Predictive Nomogram of Ultrasound Indicators for the Termination Outcome of Cesarean Scar Pregnancy

Predictive Nomogram of Ultrasound Indicators for the Termination Outcome of Cesarean Scar Pregnancy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predictive Nomogram of Ultrasound Indicators for the Termination Outcome of Cesarean Scar Pregnancy Xiaoyi Xiao, Zhichao Feng, Ting Li, Hong Qiao, Yun Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4695964/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose To construct and validate a nomogram to predict the risk of adverse events (intraoperative massive hemorrhage or retained products of conception) during the termination of Cesarean scar pregnancy (CSP). Method Data from patients diagnosed with CSP who underwent Dilation and Curettage (D&C) at two hospitals were retrospectively collected. This data formed both internal and external cohorts for analysis. The internal cohort was split randomly, with 70% of the data allocated to a training set and 30% to an internal validation set. The external cohort was used exclusively as the external validation set. LASSO and logistic regression were utilized to select variables and construct a nomogram. The nomogram's performance was assessed using various methods including C-index, calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) to evaluate its ability for identification, calibration, and clinical effectiveness. Results The prediction nomogram incorporated several predictors, including scar thickness, type of CSP, gestational sac diameter, and blood flow. This nomogram exhibited strong discrimination, as evidenced by a C-index of 0.829 (95% confidence interval: 0.770–0.887). Moreover, even in the interval validation set, a high C-index value of 0.784 was achieved, and in the external validation set, it reached 0.833. Further assessment through calibration curve analysis, DCA, and CICA revealed a robust agreement between the nomogram's predictions and actual observations, underscoring its utility and reliability. Conclusion The validated nomogram effectively predicts adverse events in CSP, showing good discrimination and calibration, making it useful in clinical settings. Health sciences/Diseases Health sciences/Health care Health sciences/Risk factors nomogram cesarean scar pregnancy adverse event LASSO Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 What does this study adds to the clinical work Our study presents a novel nomogram that incorporates scar thickness, CSP type, gestational sac diameter, and blood flow to accurately predict adverse events during CSP termination. Robust validation in both internal and external cohorts confirms the nomogram's high discriminatory power (C-index = 0.829) and clinical effectiveness, as evidenced by calibration, decision curve, and impact curve analyses. This tool provides clinicians with a valuable resource for early identification of high-risk CSP patients, facilitating personalized management strategies and ultimately improving patient outcomes. Introduction Cesarean scar pregnancy (CSP), a rare type of ectopic pregnancy, refers to the implantation of the gestational sac at the scar site from a previous uterine incision 1 . In recent years, the incidence of CSP has significantly increased due to the implementation of China's multiple birth policy, the rising rate of cesarean sections, and the widespread use of ultrasound diagnosis 2 . CSP is linked with severe complications such as massive hemorrhage and uterine rupture. Therefore, upon diagnosis of CSP, it is crucial to promptly terminate the pregnancy and remove the gestational tissue as early as possible 3 . Because the safety and effectiveness of transvaginal surgery under ultrasound or hysteroscopic guidance for uterine evacuation are relatively good, it is now widely adopted in China. However, adverse event such as intraoperative massive hemorrhage and retained products of conception (RPOC) still cause high concern among clinical doctors 4,5 . Because intraoperative massive hemorrhage may lead to more serious maternal bleeding, shock, or even death, while postoperative RPOC may result in complications such as infection, uterine trauma, and infertility 6–8 . It is crucial to predict the occurrence of the adverse events through early indicators of CSP.Ultrasonography is a safe, non-invasive, and relatively inexpensive imaging modality, thus making it the preferred method for early diagnose of CSP 9 . Through ultrasonography, doctors can thoroughly assess the uterine scar area, including scar thickness, the relationship between CSP and the scar, and parameters such as blood flow around the implant site. This non-invasive approach is important for early detection and diagnosis of CSP 10 .Therefore, if this study can construct a nomogram based on ultrasound indicators to predict the probability of adverse events during the management of CSP in the early time, it would provide more reliable evidence for clinical doctors to formulate more personalized treatment plans, thereby improving patient outcomes and survival rates. Patients and Methods Following approval from the Institutional Review Boards of Third Xiangya Hospital, Central South University, and the First Affiliated Hospital of Hunan University of Chinese Medicine, informed consent from patients was waived due to the retrospective nature of the study. The research adhered to the principles of the Helsinki Declaration. Patients This study included patients admitted to the Third Xiangya Hospital of Central South University from January 2015 to February 2024 who were diagnosed with CSP and received treatment as internal cohort. Clinical and ultrasound data of CSP patients were obtained by two independent researchers through the hospital's electronic medical record system and ultrasound department database based on inclusion and exclusion criteria. Inclusion criteria: ( 1 ) With a history of previous cesarean section; ( 2 ) Patients with elevated serum β-human chorionic gonadotropin (β-hCG); ( 3 ) Preoperative ultrasound diagnosis of CSP and ultrasound review results one month postoperatively; ( 4 ) Gestational age ≤ 12 weeks at the time of surgery and the interval between dilation and curettage (D&C) and ultrasound examination was less than 1 week; ( 5 ) Pathological results indicating pregnancy tissue. Exclusion criteria: ( 1 ) The termination of pregnancy was not done by D&C; ( 2 ) Prior treatments before D&C such as medications (e.g., methotrexate,), uterine artery embolization, and high-frequency ultrasound ablation aimed at reducing blood supply to the gestational sac and intraoperative bleeding; ( 3 ) Cases of twin or multiple pregnancies; ( 4 ) Uterine malformations and anomalies; ( 5 ) Patients with bleeding disorders; ( 6 ) CSP patients who were misdiagnosed with intrauterine pregnancy and underwent artificial or medical abortion at other hospitals. The patients who met the selection criteria and were finally included were divided into two groups: the case group (those with intraoperative blood loss exceeding 200ml 11,12 or RPOC observed on postoperative ultrasound examination 13 ) and the control group (those with intraoperative blood loss less than 50ml and no RPOC observed on postoperative ultrasound examination). The included patients were randomly divided into a training set and an internal validation set in a 7:3 ratio. Additionally, according to the same inclusion criteria, patients hospitalized for CSP at the First Affiliated Hospital of Hunan University of Chinese Medicine from January 2016 to December 2023 were used as an external cohort, namely external validation set. We de-identified all patient details to protect their identity. Ultrasound diagnosis and classification of CSP All ultrasound data relevant to our study were obtained at the time of CSP diagnosis, prior to initiating treatment. The ultrasonographic criteria used for diagnosing CSP included the following 3 : (a) The gestational sac is developing in the front part of the lower portion of the uterus. (b) The uterus and cervical canal appear empty. (c) There is no healthy muscular wall (myometrium) between the gestational sac and the bladder. According to the relationship between the gestational sac and the cesarean section diverticulum (CSD), the pregnancy sac is divided into three types 14 : Superficial: The basal decidua was partially attached to the cesarean incision scar without forming a diverticulum. Partial: The gestational sac was partially situated within the CSD. Complete: The gestational sac was entirely located within the CSD. The blood flow around the gestational sac (GS) at the site of the previous cesarean incision is categorized into four levels 15 : Grade 0: No blood flow signal detected. Grade I: Punctate blood flow observed in one to two locations. Grade II: One vessel longer than the radius of the lesion or several small vessels present. Grade III: More than four vessels or vessels interconnected forming a network. The fetal heartbeat status during gestation was categorized into two types based on ultrasound reports: absence of heartbeat and presence of heartbeat. Additionally, gestational sac diameter, the scar thickness between the gestational sac and bladder, and the presence of RPOC on ultrasound review one month postoperatively were also obtained. Clinical characteristics The ages of all patients, along with their pregnancy histories, including the number of pregnancies, cesarean sections, induced abortions, and previous CSPs, were comprehensively examined and documented. Additionally, preoperative levels of β-hCG, intervals between surgery and ultrasound examinations, and surgical procedure details were meticulously reviewed and summarized. Construction and Validation of the Prediction Model A predictive model for the occurrence of complications during the management of CSP was established using the training set. The model underwent validation using both internal and external validation datasets. To address multicollinearity among different variables, the least absolute shrinkage and selection operator (LASSO) regression analysis was utilized to identify the most relevant predictive variables. Cross-validation was applied to ascertain the suitable tuning parameter (lambda) for the LASSO logistic regression. Subsequently, in the multivariate logistic regression analysis, a nomogram model was developed using variables with a significance level of P < 0.05. Calibration curves were then created to assess the accuracy of the adverse event nomogram in predicting outcomes. In the training set, the discriminative ability of the nomogram was evaluated using metrics such as the C-index and the area under the receiver operating characteristic curve (AUC). Additionally, decision curve analysis (DCA) was conducted to assess the practical utility of the nomogram. DCA evaluates net benefits by comparing the proportion of false-positive patients to true positive patients, while considering the potential adverse consequences of forgoing any unnecessary intervention, based on a predetermined threshold probability. The clinical impact curve analysis (CICA) categorizes patients into high risk or high risk with event and compares the actual observed event rates. The predictive accuracy of the nomogram was evaluated using various metrics on both the internal and external validation sets. This evaluation included the use of ROC curves, calibration curves, DCA, and CICA. Statistical analysis Measurements that follow a normal distribution are presented as mean ± standard deviation (SD), while those that do not adhere to normal distribution are expressed as median ± interquartile range. Categorical data are represented by counts or rates. The independent-sample t-test is employed to compare parameter values between two groups, while the Mann–Whitney U-test compares nonparametric values between two groups. The chi-square test is used to compare categorical variables. Lasso regression analyses and multivariate logistic regression analyses were conducted. All tests were two-tailed, and a significance level of P < 0.05 was considered statistically significant. Statistical analyses were carried out using the R statistical software package (version 4.3.0, Vienna, Austria). Result Baseline Characteristics of the included patients The flowchart depicted in Fig. 1 outlines the screening process for the internal cohort, which consisted of 241 eligible CSP patients participating in the study. This internal cohort was subsequently divided into a training set, comprising 168 individuals, and an internal validation set, comprising 73 individuals. Within this internal cohort, the case group, characterized by experiencing adverse events, comprised 80 out of the total 241 patients (33.20%), while the control group, without adverse events, comprised 161 out of 241 patients (66.8%). Moreover, an external cohort was selected utilizing the same screening criteria, as illustrated in Supplementary Fig. 1. Table 1 displays the baseline characteristics of both the internal and external cohorts. Importantly, no statistically differences were observed between the case and control groups concerning age, menopausal duration, and the number of previous cesarean sections ( p > 0.05). Selection of Predictive Model Variables Table 1 Patient demographics and preoperative characteristics. Characteristics Internal cohort (n = 241) External cohort (n = 120) Case group (n = 80) Control group (n = 161) P value Case group (n = 40) Control group (n = 80) P value Age, years 33.52 ± 4.60 33.25 ± 3.03 0.59 34.33 ± 4.37 34.51 ± 3.41 0.81 Menopause (days) 54.61 ± 10.11 54.97 ± 10.35 0.8 52.98 ± 8.54 52.23 ± 9.83 0.68 Serum β-hCG (mIU/ml) 55451 ± 56570 47289 ± 59299 0.31 38195 ± 37424 38474 ± 37180 0.97 Gravidity 3.80 ± 1.49 4.03 ± 1.82 0.33 3.49 ± 1.58 3.88 ± 1.63 0.2 Number of cesarean sections 1.46 ± 0.61 1.48 ± 0.57 0.88 1.42 ± 0.63 1.42 ± 0.55 0.98 Number of artificial-abortion 1.80 ± 1.31 2.13 ± 1.63 0.12 1.56 ± 1.16 2.04 ± 1.57 0.08 Scar thickness(mm) 3.07 ± 1.11 2.18 ± 0.98 < 0.001 2.44 ± 1.13 3.05 ± 1.09 0.004 Percentages of grade III blood flow 20/80(25.00%) 18/161(11.18%) 0.03 15/40(37.5%) 10/80(12.5%) 0.02 Mean gestational sac diameter (mm) 33.54 ± 15.44 27.31 ± 14.47 0.03 34.88 ± 14.66 27.34 ± 14.25 0.006 Percentages of type III CSP 33/80(41.25%) 34/161(21.11%) 0.02 19/40(47.5%) 16/80(20%) 0.04 Presence of heartbeat 29/80(36.25%) 53/161(32.92%) 0.61 18/40(45%) 31/80(38.75%) 0.51 β-hCG β Human Chorionic Gonadotropin, CSP Cesarean scar pregnancy In our analysis, we employed LASSO regression to select variables, as depicted in Fig. 2 A, where we observed variations in coefficients for each variable. Additionally, utilizing tenfold cross-validation, we identified an optimal model with superior performance and minimal variables, as illustrated in Fig. 2 B. The selected variables included gestational sac diameter, scar thickness, blood flow, CSP type, and presence of heartbeat. To develop a predictive model for adverse events in patients with CSP, we conducted a multivariate logistic regression analysis on the aforementioned five variables using LASSO regression technology. As shown in Table 2 , four indicators were identified as independent risk factors influencing the occurrence of adverse events following D&C. Construction and evaluation of the nomogram Table 2 Prediction variables for the risk of adverse events in CSP management. Intercept and variable Prediction model β Odds ratio (95% CI) P -value Intercept -1.252 0.286 (0.135, 0.585) 0.000776 < 0.001 Gestational sac diameter 0.410 1.507 (1.007, 2.273) 0.047384 Scar thickness -0.902 0.406(0.261, 0.614) 3.35e-05 < 0.001 Blood flow 0.433 1.543(1.083, 2.206) 0.016600 CSP type 0.595 1.812 (1.192, 2.798) 0.006073 β is the regression coefficient, CI confidence interval A nomogram was constructed based on the four predictive factors for adverse events identified in the training set (Fig. 3 ). This nomogram revealed that scar thickness had the most significant influence on the incidence of adverse events in patients with CSP, followed by CSP type, blood flow, and gestational sac diameter. The predictive model demonstrated good discriminative ability, with a C-index of 0.829 (95% CI: 0.770–0.887) for the training set (Fig. 4 A). Furthermore, calibration curve analysis was performed to assess the degree of fit of the nomogram. The calibration curve exhibited a favorable agreement between prediction and observation in the training cohort (Fig. 4 D). The Hosmer-Lemeshow test resulted in a P-value of 0.588, indicating that the model was well-fitted in the training set. Subsequently, the clinical utility of the prediction model was evaluated through DCA and CICA. In the training set, both DCA and CICA demonstrated that the nomogram provided greater overall net benefits across a broad and clinically relevant range of threshold probabilities, thereby influencing the clinical prognosis of patients. Thus, the nomogram proves to be clinically valuable (Fig. 5 A and 5 D). Validation of Nomogram The study further validated the nomogram through internal validation set and external validation set. The C-index for the prediction nomogram were 0.784 (95% CI: 0.670–0.898) in internal validation set and 0.833 (95% CI: 0.761–0.906) in external validation set, respectively (Fig. 4 B and 4 C). The calibration curve demonstrated strong consistency between predicted and observed risk of adverse events across the two validation sets (Fig. 4 E and 4 F). The DCA (Fig. 5 B and Fig. 5 C) and CICA (Fig. 5 E and Fig. 5 F) of both the two validation sets yielded excellent net clinical benefit. Discussion Currently, research interest in CSP has shifted from image-based diagnosis to optimal management. Regarding CSP management, the primary focus is on selecting the best treatment modalities to reduce complication risks and preserve fertility. Surgical and non-surgical treatments are the main modalities for treating CSP. For different types and severities of CSP, choosing the appropriate surgical approach can significantly impact treatment outcomes. For example, Xu et al. 16 adopted different measures for different types of CSP during lesion excision surgery to achieve optimal treatment outcomes. Additionally, reducing treatment complications through improved surgical techniques is also a focal point of CSP management. For instance, Ellen Hofgaard et al. 17 achieved better clinical outcomes in treating CSP by employing robot-assisted laparoscopy. Preoperative interventions can also reduce surgical risks 18 . In our meta-analysis on CSP treatment 19 , it was found that preoperative UAE or HIFU can increase the success rate of surgery and significantly reduce postoperative complications after D&C. Long-term reproductive outcomes of CSP are also a focal point 20,21 , especially for patients with fertility requirements. In summary, before selecting the optimal treatment modality, a detailed assessment of CSP is necessary to achieve the best treatment outcomes and avoid serious complications. In previous reports, most studies 11,22–24 were single-center, utilizing univariate and multivariate logistic regression analyses to identify clinical and/or imaging indicators for determining risk factors for massive bleeding or RPOC during CSP termination of pregnancy. In this study, we developed and validated, for the first time, a column chart-based lasso regression method to select ultrasound indicators for preoperatively predicting the risk of bleeding or RPOC during CSP curettage management. This nomogram incorporates four ultrasound measurements of CSP, including scar thickness, gestational sac diameter, and blood flow, and CSP type as relevant risk factors for CSP, to achieve personalized prediction of occurrence adverse events in CSP patients. The nomogram established via ultrasound indicators assists clinicians in screening cases with high risks of postoperative complications, such as intraoperative bleeding ≥ 200 ml or RPOC, thereby guiding clinicians to formulate appropriate treatment plans for patients, such as selecting experienced emergency specialists for treatment, performing operations to reduce blood supply to the gestational sac preoperatively, and preparing for blood transfusion intraoperatively, to maximize minimizing patient suffering and ensuring patient safety to the greatest extent possible. Studies have reported 5 that patients with CSP experience significantly higher levels of bleeding during D&C compared to those during miscarriage and abortion. This may be attributed to the attachment of the CSP gestational sac to the scar tissue of the uterine muscle layer, where the lack of decidua tissue makes it easier for trophoblastic cells to invade beyond the junction of the endometrium and uterine muscle layer, reaching the deep uterine blood supply from the radial and arcuate arteries 25 , leading to a rapid increase in blood flow around the gestational sac. Therefore, forcibly separating the gestational sac during D&C may result in uncontrolled bleeding due to the weak muscle layer's inability to contract. Additionally, as the gestational age increases, the pregnancy mass enlarges, potentially elongating and thinning the lower segment of the uterus, coupled with an adequate blood supply, leading to increased bleeding during the surgical procedure. Thus, it is believed that the thinner the uterine muscle layer in the anterior wall scar, the more pronounced the increase in blood flow around the gestational sac site of the previous cesarean section incision, and the greater the likelihood of significant intraoperative bleeding. The incidence of RPOC in patients with CSP is higher than that in those after miscarriage 5,26,27 . The possible reasons for the persistent residual mass of ectopic pregnancy are as follows: 1) The CSP mass enters the uterine muscle layer or scar depth through micro-fissures, such as in type III CSP, and may sometimes invade the broad ligament, making surgical excision difficult 28 ; 2) In case of significant intraoperative bleeding before complete emptying of the pregnancy product, surgery must be stopped to ensure hemostasis; 3) Local bleeding occurs before the complete removal of the gestational sac; and 4) Scar tissue at the uterine incision site may hinder its complete removal. Additionally, CSP patients typically undergo multiple routine follow-up examinations after surgery, which helps to improve the diagnosis rate of RPOC. Time is also crucial for terminating CSP, as with time, the gestational sac and its blood vessels grow, increasing the difficulty of D&C and the likelihood of residual RPOC 4 . When trophoblastic tissue is situated at the scar site and the uterine muscle layer is thinner, there is an elevated risk of uterine perforation during D&C surgery. Additionally, a richer blood flow around the gestational sac increases the likelihood of bleeding during the procedure. These factors compound the surgical difficulty and may escalate the likelihood of RPOC. Consequently, for patients at a higher risk, personalized treatment plans such as hysteroscopy surgery or laparoscopic monitoring are preferable options. There are several limitations to this study. Firstly, both the internal and external cohorts of this study come from different hospitals in the same province, limiting the external generalizability of the results. Secondly, this study is a retrospective cohort study, which cannot control for the consistency and completeness of data collection. Thirdly, the nomogram exclusively incorporated ultrasound-related indicators, which restricts the model's practicality in patients with unclear ultrasound images. Conclusions In summary, our study has yielded a predictive nomogram specifically designed to assess the risk of adverse outcomes in CSP patients. This nomogram exhibits strong discriminatory and calibration capabilities, offering promising prospects for enhancing clinical decision-making in the management of CSP. Its potential to improve risk assessment and guide personalized patient care underscores its significance as a valuable clinical tool in this context. Declarations Author Contribution XY.X and YZ designed, drafted, and revised the paper and analyzed the data.ZC.F, HQ and TL supervised and submitted the paper for publication. Each author contributed to the article and approved the submitted version. Acknowledgement We would like to thank Dr. Liu for the assistance in the process of writing this paper. Data Availability The datasets used during the current study available from the corresponding author on reasonable request. References Jurkovic D, Hillaby K, Woelfer B, et al. First-trimester diagnosis and management of pregnancies implanted into the lower uterine segment Cesarean section scar. Ultrasound Obstet Gynecol 2003;21(3):220–7, doi: 10.1002/uog.56 Li HT, Hellerstein S, Zhou YB, et al. Trends in Cesarean Delivery Rates in China, 2008–2018. JAMA 2020;323(1):89–91, doi: 10.1001/jama.2019.17595 Society for Maternal-Fetal Medicine. Electronic address pso, Miller R, Timor-Tritsch IE, et al. 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Cite Share Download PDF Status: Published Journal Publication published 28 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Oct, 2024 Reviews received at journal 24 Oct, 2024 Reviewers agreed at journal 24 Oct, 2024 Reviews received at journal 22 Sep, 2024 Reviewers agreed at journal 22 Sep, 2024 Reviewers invited by journal 20 Jul, 2024 Editor assigned by journal 20 Jul, 2024 Editor invited by journal 11 Jul, 2024 Submission checks completed at journal 09 Jul, 2024 First submitted to journal 06 Jul, 2024 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. 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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-4695964","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":331873097,"identity":"35dbb092-fb27-4fab-a9e7-e2c52874423e","order_by":0,"name":"Xiaoyi Xiao","email":"","orcid":"","institution":"Third Xiangya Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyi","middleName":"","lastName":"Xiao","suffix":""},{"id":331873098,"identity":"90a69dea-ae01-4a53-945e-51d9455cb388","order_by":1,"name":"Zhichao Feng","email":"","orcid":"","institution":"Third Xiangya Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhichao","middleName":"","lastName":"Feng","suffix":""},{"id":331873099,"identity":"3694eeb0-5cd1-4a36-a903-075a770cf1c0","order_by":2,"name":"Ting Li","email":"","orcid":"","institution":"Third Xiangya Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Li","suffix":""},{"id":331873100,"identity":"8c736cf8-2b22-4ed4-9482-64cf068024e7","order_by":3,"name":"Hong Qiao","email":"","orcid":"","institution":"Department of Intensive Care Unit, Li County People's Hospital, Changde City, Hunan, China","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Qiao","suffix":""},{"id":331873101,"identity":"09ae2ec0-bb26-4c2c-ba75-0542be85d4a5","order_by":4,"name":"Yun Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDACCTBpkwDlMhOtJY10LYdJ0CI/u8dMmnfH+TxzieSnGxgqrBMb2M8ewKuFcc4ZoJYzt4stZ6SZ3WA4k57YwJOXgFcLs0QOUEvb7cQNN3LYbjC2HU5skOAxwKuFDaLlHFTLPyK08EC0HIBqaSBCi4REWrHl3LbkxA1nnpndSDiWbtzGk4Nfi/yM5I033rbZJW44nvzsxocaa9l+9jP4tQABiwScmQDyHSH1QMD8gQhFo2AUjIJRMJIBAEBUQ9Aztn7YAAAAAElFTkSuQmCC","orcid":"","institution":"First Affiliated Hospital of Hunan University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yun","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-07-06 09:02:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4695964/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4695964/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-82894-7","type":"published","date":"2024-12-28T15:56:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62152771,"identity":"864a6a83-738f-4b60-b286-05047965e843","added_by":"auto","created_at":"2024-08-09 20:52:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":379878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flowchart of participants’ selection for the study in the internal cohort.\u003c/strong\u003e CSP cesarean scar pregnancy, IUP intrauterine pregnancy, D\u0026amp;C dilation and curettage, HIFU high intensity focused ultrasound, UAE uterine artery embolization.\u003c/p\u003e","description":"","filename":"OnlineFigure1.patientsselection300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-4695964/v1/edbdd17f0c7f366977c0d5f0.png"},{"id":62152770,"identity":"3abc02a6-cdf0-4e9c-987c-13a1a6779d52","added_by":"auto","created_at":"2024-08-09 20:52:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":106083,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe process of predictor selection using the LASSO (Least Absolute Shrinkage and Selection Operator) regression method. \u003c/strong\u003e(A) We observe the LASSO coefficient profiles of five variables plotted against the log(λ) sequence. (B) The selection of the best penalty coefficient lambda through tenfold cross-validation and minimization criterion. The optimal parameter (λ) in the LASSO model is verified by plotting the binomial deviance curve versus log(λ), with dotted vertical lines indicating the selected lambda. Four variables with nonzero coefficients were selected based on the optimal lambda.\u003c/p\u003e","description":"","filename":"OnlineFigure2.VaribleselectionbyLASSO300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-4695964/v1/efe16755b5b1cbd5c3356958.png"},{"id":62152772,"identity":"e1f37e1f-9ff0-4f4f-85be-8d2422a8272d","added_by":"auto","created_at":"2024-08-09 20:52:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24340,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA nomogram to predict the risk of adverse events in CSP.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure3.nomogram.png","url":"https://assets-eu.researchsquare.com/files/rs-4695964/v1/249fc4ceead935b219d94cbb.png"},{"id":62152775,"identity":"4f1e1b44-0392-46ee-8346-97e1a987548b","added_by":"auto","created_at":"2024-08-09 20:52:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":172587,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve and calibration curve of nomogram model in dataset of training set, internal validation set and external validation set, respectively. \u003c/strong\u003eA-C for ROC curve and D-F for calibration curve.\u003c/p\u003e","description":"","filename":"OnlineFigure4AUCCalibrationcurve.png","url":"https://assets-eu.researchsquare.com/files/rs-4695964/v1/ca41494d5d054af7115d60de.png"},{"id":62152774,"identity":"454a8e32-6abd-4e30-afd5-4fa9c73b5f47","added_by":"auto","created_at":"2024-08-09 20:52:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":153857,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curve analysis (DCA) and clinical impact curve analysis (CICA) of nomogram model in training set, internal validation set, and external validation set. \u003c/strong\u003eA-C for DCA and D-F for CICA.\u003c/p\u003e","description":"","filename":"OnlineFigure5DCACIC.png","url":"https://assets-eu.researchsquare.com/files/rs-4695964/v1/527dcdf9fd1f723d2c6ad830.png"},{"id":72640512,"identity":"b6190674-a740-4a75-b73f-bf3c5a7e38ec","added_by":"auto","created_at":"2024-12-30 16:06:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2185701,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4695964/v1/701d8bbe-25de-4907-b061-449c9c7448ef.pdf"},{"id":62154601,"identity":"41e722da-a621-4acd-a17b-b7b216e1da0b","added_by":"auto","created_at":"2024-08-09 21:00:01","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1309300,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure1. Flowchart for External Cohort Patient Selection.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Supplementaryfigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4695964/v1/84ef74a9b2571967e1db9bd3.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Nomogram of Ultrasound Indicators for the Termination Outcome of Cesarean Scar Pregnancy","fulltext":[{"header":"What does this study adds to the clinical work","content":"\u003cp\u003eOur study presents a novel nomogram that incorporates scar thickness, CSP type, gestational sac diameter, and blood flow to accurately predict adverse events during CSP termination. Robust validation in both internal and external cohorts confirms the nomogram's high discriminatory power (C-index = 0.829) and clinical effectiveness, as evidenced by calibration, decision curve, and impact curve analyses. This tool provides clinicians with a valuable resource for early identification of high-risk CSP patients, facilitating personalized management strategies and ultimately improving patient outcomes.\u003c/p\u003e "},{"header":"Introduction","content":"\u003cp\u003eCesarean scar pregnancy (CSP), a rare type of ectopic pregnancy, refers to the implantation of the gestational sac at the scar site from a previous uterine incision\u003csup\u003e1\u003c/sup\u003e. In recent years, the incidence of CSP has significantly increased due to the implementation of China's multiple birth policy, the rising rate of cesarean sections, and the widespread use of ultrasound diagnosis\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCSP is linked with severe complications such as massive hemorrhage and uterine rupture. Therefore, upon diagnosis of CSP, it is crucial to promptly terminate the pregnancy and remove the gestational tissue as early as possible\u003csup\u003e3\u003c/sup\u003e. Because the safety and effectiveness of transvaginal surgery under ultrasound or hysteroscopic guidance for uterine evacuation are relatively good, it is now widely adopted in China. However, adverse event such as intraoperative massive hemorrhage and retained products of conception (RPOC) still cause high concern among clinical doctors \u003csup\u003e4,5\u003c/sup\u003e. Because intraoperative massive hemorrhage may lead to more serious maternal bleeding, shock, or even death, while postoperative RPOC may result in complications such as infection, uterine trauma, and infertility\u003csup\u003e6–8\u003c/sup\u003e. It is crucial to predict the occurrence of the adverse events through early indicators of CSP.Ultrasonography is a safe, non-invasive, and relatively inexpensive imaging modality, thus making it the preferred method for early diagnose of CSP \u003csup\u003e9\u003c/sup\u003e. Through ultrasonography, doctors can thoroughly assess the uterine scar area, including scar thickness, the relationship between CSP and the scar, and parameters such as blood flow around the implant site. This non-invasive approach is important for early detection and diagnosis of CSP\u003csup\u003e10\u003c/sup\u003e.Therefore, if this study can construct a nomogram based on ultrasound indicators to predict the probability of adverse events during the management of CSP in the early time, it would provide more reliable evidence for clinical doctors to formulate more personalized treatment plans, thereby improving patient outcomes and survival rates.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cp\u003e Following approval from the Institutional Review Boards of Third Xiangya Hospital, Central South University, and the First Affiliated Hospital of Hunan University of Chinese Medicine, informed consent from patients was waived due to the retrospective nature of the study. The research adhered to the principles of the Helsinki Declaration.\u003c/p\u003e\u003ch2\u003ePatients\u003c/h2\u003e\u003cp\u003eThis study included patients admitted to the Third Xiangya Hospital of Central South University from January 2015 to February 2024 who were diagnosed with CSP and received treatment as internal cohort. Clinical and ultrasound data of CSP patients were obtained by two independent researchers through the hospital's electronic medical record system and ultrasound department database based on inclusion and exclusion criteria. Inclusion criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) With a history of previous cesarean section; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Patients with elevated serum β-human chorionic gonadotropin (β-hCG); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Preoperative ultrasound diagnosis of CSP and ultrasound review results one month postoperatively; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Gestational age ≤ 12 weeks at the time of surgery and the interval between dilation and curettage (D\u0026amp;C) and ultrasound examination was less than 1 week; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Pathological results indicating pregnancy tissue. Exclusion criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) The termination of pregnancy was not done by D\u0026amp;C; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Prior treatments before D\u0026amp;C such as medications (e.g., methotrexate,), uterine artery embolization, and high-frequency ultrasound ablation aimed at reducing blood supply to the gestational sac and intraoperative bleeding; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Cases of twin or multiple pregnancies; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Uterine malformations and anomalies; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Patients with bleeding disorders; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) CSP patients who were misdiagnosed with intrauterine pregnancy and underwent artificial or medical abortion at other hospitals. The patients who met the selection criteria and were finally included were divided into two groups: the case group (those with intraoperative blood loss exceeding 200ml \u003csup\u003e11,12\u003c/sup\u003e or RPOC observed on postoperative ultrasound examination\u003csup\u003e13\u003c/sup\u003e) and the control group (those with intraoperative blood loss less than 50ml and no RPOC observed on postoperative ultrasound examination). The included patients were randomly divided into a training set and an internal validation set in a 7:3 ratio. Additionally, according to the same inclusion criteria, patients hospitalized for CSP at the First Affiliated Hospital of Hunan University of Chinese Medicine from January 2016 to December 2023 were used as an external cohort, namely external validation set. We de-identified all patient details to protect their identity.\u003c/p\u003e\u003ch3\u003eUltrasound diagnosis and classification of CSP\u003c/h3\u003e\u003cp\u003eAll ultrasound data relevant to our study were obtained at the time of CSP diagnosis, prior to initiating treatment. The ultrasonographic criteria used for diagnosing CSP included the following\u003csup\u003e3\u003c/sup\u003e: (a) The gestational sac is developing in the front part of the lower portion of the uterus. (b) The uterus and cervical canal appear empty. (c) There is no healthy muscular wall (myometrium) between the gestational sac and the bladder. According to the relationship between the gestational sac and the cesarean section diverticulum (CSD), the pregnancy sac is divided into three types\u003csup\u003e14\u003c/sup\u003e: Superficial: The basal decidua was partially attached to the cesarean incision scar without forming a diverticulum. Partial: The gestational sac was partially situated within the CSD. Complete: The gestational sac was entirely located within the CSD. The blood flow around the gestational sac (GS) at the site of the previous cesarean incision is categorized into four levels\u003csup\u003e15\u003c/sup\u003e: Grade 0: No blood flow signal detected. Grade I: Punctate blood flow observed in one to two locations. Grade II: One vessel longer than the radius of the lesion or several small vessels present. Grade III: More than four vessels or vessels interconnected forming a network. The fetal heartbeat status during gestation was categorized into two types based on ultrasound reports: absence of heartbeat and presence of heartbeat. Additionally, gestational sac diameter, the scar thickness between the gestational sac and bladder, and the presence of RPOC on ultrasound review one month postoperatively were also obtained.\u003c/p\u003e\u003cp\u003e \u003cb\u003eClinical characteristics\u003c/b\u003eThe ages of all patients, along with their pregnancy histories, including the number of pregnancies, cesarean sections, induced abortions, and previous CSPs, were comprehensively examined and documented. Additionally, preoperative levels of β-hCG, intervals between surgery and ultrasound examinations, and surgical procedure details were meticulously reviewed and summarized.\u003c/p\u003e\u003ch3\u003eConstruction and Validation of the Prediction Model\u003c/h3\u003e\u003cp\u003eA predictive model for the occurrence of complications during the management of CSP was established using the training set. The model underwent validation using both internal and external validation datasets. To address multicollinearity among different variables, the least absolute shrinkage and selection operator (LASSO) regression analysis was utilized to identify the most relevant predictive variables. Cross-validation was applied to ascertain the suitable tuning parameter (lambda) for the LASSO logistic regression. Subsequently, in the multivariate logistic regression analysis, a nomogram model was developed using variables with a significance level of P \u0026lt; 0.05. Calibration curves were then created to assess the accuracy of the adverse event nomogram in predicting outcomes. In the training set, the discriminative ability of the nomogram was evaluated using metrics such as the C-index and the area under the receiver operating characteristic curve (AUC). Additionally, decision curve analysis (DCA) was conducted to assess the practical utility of the nomogram. DCA evaluates net benefits by comparing the proportion of false-positive patients to true positive patients, while considering the potential adverse consequences of forgoing any unnecessary intervention, based on a predetermined threshold probability. The clinical impact curve analysis (CICA) categorizes patients into high risk or high risk with event and compares the actual observed event rates. The predictive accuracy of the nomogram was evaluated using various metrics on both the internal and external validation sets. This evaluation included the use of ROC curves, calibration curves, DCA, and CICA.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eMeasurements that follow a normal distribution are presented as mean ± standard deviation (SD), while those that do not adhere to normal distribution are expressed as median ± interquartile range. Categorical data are represented by counts or rates. The independent-sample t-test is employed to compare parameter values between two groups, while the Mann–Whitney U-test compares nonparametric values between two groups. The chi-square test is used to compare categorical variables. Lasso regression analyses and multivariate logistic regression analyses were conducted. All tests were two-tailed, and a significance level of \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant. Statistical analyses were carried out using the R statistical software package (version 4.3.0, Vienna, Austria).\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003eBaseline Characteristics of the included patients\u003c/p\u003e\n\u003cp\u003eThe flowchart depicted in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the screening process for the internal cohort, which consisted of 241 eligible CSP patients participating in the study. This internal cohort was subsequently divided into a training set, comprising 168 individuals, and an internal validation set, comprising 73 individuals. Within this internal cohort, the case group, characterized by experiencing adverse events, comprised 80 out of the total 241 patients (33.20%), while the control group, without adverse events, comprised 161 out of 241 patients (66.8%). Moreover, an external cohort was selected utilizing the same screening criteria, as illustrated in Supplementary Fig. 1. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e displays the baseline characteristics of both the internal and external cohorts. Importantly, no statistically differences were observed between the case and control groups concerning age, menopausal duration, and the number of previous cesarean sections (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection of Predictive Model Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatient demographics and preoperative characteristics.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eInternal cohort (n\u0026thinsp;=\u0026thinsp;241)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eExternal cohort (n\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCase group (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl group (n\u0026thinsp;=\u0026thinsp;161)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCase group (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl group (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.52\u0026thinsp;\u0026plusmn;\u0026thinsp;4.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.51\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenopause (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.97\u0026thinsp;\u0026plusmn;\u0026thinsp;10.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.98\u0026thinsp;\u0026plusmn;\u0026thinsp;8.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.23\u0026thinsp;\u0026plusmn;\u0026thinsp;9.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum \u0026beta;-hCG (mIU/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55451\u0026thinsp;\u0026plusmn;\u0026thinsp;56570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47289\u0026thinsp;\u0026plusmn;\u0026thinsp;59299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38195\u0026thinsp;\u0026plusmn;\u0026thinsp;37424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38474\u0026thinsp;\u0026plusmn;\u0026thinsp;37180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGravidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of cesarean sections\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of artificial-abortion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScar thickness(mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentages of grade III blood flow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20/80(25.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18/161(11.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15/40(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/80(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean gestational sac diameter (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.54\u0026thinsp;\u0026plusmn;\u0026thinsp;15.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.31\u0026thinsp;\u0026plusmn;\u0026thinsp;14.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.88\u0026thinsp;\u0026plusmn;\u0026thinsp;14.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.34\u0026thinsp;\u0026plusmn;\u0026thinsp;14.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentages of type III CSP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33/80(41.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34/161(21.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19/40(47.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16/80(20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresence of heartbeat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29/80(36.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53/161(32.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18/40(45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31/80(38.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u0026beta;-hCG \u0026beta; Human Chorionic Gonadotropin, CSP Cesarean scar pregnancy\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn our analysis, we employed LASSO regression to select variables, as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, where we observed variations in coefficients for each variable. Additionally, utilizing tenfold cross-validation, we identified an optimal model with superior performance and minimal variables, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB. The selected variables included gestational sac diameter, scar thickness, blood flow, CSP type, and presence of heartbeat. To develop a predictive model for adverse events in patients with CSP, we conducted a multivariate logistic regression analysis on the aforementioned five variables using LASSO regression technology. As shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, four indicators were identified as independent risk factors influencing the occurrence of adverse events following D\u0026amp;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction and evaluation of the nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrediction variables for the risk of adverse events in CSP management.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIntercept and variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePrediction model\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.286 (0.135, 0.585)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000776\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGestational sac diameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.507 (1.007, 2.273)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScar thickness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.406(0.261, 0.614)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.35e-05\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood flow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.543(1.083, 2.206)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCSP type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.812 (1.192, 2.798)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e is the regression coefficient, CI confidence interval\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA nomogram was constructed based on the four predictive factors for adverse events identified in the training set (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This nomogram revealed that scar thickness had the most significant influence on the incidence of adverse events in patients with CSP, followed by CSP type, blood flow, and gestational sac diameter. The predictive model demonstrated good discriminative ability, with a C-index of 0.829 (95% CI: 0.770\u0026ndash;0.887) for the training set (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Furthermore, calibration curve analysis was performed to assess the degree of fit of the nomogram. The calibration curve exhibited a favorable agreement between prediction and observation in the training cohort (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). The Hosmer-Lemeshow test resulted in a P-value of 0.588, indicating that the model was well-fitted in the training set. Subsequently, the clinical utility of the prediction model was evaluated through DCA and CICA. In the training set, both DCA and CICA demonstrated that the nomogram provided greater overall net benefits across a broad and clinically relevant range of threshold probabilities, thereby influencing the clinical prognosis of patients. Thus, the nomogram proves to be clinically valuable (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of Nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study further validated the nomogram through internal validation set and external validation set. The C-index for the prediction nomogram were 0.784 (95% CI: 0.670\u0026ndash;0.898) in internal validation set and 0.833 (95% CI: 0.761\u0026ndash;0.906) in external validation set, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). The calibration curve demonstrated strong consistency between predicted and observed risk of adverse events across the two validation sets (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF). The DCA (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB and Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC) and CICA (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE and Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF) of both the two validation sets yielded excellent net clinical benefit.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCurrently, research interest in CSP has shifted from image-based diagnosis to optimal management. Regarding CSP management, the primary focus is on selecting the best treatment modalities to reduce complication risks and preserve fertility. Surgical and non-surgical treatments are the main modalities for treating CSP. For different types and severities of CSP, choosing the appropriate surgical approach can significantly impact treatment outcomes. For example, Xu et al. \u003csup\u003e16\u003c/sup\u003e adopted different measures for different types of CSP during lesion excision surgery to achieve optimal treatment outcomes. Additionally, reducing treatment complications through improved surgical techniques is also a focal point of CSP management. For instance, Ellen Hofgaard et al.\u003csup\u003e17\u003c/sup\u003e achieved better clinical outcomes in treating CSP by employing robot-assisted laparoscopy. Preoperative interventions can also reduce surgical risks\u003csup\u003e18\u003c/sup\u003e. In our meta-analysis on CSP treatment \u003csup\u003e19\u003c/sup\u003e, it was found that preoperative UAE or HIFU can increase the success rate of surgery and significantly reduce postoperative complications after D\u0026amp;C. Long-term reproductive outcomes of CSP are also a focal point \u003csup\u003e20,21\u003c/sup\u003e, especially for patients with fertility requirements. In summary, before selecting the optimal treatment modality, a detailed assessment of CSP is necessary to achieve the best treatment outcomes and avoid serious complications.\u003c/p\u003e \u003cp\u003eIn previous reports, most studies\u003csup\u003e11,22\u0026ndash;24\u003c/sup\u003e were single-center, utilizing univariate and multivariate logistic regression analyses to identify clinical and/or imaging indicators for determining risk factors for massive bleeding or RPOC during CSP termination of pregnancy. In this study, we developed and validated, for the first time, a column chart-based lasso regression method to select ultrasound indicators for preoperatively predicting the risk of bleeding or RPOC during CSP curettage management. This nomogram incorporates four ultrasound measurements of CSP, including scar thickness, gestational sac diameter, and blood flow, and CSP type as relevant risk factors for CSP, to achieve personalized prediction of occurrence adverse events in CSP patients. The nomogram established via ultrasound indicators assists clinicians in screening cases with high risks of postoperative complications, such as intraoperative bleeding\u0026thinsp;\u0026ge;\u0026thinsp;200 ml or RPOC, thereby guiding clinicians to formulate appropriate treatment plans for patients, such as selecting experienced emergency specialists for treatment, performing operations to reduce blood supply to the gestational sac preoperatively, and preparing for blood transfusion intraoperatively, to maximize minimizing patient suffering and ensuring patient safety to the greatest extent possible.\u003c/p\u003e \u003cp\u003eStudies have reported\u003csup\u003e5\u003c/sup\u003e that patients with CSP experience significantly higher levels of bleeding during D\u0026amp;C compared to those during miscarriage and abortion. This may be attributed to the attachment of the CSP gestational sac to the scar tissue of the uterine muscle layer, where the lack of decidua tissue makes it easier for trophoblastic cells to invade beyond the junction of the endometrium and uterine muscle layer, reaching the deep uterine blood supply from the radial and arcuate arteries \u003csup\u003e25\u003c/sup\u003e, leading to a rapid increase in blood flow around the gestational sac. Therefore, forcibly separating the gestational sac during D\u0026amp;C may result in uncontrolled bleeding due to the weak muscle layer's inability to contract. Additionally, as the gestational age increases, the pregnancy mass enlarges, potentially elongating and thinning the lower segment of the uterus, coupled with an adequate blood supply, leading to increased bleeding during the surgical procedure. Thus, it is believed that the thinner the uterine muscle layer in the anterior wall scar, the more pronounced the increase in blood flow around the gestational sac site of the previous cesarean section incision, and the greater the likelihood of significant intraoperative bleeding.\u003c/p\u003e \u003cp\u003eThe incidence of RPOC in patients with CSP is higher than that in those after miscarriage\u003csup\u003e5,26,27\u003c/sup\u003e. The possible reasons for the persistent residual mass of ectopic pregnancy are as follows: 1) The CSP mass enters the uterine muscle layer or scar depth through micro-fissures, such as in type III CSP, and may sometimes invade the broad ligament, making surgical excision difficult \u003csup\u003e28\u003c/sup\u003e; 2) In case of significant intraoperative bleeding before complete emptying of the pregnancy product, surgery must be stopped to ensure hemostasis; 3) Local bleeding occurs before the complete removal of the gestational sac; and 4) Scar tissue at the uterine incision site may hinder its complete removal. Additionally, CSP patients typically undergo multiple routine follow-up examinations after surgery, which helps to improve the diagnosis rate of RPOC. Time is also crucial for terminating CSP, as with time, the gestational sac and its blood vessels grow, increasing the difficulty of D\u0026amp;C and the likelihood of residual RPOC \u003csup\u003e4\u003c/sup\u003e. When trophoblastic tissue is situated at the scar site and the uterine muscle layer is thinner, there is an elevated risk of uterine perforation during D\u0026amp;C surgery. Additionally, a richer blood flow around the gestational sac increases the likelihood of bleeding during the procedure. These factors compound the surgical difficulty and may escalate the likelihood of RPOC. Consequently, for patients at a higher risk, personalized treatment plans such as hysteroscopy surgery or laparoscopic monitoring are preferable options.\u003c/p\u003e \u003cp\u003eThere are several limitations to this study. Firstly, both the internal and external cohorts of this study come from different hospitals in the same province, limiting the external generalizability of the results. Secondly, this study is a retrospective cohort study, which cannot control for the consistency and completeness of data collection. Thirdly, the nomogram exclusively incorporated ultrasound-related indicators, which restricts the model's practicality in patients with unclear ultrasound images.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our study has yielded a predictive nomogram specifically designed to assess the risk of adverse outcomes in CSP patients. This nomogram exhibits strong discriminatory and calibration capabilities, offering promising prospects for enhancing clinical decision-making in the management of CSP. Its potential to improve risk assessment and guide personalized patient care underscores its significance as a valuable clinical tool in this context.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXY.X and YZ designed, drafted, and revised the paper and analyzed the data.ZC.F, HQ and TL supervised and submitted the paper for publication. Each author contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank Dr. Liu for the assistance in the process of writing this paper.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJurkovic D, Hillaby K, Woelfer B, et al. First-trimester diagnosis and management of pregnancies implanted into the lower uterine segment Cesarean section scar. 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BMJ 2012;345(e8136, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.e8136\u003c/span\u003e\u003cspan address=\"10.1136/bmj.e8136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"nomogram, cesarean scar pregnancy, adverse event, LASSO","lastPublishedDoi":"10.21203/rs.3.rs-4695964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4695964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo construct and validate a nomogram to predict the risk of adverse events (intraoperative massive hemorrhage or retained products of conception) during the termination of Cesarean scar pregnancy (CSP).\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eData from patients diagnosed with CSP who underwent Dilation and Curettage (D\u0026amp;C) at two hospitals were retrospectively collected. This data formed both internal and external cohorts for analysis. The internal cohort was split randomly, with 70% of the data allocated to a training set and 30% to an internal validation set. The external cohort was used exclusively as the external validation set. LASSO and logistic regression were utilized to select variables and construct a nomogram. The nomogram's performance was assessed using various methods including C-index, calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) to evaluate its ability for identification, calibration, and clinical effectiveness.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prediction nomogram incorporated several predictors, including scar thickness, type of CSP, gestational sac diameter, and blood flow. This nomogram exhibited strong discrimination, as evidenced by a C-index of 0.829 (95% confidence interval: 0.770\u0026ndash;0.887). Moreover, even in the interval validation set, a high C-index value of 0.784 was achieved, and in the external validation set, it reached 0.833. Further assessment through calibration curve analysis, DCA, and CICA revealed a robust agreement between the nomogram's predictions and actual observations, underscoring its utility and reliability.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe validated nomogram effectively predicts adverse events in CSP, showing good discrimination and calibration, making it useful in clinical settings.\u003c/p\u003e","manuscriptTitle":"Predictive Nomogram of Ultrasound Indicators for the Termination Outcome of Cesarean Scar Pregnancy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 20:51:55","doi":"10.21203/rs.3.rs-4695964/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-30T03:44:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-24T18:46:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19952706674170692864686835042750195029","date":"2024-10-24T17:53:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-22T21:19:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109321599979826478305557177792072016047","date":"2024-09-22T20:00:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-20T08:49:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-20T08:42:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-11T19:00:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-09T05:43:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-06T09:01:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9245c28e-ecd3-419a-9555-bc357c88e292","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":35141366,"name":"Health sciences/Diseases"},{"id":35141367,"name":"Health sciences/Health care"},{"id":35141368,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-12-30T15:59:59+00:00","versionOfRecord":{"articleIdentity":"rs-4695964","link":"https://doi.org/10.1038/s41598-024-82894-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-12-28 15:56:55","publishedOnDateReadable":"December 28th, 2024"},"versionCreatedAt":"2024-08-09 20:51:55","video":"","vorDoi":"10.1038/s41598-024-82894-7","vorDoiUrl":"https://doi.org/10.1038/s41598-024-82894-7","workflowStages":[]},"version":"v1","identity":"rs-4695964","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4695964","identity":"rs-4695964","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}