Establishment and validation of a nomogram model for predicting adverse pregnancy outcomes of pregnant women with adenomyosis

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Methods We collected the clinical data of pregnant women with adenomyosis who were treated in the First Affiliated Hospital of Chongqing Medical University and the Women and Children’s Hospital of Chongqing Medical University from January 2014 to June 2020. They were divided into the training cohort and the validation cohort, respectively. In the training cohort, we screened out risk factors associated with major adverse pregnancy outcomes and established a model, which was subsequently validated. Results In the training cohort, we found that natural conception or not, type of adenomyosis, previous parity, history of infertility or adverse pregnancy outcomes, history of uterine body surgerywere associated with major adverse pregnancy outcomes of pregnant women with adenomyosis, and based on these factors, a nomogram model was constructed. The calibration curves of the model were well fitted in both the training and validation cohorts. The receiver operating characteristic curve (ROC curve) showed that the area under the curve (AUC) was 0.862 and 0.836 in the training and validation cohorts, respectively. The optimal risk threshold of the model was 0.24, and this threshold can be applied to risk stratification of pregnant women. Conclusion The nomogram model established in this study can reliably predict the risk of major APO in pregnant women with AD. Pregnancy Adenomyosis Pregnancy complications Adverse pregnancy outcome Nomogram Figures Figure 1 Figure 2 Figure 3 1. Introduction Adenomyosis (AD) is a type of disease of unknown etiology, which can easily lead to infertility in women of reproductive age. [ 1 ] Therefore, most of these women must use assisted reproduction technology (ART) to get pregnant. [ 2 , 3 ] However, even with the help of ART, many women are also unable to get the desired fertility outcomes, conversely, adverse pregnancy outcomes such as miscarriage, premature delivery, and SGA often occur. [ 4 , 5 ] Currently, there are few studies about the effect of adenomyosis (AD) on adverse pregnancy outcomes. Studies have reported that AD increased the risk of adverse pregnancy outcomes, and women with AD are 3–5 times more likely to develop adverse pregnancy outcomes (miscarriage, premature delivery, and SGA) than without AD. [ 6 , 7 ] In addition, the probability of adverse pregnancy outcomes during pregnancy is not the same in pregnant women with AD, which is related to a series of clinical prognosis factors associated with the patient itself, including the age of the patient, type of adenomyosis (localized vs diffuse), whether natural conception, history of infertility or adverse pregnancy outcomes, and whether accompanied by pregnancy-related complications. [ 6 , 8 , 9 ] Although most patients with mild adenomyosis have good pregnancy outcomes after pregnancy, there are still many patients with adenomyosis had adverse pregnancy outcomes due to various adverse prognostic factors. Therefore, it is particularly important to accurately predict the risk of major adverse pregnancy outcomes in pregnant women with adenomyosis and to carry out hierarchical management. At present, there is a lack of a reliable tool that can comprehensively integrate various clinical prognostic factors to predict the risk of adverse pregnancy outcomes in pregnant women with adenomyosis. In view of this, this study established a nomogram model to predict the risk of major adverse pregnancy outcomes (miscarriage, premature delivery, and SGA) of pregnant women with adenomyosis and to stratify the patients according to the optimal risk threshold of the model, so as to provide a reliable reference tool for clinical diagnosis and treatment. 2. Materials And Methods 2.1 Study population The clinicopathological data of pregnant women with adenomyosis who received treatment in the outpatient and inpatient department of the first affiliated Hospital of Chongqing Medical University from January 2014 to June 2020 were collected retrospectively as the training cohort. Meanwhile we have collected the clinicopathological data of pregnant women with adenomyosis who received treatment in the Women and Children’s Hospital of Chongqing Medical University during the same period as the validation cohort. The inclusion criteria were as follows: (1) Patients with definitive diagnosis of adenomyosis; (2) This pregnancy was a singleton pregnancy with a definite pregnancy outcome; (3) Patients have conceived naturally or through ART, and B-ultrasound found embryo and primitive cardiac tube beat in the uterus during early pregnancy. The exclusion criteria were as follows:(1) Patients with assisted reproductive implantation failure, biochemical pregnancy, and ectopic pregnancy; (2) Patients with other diseases (including uterine malformations, chromosomal abnormalities, chronic hypertension, diabetes, nephropathy, thyroid disease and other basic medical diseases, immune system diseases, diseases of the blood system etc.) that would affect pregnancy outcomes before pregnancy; (3) Patients with lack of clinical data. Through the above inclusion and exclusion criteria, a total of 333 pregnant women with adenomyosis were collected as the training cohort and 233 pregnant women as the validation cohort. Adenomyosis was defined as a benign uterine disorder in which endometrial glands and stroma invaded the muscular layer of the uterus, [ 10 ] and adenomyosis was confirmed by postoperative pathological examination. The increased resolution of transvaginal ultrasonography (TVS), 3D-ultrasonography (3D-TVS) and magnetic resonance imaging (MRI) has made it possible to perform an image diagnose of AD, [ 11 , 12 ] so the diagnosis of adenomyosis in women of reproductive age was mainly based on imaging or/and postoperative pathological examination in this study. The major adverse pregnancy outcomes were defined as at least one of the following conditions: miscarriage, premature delivery, or SGA. According to WHO standards and Chinese expert consensus, premature delivery is defined as delivery between 28 and 37 weeks or less than 28 weeks with a fetal weight of more than 1000g. Miscarriage was defined as termination of pregnancy at less than 28 weeks of gestation or if the fetus weighed less than 1000g. SGA refers to a fetus whose estimated weight or abdominal circumference is below the 10th percentile of weight or abdominal circumference for the same gestational age. In this study, the pregnancy-related complications we collected included hypertensive disorder complicating pregnancy, gestational diabetes, preterm premature rupture of membranes, placenta previa, postpartum hemorrhage etc. Gravidity was defined as the number of pregnancies, and parity was defined as the number of deliveries with a pregnancy time ≥ 28 weeks at the time of delivery. As we described in the introduction, adverse pregnancy outcomes such as miscarriage, preterm delivery and SGA are much more likely to occur in pregnant women with adenomyosis than in normal women. The history of previous uterine body surgery was mainly defined as the conservative adenomyosis focal resection or caesarean section. According to the WHO, infertility was defined as a patient who had not taken any contraceptive measures for more than one year and had normal sex life without successful pregnancy. 2.2 Statistical methods The study design was shown as the supplementary Fig. 1. SPSS 25.0 statistical software was used to process the data. The measurement data was expressed as mean ± standard deviation or median [M (P25, P75)], and the intergroup comparison was based on the t-test or rank sum test; the enumeration data was expressed as cases (%), and the intergroup comparison was based on chi-square test or fisher test. The major adverse pregnancy outcomes (miscarriage, premature delivery, and SGA) were taken as outcome variables. Univariate and multivariate logistic regression analyses were used in the training cohort to screen out clinical factors associated with the major adverse pregnancy outcomes (miscarriage, preterm birth, and SGA). Based on the analysis results, we used the R language software (version 4.0.3, http://www.r-project.org ) to establish a nomogram model, and the model was validated internally and externally by using the calibration curve and the area under the curve (AUC) of the receiver operating characteristic curve (ROC). [ 13 ] The calibration curve is a graphical comparison between the predicted risk and the true risk, and the closer the predicted risk curve is to the standard curve, the better the fitness of the model is. AUC is mainly used to evaluate the prediction performance of the model, ranging from 0 to 1, and if the AUC lies between 0.5 and 0.6, between 0.6 and 0.7 or greater than 0.8, the model is considered to have poor, fair or good prediction accuracy, respectively. [ 14 ] Finally, in the training cohort, we used the receiver operating characteristic (ROC) curve and the maximum value of the Youden Index (Youden Index = Sensitivity + Specificity − 1) to find the risk threshold of the model. [ 15 ] According to the risk threshold, the training cohort and the validation cohort were divided into the high-risk group and the low-risk group respectively, and the occurrence of pregnancy-related complications among pregnant women in the high-risk and low-risk groups was compared. P < 0.05 means the difference is statistically significant. 3. Results 3.1 Basic characteristics of patients Table 1 summarized the clinical pathological data of 333 cases in the training cohort and 233 cases in the validation cohort. In the training cohort and the validation cohort, 144 (43.2%) and 108 (46.4%) pregnant women had at least three pregnancies., respectively, while most of the pregnant women gave birth for the first time. In the training cohort and the validation cohort, there were 65 (19.5%) and 45 (19.3%) pregnant women were complicated with diffuse adenomyosis respectively. In the training cohort, 113 (33.9%) patients had a history of infertility or adverse pregnancy outcomes, while 43 (12.9%) patients had a history of previous uterine body surgery. In the validation cohort, 79 (33.9%) patients had a history of infertility or adverse pregnancy outcomes, while 39 (16.7%) patients had a history of previous uterine body surgery. In the two cohorts, most pregnant women conceived naturally this time, but there were still a small number of pregnant women conceived through ART. As for the pregnancy outcomes, there were 83 pregnant women with major adverse pregnancy outcomes in the training cohort, including 25 miscarriages, 40 premature births and 32 SGA (14 patients had premature births complicated with SGA); while in the validation cohort, 62 pregnant women had adverse pregnancy outcomes, including 19 miscarriages, 30 preterm births and 11 SGA (11 patients had premature births complicated with SGA). Table 1 clinical characteristics of patients in two cohorts Characteristics Training cohort (n = 333, %) Validation cohort (n = 233, %) P value Age (mean ± standard deviation) 31.13 ± 4.77 31.12 ± 4.73 0.919 BMI (kg/m 2 ) (mean ± standard deviation) 24.85 ± 3.78 24.81 ± 3.77 0.940 Gestational weeks of termination of pregnancy [M(P 25 , P 75 )] 37.9 [37.1, 38.7] 37.9 [37.0, 38.7] 0.869 Amount of postpartum bleeding (ml) [M(P 25 , P 75 )] 250 [(150, 350)] 250 [(150, 350)] 0.345 Gravidity 1 2 ≥ 3 95(28.5) 94(28.2) 144(43.2) 58(24.9) 67(28.8) 108(46.4) 0.612 Parity 0 1 ≥ 2 24(7.2) 216(64.9) 93(27.9) 17(7.3) 143(61.4) 73(31.3) 0.668 Types of adenomyosis Localized Diffuse 268(80.5) 65(19.5) 188(80.7) 45(19.3) 0.951 History of infertility or APO Yes No 113(33.9) 220(66.1) 79(33.9) 154(66.1) 0.994 History of previous uterine body surgery* Yes No 43(12.9) 290(87.1) 39(16.7) 194(83.3) 0.203 How to get pregnant Natural conception ART 257(77.2) 76(22.8) 174(74.7) 59(25.3) 0.492 Mode of delivery Cesarean Vaginal delivery 230(69.1) 103(30.9) 160 (68.7) 73(31.3) 0.920 Pregnancy outcomes Miscarriage Preterm birth Full-term delivery 25(7.5) 40(12.0) 268(80.5) 19(8.2) 30(12.9) 184(79.0) 0.907 SGA Yes NO 32(9.6) 301(90.4) 24(10.3) 209(89.7) 0.786 M (P25, P75), median (25th percentile, 75th percentile); APO, adverse pregnancy outcomes; ART, assisted reproduction technology; SGA, small for gestational age; *, history of previous uterine body surgery includes conservative adenomyosis focus resection and caesarean section. 3.2 Univariate and multivariate logistics analysis In the training cohort, according to the outcomes of univariate logistics analysis, age, gravidity, previous parity, natural conception or not, type of adenomyosis, history of infertility or adverse pregnancy outcomes, and history of uterine body surgery were all associated with the major adverse pregnancy outcomes (P < 0.05 for all the factors). Multivariate analysis showed only previous parity (P < 0.001), natural conception or not(P = 0.011), type of adenomyosis (P = 0.003), history of infertility or adverse pregnancy outcomes (P = 0.013) and history of uterine body surgery (P = 0.007) were independent influencing factors of the major adverse pregnancy outcomes. Among them, previous parity ≥ 1 and natural conception were protective factors, while diffuse adenomyosis, with a history of previous infertility or adverse pregnancy outcomes, and with a history of uterine body surgery were risk factors (Table 2 ). Table 2 Univariate and multivariate analysis of adverse pregnancy outcomes predicted in pregnant women with adenomyosis (training cohort) Variable Univariate analysis Multivariate analysis OR 95% CI P value OR 95% CI P value Age (≥ 35 vs < 35) 1.714 1.016–2.892 0.044 1.090 0.568–2.092 0.795 Gravidity (≥ 3 vs < 3) 1.806 1.095–2.980 0.021 1.237 0.644–2.378 0.523 Previous parity (≥ 1 vs 0) 0.176 0.078–0.398 < 0.001 0.152 0.060–0.383 < 0.001 Natural conception (no vs yes) 4.021 2.320–6.972 < 0.001 2.389 1.221–4.677 0.011 Types of adenomyosis (diffuse vs localized) 6.695 3.734–12.005 < 0.001 3.183 1.498–6.764 0.003 History of infertility or APO (yes vs no) 3.566 2.127–5.977 < 0.001 2.149 1.177–3.926 0.013 History of previous uterine body surgery* (yes vs no) 5.556 2.841–10.865 < 0.001 3.083 1.361–6.981 0.007 APO, adverse pregnancy outcomes; *, history of previous uterine body surgery includes conservative adenomyosis lesion resection and caesarean section. 3.3 Establishment of the nomogram model and performance evaluation Based on the results of univariate and multivariate analysis, five independent influencing factors with P < 0.05 in multivariate analysis were used to establish a nomogram model. The five predictors were natural conception or not, previous parity, type of adenomyosis, history of infertility or adverse pregnancy outcomes, and history of uterine body surgery, respectively. The length of the line segment represented the weight of each factor. For example, the line segment of previous parity was the longest, which mean it had a large prediction weight and suggested that it had a great impact on pregnancy outcome, followed by the type of adenomyosis. The final risk score was calculated by adding up the score of each item using the nomogram model depicted in Fig. 1. The risk probability corresponding to the total score was the risk of major adverse pregnancy outcomes. The receiver operating characteristic (ROC) curve showed that the area under the curve (AUC) was 0.862 (95% CI, 0.804–0.919) and 0.836 (95% CI, 0.786–0.886) in the training and validation cohorts, respectively. The calibration curves of the model were well fitted in both the training and validation cohorts. Figure 2 indicated that the model we established can well predict the adverse pregnancy outcomes of pregnant women with adenomyosis. 3.4 The optimal threshold that the model used for risk stratification With the nomogram model, we calculated the risk of major adverse pregnancy outcomes for each pregnant women with adenomyosis, and the optimal risk threshold for the model was found to be 0.24 in the training cohort using the ROC curve and the maximum value of the Youden Index (AUC = 0.862, sensitivity = 80.6%, specificity = 80.7%) (Fig. 3). Based on this threshold, patients with model-predicted risk probability greater than the risk threshold were defined as the high-risk group for adverse pregnancy outcomes, and patients with model-predicted risk probability less than or equal to the risk threshold were defined as the low-risk group. In the training cohort, the proportions of pregnancy-associated complications in the high-risk group and the low-risk group were shown as Table 3 . Among the patients in the high-risk group, the proportion of pregnancy related complications such as hypertensive disorder complicating pregnancy (P < 0.001), PPROM (P < 0.001), GDM (P < 0.001), placenta previa (P = 0.013), and postpartum hemorrhage (P = 0.016) was higher than that in the low-risk group, and the difference was statistically significant. The high-risk group and the low-risk group in the validation cohort were also compared in the same way, as shown in Table 3 . The proportion of hypertensive disorder complicating pregnancy (P < 0.001), PPROM (P < 0.001), GDM (P < 0.001) and postpartum hemorrhage (0.046) in the high-risk group was higher than that in the low-risk group, and the difference was statistically significant, while placenta previa (P = 0.090) showed a different trend between the high-risk group and the low-risk group. In addition, there were 2 and 1 pregnant women who had uterine rupture in the high-risk group of the training group and the validation group, respectively, which were higher than those in the low-risk group, but due to the sample size limitation, no significant statistical difference was shown. Table 3 Comparison of pregnancy complications associated with patients in the high- and low-risk group of adverse pregnancy outcomes Pregnancy-associated complications Training cohort (n = 333, %) Validation cohort(n = 233, %) high-risk group (n = 114, %) low-risk group (n = 219, %) P value high-risk group (n = 83, %) low-risk group(n = 150, %) P value HDCP Yes No 33(28.9) 81(71.1) 15(6.8) 204(93.2) < 0.001 22(26.5) 61(73.5) 11(7.3) 139(92.7) < 0.001 PPROM Yes No 23(20.2) 91(79.8) 7(3.2) 212(96.8) < 0.001 21(25.3) 62(74.7) 5(3.3) 145(96.7) < 0.001 GDM Yes No 55(48.2) 59(51.8) 44(20.1) 175(79.9) < 0.001 39(47.0) 44(53.0) 33(22.0) 117(78.0) < 0.001 Placenta previa Yes No 24(21.1) 90(78.9) 24(11.0) 195(89.0) 0.013 14(16.9) 69(83.1) 14(9.3) 136(90.7) 0.090 Postpartum hemorrhage Yes No 8(7.0) 106(93.0) 3(1.4) 216(98.6) 0.016 6(7.2) 77(92.8) 2(1.3) 148(98.7) 0.046 Uterine rupture Yes No 2(1.8) 112(98.2) 0(0) 219(100) 0.117 1(1.2) 82(98.8) 0(0) 150(100) 0.356 HDCP, hypertensive disorder complicating pregnancy; PPROM, premature rupture of membranes before term; GDM, gestational diabetes mellitus. 4. Discussion In this study, we identified five factors associated with adverse pregnancy outcomes in pregnant women with adenomyosis through univariate and multivariate analysis of the training and the validation cohorts: whether natural conception, previous parity, type of adenomyosis, history of infertility or adverse pregnancy outcomes, history of uterine body surgery. Among the above factors, in addition to the type of adenomyosis, the other four factors can also reflect the severity of the effect of adenomyosis on fertility and pregnancy outcomes. For example, univariate and multivariate analysis showed that the previous parity (≥ 1), which had the largest weight in the model, was a protective factor (OR of the previous parity < 1). It was not difficult to understand, because if a patient had a previous history of delivery, it indicated that although the patient had adenomyosis, it may have not had a serious adverse impact on the patient's fertility yet, the intrauterine environment was still suitable for embryonic development until delivery at a larger gestational week. On the contrary, univariate and multivariate analysis showed the history of infertility or adverse pregnancy outcomes was a risk factor for major adverse pregnancy outcomes. Patients with a history of infertility or adverse pregnancy outcomes indicated that adenomyosis may have already seriously affected the fertility of the patient so that the patient cannot conceive normally, or even if the patient was able to conceive, the patient's intrauterine environment may no longer be suitable for the development of embryos, resulting in repeated pregnancy but also repeated abortion. The patient may have multiple pregnancies but cannot be pregnant to the larger gestational week (after 28 weeks) to give birth (that was, the gravidity increased, but the parity did not increase). In conclusion, our analysis showed that "multiple pregnancies but few births" or "multiple pregnancies with no birth" may be an important influencing factor for adverse pregnancy outcomes in pregnant women with adenomyosis, in which whether patients have given birth in the past (i.e., previous parity ≥ 1 vs 0) was a directly related factor, because it reflected whether the intrauterine environment of most patients was suitable for embryonic development. Some studies have reported that the invasion of endometrium and interstitium in patients with adenomyosis destroyed the structure of the myometrium and the continuity of the uterine junction zone, changed the function of the myometrium, and affected the remodeling of the uterine spiral artery, thus it affected the normal implantation of the embryo. Secondly, myometrial fibrosis in patients with adenomyosis affected the physiological expansion of the uterus as the gestational age increased, which increased the risk of miscarriage and premature birth in patients with adenomyosis. [ 16 – 18 ] These studies explained the pathophysiological mechanism of adenomyosis affecting the fertility of patients, and the previous parity (≥ 1 vs 0) was an important "phenotype" that reflected whether the intrauterine environment was suitable for embryonic development. Similarly, whether the patient is naturally pregnant or not, the history of previous uterine body surgery such as conservative adenomyosis lesion resection can also laterally reflect the severity of adenomyosis and the impact of adenomyosis on embryo implantation and development. [ 4 , 8 , 19 ] Based on the above situation, we established a nomogram model including the above five factors. This model can provide a comprehensive and individualized prediction of the risk of adverse pregnancy outcomes for each pregnant woman with adenomyosis, which was undoubtedly very interesting and practical. Next, we stratified the patients according to the optimal risk threshold of the model. The probability of adverse pregnancy outcomes in patients in the high-risk group was undoubtedly much higher than that in the low-risk group. This suggested that in addition to routine obstetric care and monitoring should be applied for each pregnant woman at risk of adverse pregnancy outcomes, we also need to develop a personalized diagnosis and treatment plan for them. For patients with high risk of adverse pregnancy outcomes, management and monitoring should be strengthened in the first and second trimesters of pregnancy, and the number of obstetric examinations and the items of obstetric examination should be appropriately increased, especially after 12 weeks of gestation. Because we found that the time of miscarriage in this study was mostly concentrated after 12 weeks of gestation, which may be caused by the above-mentioned myometrial fibrosis in patients with adenomyosis, with the increase of gestational weeks gestational age, the mechanical physiological expansion of the uterus was affected. Particularly, we found that for patients in the high-risk group, whether in the training cohort or in the validation cohort, the probability of developing pregnancy related complications (hypertensive disorder complicating pregnancy, PPROM, GDM, postpartum hemorrhage, etc.) was also much higher than that in the low-risk group, which undoubtedly increased the risk of adverse pregnancy outcomes on another level, thus entered a "vicious circle". [ 7 , 20 ] Therefore, it suggested that low-dose aspirin, heparin, and immunosuppressants may also should be used in early pregnancy to improve uterine blood perfusion for pregnant women in the high-risk group of adverse pregnancy outcomes. The duration and dose of administration should be adjusted according to the effect on pregnancy outcomes to reduce the probability of related complications such as preeclampsia, SGA. [ 17 , 21 ] At the same time, obstetricians should closely monitor the patient's various indicators (including various biochemical indicators and imaging examinations), be wary of the occurrence of complications (such as gestational hypertension, PPROM, GDM, etc.), and strengthen prediction and prevention of adverse pregnancy outcomes. If complications have occurred, related complications should be actively treated according to relevant guidelines. [ 22 ] For the perinatal management of patients in the high-risk group, the choice of delivery timing and delivery method should consider multidisciplinary (obstetrics, gynecology, imaging, etc.) consultation and be carefully decided according to the specific conditions of the patients. It should be noted that pregnant women with a history of uterine body surgery (such as adenomyomectomy or adenomyosis lesion resection in the past) had a progressively higher risk of uterine rupture with increasing gestational age (3 patients with uterine rupture in the training and validation cohort had a history of adenomyosis or adenomyoma surgery in the past), and these patients should be treated as high-risk pregnancy management regardless of the risk predicted by the model. Such pregnant women should terminate their pregnancy by caesarean section (especially in patients conceived through ART). [ 5 , 23 , 24 ] For intrapartum and postpartum management of patients, it had been reported in the literature that pregnant women with adenomyosis were a high-risk group for postpartum hemorrhage. In our study, the proportion of postpartum hemorrhage in the high-risk group was much greater than that in the low-risk group. Some patients should be alert to the occurrence of postpartum hemorrhage or even severe and refractory postpartum hemorrhage and should be managed served as the high-risk group of postpartum hemorrhage during delivery to strengthen prevention. In summary, for patients in the high-risk group, we should pay more attention to the management of the whole process of pregnancy. [ 21 , 25 ] This study has significant advantages. First of all, the model can predict the risk of adverse pregnancy outcomes in all pregnant women with adenomyosis. Second, the nomogram model used only 5 readily available clinicopathological factors (previous parity, natural conception or not, type of adenomyosis, history of infertility or adverse pregnancy outcomes and history of uterine body surgery) to predict the risk of adverse pregnancy outcomes in pregnant women with adenomyosis accurately. Finally, our model was externally validated and showed good accuracy and stability. This model can help clinicians to improve pregnancy outcomes by early intervention in pregnant women with adenomyosis. Of course, there were certain limitations in this study. This study was a retrospective study, and the exclusion of some cases in the access to clinical data may lead to selective bias, so we need more prospective studies to validate it further. In addition, the factors currently included in the model in this study were limited to clinical indicators, and there was a lack of objective pathophysiological, serological, and imaging indicators, and more predictive indicators should be included in the future to increase the performance of the model. 5. Conclusions All in all, we have established a nomogram model to predict the risk of major adverse pregnancy outcomes (miscarriage, premature delivery, and SGA) in pregnant women with adenomyosis, and can perform risk stratification for patients. which could provide clinicians with a reference tool when formulating a diagnosis and treatment plan for pregnant women with adenomyosis. Declarations Author Contribution Yanlin Chen: Data collection Chunxia Gong: Data collection Yicheng Hu: Data analysis, Manuscript writing Zhuoying Hu: project development Peng Jiang: Data collection or management Wei Kong: Data analysis, Manuscript writing Lingya Xu: Data collection Yang Yang: Data collection Acknowledgments Not applicable Funding There is no funding to report. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics Approval and Consent to Participate Ethics Committee of Chongqing Medical University approved this study (Ethics approval number:2021-547). Consent to participate All patients provided their informed consent before starting the treatment and gave consent to have their data published. As it was a retrospective clinical study, all the patients were contacted by telephone to obtain verbal informed consent and it was approved by the ethics committee. All data about the patients was anonymized or maintained with confidentiality. Consent to publish The authors affirm that human research participants provided informed consent for publication of the images in Table(s) 1, 2 and 3 and Figure(s) 1, 2 and 3. CRediT author statement Zhuoying Hu: Conceptualization, Methodology, Supervision, Project administration, Writing - Review & Editing Wei Kong and Yicheng Hu: Methodology, Data curation, Investigation, Software, Formal analysis, Writing- Original draft preparation, Writing - Review & Editing Peng Jiang,Chunxia Gong and Yanlin Chen: Data curation, Software, Formal analysis, Investigation Linya Xu and Yang Yang: Data curation, Supervision All authors critically reviewed the paper and had final approval of it. References Moawad G, Kheil MH, Ayoubi JM, Klebanoff JS, Rahman S, Sharara FI (2022) Adenomyosis and infertility. 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Epidemiol (Cambridge Mass) 16:73–81 Brosens I, Pijnenborg R, Benagiano G (2013) Defective myometrial spiral artery remodelling as a cause of major obstetrical syndromes in endometriosis and adenomyosis. Placenta 34:100–105 Harada T, Khine YM, Kaponis A, Nikellis T, Decavalas G, Taniguchi F (2016) The Impact of Adenomyosis on Women's Fertility. Obstet Gynecol Surv 71:557–568 Vlahos NF, Theodoridis TD, Partsinevelos GA (2017) Myomas and Adenomyosis: Impact on Reproductive Outcome. Biomed Res Int 2017:5926470 Huang Y, Zhao X, Chen Y, Wang J, Zheng W, Cao L (2020) Miscarriage on Endometriosis and Adenomyosis in Women by Assisted Reproductive Technology or with Spontaneous Conception: A Systematic Review and Meta-Analysis. Biomed Res Int 2020:4381346 Hashimoto A, Iriyama T, Sayama S, Nakayama T, Komatsu A, Miyauchi A et al (2018) Adenomyosis and adverse perinatal outcomes: increased risk of second trimester miscarriage, preeclampsia, and placental malposition. The journal of maternal-fetal & neonatal medicine: the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of. Perinat Obstet 31:364–369 Soave I, Wenger JM, Pluchino N, Marci R (2018) Treatment options and reproductive outcome for adenomyosis-associated infertility. Curr Med Res Opin 34:839–849 Munoz JL, Kimura AM, Xenakis E, Jenkins DH, Braverman MA, Ramsey PS et al Whole blood transfusion reduces overall component transfusion in cases of placenta accreta spectrum: a pilot program. The journal of maternal-fetal & neonatal medicine: the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstet 2021:1–6 Liu XY, Zhang Y, Wei Y, Li R, Zhao YY (2020) [Perinatal outcome of pregnant women with adenomyosis]. Zhonghua fu chan ke za zhi 55:743–748 Shi J, Dai Y, Zhang J, Li X, Jia S, Leng J (2021) Pregnancy outcomes in women with infertility and coexisting endometriosis and adenomyosis after laparoscopic surgery: a long-term retrospective follow-up study. BMC Pregnancy Childbirth 21:383 Tamura H, Kishi H, Kitade M, Asai-Sato M, Tanaka A, Murakami T et al (2017) Complications and outcomes of pregnant women with adenomyosis in Japan. Reproductive Med biology 16:330–336 Cite Share Download PDF Status: Published Journal Publication published 25 Jul, 2023 Read the published version in Archives of Gynecology and Obstetrics → Version 1 posted Reviewers agreed at journal 21 Nov, 2022 Reviewers invited by journal 21 Nov, 2022 Editor invited by journal 09 Oct, 2022 Editor assigned by journal 05 Oct, 2022 First submitted to journal 04 Oct, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2131358","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":153940376,"identity":"5e281f87-1a7d-4310-b85e-d41b0b5a8188","order_by":0,"name":"Yicheng Hu","email":"","orcid":"https://orcid.org/0000-0003-3095-3227","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yicheng","middleName":"","lastName":"Hu","suffix":""},{"id":153940377,"identity":"0eb4e430-1ff4-4aae-bbaa-590101e11701","order_by":1,"name":"Wei Kong","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Kong","suffix":""},{"id":153940378,"identity":"28353cb2-4757-4097-9382-26952ccd7160","order_by":2,"name":"Peng Jiang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Jiang","suffix":""},{"id":153940379,"identity":"e72344a9-5a48-453b-afeb-7a63a27ac4d5","order_by":3,"name":"Chunxia Gong","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chunxia","middleName":"","lastName":"Gong","suffix":""},{"id":153940380,"identity":"4c2b6640-827c-44ab-bdb2-1cba533d9759","order_by":4,"name":"Yanlin Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yanlin","middleName":"","lastName":"Chen","suffix":""},{"id":153940381,"identity":"ae3fa3e4-1341-4cca-9a86-eec709a6e7d4","order_by":5,"name":"Lingya Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lingya","middleName":"","lastName":"Xu","suffix":""},{"id":153940382,"identity":"9730cca2-da92-4895-82f0-bbac2d04e583","order_by":6,"name":"Yang Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yang","suffix":""},{"id":153940383,"identity":"4a0b09f3-7d43-4f33-ac3f-0419bec7ce48","order_by":7,"name":"Zhuoying Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACAwYGNhDNw8DAfIAhgUQtbAmkaQHpMiDOYeYS6c8e81QcljHnX/P5w8Mddgz87d34LbOckZBuzHPmMI/ljLfbJBLPJDNInDm7Ab/DbiQck+ZtO8xjcOPsNobENmYGA4lcQloS26Bazjz+kNhWT4yWZDaIlvM9DBKJbYeJ0HLmGZvknDPpQFvYzIBajvMQ9svx9GcSbyqs7Q3OH3788WdbtRx/ey9+LVDQzMAgkQBm8RCjHATqGBj4DxCreBSMglEwCkYaAAAzM0hDJHT7cgAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Zhuoying","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2022-10-04 10:21:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2131358/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2131358/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00404-023-07136-z","type":"published","date":"2023-07-25T21:46:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":29455680,"identity":"950bcdd5-c07a-4169-9778-72eb03357105","added_by":"auto","created_at":"2022-11-23 20:43:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33798,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-2131358/v1/ed3ecbfd8b6505b67fb2cdcc.png"},{"id":29455681,"identity":"f85ca34f-91a9-46a1-b291-381e103bd6e2","added_by":"auto","created_at":"2022-11-23 20:43:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50084,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-2131358/v1/d35852ed74c130e004c47f32.png"},{"id":29456930,"identity":"440936c2-b047-4cd3-92ad-3a9bd555b4ca","added_by":"auto","created_at":"2022-11-23 20:51:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24699,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-2131358/v1/69a2a073957f3f8dfe04fbb4.png"},{"id":44734959,"identity":"876bb376-5169-4ce3-821b-8f33475912c7","added_by":"auto","created_at":"2023-10-16 22:22:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":696797,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2131358/v1/c7851aa8-ff8c-48a2-a1c2-3683dc55b9b2.pdf"}],"financialInterests":"","formattedTitle":"Establishment and validation of a nomogram model for predicting adverse pregnancy outcomes of pregnant women with adenomyosis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAdenomyosis (AD) is a type of disease of unknown etiology, which can easily lead to infertility in women of reproductive age.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e Therefore, most of these women must use assisted reproduction technology (ART) to get pregnant. \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003eHowever, even with the help of ART, many women are also unable to get the desired fertility outcomes, conversely, adverse pregnancy outcomes such as miscarriage, premature delivery, and SGA often occur.\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e Currently, there are few studies about the effect of adenomyosis (AD) on adverse pregnancy outcomes. Studies have reported that AD increased the risk of adverse pregnancy outcomes, and women with AD are 3\u0026ndash;5 times more likely to develop adverse pregnancy outcomes (miscarriage, premature delivery, and SGA) than without AD.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e In addition, the probability of adverse pregnancy outcomes during pregnancy is not the same in pregnant women with AD, which is related to a series of clinical prognosis factors associated with the patient itself, including the age of the patient, type of adenomyosis (localized vs diffuse), whether natural conception, history of infertility or adverse pregnancy outcomes, and whether accompanied by pregnancy-related complications.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e Although most patients with mild adenomyosis have good pregnancy outcomes after pregnancy, there are still many patients with adenomyosis had adverse pregnancy outcomes due to various adverse prognostic factors. Therefore, it is particularly important to accurately predict the risk of major adverse pregnancy outcomes in pregnant women with adenomyosis and to carry out hierarchical management. At present, there is a lack of a reliable tool that can comprehensively integrate various clinical prognostic factors to predict the risk of adverse pregnancy outcomes in pregnant women with adenomyosis. In view of this, this study established a nomogram model to predict the risk of major adverse pregnancy outcomes (miscarriage, premature delivery, and SGA) of pregnant women with adenomyosis and to stratify the patients according to the optimal risk threshold of the model, so as to provide a reliable reference tool for clinical diagnosis and treatment.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThe clinicopathological data of pregnant women with adenomyosis who received treatment in the outpatient and inpatient department of the first affiliated Hospital of Chongqing Medical University from January 2014 to June 2020 were collected retrospectively as the training cohort. Meanwhile we have collected the clinicopathological data of pregnant women with adenomyosis who received treatment in the Women and Children\u0026rsquo;s Hospital of Chongqing Medical University during the same period as the validation cohort. The inclusion criteria were as follows: (1) Patients with definitive diagnosis of adenomyosis; (2) This pregnancy was a singleton pregnancy with a definite pregnancy outcome; (3) Patients have conceived naturally or through ART, and B-ultrasound found embryo and primitive cardiac tube beat in the uterus during early pregnancy. The exclusion criteria were as follows:(1) Patients with assisted reproductive implantation failure, biochemical pregnancy, and ectopic pregnancy; (2) Patients with other diseases (including uterine malformations, chromosomal abnormalities, chronic hypertension, diabetes, nephropathy, thyroid disease and other basic medical diseases, immune system diseases, diseases of the blood system etc.) that would affect pregnancy outcomes before pregnancy; (3) Patients with lack of clinical data. Through the above inclusion and exclusion criteria, a total of 333 pregnant women with adenomyosis were collected as the training cohort and 233 pregnant women as the validation cohort.\u003c/p\u003e \u003cp\u003eAdenomyosis was defined as a benign uterine disorder in which endometrial glands and stroma invaded the muscular layer of the uterus,\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e and adenomyosis was confirmed by postoperative pathological examination. The increased resolution of transvaginal ultrasonography (TVS), 3D-ultrasonography (3D-TVS) and magnetic resonance imaging (MRI) has made it possible to perform an image diagnose of AD, \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e so the diagnosis of adenomyosis in women of reproductive age was mainly based on imaging or/and postoperative pathological examination in this study. The major adverse pregnancy outcomes were defined as at least one of the following conditions: miscarriage, premature delivery, or SGA. According to WHO standards and Chinese expert consensus, premature delivery is defined as delivery between 28 and 37 weeks or less than 28 weeks with a fetal weight of more than 1000g. Miscarriage was defined as termination of pregnancy at less than 28 weeks of gestation or if the fetus weighed less than 1000g. SGA refers to a fetus whose estimated weight or abdominal circumference is below the 10th percentile of weight or abdominal circumference for the same gestational age. In this study, the pregnancy-related complications we collected included hypertensive disorder complicating pregnancy, gestational diabetes, preterm premature rupture of membranes, placenta previa, postpartum hemorrhage etc. Gravidity was defined as the number of pregnancies, and parity was defined as the number of deliveries with a pregnancy time\u0026thinsp;\u0026ge;\u0026thinsp;28 weeks at the time of delivery. As we described in the introduction, adverse pregnancy outcomes such as miscarriage, preterm delivery and SGA are much more likely to occur in pregnant women with adenomyosis than in normal women. The history of previous uterine body surgery was mainly defined as the conservative adenomyosis focal resection or caesarean section. According to the WHO, infertility was defined as a patient who had not taken any contraceptive measures for more than one year and had normal sex life without successful pregnancy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Statistical methods\u003c/h2\u003e \u003cp\u003eThe study design was shown as the supplementary Fig.\u0026nbsp;1. SPSS 25.0 statistical software was used to process the data. The measurement data was expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median [M (P25, P75)], and the intergroup comparison was based on the t-test or rank sum test; the enumeration data was expressed as cases (%), and the intergroup comparison was based on chi-square test or fisher test. The major adverse pregnancy outcomes (miscarriage, premature delivery, and SGA) were taken as outcome variables. Univariate and multivariate logistic regression analyses were used in the training cohort to screen out clinical factors associated with the major adverse pregnancy outcomes (miscarriage, preterm birth, and SGA). Based on the analysis results, we used the R language software (version 4.0.3, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to establish a nomogram model, and the model was validated internally and externally by using the calibration curve and the area under the curve (AUC) of the receiver operating characteristic curve (ROC).\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e The calibration curve is a graphical comparison between the predicted risk and the true risk, and the closer the predicted risk curve is to the standard curve, the better the fitness of the model is. AUC is mainly used to evaluate the prediction performance of the model, ranging from 0 to 1, and if the AUC lies between 0.5 and 0.6, between 0.6 and 0.7 or greater than 0.8, the model is considered to have poor, fair or good prediction accuracy, respectively.\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e Finally, in the training cohort, we used the receiver operating characteristic (ROC) curve and the maximum value of the Youden Index (Youden Index\u0026thinsp;=\u0026thinsp;Sensitivity\u0026thinsp;+\u0026thinsp;Specificity \u0026minus;\u0026thinsp;1) to find the risk threshold of the model.\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e According to the risk threshold, the training cohort and the validation cohort were divided into the high-risk group and the low-risk group respectively, and the occurrence of pregnancy-related complications among pregnant women in the high-risk and low-risk groups was compared. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 means the difference is statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Basic characteristics of patients\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarized the clinical pathological data of 333 cases in the training cohort and 233 cases in the validation cohort. In the training cohort and the validation cohort, 144 (43.2%) and 108 (46.4%) pregnant women had at least three pregnancies., respectively, while most of the pregnant women gave birth for the first time. In the training cohort and the validation cohort, there were 65 (19.5%) and 45 (19.3%) pregnant women were complicated with diffuse adenomyosis respectively. In the training cohort, 113 (33.9%) patients had a history of infertility or adverse pregnancy outcomes, while 43 (12.9%) patients had a history of previous uterine body surgery. In the validation cohort, 79 (33.9%) patients had a history of infertility or adverse pregnancy outcomes, while 39 (16.7%) patients had a history of previous uterine body surgery. In the two cohorts, most pregnant women conceived naturally this time, but there were still a small number of pregnant women conceived through ART. As for the pregnancy outcomes, there were 83 pregnant women with major adverse pregnancy outcomes in the training cohort, including 25 miscarriages, 40 premature births and 32 SGA (14 patients had premature births complicated with SGA); while in the validation cohort, 62 pregnant women had adverse pregnancy outcomes, including 19 miscarriages, 30 preterm births and 11 SGA (11 patients had premature births complicated with SGA).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eclinical characteristics of patients in two cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining cohort (n\u0026thinsp;=\u0026thinsp;333, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation cohort (n\u0026thinsp;=\u0026thinsp;233, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.13\u0026thinsp;\u0026plusmn;\u0026thinsp;4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.81\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGestational weeks of termination of pregnancy\u003c/b\u003e [M(P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.9 [37.1, 38.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.9 [37.0, 38.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmount of postpartum bleeding\u003c/b\u003e (ml) [M(P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250 [(150, 350)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250 [(150, 350)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGravidity\u003c/b\u003e\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95(28.5)\u003c/p\u003e \u003cp\u003e94(28.2)\u003c/p\u003e \u003cp\u003e144(43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(24.9)\u003c/p\u003e \u003cp\u003e67(28.8)\u003c/p\u003e \u003cp\u003e108(46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParity\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(7.2)\u003c/p\u003e \u003cp\u003e216(64.9)\u003c/p\u003e \u003cp\u003e93(27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(7.3)\u003c/p\u003e \u003cp\u003e143(61.4)\u003c/p\u003e \u003cp\u003e73(31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTypes of adenomyosis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eLocalized\u003c/p\u003e \u003cp\u003eDiffuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e268(80.5)\u003c/p\u003e \u003cp\u003e65(19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188(80.7)\u003c/p\u003e \u003cp\u003e45(19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory of infertility or APO\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113(33.9)\u003c/p\u003e \u003cp\u003e220(66.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79(33.9)\u003c/p\u003e \u003cp\u003e154(66.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory of previous uterine body surgery*\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(12.9)\u003c/p\u003e \u003cp\u003e290(87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(16.7)\u003c/p\u003e \u003cp\u003e194(83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHow to get pregnant\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNatural conception\u003c/p\u003e \u003cp\u003eART\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e257(77.2)\u003c/p\u003e \u003cp\u003e76(22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174(74.7)\u003c/p\u003e \u003cp\u003e59(25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMode of delivery\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCesarean\u003c/p\u003e \u003cp\u003eVaginal delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230(69.1)\u003c/p\u003e \u003cp\u003e103(30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (68.7)\u003c/p\u003e \u003cp\u003e73(31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePregnancy outcomes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMiscarriage\u003c/p\u003e \u003cp\u003ePreterm birth\u003c/p\u003e \u003cp\u003eFull-term delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(7.5)\u003c/p\u003e \u003cp\u003e40(12.0)\u003c/p\u003e \u003cp\u003e268(80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(8.2)\u003c/p\u003e \u003cp\u003e30(12.9)\u003c/p\u003e \u003cp\u003e184(79.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSGA\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(9.6)\u003c/p\u003e \u003cp\u003e301(90.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(10.3)\u003c/p\u003e \u003cp\u003e209(89.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eM (P25, P75), median (25th percentile, 75th percentile); APO, adverse pregnancy outcomes; ART, assisted reproduction technology; SGA, small for gestational age; *, history of previous uterine body surgery includes conservative adenomyosis focus resection and caesarean section.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Univariate and multivariate logistics analysis\u003c/h2\u003e \u003cp\u003eIn the training cohort, according to the outcomes of univariate logistics analysis, age, gravidity, previous parity, natural conception or not, type of adenomyosis, history of infertility or adverse pregnancy outcomes, and history of uterine body surgery were all associated with the major adverse pregnancy outcomes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all the factors). Multivariate analysis showed only previous parity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), natural conception or not(P\u0026thinsp;=\u0026thinsp;0.011), type of adenomyosis (P\u0026thinsp;=\u0026thinsp;0.003), history of infertility or adverse pregnancy outcomes (P\u0026thinsp;=\u0026thinsp;0.013) and history of uterine body surgery (P\u0026thinsp;=\u0026thinsp;0.007) were independent influencing factors of the major adverse pregnancy outcomes. Among them, previous parity\u0026thinsp;\u0026ge;\u0026thinsp;1 and natural conception were protective factors, while diffuse adenomyosis, with a history of previous infertility or adverse pregnancy outcomes, and with a history of uterine body surgery were risk factors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis of adverse pregnancy outcomes predicted in pregnant women with adenomyosis (training cohort)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (\u0026ge;\u0026thinsp;35 vs\u0026thinsp;\u0026lt;\u0026thinsp;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.016\u0026ndash;2.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.568\u0026ndash;2.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGravidity\u003c/b\u003e (\u0026ge;\u0026thinsp;3 vs\u0026thinsp;\u0026lt;\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.095\u0026ndash;2.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.644\u0026ndash;2.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious parity\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(\u0026ge;\u0026thinsp;1 vs 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.078\u0026ndash;0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.060\u0026ndash;0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNatural conception\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(no vs yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.320\u0026ndash;6.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.221\u0026ndash;4.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTypes of adenomyosis\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(diffuse vs localized)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.734\u0026ndash;12.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.498\u0026ndash;6.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory of infertility or APO\u003c/b\u003e (yes vs no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.127\u0026ndash;5.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.177\u0026ndash;3.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory of previous uterine body surgery*\u003c/b\u003e (yes vs no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.841\u0026ndash;10.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.361\u0026ndash;6.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAPO, adverse pregnancy outcomes; *, history of previous uterine body surgery includes conservative adenomyosis lesion resection and caesarean section.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Establishment of the nomogram model and performance evaluation\u003c/h2\u003e \u003cp\u003eBased on the results of univariate and multivariate analysis, five independent influencing factors with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in multivariate analysis were used to establish a nomogram model. The five predictors were natural conception or not, previous parity, type of adenomyosis, history of infertility or adverse pregnancy outcomes, and history of uterine body surgery, respectively. The length of the line segment represented the weight of each factor. For example, the line segment of previous parity was the longest, which mean it had a large prediction weight and suggested that it had a great impact on pregnancy outcome, followed by the type of adenomyosis. The final risk score was calculated by adding up the score of each item using the nomogram model depicted in Fig.\u0026nbsp;1. The risk probability corresponding to the total score was the risk of major adverse pregnancy outcomes.\u003c/p\u003e \u003cp\u003eThe receiver operating characteristic (ROC) curve showed that the area under the curve (AUC) was 0.862 (95% CI, 0.804\u0026ndash;0.919) and 0.836 (95% CI, 0.786\u0026ndash;0.886) in the training and validation cohorts, respectively. The calibration curves of the model were well fitted in both the training and validation cohorts. Figure\u0026nbsp;2 indicated that the model we established can well predict the adverse pregnancy outcomes of pregnant women with adenomyosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4 The optimal threshold that the model used for risk stratification\u003c/h2\u003e \u003cp\u003eWith the nomogram model, we calculated the risk of major adverse pregnancy outcomes for each pregnant women with adenomyosis, and the optimal risk threshold for the model was found to be 0.24 in the training cohort using the ROC curve and the maximum value of the Youden Index (AUC\u0026thinsp;=\u0026thinsp;0.862, sensitivity\u0026thinsp;=\u0026thinsp;80.6%, specificity\u0026thinsp;=\u0026thinsp;80.7%) (Fig.\u0026nbsp;3). Based on this threshold, patients with model-predicted risk probability greater than the risk threshold were defined as the high-risk group for adverse pregnancy outcomes, and patients with model-predicted risk probability less than or equal to the risk threshold were defined as the low-risk group.\u003c/p\u003e \u003cp\u003eIn the training cohort, the proportions of pregnancy-associated complications in the high-risk group and the low-risk group were shown as Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Among the patients in the high-risk group, the proportion of pregnancy related complications such as hypertensive disorder complicating pregnancy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PPROM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), GDM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), placenta previa (P\u0026thinsp;=\u0026thinsp;0.013), and postpartum hemorrhage (P\u0026thinsp;=\u0026thinsp;0.016) was higher than that in the low-risk group, and the difference was statistically significant. The high-risk group and the low-risk group in the validation cohort were also compared in the same way, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The proportion of hypertensive disorder complicating pregnancy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PPROM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), GDM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and postpartum hemorrhage (0.046) in the high-risk group was higher than that in the low-risk group, and the difference was statistically significant, while placenta previa (P\u0026thinsp;=\u0026thinsp;0.090) showed a different trend between the high-risk group and the low-risk group. In addition, there were 2 and 1 pregnant women who had uterine rupture in the high-risk group of the training group and the validation group, respectively, which were higher than those in the low-risk group, but due to the sample size limitation, no significant statistical difference was shown.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of pregnancy complications associated with patients in the high- and low-risk group of adverse pregnancy outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePregnancy-associated complications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTraining cohort (n\u0026thinsp;=\u0026thinsp;333, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eValidation cohort(n\u0026thinsp;=\u0026thinsp;233, %)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ehigh-risk group\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;114, %)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003elow-risk group\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;219, %)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ehigh-risk group\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;83, %)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003elow-risk group(n\u0026thinsp;=\u0026thinsp;150, %)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDCP\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33(28.9)\u003c/p\u003e \u003cp\u003e81(71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(6.8)\u003c/p\u003e \u003cp\u003e204(93.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22(26.5)\u003c/p\u003e \u003cp\u003e61(73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11(7.3)\u003c/p\u003e \u003cp\u003e139(92.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePPROM\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(20.2)\u003c/p\u003e \u003cp\u003e91(79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(3.2)\u003c/p\u003e \u003cp\u003e212(96.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21(25.3)\u003c/p\u003e \u003cp\u003e62(74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(3.3)\u003c/p\u003e \u003cp\u003e145(96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGDM\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(48.2)\u003c/p\u003e \u003cp\u003e59(51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(20.1)\u003c/p\u003e \u003cp\u003e175(79.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39(47.0)\u003c/p\u003e \u003cp\u003e44(53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33(22.0)\u003c/p\u003e \u003cp\u003e117(78.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlacenta previa\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(21.1)\u003c/p\u003e \u003cp\u003e90(78.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(11.0)\u003c/p\u003e \u003cp\u003e195(89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14(16.9)\u003c/p\u003e \u003cp\u003e69(83.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(9.3)\u003c/p\u003e \u003cp\u003e136(90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePostpartum hemorrhage\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(7.0)\u003c/p\u003e \u003cp\u003e106(93.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(1.4)\u003c/p\u003e \u003cp\u003e216(98.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(7.2)\u003c/p\u003e \u003cp\u003e77(92.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(1.3)\u003c/p\u003e \u003cp\u003e148(98.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUterine rupture\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1.8)\u003c/p\u003e \u003cp\u003e112(98.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003cp\u003e219(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(1.2)\u003c/p\u003e \u003cp\u003e82(98.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003cp\u003e150(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eHDCP, hypertensive disorder complicating pregnancy; PPROM, premature rupture of membranes before term; GDM, gestational diabetes mellitus.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we identified five factors associated with adverse pregnancy outcomes in pregnant women with adenomyosis through univariate and multivariate analysis of the training and the validation cohorts: whether natural conception, previous parity, type of adenomyosis, history of infertility or adverse pregnancy outcomes, history of uterine body surgery. Among the above factors, in addition to the type of adenomyosis, the other four factors can also reflect the severity of the effect of adenomyosis on fertility and pregnancy outcomes. For example, univariate and multivariate analysis showed that the previous parity (\u0026ge;\u0026thinsp;1), which had the largest weight in the model, was a protective factor (OR of the previous parity\u0026thinsp;\u0026lt;\u0026thinsp;1). It was not difficult to understand, because if a patient had a previous history of delivery, it indicated that although the patient had adenomyosis, it may have not had a serious adverse impact on the patient's fertility yet, the intrauterine environment was still suitable for embryonic development until delivery at a larger gestational week. On the contrary, univariate and multivariate analysis showed the history of infertility or adverse pregnancy outcomes was a risk factor for major adverse pregnancy outcomes. Patients with a history of infertility or adverse pregnancy outcomes indicated that adenomyosis may have already seriously affected the fertility of the patient so that the patient cannot conceive normally, or even if the patient was able to conceive, the patient's intrauterine environment may no longer be suitable for the development of embryos, resulting in repeated pregnancy but also repeated abortion. The patient may have multiple pregnancies but cannot be pregnant to the larger gestational week (after 28 weeks) to give birth (that was, the gravidity increased, but the parity did not increase). In conclusion, our analysis showed that \"multiple pregnancies but few births\" or \"multiple pregnancies with no birth\" may be an important influencing factor for adverse pregnancy outcomes in pregnant women with adenomyosis, in which whether patients have given birth in the past (i.e., previous parity\u0026thinsp;\u0026ge;\u0026thinsp;1 vs 0) was a directly related factor, because it reflected whether the intrauterine environment of most patients was suitable for embryonic development. Some studies have reported that the invasion of endometrium and interstitium in patients with adenomyosis destroyed the structure of the myometrium and the continuity of the uterine junction zone, changed the function of the myometrium, and affected the remodeling of the uterine spiral artery, thus it affected the normal implantation of the embryo. Secondly, myometrial fibrosis in patients with adenomyosis affected the physiological expansion of the uterus as the gestational age increased, which increased the risk of miscarriage and premature birth in patients with adenomyosis.\u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e These studies explained the pathophysiological mechanism of adenomyosis affecting the fertility of patients, and the previous parity (\u0026ge;\u0026thinsp;1 vs 0) was an important \"phenotype\" that reflected whether the intrauterine environment was suitable for embryonic development. Similarly, whether the patient is naturally pregnant or not, the history of previous uterine body surgery such as conservative adenomyosis lesion resection can also laterally reflect the severity of adenomyosis and the impact of adenomyosis on embryo implantation and development.\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBased on the above situation, we established a nomogram model including the above five factors. This model can provide a comprehensive and individualized prediction of the risk of adverse pregnancy outcomes for each pregnant woman with adenomyosis, which was undoubtedly very interesting and practical. Next, we stratified the patients according to the optimal risk threshold of the model. The probability of adverse pregnancy outcomes in patients in the high-risk group was undoubtedly much higher than that in the low-risk group. This suggested that in addition to routine obstetric care and monitoring should be applied for each pregnant woman at risk of adverse pregnancy outcomes, we also need to develop a personalized diagnosis and treatment plan for them. For patients with high risk of adverse pregnancy outcomes, management and monitoring should be strengthened in the first and second trimesters of pregnancy, and the number of obstetric examinations and the items of obstetric examination should be appropriately increased, especially after 12 weeks of gestation. Because we found that the time of miscarriage in this study was mostly concentrated after 12 weeks of gestation, which may be caused by the above-mentioned myometrial fibrosis in patients with adenomyosis, with the increase of gestational weeks gestational age, the mechanical physiological expansion of the uterus was affected. Particularly, we found that for patients in the high-risk group, whether in the training cohort or in the validation cohort, the probability of developing pregnancy related complications (hypertensive disorder complicating pregnancy, PPROM, GDM, postpartum hemorrhage, etc.) was also much higher than that in the low-risk group, which undoubtedly increased the risk of adverse pregnancy outcomes on another level, thus entered a \"vicious circle\".\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e Therefore, it suggested that low-dose aspirin, heparin, and immunosuppressants may also should be used in early pregnancy to improve uterine blood perfusion for pregnant women in the high-risk group of adverse pregnancy outcomes. The duration and dose of administration should be adjusted according to the effect on pregnancy outcomes to reduce the probability of related complications such as preeclampsia, SGA.\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e At the same time, obstetricians should closely monitor the patient's various indicators (including various biochemical indicators and imaging examinations), be wary of the occurrence of complications (such as gestational hypertension, PPROM, GDM, etc.), and strengthen prediction and prevention of adverse pregnancy outcomes. If complications have occurred, related complications should be actively treated according to relevant guidelines.\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e For the perinatal management of patients in the high-risk group, the choice of delivery timing and delivery method should consider multidisciplinary (obstetrics, gynecology, imaging, etc.) consultation and be carefully decided according to the specific conditions of the patients. It should be noted that pregnant women with a history of uterine body surgery (such as adenomyomectomy or adenomyosis lesion resection in the past) had a progressively higher risk of uterine rupture with increasing gestational age (3 patients with uterine rupture in the training and validation cohort had a history of adenomyosis or adenomyoma surgery in the past), and these patients should be treated as high-risk pregnancy management regardless of the risk predicted by the model. Such pregnant women should terminate their pregnancy by caesarean section (especially in patients conceived through ART).\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e For intrapartum and postpartum management of patients, it had been reported in the literature that pregnant women with adenomyosis were a high-risk group for postpartum hemorrhage. In our study, the proportion of postpartum hemorrhage in the high-risk group was much greater than that in the low-risk group. Some patients should be alert to the occurrence of postpartum hemorrhage or even severe and refractory postpartum hemorrhage and should be managed served as the high-risk group of postpartum hemorrhage during delivery to strengthen prevention. In summary, for patients in the high-risk group, we should pay more attention to the management of the whole process of pregnancy.\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study has significant advantages. First of all, the model can predict the risk of adverse pregnancy outcomes in all pregnant women with adenomyosis. Second, the nomogram model used only 5 readily available clinicopathological factors (previous parity, natural conception or not, type of adenomyosis, history of infertility or adverse pregnancy outcomes and history of uterine body surgery) to predict the risk of adverse pregnancy outcomes in pregnant women with adenomyosis accurately. Finally, our model was externally validated and showed good accuracy and stability. This model can help clinicians to improve pregnancy outcomes by early intervention in pregnant women with adenomyosis.\u003c/p\u003e \u003cp\u003eOf course, there were certain limitations in this study. This study was a retrospective study, and the exclusion of some cases in the access to clinical data may lead to selective bias, so we need more prospective studies to validate it further. In addition, the factors currently included in the model in this study were limited to clinical indicators, and there was a lack of objective pathophysiological, serological, and imaging indicators, and more predictive indicators should be included in the future to increase the performance of the model.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eAll in all, we have established a nomogram model to predict the risk of major adverse pregnancy outcomes (miscarriage, premature delivery, and SGA) in pregnant women with adenomyosis, and can perform risk stratification for patients. which could provide clinicians with a reference tool when formulating a diagnosis and treatment plan for pregnant women with adenomyosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYanlin Chen:\u0026nbsp;Data collection\u003c/p\u003e\n\u003cp\u003eChunxia Gong:\u0026nbsp;Data collection\u003c/p\u003e\n\u003cp\u003eYicheng Hu:\u0026nbsp;Data analysis, Manuscript writing\u003c/p\u003e\n\u003cp\u003eZhuoying Hu:\u0026nbsp;project development\u003c/p\u003e\n\u003cp\u003ePeng Jiang:\u0026nbsp;Data collection or management\u003c/p\u003e\n\u003cp\u003eWei Kong:\u0026nbsp;Data analysis, Manuscript writing\u003c/p\u003e\n\u003cp\u003eLingya Xu:\u0026nbsp;Data collection\u003c/p\u003e\n\u003cp\u003eYang Yang: Data collection\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics Committee of Chongqing Medical University approved this study (Ethics approval number:2021-547).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients provided their informed consent before starting the treatment and gave consent to have their data published. As it was a retrospective clinical study, all the patients were contacted by telephone to obtain verbal informed consent and it was approved by the ethics committee. All data about the patients was anonymized or maintained with confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors affirm that human research participants provided informed consent for publication of the images in Table(s) 1, 2 and 3 and Figure(s) 1, 2 and 3.\u003c/em\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCRediT author statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhuoying Hu: Conceptualization, Methodology, Supervision, Project administration, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eWei Kong and\u0026nbsp;Yicheng Hu: Methodology, Data curation, Investigation, Software, Formal analysis, Writing- Original draft preparation, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003ePeng Jiang,Chunxia Gong\u0026nbsp;and Yanlin Chen: Data curation, Software, Formal analysis, Investigation\u003c/p\u003e\n\u003cp\u003eLinya Xu and Yang Yang: Data curation, Supervision\u003c/p\u003e\n\u003cp\u003eAll authors critically reviewed the paper and had final approval of it.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMoawad G, Kheil MH, Ayoubi JM, Klebanoff JS, Rahman S, Sharara FI (2022) Adenomyosis and infertility. 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Curr Med Res Opin 34:839\u0026ndash;849\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunoz JL, Kimura AM, Xenakis E, Jenkins DH, Braverman MA, Ramsey PS et al Whole blood transfusion reduces overall component transfusion in cases of placenta accreta spectrum: a pilot program. The journal of maternal-fetal \u0026amp; neonatal medicine: the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstet 2021:1\u0026ndash;6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu XY, Zhang Y, Wei Y, Li R, Zhao YY (2020) [Perinatal outcome of pregnant women with adenomyosis]. Zhonghua fu chan ke za zhi 55:743\u0026ndash;748\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi J, Dai Y, Zhang J, Li X, Jia S, Leng J (2021) Pregnancy outcomes in women with infertility and coexisting endometriosis and adenomyosis after laparoscopic surgery: a long-term retrospective follow-up study. BMC Pregnancy Childbirth 21:383\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamura H, Kishi H, Kitade M, Asai-Sato M, Tanaka A, Murakami T et al (2017) Complications and outcomes of pregnant women with adenomyosis in Japan. Reproductive Med biology 16:330\u0026ndash;336\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"archives-of-gynecology-and-obstetrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arch","sideBox":"Learn more about [Archives of Gynecology and Obstetrics](https://www.springer.com/journal/404)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/arch/default.aspx","title":"Archives of Gynecology and Obstetrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pregnancy, Adenomyosis, Pregnancy complications, Adverse pregnancy outcome, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-2131358/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2131358/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/em\u003e To establish a reliable nomogram model to predict the risk of major adverse pregnancy outcomes in pregnant women with adenomyosis, and to provide a reference tool for the hierarchical management and the prenatal examination of pregnant women.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e We collected the clinical data of pregnant women with adenomyosis who were treated in the First Affiliated Hospital of Chongqing Medical University and the Women and Children’s Hospital of Chongqing Medical University from January 2014 to June 2020. They were divided into the training cohort and the validation cohort, respectively.\u003c/p\u003e\n\u003cp\u003eIn the training cohort, we screened out risk factors associated with major adverse pregnancy outcomes and established a model, which was subsequently validated.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e In the training cohort, we found that natural conception or not, type of adenomyosis, previous parity, history of infertility or adverse pregnancy outcomes, history of uterine body surgerywere associated with major adverse pregnancy outcomes of pregnant women with adenomyosis, and based on these factors, a nomogram model was constructed. The calibration curves of the model were well fitted in both the training and validation cohorts. The receiver operating characteristic curve (ROC curve) showed that the area under the curve (AUC) was 0.862 and 0.836 in the training and validation cohorts, respectively. The optimal risk threshold of the model was 0.24, and this threshold can be applied to risk stratification of pregnant women.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/em\u003e The nomogram model established in this study can reliably predict the risk of major APO in pregnant women with AD.\u003c/p\u003e","manuscriptTitle":"Establishment and validation of a nomogram model for predicting adverse pregnancy outcomes of pregnant women with adenomyosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-23 20:43:37","doi":"10.21203/rs.3.rs-2131358/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2022-11-21T20:36:11+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-11-21T18:07:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Archives of Gynecology and Obstetrics","date":"2022-10-09T19:49:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-10-05T14:37:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Gynecology and Obstetrics","date":"2022-10-04T06:20:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-gynecology-and-obstetrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arch","sideBox":"Learn more about [Archives of Gynecology and Obstetrics](https://www.springer.com/journal/404)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/arch/default.aspx","title":"Archives of Gynecology and Obstetrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7664d728-847a-40d1-8f91-9aeb4567ed3d","owner":[],"postedDate":"November 23rd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T22:05:37+00:00","versionOfRecord":{"articleIdentity":"rs-2131358","link":"https://doi.org/10.1007/s00404-023-07136-z","journal":{"identity":"archives-of-gynecology-and-obstetrics","isVorOnly":false,"title":"Archives of Gynecology and Obstetrics"},"publishedOn":"2023-07-25 21:46:45","publishedOnDateReadable":"July 25th, 2023"},"versionCreatedAt":"2022-11-23 20:43:37","video":"","vorDoi":"10.1007/s00404-023-07136-z","vorDoiUrl":"https://doi.org/10.1007/s00404-023-07136-z","workflowStages":[]},"version":"v1","identity":"rs-2131358","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2131358","identity":"rs-2131358","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}