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

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          Abstract

          Purpose

          Several mathematical models have been developed to estimate individualized chances of assisted reproduction techniques (ART) success, although with limited clinical application. Our study aimed to develop a decisional algorithm able to predict pregnancy and live birth rates after controlled ovarian stimulation (COS) phase, helping the physician to decide whether to perform oocytes pick-up continuing the ongoing ART path.

          Methods

          A single-center retrospective analysis of real-world data was carried out including all fresh ART cycles performed in 1998–2020. Baseline characteristics, ART parameters and biochemical/clinical pregnancies and live birth rates were collected. A seven-steps systematic approach for model development, combining linear regression analyses and decision trees (DT), was applied for biochemical, clinical pregnancy, and live birth rates.

          Results

          Of fresh ART cycles, 12,275 were included. Linear regression analyses highlighted a relationship between number of ovarian follicles > 17 mm detected at ultrasound before pick-up (OF17), embryos number and fertilization rate, and biochemical and clinical pregnancy rates ( p < 0.001), but not live birth rate. DT were created for biochemical pregnancy (statistical power–SP:80.8%), clinical pregnancy (SP:85.4%), and live birth (SP:87.2%). Thresholds for OF17 entered in all DT, while sperm motility entered the biochemical pregnancy’s model, and female age entered the clinical pregnancy and live birth DT. In case of OF17 < 3, the chance of conceiving was < 6% for all DT.

          Conclusion

          A systematic approach allows to identify OF17, female age, and sperm motility as pre-retrieval predictors of ART outcome, possibly reducing the socio-economic burden of ART failure, allowing the clinician to perform or not the oocytes pick-up.

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          Most cited references46

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          Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

          Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
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            Internal validation of predictive models: efficiency of some procedures for logistic regression analysis.

            The performance of a predictive model is overestimated when simply determined on the sample of subjects that was used to construct the model. Several internal validation methods are available that aim to provide a more accurate estimate of model performance in new subjects. We evaluated several variants of split-sample, cross-validation and bootstrapping methods with a logistic regression model that included eight predictors for 30-day mortality after an acute myocardial infarction. Random samples with a size between n = 572 and n = 9165 were drawn from a large data set (GUSTO-I; n = 40,830; 2851 deaths) to reflect modeling in data sets with between 5 and 80 events per variable. Independent performance was determined on the remaining subjects. Performance measures included discriminative ability, calibration and overall accuracy. We found that split-sample analyses gave overly pessimistic estimates of performance, with large variability. Cross-validation on 10% of the sample had low bias and low variability, but was not suitable for all performance measures. Internal validity could best be estimated with bootstrapping, which provided stable estimates with low bias. We conclude that split-sample validation is inefficient, and recommend bootstrapping for estimation of internal validity of a predictive logistic regression model.
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              Towards better clinical prediction models: seven steps for development and an ABCD for validation.

              Clinical prediction models provide risk estimates for the presence of disease (diagnosis) or an event in the future course of disease (prognosis) for individual patients. Although publications that present and evaluate such models are becoming more frequent, the methodology is often suboptimal. We propose that seven steps should be considered in developing prediction models: (i) consideration of the research question and initial data inspection; (ii) coding of predictors; (iii) model specification; (iv) model estimation; (v) evaluation of model performance; (vi) internal validation; and (vii) model presentation. The validity of a prediction model is ideally assessed in fully independent data, where we propose four key measures to evaluate model performance: calibration-in-the-large, or the model intercept (A); calibration slope (B); discrimination, with a concordance statistic (C); and clinical usefulness, with decision-curve analysis (D). As an application, we develop and validate prediction models for 30-day mortality in patients with an acute myocardial infarction. This illustrates the usefulness of the proposed framework to strengthen the methodological rigour and quality for prediction models in cardiovascular research.
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                Author and article information

                Contributors
                spaggiari.giorgia@aou.mo.it
                Journal
                J Assist Reprod Genet
                J Assist Reprod Genet
                Journal of Assisted Reproduction and Genetics
                Springer US (New York )
                1058-0468
                1573-7330
                27 January 2022
                : 1-14
                Affiliations
                [1 ]Department of Obstetrics and Gynaecology, Fertility Centre, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Arcispedale Santa Maria Nuova, Reggio Emilia, Italy
                [2 ]GRID grid.413363.0, ISNI 0000 0004 1769 5275, Unit of Endocrinology, Department of Medical Specialties, , Azienda Ospedaliero-Universitaria of Modena, Ospedale Civile of Baggiovara, ; Via Giardini 1355, 41126 Modena, Italy
                [3 ]GRID grid.7548.e, ISNI 0000000121697570, Department of Biomedical, Metabolic and Neural Sciences, , University of Modena and Reggio Emilia, ; Modena, Italy
                [4 ]GRID grid.7548.e, ISNI 0000000121697570, Clinical and Experimental Medicine PhD Program, , University of Modena and Reggio Emilia, ; Modena, Italy
                Author information
                http://orcid.org/0000-0002-1437-0622
                http://orcid.org/0000-0002-7089-7330
                http://orcid.org/0000-0003-1366-2081
                http://orcid.org/0000-0001-7626-9805
                http://orcid.org/0000-0002-8616-1731
                http://orcid.org/0000-0002-2133-4304
                http://orcid.org/0000-0002-0723-151X
                http://orcid.org/0000-0001-6607-7105
                Article
                2353
                10.1007/s10815-021-02353-4
                8793814
                35084638
                8ca6e5e8-16ad-48e6-a9f9-653228e9e175
                © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 24 August 2021
                : 5 November 2021
                Categories
                Assisted Reproduction Technologies

                Genetics
                assisted reproduction,controlled ovarian stimulation,oocytes,decision tree,ovarian follicles

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