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      Development a nomogram to predict fertilisation rate of infertile males with borderline semen by using semen parameters combined with AMH and INHB

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          Abstract

          The sperm quality of some males is in a critical state, making it hard for clinicians to choose the suitable fertilisation methods. This study aimed to develop an intelligent nomogram for predicting fertilisation rate of infertile males with borderline semen. 160 males underwent in vitro fertilisation (IVF), 58 of whom received rescue ICSI (R‐ICSI) due to fertilisation failure (fertilisation rate of IVF ≤30%). A least absolute shrinkage and selection operator (LASSO) regression analysis identified sperm concentration, progressively motile spermatozoa (PMS), seminal plasma anti‐Müllerian hormone (spAMH), seminal plasma inhibin (spINHB), serum AMH (serAMH) and serum INHB (serINHB) as significant predictors. The nomogram was plotted by multivariable logistic regression. This nomogram‐illustrated model showed good discrimination, calibration and clinical value. The area under the receiver operating characteristic curve (AUC) of the nomogram was 0.762 ( p < .001). Calibration curve and Hosmer–Lemeshow test ( p = .5261) showed good consistency between the predictions of the nomogram and the actual observations, and decision curve analysis showed that the nomogram was clinically useful. This nomogram may be useful in predicting fertilisation rate, mainly focused on new biomarkers, INHB and AMH. It could assist clinicians and laboratory technicians select appropriate fertilisation methods (IVF or ICSI) for male patients with borderline semen.

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          Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration

          The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.
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            Regression models in clinical studies: determining relationships between predictors and response.

            Multiple regression models are increasingly being applied to clinical studies. Such models are powerful analytic tools that yield valid statistical inferences and make reliable predictions if various assumptions are satisfied. Two types of assumptions made by regression models concern the distribution of the response variable and the nature or shape of the relationship between the predictors and the response. This paper addresses the latter assumption by applying a direct and flexible approach, cubic spline functions, to two widely used models: the logistic regression model for binary responses and the Cox proportional hazards regression model for survival time data.
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              Calibration of risk prediction models: impact on decision-analytic performance.

              Decision-analytic measures to assess clinical utility of prediction models and diagnostic tests incorporate the relative clinical consequences of true and false positives without the need for external information such as monetary costs. Net Benefit is a commonly used metric that weights the relative consequences in terms of the risk threshold at which a patient would opt for treatment. Theoretical results demonstrate that clinical utility is affected by a model';s calibration, the extent to which estimated risks correspond to observed event rates. We analyzed the effects of different types of miscalibration on Net Benefit and investigated whether and under what circumstances miscalibration can make a model clinically harmful. Clinical harm is defined as a lower Net Benefit compared with classifying all patients as positive or negative by default. We used simulated data to investigate the effect of overestimation, underestimation, overfitting (estimated risks too extreme), and underfitting (estimated risks too close to baseline risk) on Net Benefit for different choices of the risk threshold. In accordance with theory, we observed that miscalibration always reduced Net Benefit. Harm was sometimes observed when models underestimated risk at a threshold below the event rate (as in underestimation and overfitting) or overestimated risk at a threshold above event rate (as in overestimation and overfitting). Underfitting never resulted in a harmful model. The impact of miscalibration decreased with increasing discrimination. Net Benefit was less sensitive to miscalibration for risk thresholds close to the event rate than for other thresholds. We illustrate these findings with examples from the literature and with a case study on testicular cancer diagnosis. Our findings strengthen the importance of obtaining calibrated risk models.
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                Author and article information

                Contributors
                hr7424@126.com
                Journal
                Andrologia
                Andrologia
                10.1111/(ISSN)1439-0272
                AND
                Andrologia
                John Wiley and Sons Inc. (Hoboken )
                0303-4569
                1439-0272
                16 July 2021
                October 2021
                : 53
                : 9 ( doiID: 10.1111/and.v53.9 )
                : e14182
                Affiliations
                [ 1 ] Ningxia Medical University Yinchuan China
                [ 2 ] Key Laboratory of Fertility Preservation and Maintenance of Ministry of Education Ningxia Medical University Yinchuan China
                [ 3 ] Gansu Provincial Maternity and Child‐care Hospital Lanzhou, Gansu China
                [ 4 ] Reproductive Medicine Center General Hospital of Ningxia Medical University Yinchuan China
                Author notes
                [*] [* ] Correspondence

                Rong Hu, Reproductive Medicine Center, General Hospital of Ningxia Medical, University, Yinchuan, 750001, China.

                Email: hr7424@ 123456126.com

                Author information
                https://orcid.org/0000-0002-4859-6945
                Article
                AND14182
                10.1111/and.14182
                8519038
                34270116
                ba1178c2-ee7c-4420-a1b9-3994a0299b2e
                © 2021 The Authors. Andrologia published by Wiley-VCH GmbH.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 10 June 2021
                : 14 February 2021
                : 19 June 2021
                Page count
                Figures: 5, Tables: 2, Pages: 9, Words: 6851
                Funding
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 81960277
                Funded by: Key Research and Development program of Ningxia Hui Autonomous Region , doi 10.13039/100016692;
                Award ID: 2019BFG02005
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                October 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.8 mode:remove_FC converted:15.10.2021

                amh,fertilisation rate,inhb,nomogram
                amh, fertilisation rate, inhb, nomogram

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