Reducing Inter-Laboratory Differences between Semen Analyses Using Z Score and Regression Transformations.

BACKGROUND: Standardization of the semen analysis may improve reproducibility. We assessed variability between laboratories in semen analyses and evaluated whether a transformation using Z scores and regression statistics was able to reduce this variability. MATERIALS AND METHODS: We performed a retrospective cohort study. We calculated between-laboratory coefficients of variation (CVB) for sperm concentration and for morphology. Subsequently, we standardized the semen analysis results by calculating laboratory specific Z scores, and by using regression. We used analysis of variance for four semen parameters to assess systematic differences between laboratories before and after the transformations, both in the circulation samples and in the samples obtained in the prospective cohort study in the Netherlands between January 2002 and February 2004. RESULTS: The mean CVBwas 7% for sperm concentration (range 3 to 13%) and 32% for sperm morphology (range 18 to 51%). The differences between the laboratories were statistically significant for all semen parameters (all P<0.001). Standardization using Z scores did not reduce the differences in semen analysis results between the laboratories (all P<0.001). CONCLUSION: There exists large between-laboratory variability for sperm morphology and small, but statistically significant, between-laboratory variation for sperm concentration. Standardization using Z scores does not eliminate between-laboratory variability.

Semen analysis and prediction of natural conception.

STUDY QUESTION: Do two semen analyses predict natural conception better than a single semen analysis and will adding the results of repeated semen analyses to a prediction model for natural pregnancy improve predictions? SUMMARY ANSWER: A second semen analysis does not add helpful information for predicting natural conception compared with using the results of a single semen analysis and addition of the second analysis to a prediction model for natural conception did not improve predictions. WHAT IS KNOWN ALREADY: A major problem with semen analyses is the large variability of results within an individual. High-quality evidence is lacking on how many semen analyses need to be performed during the fertility workup to achieve an accurate prediction of conception. STUDY DESIGN, SIZE, DURATION: We conducted a prospective cohort study of 897 consecutive couples presenting with subfertility in two university hospitals in the period 2002-2004 in the Netherlands. PARTICIPANTS/MATERIALS, SETTING, AND METHODS: The laboratories scored sperm parameters according to the 1999 WHO criteria. Sperm concentration was counted and motility was assessed in a Makler counting chamber at a magnification of ×200. All assessments were performed by trained laboratory technicians. Follow-up started at the completion of the infertility workup and ended after 12 months. Primary end-point was natural conception resulting in an ongoing pregnancy. We constructed models for three strategies for the prediction of natural conception, using univariable and multivariable Cox hazard regression analyses. We evaluated the performance of the three strategies by comparing goodness-of-fit, discrimination and calibration. First, we analysed the semen parameters only. Secondly, we analysed the semen parameters in addition to the multivariable Hunault prediction model. MAIN RESULTS AND THE ROLE OF CHANCE: Of the 897 couples, 132 (15%) achieved a pregnancy by natural conception. Using the results of a single semen analysis only, the calculated probabilities of natural conception within 12 months across the study population ranged from 0.12 to 0.38, with a median of 0.16 (IQR: 0.16-0.17). Using the results of two semen analyses did not lead to a better goodness-of-fit. Discriminative capacity was rather poor, with an area under the ROC curve (AUC) ranging from 0.51 to 0.56. Using the Hosmer-Lemeshow test statistic we found no signs of poor calibration. Using the results of two semen analyses in combination with the Hunault model did not significantly increase goodness-of-fit compared with using a single semen analysis. The Hunault model with the addition of the semen parameters fitted the data significantly better than the Hunault model itself (difference in -2 Log likelihood: 13; 3 df; P = 0.002). Using the Hosmer-Lemeshow test statistic we found no signs of poor calibration. LIMITATIONS, REASONS FOR CAUTION: The academic setting possibly explains the relatively low natural conception rates, with only 15% achieving a natural conception within 1 year. Men with azoospermia were excluded. WIDER IMPLICATIONS OF THE FINDINGS: Performing more than one semen analysis will not increase the prognostic power of the test in clinical practice. Adding the first semen analysis to the Hunault model for the prediction of natural conception improved performance significantly compared with using the Hunault model alone. External validation, in other populations, should follow to confirm our conclusions, and to evaluate the generalizability or transportability of the extended Hunault model. STUDY FUNDING/COMPETING INTEREST(S): No external funding was involved in this study. None of the authors has any conflict of interest to declare.

Prognostic value of the postcoital test for spontaneous pregnancy.

OBJECTIVE: To evaluate the capacity of the postcoital test (PCT) to predict spontaneous pregnancy in a large cohort study of subfertile couples. DESIGN: Prospective study. SETTING: Department of reproductive medicine of 38 hospitals in the Netherlands. PATIENT(S): Between January 2002 and February 2004, we prospectively included consecutive subfertile couples who had not been evaluated previously for subfertility. INTERVENTION(S): We estimated the contribution of the PCT result to the existing prediction model for spontaneous pregnancy by calculating the adjusted hazard ratio (HR) of an abnormal PCT result. We constructed a second prediction model (PCT model) based on the reference model including the PCT. MAIN OUTCOME MEASURE(S): Primary endpoint in this study was ongoing pregnancy. We evaluated the performance of the PCT model in comparison with the reference model by calculating goodness of fit, discrimination, calibration, and the "net reclassification improvement". RESULT(S): We included 3,021 couples of whom 537 (18%) had a spontaneous pregnancy and 55 (1.8%) a nonsuccessful pregnancy; 1,316 (44%) started treatment within 12 months, 824 (27%) neither started treatment nor became pregnant, and 289 (10%) became lost to follow-up within 12 months. The adjusted HR for an abnormal PCT was 0.76 (95% confidence interval [CI]: 0.62 to 0.94). The adjusted HR for an abnormal PCT was 0.63 (95% CI: 0.47 to 0.84) in case of no spermatozoa, 0.81 (95% CI: 0.57 to 1.2) in case of nonmotile spermatozoa, and 1.2 (95% CI: 0.8 to 1.8) in case of motile, nonprogressive spermatozoa. Adding the PCT result to the reference model did not improve goodness of fit. Discrimination was equally poor for the PCT model and the reference model. The calibration plots of both models showed comparably good calibration. The net reclassification improvement of the predictions of the PCT model compared with the reference model was -1.1%. CONCLUSION(S): This study demonstrated that the PCT has prognostic value but does not add substantially to a prognostic model for spontaneous pregnancy.

Reproducibility and reliability of repeated semen analyses in male partners of subfertile couples.

OBJECTIVE: To determine the precise degree of variability that is represented by the reproducibility and reliability of semen analysis. The general assumption is that semen analyses need to be repeated because of a high degree of within-individual variability. However, the precise degree of variability is not well established in male partners of subfertile couples. DESIGN: Retrospective cohort study. SETTING: Two university hospitals in the Netherlands, which routinely perform two semen analyses in the male partner of subfertile couples. PATIENT(S): Male partners of subfertile couples. INTERVENTIONS: None. MAIN OUTCOME MEASURE(S): We assessed the test-retest reproducibility, by calculating the coefficient of variation (CV(w)) for five semen parameters. The CV(w) expresses, on a relative scale, the degree of closeness of repeated measurements taken in the same subject. We also estimated the reliability of these semen parameters, in terms of the intraclass correlation coefficient, which expresses the ratio of the between-subject variability over the total variability. RESULT(S): We analyzed the data of 5,240 men and found that the CV(w) of all semen parameters ranged from 28% to 34%. The intraclass correlation coefficients of these semen parameters were moderate to high: volume: 0.70; concentration: 0.89; motility: 0.58; morphology: 0.60; total motile count: 0.73. CONCLUSION(S): This study affirmed the presumed large within-subject variability and the limited reproducibility of semen analyses in subfertile men. Whether this degree of variability within men justifies one or more repetitions of the semen analysis in view of consequences for clinical management should be the topic of future studies. Until then it seems reasonable to perform two semen analyses.

Prediction models in reproductive medicine: a critical appraisal.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Immunoglobulin G antisperm antibodies and prediction of spontaneous pregnancy.

OBJECTIVE: To investigate the predictive capacity of immunoglobulin G ASA (direct MAR test) for spontaneous ongoing pregnancy in subfertile couples. DESIGN: Prospective cohort study. SETTING: Nine fertility centers in The Netherlands. PATIENT(S): Consecutive ovulatory subfertile couples. INTERVENTION(S): A basic fertility workup, including a mixed agglutination reaction test for IgG (MAR test) at first semen analysis. MAIN OUTCOME MEASURE(S): Spontaneous conception resulting in ongoing pregnancy. RESULT(S): We included 1,794 couples, of which 283 (16%) had a spontaneous ongoing pregnancy within 1 year. When a threshold 50% was used for an abnormal test result, the MAR test was positive in 3% of the couples. In the univariable analysis, a positive MAR test >or=50% reduced, albeit not statistically significant, the probability of spontaneous pregnancy (hazard ratio [HR] 0.76, 95% confidence interval [CI] 0.34 to 1.7). In the multivariable analysis, a positive MAR test >or=50% had no contribution in the prediction of spontaneous pregnancy (HR 0.99, 95% CI 0.40 to 2.4). CONCLUSION(S): This large cohort study shows that the MAR test is not able to predict spontaneous pregnancy chances. Its routine use in the basic fertility workup for identification of couples with low spontaneous pregnancy chances is not justified.