A case study of the applicability of a prediction model for the selection of patients undergoing in vitro fertilization for single embryo transfer in another center
ABSTRACT To evaluate the application in a different fertility clinic of a prediction model for selecting IVF patients for elective single embryo transfer.
Retrospective analysis of a large database obtained from a tertiary infertility center.
University medical center.
The model, derived at the "development center" was applied in 494 consecutive first IVF cycles carried out at the "application center."
After adjustment of embryo scoring system to be compatible with that used by the prediction model, it was applied to the development center data. A score chart for predicting the probability of singleton or twin pregnancy was constructed.
The area under the receiver operator curve (ROC) was determined to measure the ability of the model to discriminate between ongoing pregnancy and twin pregnancy. Calibration plots were made to assess agreement between predicted and observed pregnancy rates (PR).
The areas under the ROC for predicting ongoing pregnancy and twin pregnancy were 0.63 and 0.66, respectively. Insertion of a correction factor equivalent to the difference in odds ratios for ongoing PR between the two centers was required to improve the calibration of the model.
After adaptation, the model performed well in the application center.
- SourceAvailable from: Arno van Peperstraten
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- "An improved prognostic model to enable professionals to select appropriate couples for eSET would be helpful and may minimize the lower success rate reported with eSET in an unselected population (van Montfoort et al., 2006). New prediction models have recently been published (Hunault et al., 2007; van der Steeg et al., 2007), but none of them are as yet translated to daily practice. The same situation applies to embryo selection. "
ABSTRACT: Elective single embryo transfer (eSET) enables the prevention of multiple pregnancies after in vitro fertilization (IVF). However, in Europe, the multiple pregnancy rate after IVF remains stable at approximately 23%, with SET occurring in 15% of all IVF cycles. In most European clinics, the decision for the number of embryos transferred is established through a form of shared decision-making between patients and professionals. The aim of this study is to explore factors influencing this decision, in particular factors preventing eSET use. We performed explorative, semi-structured, in-depth interviews, based on two theoretical models. The interviews were performed among 19 Dutch IVF professionals and 20 patients who had just undergone IVF or were on the waiting list for IVF. The interviews were fully transcribed and two researchers independently scored the factors according to the models. We identified a wide variety of factors, potentially influencing eSET use: 37 with the professionals and 26 among the patients. Examples of factors mentioned by both patients and professionals were: uncertainty about the eSET technique, couples' lack of knowledge about essential eSET aspects, absence of a reimbursement system which favours eSET, inadequate options to select couples suitable for eSET and inferior cryopreservation success rates. This study demonstrates that both IVF professionals and patients identify numerous factors preventing eSET use in clinical practice. To estimate the impact of these factors identified, a quantitative confirmation and assessment of the magnitude of the effect is necessary.Human Reproduction 07/2008; 23(9):2036-42. DOI:10.1093/humrep/den156 · 4.59 Impact Factor
Conference Paper: Approximating sensor signals: a rough set approach[Show abstract] [Hide abstract]
ABSTRACT: This paper presents an approach to approximating sensor signals. In classical rough set theory, set approximation is carried out in non-empty, finite universes of objects. In contrast, we carry out set approximation inside non-empty, uncountable sets (universes) of points. This study is motivated by an interest in classifying sample values for various types of sensors. The result of this study is the introduction of a family of discrete rough integrals based on rough set theory. The discrete rough integrals have practical implications, since these integrals serve as an aid in approximate reasoning and in pattern recognition relative to segments of continuous signals. In the context of approximate reasoning, discrete rough integrals provide a basis for determining the relevance of sensors over a particular sampling period. In the context of pattern recognition, discrete rough integrals can be useful in doing such things as classifying radar weather data, vehicular traffic patterns, and waveforms of power system faults. By way of illustration, one form of discrete integral is used to assess the accuracy of set approximation of a sensor signal.Electrical and Computer Engineering, 2002. IEEE CCECE 2002. Canadian Conference on; 02/2002
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ABSTRACT: Prediction models have been developed in reproductive medicine to help assess the chances of a treatment-(in)dependent pregnancy. Careful evaluation is needed before these models can be implemented in clinical practice. We systematically searched the literature for papers reporting prediction models in reproductive medicine for three strategies: expectant management, intrauterine insemination (IUI) or in vitro fertilization (IVF). We evaluated which phases of development these models had passed, distinguishing between (i) model derivation, (ii) internal and/or external validation, and (iii) impact analysis. We summarized their performance at external validation in terms of discrimination and calibration. We identified 36 papers reporting on 29 prediction models. There were 9 models for the prediction of treatment-independent pregnancy, 3 for the prediction of pregnancy after IUI and 17 for the prediction of pregnancy after IVF. All of the models had completed the phase of model derivation. For six models, the validity of the model was assessed only in the population in which it was developed (internal validation). For eight models, the validity was assessed in populations other than the one in which the model was developed (external validation), and only three of these showed good performance. One model had reached the phase of impact analysis. Currently, there are three models with good predictive performance. These models can be used reliably as a guide for making decisions about fertility treatment, in patients similar to the development population. The effects of using these models in patient care have to be further investigated.Human Reproduction Update 06/2009; 15(5):537-52. DOI:10.1093/humupd/dmp013 · 8.66 Impact Factor