A nomogram based on hematological parameters for prediction of spontaneous abortion risk in pregnancies

A nomogram based on hematological parameters for prediction of spontaneous abortion risk in pregnancies | 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 Research Article A nomogram based on hematological parameters for prediction of spontaneous abortion risk in pregnancies Junmiao Xiang, Lin Liu, Ruru Bao, Zhuhua Cai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4607425/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Mar, 2025 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted 11 You are reading this latest preprint version Abstract Background Pregnancy loss significantly affects physical and mental health. A nomogram for predicting spontaneous abortion risk was developed to improve pregnancy outcomes. Methods A total of 1346 pregnant women were enrolled from The Third Affiliated Hospital of Wenzhou Medical University (May 2020 - May 2022). The training set included 941 participants, and the validation set had 405. Feature selection was optimized using a random forest model, and a predictive model was constructed via multivariable logistic regression. The nomogram’s performance was assessed with receiver operator characteristic (ROC), Hosmer-Lemeshow test, calibration curve, and clinical impact curve (CIC). Discrimination and clinical utility were compared between the nomogram and its individual variables. Results Antithrombin III (AT-III), homocysteine (Hcy), complement component 3 (C3), protein C (PC), and anti-β2 glycoprotein I antibody (anti-β2GP1) were identified as risk factors. The nomogram demonstrated satisfactory discrimination (Training AUC: 0.813, 95% CI: 0.790–0.842; Validation AUC: 0.792, 95% CI: 0.741–0.838). The Hosmer-Lemeshow test (P = .331) indicated a good fit, and the CIC showed clinical net benefit. The nomogram outperformed individual variables in discrimination (AUC: 0.804, 95% CI: 0.779–0.829). Conclusion The developed nomogram, incorporating AT-III, Hcy, C3, PC, and anti-β2GP1, aids clinicians in identifying pregnant women at high risk for spontaneous abortion. Abortion Hematological parameters Nomogram Receiver operator characteristic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Pregnancy loss is a common complication of early pregnancy. The incidence of clinically confirmed miscarriage in all pregnancies is 15%, and the average prevalence of women with one miscarriage is 11%[ 1 ]. If biochemical pregnancy is included, the estimated pregnancy loss rate will be as high as 57%[ 2 ]. Miscarriage not only inflicts serious physical damage on the patient, but also profoundly impacts their mental well-being, adds to the financial strain on their family, and even jeopardizes the harmony and stability of their family and society. Research shows that early pregnancy loss can cause women to suffer from high levels of post-traumatic stress, anxiety, and depression. Although their distress decreases over time, it still remains at a clinically significant level after 9 months[ 3 ]. By accurately predicting the outcome of pregnancy, we can avoid unnecessary interventions, save time and medical resources, and also provide scientific guidance for the next steps of diagnosis and treatment. Therefore, in early pregnancy, clinicians usually predict the outcome of early pregnancy by measuring the expression level and dynamic changes of a specific hematological parameters, combined with ultrasound examination results and clinical manifestations of pregnant women[ 4 , 5 ]. Although the exact mechanisms behind miscarriage are not fully understood, the main causes are chromosomal abnormalities, immunologic diseases, infections, endocrine diseases, thrombophilia, and inflammation[ 6 ]. At present, the hematological parameters used clinically to predict the outcome of early pregnancy include thromboelastography, lymphocyte subsets, thyroid function, antiphospholipid antibodies, antinuclear antibodies, inflammatory markers, etc. Thrombophilia is positively correlated with adverse pregnancy outcomes. Pregnant women with coagulation, anticoagulation dysfunction or disorder can cause maternal hypercoagulable state, which worsens with the progress of pregnancy and increases the incidence of various pregnancy complications[ 7 ]. The thrombophilia workup included blood cell counts, coagulation parameters, factor levels, d-dimer, fibrinogen levels, proteins C and S, etc[ 8 ]. Autoimmune diseases can significantly increase the risk of adverse pregnancy outcomes such as spontaneous abortion[ 9 , 10 ]. It has been confirmed that the incidence of adverse pregnancy outcomes such as miscarriage, stillbirth, and fetal death in patients with systemic lupus erythematosus (SLE) is significantly higher than that in the normal population. The risk of severe complications for SLE pregnant women and newborns is also significantly higher than that in the normal population[ 11 ]. Experts suggest that patients with recurrent miscarriages should undergo a comprehensive and systematic screening of immune-related indicators, such as antinuclear antibody, thyroid autoantibodies, antiphospholipid antibody, lupus anticoagulant, etc., to exclude the possibility of autoimmune factors contributing to the miscarriages[ 12 ]. Endocrine diseases such as thyroid dysfunction and abnormal glucose metabolism can increase the risk of pregnancy loss[ 13 ]. Some literature has pointed out that systemic inflammation markers such as cytokines, white blood cell count, neutrophil count, C-reactive protein, etc., are also associated with pregnancy loss[ 14 ]. The aim of this study was to establish accurate and personalized prediction nomograms for the expression level of hematological parameters in early pregnancy, to predict clinical pregnancy loss. 2. Material and Methods 2.1 Patients The retrospective study was conducted at the Department of Gynecology and Obstetrics, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China, from May 2020 to May 2022. The protocol was approved by the Research Ethics Committee of The Third Affiliated Hospital of Wenzhou Medical University. All patients provided informed consent. The flow chart of the study population’s inclusion and exclusion criteria and the research process diagram are shown (Fig. 1 ). The study enrolled 1584 women with a history of at least one pregnancy loss. They were confirmed to be pregnant based on elevated serum human chorionic gonadotropin (hCG) levels and the date of their last menstrual period, as recorded in the electronic medical records. Patients were excluded from the final analysis for the following reasons: 1) missing complete analyzed variables of hematological parameters (n = 141), 2) lost to follow-up (n = 50), 3) elective abortion (included induced labor due to fetal anomalies) (n = 15), 4) genital tract infection (n = 12); 5) malformations of the genital tract (n = 11); 6) have systemic co-morbid diseases (n = 9). 1346 patients were included in this study. Then, based on a training set ratio verification set of approximatively 7: 3, 941 patients were included in the training set and 405 patients were included in the validation set. 2.2 Definitions In China, spontaneous abortion refers to pregnancy loss that occurs before 28 weeks of gestation or with a fetal weight of less than 1000g [ 17 ]. The European Society of Human Reproduction and Embryology (ESHRE) and the American Society for Reproductive Medicine (ASRM) defines miscarriage as the termination of an intrauterine pregnancy confirmed by ultrasound or histology before 24 weeks, and uses the term pregnancy loss to include miscarriage, biochemical pregnancy, and ectopic pregnancy [ 2 , 18 ]. We classified pregnancy outcomes into two groups: successful pregnancy and pregnancy loss. Successful pregnancy was defined as the presence of a viable intrauterine fetus beyond 24 weeks of gestation. Pregnancy loss was defined as any spontaneous termination of pregnancy before 24 weeks of gestation, including stillbirth, embryonic demise, biochemical pregnancy, etc. 2.3 Date Collection We collected the clinical parameters that included the maternal ages, medical histories, obstetric histories (number of gravidity, parity, previous miscarriage, living child), body mass index (BMI), and blood pressure from the electronic medical records of these patients, meanwhile, the following laboratory tests were also recorded: (1) Endocrine function examination, including thyroid function, fasting blood glucose (FBG); (2) immune system examination, including antiphospholipid antibodies (anti-cardiolipin antibodies (ACA), anti-β2 glycoprotein 1 antibodies (anti-β2GP1) and lupus anticoagulant (LAC)), anti-nuclear antibody spectrum (ANAs), complement, immunoglobulin, lymphocyte subpopulation, thyroid peroxidase antibodies and thyroglobulin antibodies (TPOAb/TGAb); (3) prethromboticstate (PTS), including routine coagulation tests, protein C (PC), protein S (PS), antithrombin III (AT-III), D-dimer, thromboela-stogram (TEG), homocysteine (Hcy), platelet count (PLT); (4) systemic inflammation markers, including white blood cell (WBC) count, neutrophil count (Neu), monocytes count, Lymphocyte count, cytokine serum (interleukin-2, -4, -6, -10 (IL-2, -4, -6, -10), Tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ)); (5) other blood biochemical parameters, including methylenetetrahydrofolate (MTHFR), 25-hydroxyvitamin D (25(OH)D), uric acid, creatinine, bilirubin, blood lipid profiles (triglyceride (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C)), hemoglobin (Hb), red blood cell count (RBC) and alanine aminotransferase (ALT). Various laboratory tests are performed on blood samples taken from pregnant women in the 4th to 6th week of pregnancy. 2.4 Construction of the nomogram We used a random forest classifier to train the data. A random forest model is a type of ensemble learning that combines multiple decision trees. It trains multiple decision trees simultaneously and determines the outcome by the majority vote of all trees. To avoid overfitting, we performed cross-validation on the training cohort to construct and evaluate the ML models. We then used the validation database to test the predictive power of the models. Next, the factors with a p value of 0.05 or less in univariate analysis will be further analyzed using multivariate logistic regression. Then, we applied multivariable logistic regression analysis with the features selected by the random forest model to identify statistically significant predictors. We established a clinical nomogram that contains independent clinical predictors, reported the features as odds ratio (OR), 95% confidence interval (95% CI), and p-value. Then the performance of the nomogram in discriminating ability, calibration ability and clinical usefulness was evaluated. Finally, we developed a nomogram that predicts the risk of spontaneous abortion in pregnancies based on the result of the multivariate logistic regression. 2.5. Validation of the nomogram To assess the calibration of the nomogram, we plotted the calibration curve and calculated the Hosmer-Lemeshow test[ 15 ]. A significant result of the test indicates that the nomogram is not well calibrated. We used the area under the curve (AUC) of the receiver operator characteristic (ROC) curve to evaluate the discrimination of the nomogram. To measure the net benefits of different threshold probabilities in the spontaneous abortion cohort, we performed clinical impact curve (CIC) [ 16 ] to evaluate the clinical benefits of the nomogram. 2.6. Models comparison We plotted ROC and decision curve analysis (DCA) curves to compare the discrimination and clinical utility of the nomogram and its individual variables. We used the Delong test[ 17 ] to compare the AUC values. 2.7. Statistical analysis Normally distributed continuous variables were described as means with standard deviations (SD), and parametric t-tests were used to test for statistical significance between the two groups; otherwise, medians with interquartile range (IQR) and non-parametric Mann–Whitney U tests were applied for variable description and two comparisons. All statistical analyses were executed utilizing R statistical software, version 4.3.1 (R Foundation for Statistical Computing). Logistic regression analyses, nomogram development, and calibration plot generation were facilitated through the ‘rms’ package. ROC curves were delineated employing the ‘pROC’ package. The Hosmer-Lemeshow goodness-of-fit test was applied via the ‘resourceselection’ package. Both CIC and DCA were conducted using the ‘rmda’ function. All statistical tests were 2‐tailed, and P < 0.05 were considered statistically significant. 3. Results 3.1 Patients' Characteristics The final study cohort comprised 1346 pregnancies. Of these, 941 were assigned to the training set, which included 262 cases of pregnancy loss and 697 instances of successful pregnancy. The remaining 405 pregnancies constituted the validation set, encompassing 292 cases of pregnancy loss and 113 successful pregnancies. All patients’ baseline clinical parameters are given in (Table 1 ). The AUC of the 70 laboratory tests in the training set is 0.799 (95% CI: 0.768 to 0.821) (Fig. 2 b), similarly, the AUC of the tests in the validation set is 0.790, with a 95% CI of 0.741 to 0.840 (Fig. 2 c). This means that the test performs well on new data and has a good generalizability. Of the 70 laboratory tests collected from pregnancies, 30 features were chosen based on random forest classifier (Fig. 2 a). These features were subsequently included in univariate and multivariate logistic regression analysis. The characteristics of laboratory tests between successful pregnancy and pregnancy loss groups in the training sets are summarized (Table 2 ). The risk of abortion is significantly positively associated with Hcy, C3, C4, WBC, Neu, Th cell (CD4+), CD4+/CD8+, LAC and anti-β2GP1 (P < 0.05). On the other hand, the risk of abortion is significantly negatively associated with AT-III, suppressor/cytotoxic T cells (CD8+) and PC (P < 0.05). Table 1 Baseline characteristics of the investigated patients in the training and validation sets (n = 1346) Characteristic Training set (n = 941) Validation set (n = 405) P Age (years) 29.14 ± 4.50 28.88 ± 4.61 0.336 BMI (kg/m2) 21.66 ± 3.30 21.56 ± 3.12 0.611 Previous abortions 0.886 1 612/941(65.0%) 268/405(66.2%) 2 185/941(19.7%) 79/405(19.5%) ≥ 3 144/941(15.3%) 58/405(14.3%) Previous birth 0.524 Nulliparous 635/941(67.5%) 281/405(69.4%) Multiparous 306941(32.5%) 124/405(30.6%) Systolic blood pressure (mmHg) 116.27 ± 12.30 115.83 ± 12.23 0.553 Diastolic blood pressure (mmHg) 70.68 ± 9.46 70.62 ± 9.60 0.918 BMI: body mass index Table 2 Differences between laboratory tests of successful pregnancy and pregnancy loss groups in the training sets (n = 941) Characteristic successful pregnancy (n = 697) pregnancy loss (n = 262) P PT (s) 11.61 ± 0.69 11.56 ± 0.74 0.406 TT (s) 16.57 ± 1.45 16.73 ± 1.82 0.149 AT-III (%) 93.66 ± 8.98 89.31 ± 10.65 < 0.001 Angle (deg) 71.05 ± 5.65 70.99 ± 5.68 0.868 CI 1.50 (0.90, 2.80) 1.49 (0.70, 3.00) 0.929 Hcy (µmol/L) 5.56 ± 1.16 6.89 ± 2.07 < 0.001 25(OH)D (ng/mL) 20.22 ± 7.15 19.32 ± 7.45 0.08 IgG (g/L) 12.38 ± 2.81 12.52 ± 3.01 0.527 C3 (g/L) 0.91 ± 0.21 1.02 ± 0.23 < 0.001 C4 (g/L) 0.21 ± 0.07 0.23 ± 0.08 0.001 TG (mmol/L) 1.09 ± 0.63 1.18 ± 0.62 0.055 WBC (*10^9/L) 9.71 ± 5.43 11.13 ± 7.01 0.001 Neu (*10^9/L) 6.98 ± 5.03 8.33 ± 6.45 0.001 Th cell (CD4+) (%) 39.17 ± 7.21 40.89 ± 7.57 0.001 suppressor/cytotoxic T cells (CD8+) (%) 27.55 ± 5.87 26.07 ± 5.94 0.001 CD4+/CD8+ 1.53 ± 0.54 1.70 ± 0.67 < 0.001 PC (%) 112.87 ± 26.85 99.90 ± 24.98 < 0.001 PS (%) 66.41 ± 16.16 66.37 ± 15.98 0.971 anti-β2GP1 (AU/mL) 4.64 (2.00, 3.60) 9.33 (2.00, 5.93) 0.002 LAC 1.03 ± 0.09 1.05 ± 0.15 0.025 PT: prothrombin time; TT: thrombin time; AT-III: antithrombin III; Angle: kinetics of clot development; Hcy: homocysteine; 25(OH)D: 25-hydroxyvitamin D; IgG: Immunoglobulin G; C3: Complement protein 3; C4: Complement protein 4; TG: triglyceride; WBC: white blood cell; Neu: neutrophil; Th cell (CD4+): CD4(+) T lymphocytes; PC: protein C; PS: protein S; anti-β2GP1: anti-β2 glycoprotein 1 antibody; LAC: lupus anticoagulant 3.2 Nomogram construction and performance assessment Eleven candidate variables, including AT-III, Hcy, C3, C4, PC, WBC, Neu, Th cell (CD4+), suppressor/cytotoxic T cells (CD8+), CD4+/CD8+, LAC and anti-β2GP1 (all P < 0.05) were significantly associated with spontaneous abortion in pregnancies in the univariate logistic regression analyses (Table 3 ). Among them, AT-III (P < 0.001), Hcy (P < 0.001), C3 (P < 0.001), PC (P < 0.001) and anti-β2GP1 (P = 0.001) were identified as independent risk factors for spontaneous abortion by the subsequent multivariate regression analysis (Table 3 ). Thereafter, a nomogram was developed by incorporating these five predictors (Fig. 3 ). Table 3 Univariate and multivariate logistic regression analysis of the candidate predictors in the training set Variables Univariate analysis Multivariate analysis OR (95% CI) P OR (95% CI) P PT (s) 0.917 (0.748–1.125) 0.405 TT (s) 1.066 (0.977–1.163) 0.152 AT-III (%) 0.953 (0.939–0.968) < 0.001 0.955 (0.936–0.974) < 0.001 Angle (deg) 0.998 (0.973–1.023) 0.868 CI 0.999 (0.940–1.061) 0.965 Hcy (µmol/L) 1.917 (1.694–2.171) < 0.001 1.943 (1.685–2.241) < 0.001 25(OH)D (ng/mL) 0.982 (0.962–1.003) 0.089 IgG (g/L) 1.016 (0.967–1.067) 0.527 C3 (g/L) 9.252 (4.770-17.945) < 0.001 14.685 (5.103–42.264) < 0.001 C4 (g/L) 24.136 (3.547-164.234) 0.001 TG (mmol/L) 1.231 (0.992–1.527) 0.059 WBC (*10^9/L) 1.038 (1.015–1.062) 0.001 Neu (*10^9/L) 1.042 (1.017–1.068) 0.001 Th cell (CD4+) (%) 1.032 (1.012–1.053) 0.001 suppressor/cytotoxic T cells (CD8+) (%) 0.957 (0.934–0.981) 0.001 CD4+/CD8+ 1.621 (1.278–2.055) < 0.001 PC (%) 0.98 (0.974–0.986) < 0.001 0.976 (0.967–0.984) < 0.001 PS (%) 1.000 (0.991–1.009) 0.971 anti-β2GP1 (AU/mL) 1.014 (1.003–1.025) 0.013 1.013 (1.003–1.022) 0.010 LAC 4.268 (1.106–16.473) 0.035 PT: prothrombin time; TT: thrombin time; AT-III: antithrombin III; Angle: kinetics of clot development; Hcy: homocysteine; 25(OH)D: 25-hydroxyvitamin D; IgG: Immunoglobulin G; C3: Complement protein 3; C4: Complement protein 4; TG: triglyceride; WBC: white blood cell; Neu: neutrophil; Th cell (CD4+): CD4(+) T lymphocytes; PC: protein C; PS: protein S; anti-β2GP1: anti-β2 glycoprotein 1 antibody; LAC: lupus anticoagulant; 95% CI: 95% confidence interval; OR: odds ratio; The nomogram showed favorable discrimination with an AUC of 0.813 (95% CI: 0.790 to 0.842) in the training set (Fig. 4 a). The calibration curve suggested good agreement between model prediction and actual observation in the training set (Fig. 4 c). Besides, the Hosmer-Lemeshow test yielded a nonsignificant P value of 0.331, indicating good calibration power. The clinical impact curve in the training set shows the stratification of spontaneous abortion probability for 1000 samples based on the predicted probabilities (Fig. 4 e). The predictive abortion number was close to the actual number of positive cases when the threshold probability was greater than 0.8. At this time, the cost-to-benefit ratio was 0.6. 3.3 Validation of the nomogram The satisfactory discrimination of the nomogram was confirmed using the validation set, with an AUC of 0.792 (95% CI: 0.741 to 0.838) (Fig. 4 b). Good calibration was also observed in the validation set, with a nonsignificant P value of 0.675 derived from the Hosmer-Lemeshow test (Fig. 4 d). The CIC demonstrated that when the threshold probabilities greater than 0.4, the nomogram also showed a higher net benefit in the validation set (Fig. 4 f). 3.4 Models comparison The nomogram showed higher discriminatory accuracy with an AUC of 0.804 (95% CI: 0.779 to 0.829) for predicting spontaneous abortion in pregnancies than any of the variables incorporated in the nomogram alone (P < 0.001) (Fig. 5 a). The DCAs showed that the nomogram had a higher overall net benefit than the models containing only risk factors incorporated in the nomogram across a wide range of threshold probabilities (Fig. 5 b). 4. Discussion Although many previous studies have reported the association between miscarriage and immunological, endocrinological, systemic inflammation, thrombophilic and other blood biochemical parameters[ 7 , 9 , 10 , 13 ], few studies have combined these markers to predict the risk of miscarriage. Therefore, we constructed this prediction nomogram using hematological risk factors which demonstrated favorable diagnostic accuracy to make accurate assessments of the risk of miscarriage, so that clinicians can provide precise and scientific treatment. Thrombophilia is a pathological state of coagulation-anticoagulation imbalance caused by various factors, including platelets, coagulation-fibrinolysis system, and hemorheology. Mothers with thrombophilia may develop placental thrombosis, which leads to reduced placental perfusion and ultimately affects the material exchange between the mother and the fetus, inducing the occurrence of spontaneous abortion[ 18 ]. This study found that AT-III and PC, two coagulation-related factors, are one of the key indicators for predicting miscarriage. AT-III is a heparin-dependent serine protease inhibitor that can bind to and inactivate various serine-containing coagulation factors, thrombin, and plasmin, thus maintaining normal coagulation and preventing thrombosis[ 19 ]. PC is a protein that is activated in the presence of calcium ions and inactivates factor V and factor VIII, exerting anticoagulant effects[ 20 ]. In normal pregnant women, the synthesis of coagulation factors is accelerated, the production of thrombin increases, and the anticoagulant activity of AT-III is strong. Subsequently, the thrombin-antithrombin complex is strengthened, the consumption of AT-III is reduced, and finally resulting in increased AT-III level. However, in abnormal pregnant women, this cascade is interrupted[ 21 ]. Studies have shown that the decrease of AT-III and PC activities increases the risk of thrombosis and miscarriage[ 22 ]. Wang, P., et al[ 21 ] found that the AT-III level decreased along with the increase of the number of prior abortions, and that patients with four or more prior abortions had significantly lower AT-III levels than patients with normal fertility, and the difference was statistically significant (P = .0111). Sugiura, M [ 23 ] pointed out that PC deficiency during pregnancy increases the risk of thrombosis by 3–10% antepartum and 7–19% postpartum, and is also associated with recurrent miscarriages in the first, second and third trimesters. Immune system is essential for maintaining a healthy pregnancy, as it regulates the maternal-fetal interface and protects the fetus from pathogens. However, immune dysregulation, such as autoimmune disorders, alloimmune reactions, and chronic inflammation, can lead to pregnancy loss or complications. Complement C3 is the key protein of the complement system and composed of two polypeptide chains, α- and β-chain. C3—the central protein of the complement cascade—appears to have an important role in the early phase of pregnancy and in the development of the placenta[ 24 ]. The activation of complement C3 can increase the inflammatory response of leukocytes and the activity of vascular endothelial cells, amplify the procoagulant effect of APL antibodies, cause thrombosis, placental tissue ischemia and hypoxia, and eventually lead to miscarriage[ 25 ]. To confirm this hypothesis, Al Jameil N[ 26 ] injected Crry-IG, a C3 convertase inhibitor, into pregnant APL antibody-positive mice, blocking the classical and alternative pathways of complement C3 activation, which prevented fetal loss and growth restriction. Research also shows that after pregnancy in patients with SLE, complement activation occurs in the placenta and produces anaphylatoxins, as well as cytokines such as C3 and C1q, which mediate the activation of effector cells, accelerating the rejection of the embryo[ 27 ]. β2GP1 activity as an in vitro inhibitor of the intrinsic blood coagulation pathway, adenosine diphosphate–dependent aggregation of platelets, and prothrombinase activity of activated platelets[ 28 ]. Anti-β2GP1 is the main diagnostic indicators of antiphospholipid syndrome, which can cause miscarriage by affecting the implantation of fertilized eggs through direct or indirect damage to trophoblasts and thrombosis formation. Previous studies have confirmed that[ 29 ], the expression of β2GP1 in the uterus of parturient can inhibit platelet prothrombin activity and endogenous aggregation pathway, which, in combination with thrombosis in the uterus, eventually results in adverse pregnancy outcomes. Hcy is a sulfur-containing amino acid that is produced by the demethylation of methionine. Hyperhomocysteinemia (HHcy) is a condition where Hcy accumulates excessively in the body due to innate metabolic enzyme disorders, folic acid, vitamin B6 and B12 deficiencies, or abnormal Hcy metabolism[ 30 ]. High levels of Hcy in the blood can increase the risk of vascular diseases, coronary artery dysfunction, atherosclerosis, and embolic diseases. HHcy during pregnancy can cause endothelial cell damage, make the blood hypercoagulable, harm the placental blood vessels, and increase the risk of placental embolism. The production of nitric oxide (NO) and prostacyclin may be the reasons why HHcy leads to thrombosis, which triggers a coagulation process, resulting in endothelial injury[ 31 ]. This may be an underlying mechanism for abortion. Several studies[ 32 , 33 ]have shown that high serum Hcy levels are strongly correlated with the occurrence of RSA. Studies have also shown that women with HHcy have a higher rate of three or more consecutive spontaneous abortions[ 32 ]. Our limitation is that we only used hematological parameters to predict the risk of miscarriage in pregnancies. Future studies will combine clinical indicators such as maternal age, BMI, medical history, and symptoms to further improve the accuracy of the prediction model. 5. Conclusion We have developed and validated a novel tool to assess the risk of spontaneous abortion in pregnancies. This tool is a nomogram that incorporates several biomarkers and antibodies, such as AT-III, Hcy, C3, PC, and anti-β2GP1. It demonstrates reasonable accuracy and discrimination in predicting the outcome of pregnancy. Based on individual risk assessments, clinicians and patients can take more effective steps for treatment. However, this tool requires further validation from large multicenter studies before it can be widely implemented in clinical practice. Abbreviations AT-III Antithrombin III Hcy Homocysteine C3 Complement component 3 PC Protein C Anti-β2GP1 Anti-β2 glycoprotein I antibody Declarations Acknowledgments The authors thank the Department of Gynecology of The Third Affiliated Hospital of Wenzhou Medical University, for their continuous support during the study Author contributions Junmiao Xiang: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Funding Acquisition, Writing - Original Draft, Writing - Review & Editing; Lin Liu: Data Curation, Writing - Original Draft; Ruru Bao: Visualization, Validation, Writing - Original Draft; Zhuhua Cai: Conceptualization, Resources, Supervision, Writing - Review & Editing. Funding This work was supported by Foundation of Wenzhou Municipal Health Commission (2020041) Data Availability Statement Data available on request from the authors Conflict of interest The authors declare that they have no competing interests. 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Stansfield BK, Wise L, Ham PB, 3rd, Patel P, Parman M, Jin C, Mathur S, Harshfield G, Bhatia J: Outcomes following routine antithrombin III replacement during neonatal extracorporeal membrane oxygenation . J Pediatr Surg 2017, 52 (4):609-613. Gardiner JE, Griffin JH: Studies on human protein C inhibitor in normal and Factor V/VIII deficient plasmas . Thromb Res 1984, 36 (3):197-203. Wang P, Yang H, Wang G, Tian J: Predictive value of thromboelastography parameters combined with antithrombin III and D-Dimer in patients with recurrent spontaneous abortion . American journal of reproductive immunology (New York, NY : 1989) 2019, 82 (4):e13165. Wang Y, Lin X, Wu Q, Zhao M, Xian S, Lin D, Sun L, He J, Bao Y, Duan C: Thrombophilia Markers in Patients with Recurrent Early Miscarriage . Clin Lab 2015, 61 (11):1787-1794. Sugiura M: Pregnancy and delivery in protein C-deficiency . Curr Drug Targets 2005, 6 (5):577-583. Mohlin FC, Gros P, Mercier E, Gris JR, Blom AM: Analysis of C3 Gene Variants in Patients With Idiopathic Recurrent Spontaneous Pregnancy Loss . Frontiers in immunology 2018, 9 :1813. De Carolis S, Botta A, Santucci S, Salvi S, Moresi S, Di Pasquo E, Del Sordo G, Martino C: Complementemia and obstetric outcome in pregnancy with antiphospholipid syndrome . Lupus 2012, 21 (7):776-778. Al Jameil N, Tyagi P, Al Shenefy A: Incidence of anticardiolipin antibodies and lupus anticoagulant factor among women experiencing unexplained recurrent abortion and intrauterine fetal death . International journal of clinical and experimental pathology 2015, 8 (3):3204-3209. Lood C, Tydén H, Gullstrand B, Sturfelt G, Jönsen A, Truedsson L, Bengtsson AA: Platelet activation and anti-phospholipid antibodies collaborate in the activation of the complement system on platelets in systemic lupus erythematosus . PloS one 2014, 9 (6):e99386. Franklin RD, Hollier N, Kutteh WH: beta2-Glycoprotein 1 as a marker of antiphospholipid syndrome in women with recurrent pregnancy loss . Fertility and sterility 2000, 73 (3):531-535. Chopra A, Radhakrishnan R, Sharma M: Porphyromonas gingivalis and adverse pregnancy outcomes: a review on its intricate pathogenic mechanisms . Critical reviews in microbiology 2020, 46 (2):213-236. Li J, Feng D, He S, Wu Q, Su Z, Ye H: Meta-analysis: association of homocysteine with recurrent spontaneous abortion . Women Health 2021, 61 (7):713-720. Freyburger G, Labrouche S, Sassoust G, Rouanet F, Javorschi S, Parrot F: Mild hyperhomocysteinemia and hemostatic factors in patients with arterial vascular diseases . Thromb Haemost 1997, 77 (3):466-471. Nelen WL, Blom HJ, Steegers EA, den Heijer M, Thomas CM, Eskes TK: Homocysteine and folate levels as risk factors for recurrent early pregnancy loss . Obstetrics and gynecology 2000, 95 (4):519-524. Raziel A, Kornberg Y, Friedler S, Schachter M, Sela BA, Ron-El R: Hypercoagulable thrombophilic defects and hyperhomocysteinemia in patients with recurrent pregnancy loss . American journal of reproductive immunology (New York, NY : 1989) 2001, 45 (2):65-71. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Mar, 2025 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted Editorial decision: Revision requested 22 Oct, 2024 Reviews received at journal 21 Oct, 2024 Reviews received at journal 03 Oct, 2024 Reviewers agreed at journal 01 Oct, 2024 Reviewers agreed at journal 14 Sep, 2024 Reviewers agreed at journal 26 Aug, 2024 Reviewers agreed at journal 23 Jun, 2024 Reviewers invited by journal 21 Jun, 2024 Editor assigned by journal 20 Jun, 2024 Submission checks completed at journal 20 Jun, 2024 First submitted to journal 19 Jun, 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-4607425","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322709835,"identity":"5df738f8-8341-461e-ad68-baf7bb2227bc","order_by":0,"name":"Junmiao Xiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIie3PMQqDQBBA0YGFsRljF1a28AobBKuAV9kDpBBsLQKC6awNOUVuICxomQMEQsQiBwhYSiSkCoHVLsU+ppzPMACW9Y/YewDQ2UO9MKF6bgKfBLiauS5b1vVJdotX/qPTSQaBtzYc83MMw6pJGYqd1FUDm+PJcM5jEAlChVMCmhCUvBoSZM4gaFSEfnvXNM5IPEaRcAvFkYPUbjEj8XNKw2OpJNL0i1ty8y/y0p77ZFBxcGj7Jw3bwBOG5Btftm5ZlmX99gJ2IzbEACL7fQAAAABJRU5ErkJggg==","orcid":"","institution":"The Third Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Junmiao","middleName":"","lastName":"Xiang","suffix":""},{"id":322709836,"identity":"c12ff1ce-a30d-4b49-9d7b-6d3b0381fcc7","order_by":1,"name":"Lin Liu","email":"","orcid":"","institution":"Diagnostics Group Co.,Ltd","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Liu","suffix":""},{"id":322709837,"identity":"4653fafa-25f0-4ec2-b157-d1a64ea40490","order_by":2,"name":"Ruru Bao","email":"","orcid":"","institution":"The Third Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruru","middleName":"","lastName":"Bao","suffix":""},{"id":322709838,"identity":"0e0b7f9d-2cc1-4d13-a36a-d16d3fbf7bdd","order_by":3,"name":"Zhuhua Cai","email":"","orcid":"","institution":"The Third Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhuhua","middleName":"","lastName":"Cai","suffix":""}],"badges":[],"createdAt":"2024-06-19 17:30:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4607425/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4607425/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12884-025-07396-4","type":"published","date":"2025-03-11T15:58:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60602107,"identity":"31f70c44-5472-4e79-bf0d-da9bc97c19d9","added_by":"auto","created_at":"2024-07-18 16:10:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68585,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart of the study population’s inclusion and exclusion criteria and the research process diagram. The research process diagram describes the steps and procedures of the study, such as the data collection, analysis, and reporting methods. AT-III: antithrombin III; Hcy: homocysteine; C3: Complement protein 3; PC: protein C; anti-β2GP1: anti-beta 2 glycoprotein 1 antibody.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4607425/v1/9224c05e0c436653bba44de3.png"},{"id":60602104,"identity":"0eac9913-12f9-406c-a669-d6310c93aae9","added_by":"auto","created_at":"2024-07-18 16:10:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1331799,"visible":true,"origin":"","legend":"\u003cp\u003eTop 30 important variables in the random forest model and the AUC of the 70 laboratory tests in the training and validation sets. Feature importance derived from the random forest model. The plot shows the relative importance of the variables in the random forest model (a). The AUC, or area under the curve, of the 70 laboratory tests in the training set (b) and the validation set (c) shows that the test performs well on new data and has good generalizability.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4607425/v1/eaa8870a3f59455eed48d2d3.png"},{"id":60602105,"identity":"f6f83bcc-a66a-4afb-942b-788afaaa0ee9","added_by":"auto","created_at":"2024-07-18 16:10:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":157847,"visible":true,"origin":"","legend":"\u003cp\u003eA nomogram for predicting the risk of spontaneous abortion in pregnancies. Covariates were assessed for the pregnancies and given a point in the nomogram. A higher total number of points indicated a higher likelihood of spontaneous abortion. AT-III: antithrombin III; Hcy: homocysteine; C3: Complement protein 3; PC: protein C; anti-β2GP1: anti-β2 glycoprotein 1 antibody.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4607425/v1/96fcb61999d0677fd08a0136.png"},{"id":60602808,"identity":"30240aea-dfbe-4c02-b524-4077580ec727","added_by":"auto","created_at":"2024-07-18 16:18:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":712192,"visible":true,"origin":"","legend":"\u003cp\u003eDiscrimination and calibration of the nomogram for predicting spontaneous abortion risk in the pregnancies and clinical impact curve depicting the clinical net benefit of the nomogram. Receiver operator characteristic curve of the nomogram in the training set (a) and validation set (b). Calibration curve of the nomogram in the training set (c) and validation set (d). Clinical impact curve for the nomogram in the training set (e) and validation set (f). For the calibration curve, the y‐axis represents the actual observed spontaneous abortion probabilities, and the x‐axis represents nomogram‐predicted probabilities. The calibration curve shows how well the predicted probabilities agree with the observed probabilities. The diagonal blue dashed line represents a perfect prediction by an ideal model, and the green solid line reflects the performance of the nomogram; a closer fit to the diagonal dashed line indicates a better prediction. Clinical impact curve to predict the improved number for a population size of 1000. The red solid curve shows the predicted number of spontaneous abortions at different threshold probabilities, and the blue dashed curve represents the actual number of spontaneous abortions in the pregnancies. AUC: area under the curve;\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4607425/v1/62545c1ad7f9e8c707069f5e.png"},{"id":60602108,"identity":"56964525-9df2-48bd-a750-d970e6dfcbd2","added_by":"auto","created_at":"2024-07-18 16:10:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":299592,"visible":true,"origin":"","legend":"\u003cp\u003eModels comparison in the whole study cohort. (a) Receiver operator characteristic curves of the models are presented to compare their discriminatory accuracy for predicting spontaneous abortion. P values show the difference between the AUC for the nomogram and the AUCs for other variables incorporated in the nomogram alone. (b) Decision curve analyses comparing the net benefit of the nomogram and the other variables incorporated in the nomogram alone are shown. AUC: area under the curve; CI: confidence interval; AT-III: antithrombin III; Hcy: homocysteine; C3: Complement protein 3; PC: protein C; anti-β2GP1: anti-β2 glycoprotein 1antibody;\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4607425/v1/acd33005f2b2ff4f280c3885.png"},{"id":78689025,"identity":"e0969a71-d455-4001-b0cd-87464890a964","added_by":"auto","created_at":"2025-03-17 16:10:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4608480,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4607425/v1/58d35135-ab32-4066-baab-bfc910799304.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A nomogram based on hematological parameters for prediction of spontaneous abortion risk in pregnancies","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePregnancy loss is a common complication of early pregnancy. The incidence of clinically confirmed miscarriage in all pregnancies is 15%, and the average prevalence of women with one miscarriage is 11%[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. If biochemical pregnancy is included, the estimated pregnancy loss rate will be as high as 57%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMiscarriage not only inflicts serious physical damage on the patient, but also profoundly impacts their mental well-being, adds to the financial strain on their family, and even jeopardizes the harmony and stability of their family and society. Research shows that early pregnancy loss can cause women to suffer from high levels of post-traumatic stress, anxiety, and depression. Although their distress decreases over time, it still remains at a clinically significant level after 9 months[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBy accurately predicting the outcome of pregnancy, we can avoid unnecessary interventions, save time and medical resources, and also provide scientific guidance for the next steps of diagnosis and treatment. Therefore, in early pregnancy, clinicians usually predict the outcome of early pregnancy by measuring the expression level and dynamic changes of a specific hematological parameters, combined with ultrasound examination results and clinical manifestations of pregnant women[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the exact mechanisms behind miscarriage are not fully understood, the main causes are chromosomal abnormalities, immunologic diseases, infections, endocrine diseases, thrombophilia, and inflammation[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. At present, the hematological parameters used clinically to predict the outcome of early pregnancy include thromboelastography, lymphocyte subsets, thyroid function, antiphospholipid antibodies, antinuclear antibodies, inflammatory markers, etc. Thrombophilia is positively correlated with adverse pregnancy outcomes. Pregnant women with coagulation, anticoagulation dysfunction or disorder can cause maternal hypercoagulable state, which worsens with the progress of pregnancy and increases the incidence of various pregnancy complications[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The thrombophilia workup included blood cell counts, coagulation parameters, factor levels, d-dimer, fibrinogen levels, proteins C and S, etc[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Autoimmune diseases can significantly increase the risk of adverse pregnancy outcomes such as spontaneous abortion[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It has been confirmed that the incidence of adverse pregnancy outcomes such as miscarriage, stillbirth, and fetal death in patients with systemic lupus erythematosus (SLE) is significantly higher than that in the normal population. The risk of severe complications for SLE pregnant women and newborns is also significantly higher than that in the normal population[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Experts suggest that patients with recurrent miscarriages should undergo a comprehensive and systematic screening of immune-related indicators, such as antinuclear antibody, thyroid autoantibodies, antiphospholipid antibody, lupus anticoagulant, etc., to exclude the possibility of autoimmune factors contributing to the miscarriages[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Endocrine diseases such as thyroid dysfunction and abnormal glucose metabolism can increase the risk of pregnancy loss[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Some literature has pointed out that systemic inflammation markers such as cytokines, white blood cell count, neutrophil count, C-reactive protein, etc., are also associated with pregnancy loss[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aim of this study was to establish accurate and personalized prediction nomograms for the expression level of hematological parameters in early pregnancy, to predict clinical pregnancy loss.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003eThe retrospective study was conducted at the Department of Gynecology and Obstetrics, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China, from May 2020 to May 2022. The protocol was approved by the Research Ethics Committee of The Third Affiliated Hospital of Wenzhou Medical University. All patients provided informed consent. The flow chart of the study population\u0026rsquo;s inclusion and exclusion criteria and the research process diagram are shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study enrolled 1584 women with a history of at least one pregnancy loss. They were confirmed to be pregnant based on elevated serum human chorionic gonadotropin (hCG) levels and the date of their last menstrual period, as recorded in the electronic medical records. Patients were excluded from the final analysis for the following reasons: 1) missing complete analyzed variables of hematological parameters (n\u0026thinsp;=\u0026thinsp;141), 2) lost to follow-up (n\u0026thinsp;=\u0026thinsp;50), 3) elective abortion (included induced labor due to fetal anomalies) (n\u0026thinsp;=\u0026thinsp;15), 4) genital tract infection (n\u0026thinsp;=\u0026thinsp;12); 5) malformations of the genital tract (n\u0026thinsp;=\u0026thinsp;11); 6) have systemic co-morbid diseases (n\u0026thinsp;=\u0026thinsp;9). 1346 patients were included in this study. Then, based on a training set ratio verification set of approximatively 7: 3, 941 patients were included in the training set and 405 patients were included in the validation set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Definitions\u003c/h2\u003e \u003cp\u003eIn China, spontaneous abortion refers to pregnancy loss that occurs before 28 weeks of gestation or with a fetal weight of less than 1000g [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The European Society of Human Reproduction and Embryology (ESHRE) and the American Society for Reproductive Medicine (ASRM) defines miscarriage as the termination of an intrauterine pregnancy confirmed by ultrasound or histology before 24 weeks, and uses the term pregnancy loss to include miscarriage, biochemical pregnancy, and ectopic pregnancy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe classified pregnancy outcomes into two groups: successful pregnancy and pregnancy loss. Successful pregnancy was defined as the presence of a viable intrauterine fetus beyond 24 weeks of gestation. Pregnancy loss was defined as any spontaneous termination of pregnancy before 24 weeks of gestation, including stillbirth, embryonic demise, biochemical pregnancy, etc.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Date Collection\u003c/h2\u003e \u003cp\u003eWe collected the clinical parameters that included the maternal ages, medical histories, obstetric histories (number of gravidity, parity, previous miscarriage, living child), body mass index (BMI), and blood pressure from the electronic medical records of these patients, meanwhile, the following laboratory tests were also recorded: (1) Endocrine function examination, including thyroid function, fasting blood glucose (FBG); (2) immune system examination, including antiphospholipid antibodies (anti-cardiolipin antibodies (ACA), anti-β2 glycoprotein 1 antibodies (anti-β2GP1) and lupus anticoagulant (LAC)), anti-nuclear antibody spectrum (ANAs), complement, immunoglobulin, lymphocyte subpopulation, thyroid peroxidase antibodies and thyroglobulin antibodies (TPOAb/TGAb); (3) prethromboticstate (PTS), including routine coagulation tests, protein C (PC), protein S (PS), antithrombin III (AT-III), D-dimer, thromboela-stogram (TEG), homocysteine (Hcy), platelet count (PLT); (4) systemic inflammation markers, including white blood cell (WBC) count, neutrophil count (Neu), monocytes count, Lymphocyte count, cytokine serum (interleukin-2, -4, -6, -10 (IL-2, -4, -6, -10), Tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ)); (5) other blood biochemical parameters, including methylenetetrahydrofolate (MTHFR), 25-hydroxyvitamin D (25(OH)D), uric acid, creatinine, bilirubin, blood lipid profiles (triglyceride (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C)), hemoglobin (Hb), red blood cell count (RBC) and alanine aminotransferase (ALT). Various laboratory tests are performed on blood samples taken from pregnant women in the 4th to 6th week of pregnancy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Construction of the nomogram\u003c/h2\u003e \u003cp\u003eWe used a random forest classifier to train the data. A random forest model is a type of ensemble learning that combines multiple decision trees. It trains multiple decision trees simultaneously and determines the outcome by the majority vote of all trees. To avoid overfitting, we performed cross-validation on the training cohort to construct and evaluate the ML models. We then used the validation database to test the predictive power of the models. Next, the factors with a p value of 0.05 or less in univariate analysis will be further analyzed using multivariate logistic regression. Then, we applied multivariable logistic regression analysis with the features selected by the random forest model to identify statistically significant predictors. We established a clinical nomogram that contains independent clinical predictors, reported the features as odds ratio (OR), 95% confidence interval (95% CI), and p-value. Then the performance of the nomogram in discriminating ability, calibration ability and clinical usefulness was evaluated. Finally, we developed a nomogram that predicts the risk of spontaneous abortion in pregnancies based on the result of the multivariate logistic regression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Validation of the nomogram\u003c/h2\u003e \u003cp\u003eTo assess the calibration of the nomogram, we plotted the calibration curve and calculated the Hosmer-Lemeshow test[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A significant result of the test indicates that the nomogram is not well calibrated. We used the area under the curve (AUC) of the receiver operator characteristic (ROC) curve to evaluate the discrimination of the nomogram. To measure the net benefits of different threshold probabilities in the spontaneous abortion cohort, we performed clinical impact curve (CIC) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] to evaluate the clinical benefits of the nomogram.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Models comparison\u003c/h2\u003e \u003cp\u003eWe plotted ROC and decision curve analysis (DCA) curves to compare the discrimination and clinical utility of the nomogram and its individual variables. We used the Delong test[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] to compare the AUC values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical analysis\u003c/h2\u003e \u003cp\u003eNormally distributed continuous variables were described as means with standard deviations (SD), and parametric t-tests were used to test for statistical significance between the two groups; otherwise, medians with interquartile range (IQR) and non-parametric Mann\u0026ndash;Whitney U tests were applied for variable description and two comparisons.\u003c/p\u003e \u003cp\u003eAll statistical analyses were executed utilizing R statistical software, version 4.3.1 (R Foundation for Statistical Computing). Logistic regression analyses, nomogram development, and calibration plot generation were facilitated through the \u0026lsquo;rms\u0026rsquo; package. ROC curves were delineated employing the \u0026lsquo;pROC\u0026rsquo; package. The Hosmer-Lemeshow goodness-of-fit test was applied via the \u0026lsquo;resourceselection\u0026rsquo; package. Both CIC and DCA were conducted using the \u0026lsquo;rmda\u0026rsquo; function. All statistical tests were 2‐tailed, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patients' Characteristics\u003c/h2\u003e \u003cp\u003eThe final study cohort comprised 1346 pregnancies. Of these, 941 were assigned to the training set, which included 262 cases of pregnancy loss and 697 instances of successful pregnancy. The remaining 405 pregnancies constituted the validation set, encompassing 292 cases of pregnancy loss and 113 successful pregnancies. All patients\u0026rsquo; baseline clinical parameters are given in (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The AUC of the 70 laboratory tests in the training set is 0.799 (95% CI: 0.768 to 0.821) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), similarly, the AUC of the tests in the validation set is 0.790, with a 95% CI of 0.741 to 0.840 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). This means that the test performs well on new data and has a good generalizability. Of the 70 laboratory tests collected from pregnancies, 30 features were chosen based on random forest classifier (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). These features were subsequently included in univariate and multivariate logistic regression analysis. The characteristics of laboratory tests between successful pregnancy and pregnancy loss groups in the training sets are summarized (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The risk of abortion is significantly positively associated with Hcy, C3, C4, WBC, Neu, Th cell (CD4+), CD4+/CD8+, LAC and anti-β2GP1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). On the other hand, the risk of abortion is significantly negatively associated with AT-III, suppressor/cytotoxic T cells (CD8+) and PC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eBaseline characteristics of the investigated patients in the training and validation sets (n\u0026thinsp;=\u0026thinsp;1346)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining set (n\u0026thinsp;=\u0026thinsp;941)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation set (n\u0026thinsp;=\u0026thinsp;405)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious abortions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e612/941(65.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268/405(66.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185/941(19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79/405(19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144/941(15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58/405(14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNulliparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e635/941(67.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281/405(69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e306941(32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124/405(30.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.27\u0026thinsp;\u0026plusmn;\u0026thinsp;12.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115.83\u0026thinsp;\u0026plusmn;\u0026thinsp;12.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.68\u0026thinsp;\u0026plusmn;\u0026thinsp;9.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI: body mass index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\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\u003eDifferences between laboratory tests of successful pregnancy and pregnancy loss groups in the training sets (n\u0026thinsp;=\u0026thinsp;941)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esuccessful pregnancy (n\u0026thinsp;=\u0026thinsp;697)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epregnancy loss (n\u0026thinsp;=\u0026thinsp;262)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT-III (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.66\u0026thinsp;\u0026plusmn;\u0026thinsp;8.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.31\u0026thinsp;\u0026plusmn;\u0026thinsp;10.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eAngle (deg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.05\u0026thinsp;\u0026plusmn;\u0026thinsp;5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.99\u0026thinsp;\u0026plusmn;\u0026thinsp;5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50 (0.90, 2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49 (0.70, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHcy (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003e25(OH)D (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.22\u0026thinsp;\u0026plusmn;\u0026thinsp;7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.32\u0026thinsp;\u0026plusmn;\u0026thinsp;7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgG (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eC4 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (*10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.71\u0026thinsp;\u0026plusmn;\u0026thinsp;5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.13\u0026thinsp;\u0026plusmn;\u0026thinsp;7.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeu (*10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.98\u0026thinsp;\u0026plusmn;\u0026thinsp;5.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTh cell (CD4+) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.17\u0026thinsp;\u0026plusmn;\u0026thinsp;7.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.89\u0026thinsp;\u0026plusmn;\u0026thinsp;7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esuppressor/cytotoxic T cells (CD8+) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.55\u0026thinsp;\u0026plusmn;\u0026thinsp;5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.07\u0026thinsp;\u0026plusmn;\u0026thinsp;5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4+/CD8+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003ePC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112.87\u0026thinsp;\u0026plusmn;\u0026thinsp;26.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.90\u0026thinsp;\u0026plusmn;\u0026thinsp;24.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003ePS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.41\u0026thinsp;\u0026plusmn;\u0026thinsp;16.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.37\u0026thinsp;\u0026plusmn;\u0026thinsp;15.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eanti-β2GP1 (AU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.64 (2.00, 3.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.33 (2.00, 5.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePT: prothrombin time; TT: thrombin time; AT-III: antithrombin III; Angle: kinetics of clot development; Hcy: homocysteine; 25(OH)D: 25-hydroxyvitamin D; IgG: Immunoglobulin G; C3: Complement protein 3; C4: Complement protein 4; TG: triglyceride; WBC: white blood cell; Neu: neutrophil; Th cell (CD4+): CD4(+) T lymphocytes; PC: protein C; PS: protein S; anti-β2GP1: anti-β2 glycoprotein 1 antibody; LAC: lupus anticoagulant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Nomogram construction and performance assessment\u003c/h2\u003e \u003cp\u003eEleven candidate variables, including AT-III, Hcy, C3, C4, PC, WBC, Neu, Th cell (CD4+), suppressor/cytotoxic T cells (CD8+), CD4+/CD8+, LAC and anti-β2GP1 (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were significantly associated with spontaneous abortion in pregnancies in the univariate logistic regression analyses (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among them, AT-III (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Hcy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), C3 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and anti-β2GP1 (P\u0026thinsp;=\u0026thinsp;0.001) were identified as independent risk factors for spontaneous abortion by the subsequent multivariate regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Thereafter, a nomogram was developed by incorporating these five predictors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eUnivariate and multivariate logistic regression analysis of the candidate predictors in the training set\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.917 (0.748\u0026ndash;1.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.066 (0.977\u0026ndash;1.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT-III (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.953 (0.939\u0026ndash;0.968)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.955 (0.936\u0026ndash;0.974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eAngle (deg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.998 (0.973\u0026ndash;1.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.999 (0.940\u0026ndash;1.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHcy (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.917 (1.694\u0026ndash;2.171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.943 (1.685\u0026ndash;2.241)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003e25(OH)D (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.982 (0.962\u0026ndash;1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgG (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.016 (0.967\u0026ndash;1.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.252 (4.770-17.945)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.685 (5.103\u0026ndash;42.264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eC4 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.136 (3.547-164.234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.231 (0.992\u0026ndash;1.527)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (*10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.038 (1.015\u0026ndash;1.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeu (*10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.042 (1.017\u0026ndash;1.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTh cell (CD4+) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.032 (1.012\u0026ndash;1.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esuppressor/cytotoxic T cells (CD8+) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.957 (0.934\u0026ndash;0.981)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4+/CD8+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.621 (1.278\u0026ndash;2.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.974\u0026ndash;0.986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.976 (0.967\u0026ndash;0.984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003ePS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000 (0.991\u0026ndash;1.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eanti-β2GP1 (AU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.014 (1.003\u0026ndash;1.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.013 (1.003\u0026ndash;1.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.268 (1.106\u0026ndash;16.473)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ePT: prothrombin time; TT: thrombin time; AT-III: antithrombin III; Angle: kinetics of clot development; Hcy: homocysteine; 25(OH)D: 25-hydroxyvitamin D; IgG: Immunoglobulin G; C3: Complement protein 3; C4: Complement protein 4; TG: triglyceride; WBC: white blood cell; Neu: neutrophil; Th cell (CD4+): CD4(+) T lymphocytes; PC: protein C; PS: protein S; anti-β2GP1: anti-β2 glycoprotein 1 antibody; LAC: lupus anticoagulant; 95% CI: 95% confidence interval; OR: odds ratio;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe nomogram showed favorable discrimination with an AUC of 0.813 (95% CI: 0.790 to 0.842) in the training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The calibration curve suggested good agreement between model prediction and actual observation in the training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Besides, the Hosmer-Lemeshow test yielded a nonsignificant P value of 0.331, indicating good calibration power.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe clinical impact curve in the training set shows the stratification of spontaneous abortion probability for 1000 samples based on the predicted probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). The predictive abortion number was close to the actual number of positive cases when the threshold probability was greater than 0.8. At this time, the cost-to-benefit ratio was 0.6.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Validation of the nomogram\u003c/h2\u003e \u003cp\u003eThe satisfactory discrimination of the nomogram was confirmed using the validation set, with an AUC of 0.792 (95% CI: 0.741 to 0.838) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Good calibration was also observed in the validation set, with a nonsignificant P value of 0.675 derived from the Hosmer-Lemeshow test (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). The CIC demonstrated that when the threshold probabilities greater than 0.4, the nomogram also showed a higher net benefit in the validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Models comparison\u003c/h2\u003e \u003cp\u003eThe nomogram showed higher discriminatory accuracy with an AUC of 0.804 (95% CI: 0.779 to 0.829) for predicting spontaneous abortion in pregnancies than any of the variables incorporated in the nomogram alone (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe DCAs showed that the nomogram had a higher overall net benefit than the models containing only risk factors incorporated in the nomogram across a wide range of threshold probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAlthough many previous studies have reported the association between miscarriage and immunological, endocrinological, systemic inflammation, thrombophilic and other blood biochemical parameters[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], few studies have combined these markers to predict the risk of miscarriage. Therefore, we constructed this prediction nomogram using hematological risk factors which demonstrated favorable diagnostic accuracy to make accurate assessments of the risk of miscarriage, so that clinicians can provide precise and scientific treatment.\u003c/p\u003e \u003cp\u003eThrombophilia is a pathological state of coagulation-anticoagulation imbalance caused by various factors, including platelets, coagulation-fibrinolysis system, and hemorheology. Mothers with thrombophilia may develop placental thrombosis, which leads to reduced placental perfusion and ultimately affects the material exchange between the mother and the fetus, inducing the occurrence of spontaneous abortion[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This study found that AT-III and PC, two coagulation-related factors, are one of the key indicators for predicting miscarriage.\u003c/p\u003e \u003cp\u003eAT-III is a heparin-dependent serine protease inhibitor that can bind to and inactivate various serine-containing coagulation factors, thrombin, and plasmin, thus maintaining normal coagulation and preventing thrombosis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. PC is a protein that is activated in the presence of calcium ions and inactivates factor V and factor VIII, exerting anticoagulant effects[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In normal pregnant women, the synthesis of coagulation factors is accelerated, the production of thrombin increases, and the anticoagulant activity of AT-III is strong. Subsequently, the thrombin-antithrombin complex is strengthened, the consumption of AT-III is reduced, and finally resulting in increased AT-III level. However, in abnormal pregnant women, this cascade is interrupted[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Studies have shown that the decrease of AT-III and PC activities increases the risk of thrombosis and miscarriage[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Wang, P., et al[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] found that the AT-III level decreased along with the increase of the number of prior abortions, and that patients with four or more prior abortions had significantly lower AT-III levels than patients with normal fertility, and the difference was statistically significant (P\u0026thinsp;=\u0026thinsp;.0111). Sugiura, M [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] pointed out that PC deficiency during pregnancy increases the risk of thrombosis by 3\u0026ndash;10% antepartum and 7\u0026ndash;19% postpartum, and is also associated with recurrent miscarriages in the first, second and third trimesters.\u003c/p\u003e \u003cp\u003eImmune system is essential for maintaining a healthy pregnancy, as it regulates the maternal-fetal interface and protects the fetus from pathogens. However, immune dysregulation, such as autoimmune disorders, alloimmune reactions, and chronic inflammation, can lead to pregnancy loss or complications. Complement C3 is the key protein of the complement system and composed of two polypeptide chains, α- and β-chain. C3\u0026mdash;the central protein of the complement cascade\u0026mdash;appears to have an important role in the early phase of pregnancy and in the development of the placenta[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The activation of complement C3 can increase the inflammatory response of leukocytes and the activity of vascular endothelial cells, amplify the procoagulant effect of APL antibodies, cause thrombosis, placental tissue ischemia and hypoxia, and eventually lead to miscarriage[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. To confirm this hypothesis, Al Jameil N[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] injected Crry-IG, a C3 convertase inhibitor, into pregnant APL antibody-positive mice, blocking the classical and alternative pathways of complement C3 activation, which prevented fetal loss and growth restriction. Research also shows that after pregnancy in patients with SLE, complement activation occurs in the placenta and produces anaphylatoxins, as well as cytokines such as C3 and C1q, which mediate the activation of effector cells, accelerating the rejection of the embryo[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. β2GP1 activity as an in vitro inhibitor of the intrinsic blood coagulation pathway, adenosine diphosphate\u0026ndash;dependent aggregation of platelets, and prothrombinase activity of activated platelets[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Anti-β2GP1 is the main diagnostic indicators of antiphospholipid syndrome, which can cause miscarriage by affecting the implantation of fertilized eggs through direct or indirect damage to trophoblasts and thrombosis formation. Previous studies have confirmed that[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], the expression of β2GP1 in the uterus of parturient can inhibit platelet prothrombin activity and endogenous aggregation pathway, which, in combination with thrombosis in the uterus, eventually results in adverse pregnancy outcomes.\u003c/p\u003e \u003cp\u003eHcy is a sulfur-containing amino acid that is produced by the demethylation of methionine. Hyperhomocysteinemia (HHcy) is a condition where Hcy accumulates excessively in the body due to innate metabolic enzyme disorders, folic acid, vitamin B6 and B12 deficiencies, or abnormal Hcy metabolism[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. High levels of Hcy in the blood can increase the risk of vascular diseases, coronary artery dysfunction, atherosclerosis, and embolic diseases. HHcy during pregnancy can cause endothelial cell damage, make the blood hypercoagulable, harm the placental blood vessels, and increase the risk of placental embolism. The production of nitric oxide (NO) and prostacyclin may be the reasons why HHcy leads to thrombosis, which triggers a coagulation process, resulting in endothelial injury[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This may be an underlying mechanism for abortion. Several studies[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]have shown that high serum Hcy levels are strongly correlated with the occurrence of RSA. Studies have also shown that women with HHcy have a higher rate of three or more consecutive spontaneous abortions[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur limitation is that we only used hematological parameters to predict the risk of miscarriage in pregnancies. Future studies will combine clinical indicators such as maternal age, BMI, medical history, and symptoms to further improve the accuracy of the prediction model.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWe have developed and validated a novel tool to assess the risk of spontaneous abortion in pregnancies. This tool is a nomogram that incorporates several biomarkers and antibodies, such as AT-III, Hcy, C3, PC, and anti-β2GP1. It demonstrates reasonable accuracy and discrimination in predicting the outcome of pregnancy. Based on individual risk assessments, clinicians and patients can take more effective steps for treatment. However, this tool requires further validation from large multicenter studies before it can be widely implemented in clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAT-III \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Antithrombin III\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHcy \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Homocysteine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC3 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Complement component 3\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Protein C\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnti-\u0026beta;2GP1 \u0026nbsp; \u0026nbsp; Anti-\u0026beta;2 glycoprotein I antibody\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Department of Gynecology of The Third Affiliated Hospital of Wenzhou Medical University, for their continuous support during the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJunmiao Xiang: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Funding Acquisition, Writing - Original Draft, Writing - Review \u0026amp; Editing;\u0026nbsp;Lin Liu: Data Curation, Writing - Original Draft;\u0026nbsp;Ruru Bao: Visualization, Validation, Writing - Original Draft; Zhuhua Cai: Conceptualization, Resources, Supervision, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Foundation of Wenzhou Municipal Health Commission (2020041)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData available on request from the authors\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eBefore participating in the study, all participants signed up with informed permission.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Ethics Committee of The Third Affiliated Hospital of Wenzhou Medical University. The patients/participants provided written informed consent to participate in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eQuenby S, Gallos ID, Dhillon-Smith RK, Podesek M, Stephenson MD, Fisher J, Brosens JJ, Brewin J, Ramhorst R, Lucas ES: \u003cstrong\u003eMiscarriage matters: the epidemiological, physical, psychological, and economic costs of early pregnancy loss\u003c/strong\u003e. \u003cem\u003eThe Lancet \u003c/em\u003e2021, \u003cstrong\u003e397\u003c/strong\u003e(10285):1658-1667.\u003c/li\u003e\n\u003cli\u003eNo RG-tG: \u003cstrong\u003eThe investigation and treatment of couples with recurrent first-trimester and second-trimester miscarriage\u003c/strong\u003e. \u003cem\u003eRCOG: London, UK \u003c/em\u003e2011.\u003c/li\u003e\n\u003cli\u003eFarren J, Jalmbrant M, Falconieri N, Mitchell-Jones N, Bobdiwala S, Al-Memar M, Tapp S, Van Calster B, Wynants L, Timmerman D\u003cem\u003e et al\u003c/em\u003e: 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reproductive immunology (New York, NY : 1989) \u003c/em\u003e2001, \u003cstrong\u003e45\u003c/strong\u003e(2):65-71.\u003c/li\u003e\n\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":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Abortion, Hematological parameters, Nomogram, Receiver operator characteristic","lastPublishedDoi":"10.21203/rs.3.rs-4607425/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4607425/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePregnancy loss significantly affects physical and mental health. A nomogram for predicting spontaneous abortion risk was developed to improve pregnancy outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 1346 pregnant women were enrolled from The Third Affiliated Hospital of Wenzhou Medical University (May 2020 - May 2022). The training set included 941 participants, and the validation set had 405. Feature selection was optimized using a random forest model, and a predictive model was constructed via multivariable logistic regression. The nomogram\u0026rsquo;s performance was assessed with receiver operator characteristic (ROC), Hosmer-Lemeshow test, calibration curve, and clinical impact curve (CIC). Discrimination and clinical utility were compared between the nomogram and its individual variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAntithrombin III (AT-III), homocysteine (Hcy), complement component 3 (C3), protein C (PC), and anti-β2 glycoprotein I antibody (anti-β2GP1) were identified as risk factors. The nomogram demonstrated satisfactory discrimination (Training AUC: 0.813, 95% CI: 0.790\u0026ndash;0.842; Validation AUC: 0.792, 95% CI: 0.741\u0026ndash;0.838). The Hosmer-Lemeshow test (P\u0026thinsp;=\u0026thinsp;.331) indicated a good fit, and the CIC showed clinical net benefit. The nomogram outperformed individual variables in discrimination (AUC: 0.804, 95% CI: 0.779\u0026ndash;0.829).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe developed nomogram, incorporating AT-III, Hcy, C3, PC, and anti-β2GP1, aids clinicians in identifying pregnant women at high risk for spontaneous abortion.\u003c/p\u003e","manuscriptTitle":"A nomogram based on hematological parameters for prediction of spontaneous abortion risk in pregnancies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 16:10:11","doi":"10.21203/rs.3.rs-4607425/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-22T21:20:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-21T07:32:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-03T15:17:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194285256872449517638681772523768154520","date":"2024-10-01T06:47:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131643683952539656411748116035515620897","date":"2024-09-14T13:08:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237524006086791047646253091425143516811","date":"2024-08-26T04:46:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292722133505273122016428190924547441952","date":"2024-06-24T00:16:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-21T18:49:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-20T04:47:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-20T04:46:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2024-06-19T17:28:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9db8f680-ce4d-4d1a-812e-7092bde759ba","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-17T16:04:12+00:00","versionOfRecord":{"articleIdentity":"rs-4607425","link":"https://doi.org/10.1186/s12884-025-07396-4","journal":{"identity":"bmc-pregnancy-and-childbirth","isVorOnly":false,"title":"BMC Pregnancy and Childbirth"},"publishedOn":"2025-03-11 15:58:18","publishedOnDateReadable":"March 11th, 2025"},"versionCreatedAt":"2024-07-18 16:10:11","video":"","vorDoi":"10.1186/s12884-025-07396-4","vorDoiUrl":"https://doi.org/10.1186/s12884-025-07396-4","workflowStages":[]},"version":"v1","identity":"rs-4607425","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4607425","identity":"rs-4607425","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}