OBJECTIVE: We assessed the predictive value added by Anti-Mullerian Hormone (AMH) to currently validated live birth (LB) prediction models. METHODS: Based on recent data from our center, we compared the external validity of the Templeton Model (TM) and its recent improvement (TMA) to select our model of reference. The added predictive value of AMH was assessed in testing the likelihood ratio significance and the Net Reclassification Index (NRI). The surrogate utility of AMH was tested by conducting an exploratory stepwise logistic regression. RESULTS: Based on 715 cycles, the original TM had poor performances (auROC C = 0.61 [0.58, 0.66], improving by fitting TM to our data (C = 0.71[0.66, 0.75]. TMA fitting proved better (C = 0.76; 95 %CI: 0.71, 0.80) and was selected as model of reference. Adding AMH to TMA or TM had no effect on discrimination (C = 0.76; 95 %CI: 0.72, 0.80), the likelihood ratio test was significant (p = 0.023), but the NRI was not (6.7 %; p = 0.055). A stepwise exploratory logistic regression identified the effects of age, previous IVF resulting in LB, time trend and AMH, leading to a prediction model reduced to four predictors (C = 0.75 [0.70, 0.81]). CONCLUSION: The added predictive value of AMH is limited. A possible surrogate/simplifying effect of AMH was found in eliminating 9/13 predictors from the model of reference. We conclude that whereas AMH does not add significant predictive value to the existing model, it contributes to simplifying the equation to reliable, easy to collect, and available in all databases predictors: age, AMH, time trend and female previous fertility history.
OBJECTIVE: We assessed the predictive value added by Anti-Mullerian Hormone (AMH) to currently validated live birth (LB) prediction models. METHODS: Based on recent data from our center, we compared the external validity of the Templeton Model (TM) and its recent improvement (TMA) to select our model of reference. The added predictive value of AMH was assessed in testing the likelihood ratio significance and the Net Reclassification Index (NRI). The surrogate utility of AMH was tested by conducting an exploratory stepwise logistic regression. RESULTS: Based on 715 cycles, the original TM had poor performances (auROC C = 0.61 [0.58, 0.66], improving by fitting TM to our data (C = 0.71[0.66, 0.75]. TMA fitting proved better (C = 0.76; 95 %CI: 0.71, 0.80) and was selected as model of reference. Adding AMH to TMA or TM had no effect on discrimination (C = 0.76; 95 %CI: 0.72, 0.80), the likelihood ratio test was significant (p = 0.023), but the NRI was not (6.7 %; p = 0.055). A stepwise exploratory logistic regression identified the effects of age, previous IVF resulting in LB, time trend and AMH, leading to a prediction model reduced to four predictors (C = 0.75 [0.70, 0.81]). CONCLUSION: The added predictive value of AMH is limited. A possible surrogate/simplifying effect of AMH was found in eliminating 9/13 predictors from the model of reference. We conclude that whereas AMH does not add significant predictive value to the existing model, it contributes to simplifying the equation to reliable, easy to collect, and available in all databases predictors: age, AMH, time trend and female previous fertility history.
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