍胚胎資料,協助孕育之方式和使用陽性血清 hCG作為懷孕結果等。
  
  Yao醫師的團隊現在分析順產嬰兒的四年追蹤結果— 而非陽性 beta-hCG — 作為研究終點。
  
  研究作者結論表示,雖然胚胎特定參數可以幫助確認胚胎,使每對夫妻可以有立即懷孕的最大機會,未來,瞭解世代特定參數,對於進行諮商的病患、改善治療、發展特定機制且更客製化的治療而言,都是重要的。
  
  國家健康研究中心與March of Dimes贊助此研究。
  
  PLOS One線上發表於2008年7月2日。

Four Combined Factors Predict Success of In Vitro Fertilization

By Laurie Barclay, MD
Medscape Medical News

July 2, 2008 — Four factors (total number of embryos, number of 8-cell embryos, percentage of embryos that stopped dividing, and maternal follicle-stimulating hormone [FSH]), when combined, were 70% accurate in predicting pregnancy following in vitro fertilization (IVF), according to a study published online July 2 in PLOS One.

"If you talk with IVF patients or doctors, they wouldn't be surprised to hear that the quality of all embryos in a cycle — not just the transferred one — matters," Mylene W. M. Yao, MD, from Stanford University School of Medicine in California, says in a news release. "But it's important to go beyond intuition and to prove it scientifically, in order to move the field forward."

The hypothesis of this study was that embryo cohort–specific variables describing sibling embryos as a group could predict developmental competence, as reflected in pregnancy outcomes of IVF cycles.

The investigators analyzed data for all 1117 IVF cycles performed during 2005 at Stanford University Medical Center, as well as additional clinical data from the 665 fresh IVF, nondonor cycles and their associated 4144 embryos. Regression tree models were used for the unbiased analysis of 30 variables related to patient characteristics, clinical diagnoses, treatment protocol, and embryo characteristics, based on pregnancy outcomes measured by positive serum beta-human chorionic gonadotropin (beta-hCG).

The most accurate prediction of IVF cycle outcomes was approximately 70% and resulted from the use of 4 embryo cohort–specific variables. Surprisingly, these variables were more predictive than were any variables related to individual, transferred embryos.

"People make decisions based on probability," said Dr. Yao. "At that point, it's really important to give a more accurate prediction."

The 4 most predictive cohort factors were total number of embryos, number of 8-cell embryos, rate (percentage) of cleavage arrest in the cohort, and FSH level on day 3. Of these 4 variables, only the rate of cleavage arrest was independent of any known variables.

"Our findings support defining human embryo phenotypes by non-redundant, prognostic variables that are specific to sibling embryos in a cohort," the study authors write. "The concept of cohort-specific determinants suggest[s] a paradigm shift from strictly focusing research efforts on selecting the 'best' embryos to identifying methods that would improve the quality of the entire cohort."

Limitations of this study include lack of data on cryopreservation of sibling embryos and assisted hatching and use of positive serum hCG status as a surrogate outcome for pregnancy.

Dr. Yao's group is now analyzing results from a follow-up study with 4 years of data using live birth — rather than positive beta-hCG — as the endpoint.

"While embryo-specific parameters may help to identify embryos that would maximize the immediate pregnancy outcome for each couple, in the long term, understanding cohort-specific parameters is critical in counseling patients, improving treatment, and ultimately in developing mechanism-specific and more customized treatments," the study authors conclude.

The National Institutes of Health and the March of Dimes supported this study.

PLOS One. Published online July 2, 2008.

    
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