Establishment of a predictive nomogram for clinical pregnancy rate in patients with endometriosis undergoing fresh embryo transfer

doi:10.12122/j.issn.1673-4254.2024.07.21

Abstract

Abstract:

Objective To establish a nomogram model for predicting clinical pregnancy rate in patients with endometriosis undergoing fresh embryo transfer. Methods We retrospectively collected the data of 464 endometriosis patients undergoing fresh embryo transfer, who were randomly divided into a training dataset (60%) and a testing dataset (40%). Using univariate analysis, multiple logistic regression analysis, and LASSO regression analysis, we identified the factors associated with the fresh transplantation pregnancy rate in these patients and developed a nomogram model for predicting the clinical pregnancy rate following fresh embryo transfer. We employed an integrated learning approach that combined GBM, XGBOOST, and MLP algorithms for optimization of the model performance through parameter adjustments. Results The clinical pregnancy rate following fresh embryo transfer was significantly influenced by female age, Gn initiation dose, number of assisted reproduction cycles, and number of embryos transferred. The variables included in the LASSO model selection included female age, FSH levels, duration and initial dose of Gn usage, number of assisted reproduction cycles, retrieved oocytes, embryos transferred, endometrial thickness on HCG day, and progesterone level on HCG day. The nomogram demonstrated an accuracy of 0.642 (95% CI: 0.605-0.679) in the training dataset and 0.652 (95% CI: 0.600-0.704) in the validation dataset. The predictive ability of the model was further improved using ensemble learning methods and achieved predicative accuracies of 0.725 (95% CI: 0.680-0.770) in the training dataset and 0.718 (95% CI: 0.675-0.761) in the validation dataset. Conclusions The established prediction model in this study can help in prediction of clinical pregnancy rates following fresh embryo transfer in patients with endometriosis.

Key words: endometriosis, infertility, clinical pregnancy rate, predictive model, nomogram

Shenhao PAN, Yankun LI, Zhewei WU, Yuling MAO, Chunyan WANG. Establishment of a predictive nomogram for clinical pregnancy rate in patients with endometriosis undergoing fresh embryo transfer[J]. Journal of Southern Medical University, 2024, 44(7): 1407-1415.

Figures/Tables 8

Fig 1 Flowchart of subject selection. Sample size (n) is indicated in each category. PGT: Preimplantation genetic testing; D-IVF: Sperm donation-in vitro fertilization; DOR: Diminished ovarian reserve; POR: Poor ovarian response; FET: Frozen embryo transfer.

Fig.2 Proposed methodology for research work. LightGBM: Light gradient boosting machine; XGBoost: Extreme Gradient Boosting; MLP: Multi-Layer Perceptron.

Tab.1 Univariate analysis of factors affecting clinical pregnancy

Variables	Pregnancy (n=254)	Unpregnancy (n=210)	t/χ²	P
Age (year)	32.19±3.58	33.70±4.43	-4.008	<0.001
BMI(kg/m²)	21.22±3.00	21.66±2.87	-1.596	0.111
AMH (ng/mL)	3.19±2.23	2.81±2.10	1.872	0.062
Infertility time in years	4.00±2.55	4.35±2.81	-1.358	0.175
Type of subfertility			0.345	0.571
Primary infertility	102	90
Secondary infertility	152	120
FSH (U/L)	5.92±2.44	6.54±3.06	-2.447	0.015
LH (U/L)	4.35±5.80	3.70±2.93	1.554	0.121
P (nmol/L)	1.69±6.80	1.61±5.55	0.137	0.891
PRL (ng/mL)	17.24±8.85	16.31±8.87	1.124	0.262
T (nmol/L)	0.98±0.46	1.00±0.41	-0.611	0.541
E2 (pg/mL)	220.95±365.46	179.90±196.84	1.540	0.124
Dates of Gn Duration	11.14±2.28	10.78±2.38	1.650	0.100
Total Gn (IU)	2356.74±1166.58	2423.30±1018.86	-0.652	0.515
Gn initation dose	190.25±60.69	211.22±67.40	-3.526	<0.001
E2 on HCG day (pg/mL)	8062.67±3937.74	7527.33±4201.39	1.414	0.158
P on HCG day (nmol/L)	2.04±0.94	2.20±1.01	-1.714	0.087
Follicles≥14 mm on HCG day	6.45±4.32	5.80±4.19	1.633	0.103
Endometrium thickness on HCG day (mm)	11.46±2.22	11.10±2.42	1.665	0.097
Oocytes retrieved	9.09±3.92	8.19±4.31	2.366	0.018
ART Cycles	1.33±0.68	1.55±1.07	-2.607	0.010
Fertilization rate (%)	83.58±17.52	81.50±20.29	1.174	0.241
Normal Fertilization rate (%)	59.92±23.11	60.99±23.67	-0.492	0.623
degeneration rate(%)	2.97±8.24	3.24±1.43	-0.252	0.801
Cleavage rate (%)	98.59±5.00	97.45±10.38	1.495	0.136
Usable embryos rate (%)	56.53±26.37	53.74±26.88	1.127	0.260
High quality day 3 embyo (%)	28.43±26.93	24.61±30.07	1.442	0.150
Blastulation rate (%)	33.08±33.81	31.50±33.41	0.506	0.613
Number of frozen embryos	2.08±2.05	1.58±1.91	2.716	0.007
Embryo transfer (n)	1.65±0.48	1.48±0.50	3.700	<0.001

Tab.2 Multivariate logistic regression analysis of key clinical features affecting pregnancy outcomes

Variables	B	χ²	P	OR	95% CI
Age (year)	-0.075	7.990	0.005	0.928	0.881-0.977
Gn initation dose	-0.003	4.495	0.034	0.997	0.993-1.000
ART Cycles	-0.247	4.207	0.040	0.781	0.617-0.989
Embryo transfer (n)	0.838	17.492	<0.001	2.311	1.561-3.422

Fig.3 LASSO regression model. A: Coefficient distribution chart by lasso regression. Each curve represents a coefficient, and the x-axis represents the regularization penalty parameter. As λ changes, a coefficient that becomes non-zero enters the LASSO regression model. B: Ten-fold cross-validation diagram. The red dotted vertical line crosses over the optimal log λ. The two dotted lines represent one standard deviation from the minimum value, with 1se as the criterion (λ=0.006438551).

Fig.4 Nomogram of the prediction model for clinical pregnancy in endometriosis patients undergoing fresh embryo transfer. The nomogram is applied by drawing a perpendicular line from each risk factor's corresponding axis to intersect with the "Points" top line, followed by calculation of the total score as the sum of points for all risk factors, drawing a descending line from the "Total points" axis to intercept with the lower line, and determination of the probability of clinical pregnancy.

Fig.5 Validation of accuracy and discrimination of the nomogram model. A, B: ROC for training and validation cohorts. C, D: Calibration curves for evaluating calibration of the model: The horizontal axis is the predicted probability provided by this model, and the vertical axis is the observed incidence of pregnancy failure. The ideal line with 45° slope represents a perfect prediction (the predicted probability equals the observed probability; E, F: Decision curve of the prediction model.

Fig.6 Ensemble learning improves the performance of the predictive models. A, B: ROC for training and validation cohorts; C, D: Calibration curves for evaluating calibration of the training and validation cohorts. E, F: Decision curve of the training and validation cohorts.

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