Abstract


OBJECTIVE: All the existing predicting models of live birth were developed from separate retrospective data without prior hypothesis based on previous researches: These models produce conflicting and non generalizable predictions. A meta-analysis (MAL) of existing models may provide the best synthesis of prior evidence, from which a new research can be based. In this paper, we suggest adequate meta-analytical technique to aggregate predicting models and we provide the first MAL synthesizing prior evidence of existing predicting model on Intra-Uterine Insemination (IUI) view more

OBJECTIVE: All the existing predicting models of live birth were developed from separate retrospective data without prior hypothesis based on previous researches: These models produce conflicting and non generalizable predictions. A meta-analysis (MAL) of existing models may provide the best synthesis of prior evidence, from which a new research can be based. In this paper, we suggest adequate meta-analytical technique to aggregate predicting models and we provide the first MAL synthesizing prior evidence of existing predicting model on Intra-Uterine Insemination (IUI)
DESIGN: Meta-analysis
MATERIALS AND METHODS: We conducted our MAL according PRISMA guidelines for study selection and research. All the existing predicting models of LB in IUI were selected without restriction. The Multivariate General least Square (MGLS) was a priori the more appropriate method to account for existing inter-correlation when estimating multivariable logistic regression slopes, and was compared with the classical random MAL model.
RESULTS: 12 studies (33925 cycles, 15266 patients) were identified and included into the MAL. Overall, we found 17 predictors, 11 out of them reported more than once. Multiple standardizations were needed in particular conversions into continuous covariates. The MGLS technique provided more accurate results compared with the classical random model. Only 7 assumed inter-correlated predictors were found significantly determinant.

Odds Ratios, 95%CI and related I² heterogeneity score, by order of decreasing determination
Predictors Variable Pooled, [95%CI] I² (%)
Female age Continuous .97 [ .96;.99] 8
NTMS (log) Continuous 1.95 [1.65-2.25] 16
Number of Dominant Follicles Count 1.31, [1.02-1.6] 24
Ovarian Stimulation Binary 1.89, [1.06-3.5] 45
Infertility Duration Continuous .92, [.87-.97] 15
Number of previous IUI cycles Count .82, [.76-.89] 12
Basal FSH>10iu/L Binary .78,[.56-.98] 35

Female age, sperm motility, infertility duration and number of previous cycles were highly significant and homogeneous among studies (i2<15%). The number of dominant follicles and ovarian stimulation and Basal FSH>10 iu/l show an important effect however characterized by higher between-study heterogeneity. Other variables like AMH, unexplained infertility, cervical factor, endometriosis, or abnormal weight were not found significant. The Intercept, measuring the success rates adjusted for patients characteristics, shows considerable difference between center performances (5.9% until 21.7%).
CONCLUSIONS: Our analysis provides an overall synthesis of 12 studies totalizing 33925 cycles, based on an appropriate MAL technique. Our table constitutes the basis for a collaborative adaptive meta-analysis: new studies based on recent data can compare the odds ratio with our confidence interval, and new predictors can be added for improving estimate precision.

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