Review article| Volume 265, P190-202, October 2021

Prediction models in gynaecology: Transparent reporting needed for clinical application


      • Clinical application of prediction models is increasing within the field of OBGYN.
      • Correct model development and transparent reporting is important for clinical use.
      • We advise using the TRIPOD criteria for developing and validating prediction models.


      The clinical application of prediction models is increasing within the field of gynaecology and obstetrics. This is mostly due to the fact that clinicians and patients prefer individualized counselling and person specific, more objective outcome assessment. To prevent using inadequate models, it is important to construct and perform prediction model studies correctly. Therefore, the TRIPOD statement (the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) was developed. The aim of this review is to obtain an overview of the existing published prediction models for benign gynaecology and to investigate to what extent these studies meet the TRIPOD criteria.
      We performed a literature search in the databases PubMed, Embase and Cochrane Library from inception to August 2020. Searching the cross-references of the relevant studies within our search identified additional articles. Publications were included if the aim of the study was to develop a multivariable prediction model within the field of benign gynaecology. Two independent reviewers extracted the data. Analysis of the studies was performed by using a checklist derived from the TRIPOD criteria.
      Based on our search, 2487 studies were selected, including potential duplications. Eventually, a total of twenty-two studies were selected. 91% of these studies handled their predictors by univariable analysis before developing a multivariable prediction model. Fifteen studies described having missing data, but not all of them (9%) handled these missing data. Four different internal validation methods were used in twenty studies. Fifteen studies (68%) had prediction models with a C-index ≥ 0.7, which indicates a good model. Half of the studies (50%) did not measure the calibration, overall performance was described in two studies (9%). External validation was performed in 9% of the studies.
      The correct development of a prediction model within benign gynaecology and subsequent transparent reporting of the model development is important to facilitate clinical use. Without transparent reporting, wrong assumptions can be made leading to incorrect application of a specific prediction model. This overview shows that excepting carrying out an external validation, only one article met all the criteria. Therefore, we strongly recommend use of the TRIPOD criteria for developing and validating a prediction model (study). In addition, prior to publication, content experts should critically and statistically review the prediction model. If too many criteria are not met, refusing publication should be considered.


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        • van Delft K.
        • Thakar R.
        • Sultan A.H.
        • Schwertner-Tiepelmann N.
        • Kluivers K.
        Levator ani muscle avulsion during childbirth: a risk prediction model.
        BJOG. 2014; 121 (discussion 1163): 1155-1163
        • Fagerberg M.C.
        • Marsal K.
        • Kallen K.
        Predicting the chance of vaginal delivery after one cesarean section: validation and elaboration of a published prediction model.
        Eur J Obstet Gynecol Reprod Biol. 2015; 188: 88-94
      1. Steyerberg EW. Clinical prediction models. A practical approach to development, validation and updating. Springer, editor. New York; 2009.

      2. G. E, D. F, R. T, N. J-W, A. L, P. K, et al. Shared decision making: A model for clinical practice. J Gen Intern Med. 2012;

        • Laupacis A.
        • Sekar N.
        • Stiell I.G.
        Clinical prediction rules: a review and suggested modifications of methodological standards.
        J Am Med Assoc. 1997;
        • Moons K.G.M.
        • Kengne A.P.
        • Woodward M.
        • Royston P.
        • Vergouwe Y.
        • Altman D.G.
        • et al.
        Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker.
        Heart. 2012;
      3. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. Br J Surg [Internet]. 2015 Feb [cited 2018 Dec 3];102(3):148–58. Available from:

        • Reilly B.M.
        • Evans A.T.
        Translating clinical research into clinical practice: Impact of using prediction rules to make decisions.
        Ann Intern Med. 2006;
        • McGinn T.G.
        • Guyatt G.H.
        • Wyer P.C.
        • Naylor C.D.
        • Stiell I.G.
        • Richardson W.S.
        Users’ guides to the medical literature XXII: How to use articles about clinical decision rules.
        J Am Med Assoc. 2000;
        • Visser M.
        Dwalingen in de methodologie. XXXIV. Predictiemodellen stellen vaak teleur.
        Ned Tijdschr Geneeskd. 2001;
        • Moons K.G.M.
        • Altman D.G.
        • Reitsma J.B.
        • Ioannidis J.P.A.
        • Macaskill P.
        • Steyerberg E.W.
        • et al.
        Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration.
        Ann Intern Med. 2015;
        • Moons K.G.M.
        • Altman D.G.
        • Reitsma J.B.
        • Collins G.S.
        New guideline for the reporting of studies developing, validating, or updating a multivariable clinical prediction model: the TRIPOD statement.
        Adv Anatomic Pathol. 2015;
        • Fagerberg M.C.
        • Maršál K.
        • Källén K.
        Predicting the chance of vaginal delivery after one cesarean section: validation and elaboration of a published prediction model.
        Eur J Obstet Gynecol Reprod Biol. 2015;
        • Timmerman D.
        • Testa A.C.
        • Bourne T.
        • Ferrazzi E.
        • Ameye L.
        • Konstantinovic M.L.
        • et al.
        Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the International Ovarian Tumor Analysis Group.
        J Clin Oncol. 2005;
        • Ulusoy S.
        • Akbayir O.
        • Numanoglu C.
        • Ulusoy N.
        • Odabas E.
        • Gulkilik A.
        The risk of malignancy index in discrimination of adnexal masses.
        Int J Gynecol Obstet. 2007;
      4. Bouquier J, Huchon C, Panel P, Fauconnier A. A self-assessed questionnaire can help in the diagnosis of pelvic inflammatory disease. Sex Transm Dis. 2014/08/15. 2014;41(9):525–31.

        • Chung Y.J.
        • Kang S.Y.
        • Chun H.J.
        • Rha S.E.
        • Cho H.H.
        • Kim J.H.
        • et al.
        Development of a model for the prediction of treatment response of uterine leiomyomas after uterine artery embolization.
        Int J Med Sci. 2018; 15: 1771-1777
      5. Stanhiser J, Chagin K, Jelovsek JE. A model to predict risk of blood transfusion after gynecologic surgery. Am J Obstet Gynecol. 2017/01/21. 2017;216(5):506.e1-506.e14.

      6. Stevens KYR, Meulenbroeks D, Houterman S, Gijsen T, Weyers S, Schoot BC. Prediction of unsuccessful endometrial ablation: a retrospective study. Gynecol Surg [Internet]. 2019;16(1):7. Available from:

        • Cobellis L.
        • Castaldi M.A.
        • Giordano V.
        • De Franciscis P.
        • Signoriello G.
        • Colacurci N.
        Is it possible to predict office hysteroscopy failure?.
        Eur J Obstet Gynecol Reprod Biol. 2014; 181: 328-333
        • Erekson E.A.
        • Yip S.O.
        • Martin D.K.
        • Ciarleglio M.M.
        • Connell K.A.
        • Fried T.R.
        Major postoperative complications after benign gynecologic surgery: a clinical prediction tool.
        Female Pelvic Med Reconstr Surg. 2012; 18: 274-280
        • Fauconnier A.
        • Mabrouk A.
        • Salomon L.J.
        • Bernard J.P.
        • Ville Y.
        Ultrasound assessment of haemoperitoneum in ectopic pregnancy: derivation of a prediction model.
        World J Emerg Surg. 2007; : 2-23
        • Fouks Y.
        • Cohen A.
        • Shapira U.
        • Solomon N.
        • Almog B.
        • Levin I.
        Surgical intervention in patients with tubo-ovarian abscess: clinical predictors and a simple risk score.
        J Minim Invasive Gynecol. 2019; 26: 535-543
        • Heisler C.A.
        • Aletti G.D.
        • Weaver A.L.
        • Melton L.J.
        • Cliby W.A.
        • Gebhart J.B.
        Improving quality of care: development of a risk-adjusted perioperative morbidity model for vaginal hysterectomy.
        Am J Obstet Gynecol. 2010;
        • Lee J.H.
        • Kim S.
        • Lee I.
        • Yun J.
        • Yun B.H.
        • Choi Y.S.
        • et al.
        A risk prediction model for medical treatment failure in tubal pregnancy.
        Eur J Obstet Gynecol Reprod Biol. 2018; 225: 148-154
        • Perello M.
        • Martinez-Zamora M.A.
        • Torres X.
        • Munros J.
        • Llecha S.
        • De Lazzari E.
        • et al.
        Markers of deep infiltrating endometriosis in patients with ovarian endometrioma: a predictive model.
        Eur J Obstet Gynecol Reprod Biol. 2017; 209: 55-60
        • Pepin K.J.
        • Cook E.F.
        • Cohen S.L.
        Risk of complication at the time of laparoscopic hysterectomy: a prediction model built from the National Surgical Quality Improvement Program database.
        Am J Obstet Gynecol. 2020;
        • Verket N.J.
        • Falk R.S.
        • Qvigstad E.
        • Tanbo T.G.
        • Sandvik L.
        Development of a prediction model to aid primary care physicians in early identification of women at high risk of developing endometriosis: cross-sectional study.
        BMJ Open. 2019; 9 (e030346)
        • Buckley R.G.
        • King K.J.
        • Disney J.D.
        • Ambroz P.K.
        • Gorman J.D.
        • Klausen J.H.
        Derivation of a clinical prediction model for the emergency department diagnosis of ectopic pregnancy.
        Acad Emerg Med [Internet]. 1998; 5 (Available from): 951-960
        • Condous G.
        • Okaro E.
        • Khalid A.
        • Timmerman D.
        • Lu C.
        • Zhou Y.
        • et al.
        The use of a new logistic regression model for predicting the outcome of pregnancies of unknown location.
        Hum Reprod. 2004;
        • Condous G.
        • Van Calster B.
        • Kirk E.
        • Haider Z.
        • Timmerman D.
        • Van Huffel S.
        • et al.
        Prediction of ectopic pregnancy in women with a pregnancy of unknown location.
        Ultrasound Obstet Gynecol. 2007;
        • Lafay Pillet M.C.
        • Huchon C.
        • Santulli P.
        • Borghese B.
        • Chapron C.
        • Fauconnier A.
        A clinical score can predict associated deep infiltrating endometriosis before surgery for an endometrioma.
        Hum Reprod. 2014; 29: 1666-1676
        • Reid S.
        • Lu C.
        • Condous G.
        Can we improve the prediction of pouch of Douglas obliteration in women with suspected endometriosis using ultrasound-based models? A multicenter prospective observational study.
        Acta Obstet Gynecol Scand. 2015;
        • Tellum T.
        • Nygaard S.
        • Skovholt E.K.
        • Qvigstad E.
        • Lieng M.
        Development of a clinical prediction model for diagnosing adenomyosis.
        Fertil Steril. 2018; 110: 957-964.e3
        • Visser N.C.
        • Breijer M.C.
        • Herman M.C.
        • Bekkers R.L.
        • Veersema S.
        • Opmeer B.C.
        • et al.
        Factors attributing to the failure of endometrial sampling in women with postmenopausal bleeding.
        Acta Obstet Gynecol Scand. 2013; 92: 1216-1222
        • Vonk Noordegraaf A.
        • Anema J.R.
        • Louwerse M.D.
        • Heymans M.W.
        • van Mechelen W.
        • Brolmann H.A.
        • et al.
        Prediction of time to return to work after gynaecological surgery: a prospective cohort study in the Netherlands.
        BJOG. 2014; 121: 487-497
        • Van Calster B.
        • Bobdiwala S.
        • Guha S.
        • Van Hoorde K.
        • Al-Memar M.
        • Harvey R.
        • et al.
        Managing pregnancy of unknown location based on initial serum progesterone and serial serum hCG levels: development and validation of a two-step triage protocol.
        Ultrasound Obstet Gynecol. 2016; 48: 642-649
        • Buckley R.G.
        • King K.J.
        • Disney J.D.
        • Gorman J.D.
        • Klausen J.H.
        History and physical examination to estimate the risk of ectopic pregnancy: validation of a clinical prediction model.
        Ann Emerg Med. 1999;
        • Van Calster B.
        • Abdallah Y.
        • Guha S.
        • Kirk E.
        • Van Hoorde K.
        • Condous G.
        • et al.
        Rationalizing the management of pregnancies of unknown location: temporal and external validation of a risk prediction model on 1962 pregnancies.
        Hum Reprod [Internet]. 2013; 28 (Available from): 609-616
        • Stevens K.
        • Houterman S.
        • Muller I.
        • Weyers S.
        • van Vliet H.
        • Schoot B.
        Models to predict unsuccessful endometrial ablation: external validation.
        J Minim Invasive Gynecol. 2019;
        • Mann c.J.
        Observational research methods. Research design II.
        Emerg Med J. 2003;
        • Bouwmeester W.
        • Zuithoff N.P.A.
        • Mallett S.
        • Geerlings M.I.
        • Vergouwe Y.
        • Steyerberg E.W.
        • et al.
        Reporting and methods in clinical prediction research: a systematic review.
        PLoS Med. 2012; 9
        • Steyerberg E.W.
        • Vergouwe Y.
        Towards better clinical prediction models: seven steps for development and an ABCD for validation.
        Eur Heart J. 2014;
      7. Steyerberg EW, Eijkemans MJ, Harrell FE, Habbema JD. Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. Stat Med [Internet]. 2000 Apr 30 [cited 2018 Dec 3];19(8):1059–79. Available from:

      8. Steyerberg EW, Eijkemans MJ, Habbema JD. Stepwise selection in small data sets: a simulation study of bias in logistic regression analysis. J Clin Epidemiol [Internet]. 1999 Oct [cited 2018 Dec 3];52(10):935–42. Available from:

        • Gorelick M.H.
        Bias arising from missing data in predictive models.
        J Clin Epidemiol. 2006;
      9. van der Heijden GJMG, T. Donders AR, Stijnen T, Moons KGM. Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: A clinical example. J Clin Epidemiol. 2006.

        • Melgaard L.
        • Gorst-Rasmussen A.
        • Lane D.A.
        • Rasmussen L.H.
        • Larsen T.B.
        • Lip G.Y.H.
        Assessment of the CHA2DS2-VASc score in predicting ischemic stroke, thromboembolism, and death in patients with heart failure with and without atrial fibrillation.
        JAMA - J Am Med Assoc. 2015;
        • Moons K.G.M.
        • Kengne A.P.
        • Grobbee D.E.
        • Royston P.
        • Vergouwe Y.
        • Altman D.G.
        • et al.
        Risk prediction models: II. External validation, model updating, and impact assessment.
        Heart. 2012;
        • Collins G.S.
        • De Groot J.A.
        • Dutton S.
        • Omar O.
        • Shanyinde M.
        • Tajar A.
        • et al.
        External validation of multivariable prediction models: a systematic review of methodological conduct and reporting.
        BMC Med Res Method. 2014;
        • Debray T.P.A.
        • Vergouwe Y.
        • Koffijberg H.
        • Nieboer D.
        • Steyerberg E.W.
        • Moons K.G.M.
        A new framework to enhance the interpretation of external validation studies of clinical prediction models.
        J Clin Epidemiol. 2015;
        • Moons K.G.M.
        • Wolff R.F.
        • Riley R.D.
        • Whiting P.F.
        • Westwood M.
        • Collins G.S.
        • et al.
        PROBAST: a tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration.
        Ann Intern Med. 2019;
      10. Stevens KYR, Houterman S, Muller I, Weyers S, van Vliet H, Schoot BC. Models to Predict Unsuccessful Endometrial Ablation: External Validation. J Minim Invasive Gynecol [Internet]. 2019;26 (7 Supp:S46. Available from:

      11. Moher D, Liberati A, Tetzlaff J AD. PRISMA 2009 Flow Diagram. The PRISMA statement. 2009.