Literature DB >> 16827767

Do clinical prediction models improve concordance of treatment decisions in reproductive medicine?

J W van der Steeg1, P Steures, M J C Eijkemans, J D F Habbema, P M M Bossuyt, P G A Hompes, F van der Veen, B W J Mol.   

Abstract

OBJECTIVE: To assess whether the use of clinical prediction models improves concordance between gynaecologists with respect to treatment decisions in reproductive medicine.
DESIGN: We constructed 16 vignettes of subfertile couples by varying fertility history, postcoital test, sperm motility, follicle-stimulating hormone level and Chlamydia antibody titre.
SETTING: Thirty-five gynaecologists estimated three probabilities, i.e. the 1-year probability of spontaneous pregnancy, the pregnancy chance after intrauterine insemination (IUI) and the pregnancy chance after in vitro fertilisation (IVF). Subsequently they proposed therapeutic regimens for these 16 fictional couples, i.e. expectant management, IUI or IVF. Three months later, the participant gynaecologists again had to propose therapeutic regimes for the same 16 fictional cases but this time accompanied by pregnancy chances obtained from prediction models: predictions on spontaneous pregnancy, IUI and IVF. POPULATION: Thirty-five gynaecologists working in academic and nonacademic hospitals in the Netherlands.
METHODS: Setting section. Main outcome measures The concordance between gynaecologists of probability estimates, expressed as interclass correlation coefficient (ICC) and the concordance between gynaecologists of treatment decisions, analysed by calculating Cohen's kappa (kappa).
RESULTS: The gynaecologists differed widely in estimating pregnancy chances (ICC: 0.34). Furthermore, there was a huge variation in the proposed therapeutic regimens (kappa: 0.21). The treatment decisions made by gynaecologists were consistent with the ranking of their probability estimates. When prediction models were used, the concordance (kappa) for treatment decisions increased from 0.21 to 0.38. The number of gynaecologists counselling for expectant management increased from 39 to 51%, whereas counselling for IVF dropped from 23 to 14%.
CONCLUSION: Gynaecologists differed widely in their estimation of prognosis in 16 fictional cases of subfertile couples. Their therapeutic regimens showed likewise huge variation. After confrontation with prediction models in the same 16 fictional cases, the proposed therapeutic regimens showed only slightly better concordance. Therefore a simple introduction of validated prediction models is insufficient to introduce concordant management between doctors.

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Year:  2006        PMID: 16827767     DOI: 10.1111/j.1471-0528.2006.00992.x

Source DB:  PubMed          Journal:  BJOG        ISSN: 1470-0328            Impact factor:   6.531


  8 in total

1.  External validation and calibration of IVFpredict: a national prospective cohort study of 130,960 in vitro fertilisation cycles.

Authors:  Andrew D A C Smith; Kate Tilling; Debbie A Lawlor; Scott M Nelson
Journal:  PLoS One       Date:  2015-04-08       Impact factor: 3.240

Review 2.  Prediction models in in vitro fertilization; where are we? A mini review.

Authors:  Laura van Loendersloot; S Repping; P M M Bossuyt; F van der Veen; M van Wely
Journal:  J Adv Res       Date:  2013-05-09       Impact factor: 10.479

Review 3.  A Review of Machine Learning Approaches in Assisted Reproductive Technologies.

Authors:  Behnaz Raef; Reza Ferdousi
Journal:  Acta Inform Med       Date:  2019-09

4.  Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study.

Authors:  Qingsong Xi; Qiyu Yang; Meng Wang; Bo Huang; Bo Zhang; Zhou Li; Shuai Liu; Liu Yang; Lixia Zhu; Lei Jin
Journal:  Reprod Biol Endocrinol       Date:  2021-04-05       Impact factor: 5.211

5.  The (decision) tree of fertility: an innovative decision-making algorithm in assisted reproduction technique.

Authors:  Maria Teresa Villani; Daria Morini; Giorgia Spaggiari; Chiara Furini; Beatrice Melli; Alessia Nicoli; Francesca Iannotti; Giovanni Battista La Sala; Manuela Simoni; Lorenzo Aguzzoli; Daniele Santi
Journal:  J Assist Reprod Genet       Date:  2022-01-27       Impact factor: 3.412

6.  Predicting cumulative live birth for couples beginning their second complete cycle of in vitro fertilization treatment.

Authors:  Mariam B Ratna; Siladitya Bhattacharya; N van Geloven; David J McLernon
Journal:  Hum Reprod       Date:  2022-08-25       Impact factor: 6.353

7.  Nomogram for the cumulative live birth in women undergoing the first IVF cycle: Base on 26, 689 patients in China.

Authors:  Pengfei Qu; Lijuan Chen; Doudou Zhao; Wenhao Shi; Juanzi Shi
Journal:  Front Endocrinol (Lausanne)       Date:  2022-08-25       Impact factor: 6.055

Review 8.  Models Predicting Success of Infertility Treatment: A Systematic Review.

Authors:  Alireza Zarinara; Hojjat Zeraati; Koorosh Kamali; Kazem Mohammad; Parisa Shahnazari; Mohammad Mehdi Akhondi
Journal:  J Reprod Infertil       Date:  2016 Apr-Jun
  8 in total

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