Nomogram model to predict the probability of spontaneous abortion in human assisted reproductive technology

doi:10.21203/rs.3.rs-2608385/v1

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Nomogram model to predict the probability of spontaneous abortion in human assisted reproductive technology

https://doi.org/10.21203/rs.3.rs-2608385/v1

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Background: Spontaneous abortion is one of the most common complications of pregnancy in assisted reproductive technology (ART), it has become a thorny issue that poses a threat to pregnant women and their unborn children. Prevention is more important than treatment as for spontaneous abortion, Unfortunately, there is no accurate model to predict and prevent its occurrence.

Methods: This retrospective cohort study evaluated clinical data from 15,723 patients successfully conceived who underwent IVF/ICSI in the first people’s hospital of shangqiu and the affiliated hospital of zhengzhou university from december 2015 to February 2021. Patients were divided into an "abortion group" and a "non-abortion group" according to the outcome of abortion. We further performed a multi-factor and single-factor logistic regression analysis to determine the independent factors, then we establish nomogram model to avoid the occurrence of spontaneous abortion in ART patients based on the independent influencing factors.

Results: In this study, 15,723 patients had successfully conceived with ART, of which 2,336 patients (14.86%) had abortion, and 13,387 patients (85.14%) did not. Logistic regression analysis showed that female age (OR 1.050; 95% CI 1.029 ~ 1.061; P<0.001), male age (OR 1.102; 95% CI 1.086 ~ 1.115; P<0.001), follicle-stimulating hormone (OR 1.044; 95% CI 1.015 ~ 1.069; P<0.001), anti-Mullerian hormone (OR 0.891; 95% CI 0.862 ~ 0.925; P<0.001) were risk factors for spontaneous abortion. Then a nomogram model of abortion occurrence was constructed according to the above factors, and the AUC of the modeling group is 0.746 (95%CI 0.707-0.784), the AUC of the verification group is 0.718 (95%CI 0.999-0.737).

Conclusions: Our study found that female age, male age, follicle-stimulating hormone, anti-mullerian hormone were independent risk factors for spontaneous abortion, and the Nomogram model is an effective, simple and intuitive clinical model for predicting the probability of spontaneous abortion in ART.

Assisted reproductive technology

Spontaneous abortion

Nomogram model

Age

Endocrine

Spontaneous abortion is defined as the loss of an intrauterine pregnancy before the 20th week, which is also the most common abnormal pregnancy outcome in clinical work[1, 2]. Spontaneous abortion brings serious psychological and physical burdens to patients, especially infertility patients who are receiving assisted reproductive technology (ART)[3]. At present, the causes of spontaneous abortion are complex, despite considerable research, it has not been entirely clear what causes spontaneous abortions and how they are influenced[1, 4]. Prevention of spontaneous abortion is far more important than treatment, therefore, it is necessary to explore the related risk factors and establish an accurate prediction model for predicting the probability of miscarriage, in order to better guide the clinical application and provide better treatment options for infertility patients.

In the years since 1978, more and more women have been able to conceive using ART, however, ART does not prevent and avoid spontaneous abortion[5]. One study showed that the incidence of first-trimester pregnancy loss in ART patients who received fresh embryo transfer was 14.9%[6]. In addition, there have been several studies examining the relationship between ART and miscarriage risk factors, Shuai et al showed that Maternal age, number of embryos transferred, Body Mass Index, and frozen-thawed transfer were independent risk factors for early spontaneous abortion in ART treatment cycles[3]. Hahn KA et al showed that female's age affects the quality of her oocytes and endometrium, which results in abortion during ART[7]. Du Fossé et al show that advanced paternal age is also associated with an increased risk of spontaneous abortion[8]. Studies have also shown that assisted fertility treatment itself can lead to reduced embryo quality and endometrial receptivity changes, which are abortion risk factors[9]. However, few studies have systematically investigated the clinical management of Spontaneous abortion in ART patients based on large sample volumes, and attempted to analyzed independent influencing factors to establish nomogram model to predict the occurrence of abortion in ART patients.

Considering the psychological burden and physical hazards imposed by spontaneous abortion on infertile patients, we retrospective cohort study evaluated clinical data from 15,723 patients successfully conceived who underwent ART in the first people’s hospital of shangqiu and the affiliated hospital of zhengzhou university from December 2015 to February 2021, and establish nomogram model to explore the influencing factors of Spontaneous abortion in ART patients, The nomogram provides graphical representations of each variable in the model and allows users to compute output probabilities easily, it is an accurate, effective, and intuitive new clinical model[10]. Up to now, there is no report indicating logistic regression-based nomogram model that has been used to predict the risk factors of spontaneous abortion in infertile patients treated with ART. Therefore, it is crucial to identify different risk factors and develop a special nomogram model using logistic regression analysis, by using the nomogram model, clinicians can achieve better clinical outcomes and improved safety in the infertile couples receiving the ART treatment.

This retrospective cohort study evaluated clinical data from 15,723 patients successfully conceived who underwent IVF/ICSI in the first people’s hospital of shangqiu and the affiliated hospital of zhengzhou university from December 2015 to February 2021. The age of female patients ranged from 20 to 48 years, with an average age of (31.2 ± 5.8) years. The infertility time span was 0.5–21 years, with an average of (3.8 ± 2.9) years. Follicle-generating hormone was 0.3–33.7 IU/L with an average of (7.2 ± 2.2) IU/L. Luteinizing hormone was 0.2–25.9 IU/L, with an average of (5.6 ± 3.8) IU/L. Anti-mullerian hormone 1.2–23.1 ng/mL, with an average of (3.5 ± 2.3) ng/mL. This study was a retrospective study approved by the Ethics Committee of Reproductive Center, the First People's Hospital of Shangqiu, China, all research was conducted in accordance with relevant guidelines. Ethics committee approval waived informed consent.

Based on abortion outcome, experimental subjects were grouped into abortion and non-abortion groups, and there were 2,336 in the abortion group and 13,387 in the non-abortion group. Our study compared the experimental data between patients undergoing spontaneous abortion and those who did not, and looked for statistically significant differences between the two groups. Based on this, we further performed a multi-factor and single-factor logistic regression analysis to determine the independent factors, then we establish nomogram model to avoid the occurrence of spontaneous abortion in ART patients based on the independent influencing factors. Finally, bootstrap equivalent repeated sampling 1000 times was used to verify the prediction performance of the nomogram model, each patient was identified by their unique medical record number, and these datas were randomly divided into a modeling group (3,086 cases) and a verification group (12,637 cases).

Patients were randomized to either a GnRH-agonist protocol or a GnRH antagonist protocol for ART, Patients were given an initial dose of gonadotropin (Puregon, The Netherlands) based on their sinus follicle count, woman age, BMI, and response to their previous cycle of ovarian stimulation. Afterwards, the doses of gonadotropin could be adjusted sequentially based on the patient's response. It was estimated that 60% of all follicles would be dominant follicles of > 16mm in diameter according to guidelines issued by the Chinese Medical Association, or when 3 follicles > 17mm. Under ultrasound guidance, eggs were collected 37 hours after injection of 250ug of human chorionic gonadotropin (Livzon Pharmaceuticals, China). Corpus luteum support therapy was administered after embryo transfer. The diagnosis of spontaneous abortion is made after the pregnancy is confirmed, the physical examination, the female endocrine test, and the vaginal ultrasound confirm that the embryo has stopped developing before the week 20[11].

Statistical analysis

SPSS22.0 (IBM, Armonk, NY, USA) and R3.6.1 statistical package (The R Foundation; http://www.r-project.org)software were used to statistically process the experimental data. The measurement data of female age, male age, basal FSH, basal LH, basal E2, basal P, anti-Mullerin hormone, prolactin, and the number of excretion promotion cycles met the normal distribution test. Homogeneity of variance was tested by Barlett's method, the Continuous variables data are expressed as Mean ± SD, and were compared using Student’s t-test. Categorical variables are expressed as frequencies, and were compared using the chi-square test. Univariate and multivariate logistic regression analysis was used to analyze the factors related to abortion. Odds ratio (OR) and 95% confidence interval (CI) were calculated. OR < 1 was a risk factor, OR > 1 was a protective factor, and P < 0.05 was statistically significant.

According to the results of multivariate Logistic regression analysis and the regression coefficients of relevant variables, the corresponding nomogram prediction model was established by R language software, and the prediction performance of the nomogram model was verified by Bootstrap repeated sampling for 1000 times. The receiver operating characteristic (ROC) curve was used to determine the optimal critical value, corresponding sensitivity and specificity of nomogram model prediction. The area under the ROC curve was 0.50–0.70 for low accuracy, 0.71–0.90 for medium accuracy, and higher than 0.90 for high accuracy. P < 0.05 indicates statistical significance.

We collected data from 15,723 patients who had successfully conceived with ART. Among them, 2,336 patients (14.86%) had abortion, and 13,387 patients (85.14%) did not have abortion. There were significant differences in female age, male age, basal FSH, basal P, body mass index, anti-Mullerian hormone, prolactin and the number of excretion promotion cycles between the two groups (Table 1).

Table 1

Comparison of baseline data between abortion group and non-abortion group
projects	abortion group(n = 2336)	non-abortion group(n = 13387)	t/x²value	P value
Female age (years)	33.16 ± 5.53	30.72 ± 4.35	-16.372	< 0.001
Male age (years)	39.20 ± 7.63	34.16 ± 8.61	-17.661	< 0.001
Basal FSH (IU/L)	7.32 ± 3.12	6.53 ± 2.16	-4.536	< 0.001
Basal LH (IU/L)	5.53 ± 4.19	5.53 ± 4.03	0.635	0.836
Basal E2 (ng/L)	58.43 ± 42.17	54.63 ± 39.22	-0.758	0.563
Basal P (µg/L)	0.44 ± 0.36	0.50 ± 0.33	3.006	0.002
BMI (Kg/M2)	23.53 ± 3.20	22.71 ± 3.14	-5.236	< 0.001
AMH (ng/mL)	3.43 ± 2.03	3.73 ± 2.39	4.294	< 0.001
TSH (mlU/ml)	2.21 ± 1.13	2.69 ± 1.25	1.977	0.193
FT3 (pmol/L)	5.22 ± 0.80	5.34 ± 0.63	0.979	0.402
FT4 (pmol/L)	11.40 ± 1.81	11.98 ± 1.92	0.727	0.583
Blood glucose (mmol/L)	4.71 ± 0.38	4.92 ± 0.51	0.602	0.881
PRL (ng/L)	19.19 ± 12.22	20.51 ± 17.34	1.019	0.213
No. of treatment cycles	1.37 ± 0.71	1.25 ± 0.60	-7.005	< 0.001
Method of fertilization			3.689 0.056
IVF	70.7(1652/2336)	72.6(9725/13387)
ICSI	29.3(684/2336)	27.4(3662/13387)
Embryo stage			0.725 0.404
D3	78.3(1829/2336)	77.5(10375/13387)
D5, D6	21.7(507/2336)	22.5(3012/13387)
Semen quality			0.484 0.496
normal spermatozoa	84.0(1963/2336)	84.6(11325/13387)
non-normal spermatozoa	16.0(373/2336)	15.4(2062/13387)
Data are shown as Mean ± SD or N (%). FSH, follicular-stimulating hormone; LH, luteinizing hormone; E2, estradiol; P, progesterone; BMI, body mass index; AMH, anti-Müllerian hormone; PRL, prolactin.

Univariate and multivariate logistic regression analysis was used to analyze the factors related to abortion. Univariate logistic regression analysis showed that female age (OR 1.109; 1.086 ~ 1.116 95%; P < 0.001), male age (OR 1.044; 1.033 ~ 1.061 95%; P < 0.001), body mass index (OR 1.018; 1.009 ~ 1.026 95%; P < 0.001), basal FSH (OR 1.051; 1.028 ~ 1.065 95%; P < 0.001), basal P (OR 0.835; 0.751 ~ 0.929 95%; P = 0.001), anti-Mullerian hormone (OR 0.953; 0.899 ~ 0.931 95%; P < 0.001), the number of induction cycles (OR 1.216; 1.127 ~ 1.351 95%; P < 0.001) was a predictor of spontaneous abortion. Multivariate logistic regression analysis showed that female age (OR 1.050; 1.029 ~ 1.061 95%; P < 0.001), male age (OR 1.102; 1.088 ~ 1.119 95%; P < 0.001), basal FSH (OR 1.044; 1.015 ~ 1.069 95%; P < 0.001), anti-Mullerian hormone (OR 0.891; 0.859 ~ 0.917 95%; P < 0.001) were independent predictors of spontaneous abortion (Table 2).

Table 2

Logistic regression analysis of factors related to spontaneous abortion
projects	Unadjusted			Adjusted
projects	OR	95% C.I	P value	OR	95% C.I	P value
Female age (years)	1.109	1.086 ~ 1.116	< 0.001	1.050	1.029 ~ 1.061	< 0.001
Male age (years)	1.044	1.033 ~ 1.061	< 0.001	1.102	1.088 ~ 1.119	< 0.001
Basal FSH (IU/L)	1.051	1.028 ~ 1.065	< 0.001	1.044	1.015 ~ 1.069	< 0.001
Basal P (µg/L)	0.851	0.736 ~ 0.910	0.012	1.032	0.906 ~ 1.151	0.836
BMI (Kg/M2)	1.018	1.009 ~ 1.026	< 0.001	1.020	0.995 ~ 1.056	0.311
AMH (ng/mL)	0.902	0.899 ~ 0.931	< 0.001	0.891	0.859 ~ 0.917	< 0.001
PRL (ng/L)	0.983	0.996 ~ 1.005	0.202	1.001	0.983 ~ 1.012	0.965
No. of treatment cycles	1.216	1.127 ~ 1.351	< 0.001	1.106	0.959 ~ 1.195	0.063
Data are shown as Mean ± SD, FSH, follicular-stimulating hormone; P, progesterone; BMI, body mass index; AMH, anti-Müllerian hormone; PRL, prolactin;

Based on multiple univariate logistic regression analysis, four independent influencing factors, female age (OR 1.050; 1.029 ~ 1.061 95%; P < 0.001), male age (OR 1.102; 1.088 ~ 1.119 95%; P < 0.001), basal FSH (OR 1.044; 1.015 ~ 1.069 95%; P < 0.001), anti-Mullerian hormone (OR 0.891; 0.859 ~ 0.917 95%; P < 0.001) was an independent risk factor for spontaneous abortion, which was included in the prediction model to establish a nomogram prediction model. Spontaneous abortion occurrence nomogram prediction model is as follows: Full model from observed data: -7.48028 + 0.04316*female age + 0.10784*male age + 0.00526*FSH-0.13877 *AMH (Fig. 1).

According to the internal validation grouping principle, with the help of SPSS 22.0's automated random number generators, we can implement the principle of random grouping. The experimental data were divided into modeling group (3,086) and validation group (12,637), and the data ratio of the two groups was about (1:4), and there was no statistically significant difference in baseline data between the two groups (Table 3).

Table 3

Comparison of baseline data between validation group and modeling group.
projects	abortion group(n = 3086)	non-abortion group(n = 12637)	t/x²value	P value
Female age (years)	30.43 ± 4.67	30.55 ± 4.63	1.312	0.189
Male age (years)	34.47 ± 9.20	34.70 ± 9.25	1.267	0.205
Basal FSH (IU/L)	6.94 ± 2.45	6.92 ± 2.73	0.536	0.592
Basal LH (IU/L)	5.74 ± 4.80	5.62 ± 4.39	-1.800	0.072
Basal E2 (ng/L)	52.98 ± 136.50	53.91 ± 140.57	0.334	0.738
Basal P (µg/L)	0.48 ± 0.47	0.50 ± 0.56	1.713	0.087
BMI (Kg/M2)	22.69 ± 3.21	22.76 ± 3.36	1.082	0.279
AMH (ng/mL)	3.70 ± 2.92	3.60 ± 2.84	-1.284	0.199
TSH (mlU/ml)	2.43 ± 1.53	2.56 ± 1.23	1.256	0.243
FT3 (pmol/L)	5.26 ± 0.72	5.32 ± 0.59	0.899	0.516
FT4 (pmol/L)	11.58 ± 1.83	11.81 ± 1.88	0.532	0.572
Blood glucose (mmol/L)	4.78 ± 0.40	4.86 ± 0.46	0.405	0.693
PRL (ng/L)	21.31 ± 37.14	22.26 ± 124.76	0.422	0.673
No. of treatment cycles	1.23 ± 0.57	1.25 ± 0.62	1.947	0.052
Semen quality			3.655 0.057
normal spermatozoa	86.8(2679/3086)	87.3(11032/12637)
non-normal spermatozoa	13.2(407/3086)	12.7(1605/12637)
FSH, follicular-stimulating hormone; LH, luteinizing hormone; E2, estradiol; P, progesterone; BMI, body mass index; AMH, anti-Müllerian hormone; AFC.

The AUC of the modeling group was 0.746 (95%CI 0.707–0.784), the specificity was 0.701, the sensitivity was 0.715, and the accuracy was 0.703. In the validation group, the AUC was 0.718(95%CI 0.699–0.737), the specificity was 0.736, the sensitivity was 0.613, and the accuracy was 0.718. In the calibration figures of the model group and the validation group, the standard curve fitted well with the calibration prediction curve, indicating that the predicted value derived from the model was in good agreement with the observed value (Figs. 2).

Principal Findings

ART such as IVF and ICSI are commonly used in infertility treatments, more and more infertility patients have been able to conceive using ART[12], however, ART does not prevent and avoid spontaneous abortion. In fact, prevention and premanagement of spontaneous abortion is more important than treatment[13, 14]. Therefore, it is important to correctly assess and identify the risk factors for spontaneous abortion and conduct clinical intervention as early as possible. Our study explored the risk factors of spontaneous abortion in patients receiving ART, univariate and multivariate logistic regression analysis showed that female age, male age, basic FSH, and AMH were independent risk factors for spontaneous abortion. In addition, we successfully constructed a nomogram prediction model based on the above independent influencing factors, and these findings can provide preventive and personalized interventions for clinicians and infertile couples. The aim of this study was to analyze and construct a nomogram model based on big clinical data to identify abortion risk factors, we quantified, visualized and graphed the results of logistic regression, and calculated the value of variables by analyzing the graph, so as to predict the probability of abortion in patients treated with ART. The nomogram model is more intuitive to show the prediction results, and its AUC is 0.746 (95%CI = 0.707–0.784), which shows that it has good prediction accuracy and specificity. Different from other clinical models of assisted reproduction in the past, nomogram is more intuitive and practical, which is convenient for clinicians to calculate the probability of abortion in ART treatment cycle according to patients' own conditions.

Results

Previous studies have pointed out that ART does not increase the risk of spontaneous abortion[15, 16], the spontaneous abortion rate among ART pregnancies was 14.7%. There was a 10.1% spontaneous abortion risk among women 20–29 years old and a 39.3% spontaneous abortion risk among women older than 43, the spontaneous abortion risk was 13.1% among pregnancies conceived with donor eggs[17]. This conclusion was similar to a Chinese study, a large Chinese study in 2017 showed that the incidence of early pregnancy loss in ART patients receiving fresh embryo transfer was 14.9%[18]. In addition, it has been shown that the incidence of early spontaneous abortion after ART is about 10.6%[19]. Regardless of the specific data about spontaneous abortion, this is quite a high percentage considering the time-consuming and money-taking on ART. However, research currently shows that the causes of miscarriage are not fully understood[20, 21], and there are few methods of prevention and intervention. Furthermore, there is no predictive model that can be used to guide clinical treatment based on the individual parameters and laboratory data of such patients. For assisted reproduction experts, it is imperative to minimize spontaneous abortion. In view of this, we screened out independent risk factors and establish an accurate prediction model for predicting the probability of miscarriage, in order to better guide the clinical application and provide better treatment options for infertility patients.

Clinical Implications

According to our study, female age is an independent predictor of spontaneous abortion and is positively correlated with it, a spontaneous abortion is more likely to occur at an older age. It is well known that the quality and number of oocytes decrease with age[22, 23], the ATP content in the cytoplasm decreases, and the proportion of abnormal embryos chromosome structure increases[24]. The number of pregnant women over 35 years old who spontaneously aborted in patients receiving ART was significantly higher than that of pregnant women under 35[25]. In addition, as women age, their endometrial receptivity decreases and abnormal blood flow occurs, which are causes of an increase in spontaneous abortion[26]. Similarly, in our study, we found that male age is an independent risk factor for spontaneous abortion in ART patients. A meta-analysis of 975 original clinical studies showed that spontaneous abortion are associated with advanced paternal age, the pooled risk estimates for spontaneous abortion for age categories 30–34, 35–39, 40–44 and ≥ 45 years of age were 1.04 (95% CI 0.90, 1.21), 1.15 (95% CI 0.92, 1.43), 1.23 (95% CI 1.06, 1.43) and 1.43 (95% CI 1.13, 1.81) respective[8]. In another study involving patients with natural conception, spontaneous abortion risks increased by 1.26 times in men over 35 years of age, and they were not influenced by alcohol consumption[27]. Despite the difference in target population, the findings were similar to our own. In a study by Kleinhaus et al., spontaneous abortion risk in males > 40 years of age was 1.6, independent of female age and other factors[28]. In addition, Frattarelli et al. found that spontaneous abortion increased with the age of the father, among those over 50, spontaneous abortions were 41.5%, and among those under 50, they were 24.4%[29]. With the increase of age, there are a variety of factors associated with increasing age that will destroy the DNA replication and repair mechanism and increase the possibility of DNA breakage, causing abortions, which leads to abortion[30, 31]. The relationship between male age and the probability of spontaneous abortions could provide genetic counseling for older men.

Research Implications

As we presented above, this study found that female age and male age were independent risk factors for spontaneous abortion. Apart from the above, we found that basic FSH and AMH are also important factors contributing to spontaneous abortions during ART. In assisted reproduction (ART), when ovarian reserve decreases, basic FSH levels increase, while AMH levels decrease[32, 33]. The ovarian reserve and endometrial conditions are essential to the implantation and growth of embryos[34], therefore, fluctuations in hormone levels can lead to spontaneous abortions. In addition, poor ovarian reserve may induce us to use more follicle stimulating hormone during ART, large doses of follicle stimulating hormone affect the level of female endocrine hormones and the oocyte quality[35, 36]. Based on these research results and the Nomogram model, we can predict the probability of spontaneous abortions based on the results of laboratory examinations and the individual parameters of infertile patients in ART.

Strengths And Limitations

Our study has some strengths. First, to our knowledge there is no report indicating logistic regression-based nomogram model that has been used to predict the risk factors of spontaneous abortion in infertile patients treated with ART, thus, our study is innovative in this field. Second, our sample size is relatively large and the analysis was comprehensive compared with previous studies. Third, in this analysis, we present the target independent variables as both categorical variables and continuous variables, which reduces the amount of contingency in the analysis and enhances its robustness. Our study has the major limitation of being retrospective, which cannot exclude all possible biases, selection bias can skew results in some ways. In addition, although the nomogram model can better predict the probability of spontaneous abortion and provide reference for clinicians and patients, but no nomogram model is perfect. In the future, larger cohort studies and better data collection methods are needed to improve the prediction accuracy of nomogram model.

In conclusion, the nomogram prediction model successfully established in this study can effectively predict the probability of spontaneous abortion risk in ART treatment, and can provide positive, effective and targeted individual interventions. Compared with the traditional logistic regression model, nomogram has the advantages of simplicity, intuition and strong practicability[37, 38], and has good clinical application value and promotion value.

Authors’ roles: F.L. and X.Y.D. conceived of and designed the experiments. X.M. and M.M.L. selected and supervised suitable patients. F.L provided overall supervision. F.L. and X.Y.D. drafted the manuscript. All authors reviewed this manuscript.

Funding: This work was supported by the Key Science and Technology Foundation of Henan Province (212102310049 to FL), Joint Construction Project of Henan Province and Ministry (LHGJ20200933 to FL).

Institutional Review Board Statement: This study was a retrospective study approved by the Ethics Committee of Reproductive Center, the First People's Hospital of Shangqiu, China (HS2021016).

Informed Consent Statement: Not applicable.

Acknowledgements: The authors thank the women who participated in this study and all the physicians and nurses at the Reproductive Medicine Center, the Shangqiu First People's Hospital, China, for their support in collecting the data.

Conflicts of Interest: The authors declare no conflict of interest.

Ethical Approval：This study was a retrospective study approved by the Ethics Committee of Reproductive Center, the First People's Hospital of Shangqiu (HS2021016), China, all research was conducted in accordance with relevant guidelines. Ethics committee approval waived informed consent.

Availability of data and materials: The datasets generated during and/or analyzed during the current study are not publicly available due to patient privacy, but are available from the corresponding author on reasonable request.

Supplementary data: The data presented in this study are available on request from the corresponding author.

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No competing interests reported.

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Nomogram model to predict the probability of spontaneous abortion in human assisted reproductive technology

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