A nomogram prediction model for ovarian endometrioma in patients with endometriosis: a retrospective study based on clinical indicators

A nomogram prediction model for ovarian endometrioma in patients with endometriosis: a retrospective study based on clinical indicators | 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 prediction model for ovarian endometrioma in patients with endometriosis: a retrospective study based on clinical indicators Li Shan, Lin Zhong, Li Weihong, Meng Fengyun, Bai Rui, Tang Zhenyu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9823695/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract Background Among the subtypes of endometriosis (EMs), ovarian endometrioma (OE) causes the most direct and progressive damage to ovarian reserve function. This study aimed to identify independent risk factors for OE in patients with EMs, and to develop and validate a clinical prediction model to support early identification and stratified intervention in high-risk patients. Methods A retrospective study was conducted on 342 patients with pathologically confirmed EMs admitted to the First Affiliated Hospital of Guangxi University of Chinese Medicine from January 2021 to December 2025. Among them, 103 patients had OE (OE group) and 239 had other types of EMs (non-OE group). Patients were randomly divided into training and validation sets at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression was used for preliminary feature selection, followed by univariate and multivariate logistic regression analyses to identify independent predictors of OE. A nomogram prediction model was subsequently constructed. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC), calibration was evaluated using calibration curves comparing predicted probabilities with observed outcomes, and clinical utility was assessed using decision curve analysis (DCA). Results Multivariate logistic regression analysis revealed that history of dysmenorrhea (OR = 60.44, 95%CI: 20.95–213.90), infertility (OR = 13.10, 95%CI: 4.85–40.21), elevated fibrinogen levels (OR = 1.84, 95%CI: 1.13–3.01), and decreased lymphocyte count (OR = 0.42, 95%CI: 0.16–0.99) were independent predictors of OE in patients with EMs (all P < 0.05). The nomogram model constructed based on these factors demonstrated excellent discrimination in both the training and validation sets (AUC = 0.945 and 0.948, respectively), good calibration, and positive clinical net benefit across a wide range of threshold probabilities as shown by DCA. Conclusion This study successfully developed and validated a nomogram model integrating clinical symptoms (history of dysmenorrhea, infertility) and routine laboratory indicators (fibrinogen, lymphocyte count), which can effectively predict the individualized risk of OE in patients with EMs. This model provides a simple and practical quantitative tool for clinicians to identify high-risk OE populations, implement risk-stratified management, and formulate individualized intervention strategies. Endometriosis Ovarian endometrioma Nomogram Dysmenorrhea Fibrinogen Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Endometriosis (EMs) is a condition characterized by the presence, growth, and infiltration of active endometrial tissue outside the uterine cavity, accompanied by recurrent bleeding and varying degrees of inflammatory response [ 1 ] . As a common disease in women of reproductive age, the prevalence of EMs is approximately 5%-10%, and can reach as high as 30%-50% in women with infertility [ 2 – 3 ] . Based on the site of involvement and depth of infiltration, EMs can be classified into peritoneal, ovarian, and deep infiltrating subtypes, each with distinct clinical manifestations, impacts on fertility, and clinical management strategies [1 ]. Ovarian endometrioma (OE), commonly known as "chocolate cyst," is one of the most frequent types of EMs, accounting for approximately 17%-44% of patients with EMs [ 4 – 5 ] . Compared with other subtypes, OE causes the most direct and progressive damage to ovarian reserve function [ 6 ] . The underlying pathological mechanisms include: mass effect of the cyst compressing the normal ovarian cortex, reducing the volume of functional ovarian tissue; and leakage of free iron, reactive oxygen species, inflammatory factors, and proteolytic enzymes from the old hemorrhagic fluid within the cyst cavity into the surrounding cortex, inducing oxidative stress and fibrosis, and leading to accelerated follicular depletion [ 7 ] . Recent prospective studies have shown that even with expectant management, ovarian reserve indicators—anti-Müllerian hormone (AMH)—decline in patients with OE over 6 months [ 8 ] . Although cystectomy can remove the lesion, the surgery itself may cause incidental loss of normal ovarian tissue, leading to further decline in postoperative AMH levels [ 9 ] . Therefore, the formation of OE not only marks disease progression but also signifies irreversible damage to ovarian function, representing a critical turning point affecting the long-term fertility outcomes of patients with EMs. Based on the above understanding, early identification of individuals at high risk of progressing to OE within the EMs patient population, followed by proactive intervention, may delay or prevent the formation of OE, thereby preserving ovarian function and improving fertility prognosis. Potential intervention strategies for high-risk OE populations include: ① enhanced imaging surveillance (e.g., pelvic ultrasound follow-up every 6–12 months); ② lifestyle interventions (anti-inflammatory diet, regular exercise); ③ prophylactic medication (oral contraceptives, progestins, or GnRH-a, individualized according to fertility needs); ④ fertility preservation counseling (oocyte or embryo cryopreservation); and ⑤ elective surgical intervention (for those with small cysts showing progression tendency) [ 10 – 12 ] . The effective implementation of these strategies depends on accurate prediction of OE risk. Although studies have explored the association of serum CA-125, neutrophil-to-lymphocyte ratio (NLR) and other indicators with OE [ 13 – 14 ] , most research has focused on the diagnosis of OE or prediction of postoperative recurrence. No study has specifically developed a quantitative tool for predicting OE risk in patients with established EMs. In clinical practice, historical information such as dysmenorrhea and infertility, as well as routine laboratory tests including blood counts and coagulation function, are readily accessible at all levels of medical institutions. If these simple indicators could be used to construct a prediction model, the accessibility of screening for high-risk OE populations would be greatly enhanced. Therefore, based on data from a single-center retrospective cohort of patients with pathologically confirmed EMs, this study aimed to: ① identify independent risk factors for OE in patients with EMs; ② construct a nomogram prediction model integrating these factors; and ③ evaluate the model's discrimination, calibration, and clinical utility through internal validation, thereby providing evidence-based support for early identification and stratified management of high-risk OE populations. 1 Methods 1.1 Study Design and Population This was a single-center retrospective study. Using the hospital electronic medical record system, patients who were hospitalized at the First Affiliated Hospital of Guangxi University of Chinese Medicine from January 2021 to December 2025 and diagnosed with endometriosis (EMs) by laparoscopic surgery and postoperative pathology were consecutively enrolled. Based on intraoperative findings and pathological reports, patients were divided into two groups: Ovarian endometrioma group (OE group) Presence of ovarian endometrioma, defined as a cyst located within the ovarian cortex containing chocolate-like old hemorrhagic fluid, with pathological confirmation of endometrial glands and stroma in the cyst wall. Non-ovarian endometrioma group (non-OE group) Patients with other types of EMs, including peritoneal and deep infiltrating subtypes, without OE. Inclusion criteria ① age 18–50 years; ② regular menstrual cycles (21–35 days); ③ complete clinical medical records. Exclusion criteria ① complicated by immune system diseases, hematological disorders, active infections, or malignancies; ② history of hormonal therapy (including oral contraceptives, progestins, gonadotropin-releasing hormone agonists, etc.) or anticoagulant therapy within the past month; ③ severe hepatic, renal, or cardiac insufficiency; ④ pregnancy or lactation; ⑤ missing key variables in medical records. This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi University of Chinese Medicine (Approval No: YJS2023-048) with waiver of informed consent. 1.2 Data Collection The following data were collected from the electronic medical record system: Demographic characteristics Age, body mass index (BMI). Clinical symptoms:History of dysmenorrhea : defined as periodic menstrual lower abdominal pain requiring medication or affecting daily life (yes/no);Infertility: defined as failure to conceive after ≥ 1 year of regular unprotected intercourse (yes/no), including primary and secondary infertility. Laboratory indicators : Fasting venous blood samples were collected on the morning after admission for testing of the following indicators:lymphocyte count (LYM), neutrophil count (NEUT), white blood cell count, hemoglobin, platelet count, etc.;Liver function: total bilirubin (TBIL), indirect bilirubin (IBIL), adenosine deaminase (ADA), alanine aminotransferase, aspartate aminotransferase, etc.;Coagulation function: fibrinogen (FIB), prothrombin time, activated partial thromboplastin time, D-dimer, etc.;Electrolytes: chloride (Cl), sodium, potassium, etc.;Inflammatory markers: C-reactive protein (CRP). 1.3 Data Grouping and Preprocessing All 342 patients were randomly divided into training and validation sets at a 7:3 ratio. To ensure consistent distribution of outcome events between the two datasets, stratified random sampling was used: first, patients in the OE group (103 cases) were randomly assigned 7:3, yielding 72 cases in the training set and 31 cases in the validation set; then, patients in the non-OE group (239 cases) were randomly assigned 7:3, yielding 167 cases in the training set and 72 cases in the validation set. After combination, the training set comprised 239 cases (72 OE, 167 non-OE), and the validation set comprised 103 cases (31 OE, 72 non-OE). Continuous variables were tested for normality using the Shapiro-Wilk test. Normally distributed data were expressed as mean ± standard deviation and compared between groups using independent samples t -test; non-normally distributed data were expressed as median (interquartile range) and compared using Mann-Whitney U test. Categorical variables were expressed as number of cases (percentage) [n (%)] and compared using χ² test or Fisher's exact test. 1.4 Variable Selection and Model Construction Variable selection In the training set, least absolute shrinkage and selection operator (LASSO) regression was used for feature selection. The optimal penalty parameter λ was determined by 10-fold cross-validation, and variables with non-zero coefficients at the λ value corresponding to the maximum λ within one standard error of the minimum mean squared error (lambda.1se) were selected as candidate variables significantly associated with OE. Model construction Candidate variables selected by LASSO were included in univariate logistic regression analysis to calculate odds ratios (OR) and their 95% confidence intervals (CI). Variables with P < 0.1 in univariate analysis were further entered into multivariate logistic regression analysis (forward selection method), with P < 0.05 considered statistically significant, to identify independent predictors of OE. Based on the partial regression coefficients from multivariate analysis, a nomogram prediction model was constructed using the "rms" package in R software. 1.5 Model Validation and Evaluation Discrimination assessment Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) with its 95% confidence interval was calculated. An AUC value closer to 1 indicates better discriminatory ability of the model (AUC > 0.75 is generally considered to indicate good discrimination). Calibration assessment Calibration curves were plotted, and bootstrap resampling (1000 repetitions) was used for internal validation of the model to assess the consistency between predicted probabilities and observed outcomes. A calibration curve closer to the diagonal (ideal line) indicates better model calibration. Clinical utility assessment Decision curve analysis (DCA) was performed to quantify the clinical net benefit of the model at different threshold probabilities, evaluating its clinical application value. If the DCA curve shows positive net benefit across a range of threshold probabilities compared to the two extreme strategies of "intervene in all patients" and "intervene in none," the model is considered to have positive clinical utility. 1.6 Statistical Software Data analysis was performed using R software (version 4.3.1, R Foundation for Statistical Computing, Vienna, Austria) and SPSS 26.0 (IBM Corp., Armonk, NY, USA). All statistical tests were two-sided, and P < 0.05 was considered statistically significant. 2 Results 2.1 Baseline Characteristics of Patients A total of 342 patients with pathologically confirmed endometriosis (EMs) were included in this study, comprising 103 patients with ovarian endometrioma (OE group, 30.1%) and 239 patients with other types of EMs (non-OE group, 69.9%). The baseline clinical characteristics of the two groups are compared in Table 1 . Compared with the non-OE group, patients in the OE group had significantly higher proportions of dysmenorrhea (81.6% vs. 11.3%, P < 0.001), infertility (80.6% vs. 17.6%, P < 0.001), higher fibrinogen levels [3.40 (3.00, 4.27) g/L vs. 3.20 (2.82, 3.64) g/L, P = 0.004], and lower lymphocyte counts (1.59 ± 0.65 vs. 1.76 ± 0.54, P = 0.021) and total bilirubin levels [10.80 (8.35, 14.85) µmol/L vs. 12.50 (9.60, 16.00) µmol/L, P = 0.016]. No statistically significant differences were observed in the remaining indicators between the two groups ( P > 0.05). Using stratified random sampling at a 7:3 ratio, all 342 patients were divided into training set (239 cases) and validation set (103 cases). The training set contained 72 OE cases (30.1%) and 167 non-OE cases (69.9%); the validation set contained 31 OE cases (30.1%) and 72 non-OE cases (69.9%). Comparison of baseline characteristics between the two sets (Table 1 ) showed no statistically significant differences in any indicator ( P > 0.05), indicating balanced randomization and comparability. Table 1 Baseline clinical characteristics of patients Variable Total EMs (N = 342) Training Set (N = 239) Validation Set (N = 103) P -value age 35.00 [31.00;43.00] 35.00 [31.00;42.00] 37.00 [30.00;44.00] 0.395 CHE 7874.50 [6962.00;9061.50] 7931.00 [7109.50;9099.00] 7684.00 [6650.00;9019.50] 0.097 ALB 42.50 [40.00;44.60] 42.50 [40.25;44.40] 42.10 [39.20;44.90] 0.680 IBIL 8.50 [6.62;11.17] 9.10 [7.00;11.45] 7.60 [6.10;10.20] 0.003 GGT 14.00 [11.00;19.00] 14.00 [12.00;19.00] 15.00 [11.00;21.00] 0.540 ADA 8.00 [6.00;9.75] 8.00 [7.00;10.00] 7.00 [5.00;8.50] < 0.001 PAB 215.00 [192.00;241.00] 213.00 [193.00;238.50] 217.00 [181.50;245.50] 0.642 TP 70.70 [67.00;73.47] 70.90 [67.45;73.35] 69.70 [65.10;74.05] 0.227 DBIL 3.50 [2.50;4.60] 3.60 [2.65;4.55] 3.30 [2.10;4.60] 0.185 ALP 59.00 [50.00;66.00] 58.00 [50.00;64.00] 60.00 [48.50;71.00] 0.099 AST 18.00 [15.00;22.00] 18.00 [15.00;22.00] 19.00 [15.00;22.00] 0.676 A.G 1.50 [1.40;1.60] 1.50 [1.40;1.60] 1.50 [1.40;1.70] 0.265 TBIL 12.10 [9.40;15.88] 12.50 [9.60;16.00] 10.80 [8.35;14.85] 0.016 GB 28.05 [26.20;30.48] 28.30 [26.45;30.60] 27.70 [25.45;29.80] 0.068 TBA 2.60 [1.50;4.50] 2.60 [1.50;4.50] 2.50 [1.55;4.35] 0.754 AFU 22.00 [19.00;26.00] 23.00 [20.00;26.00] 20.00 [16.00;25.00] < 0.001 ALT 12.00 [9.00;18.00] 12.00 [9.00;17.00] 13.00 [10.00;21.00] 0.033 HB 120.00 [110.00;130.75] 123.00 [112.50;130.00] 118.00 [104.00;131.00] 0.133 NEUT 4.47 [3.42;6.30] 4.30 [3.40;6.02] 4.71 [3.51;7.25] 0.100 RBC 4.34 [4.04;4.65] 4.37 [4.07;4.64] 4.32 [3.99;4.65] 0.335 PLT 281.50 [236.25;332.75] 281.00 [238.00;336.50] 284.00 [220.50;320.50] 0.319 WBC 6.91 [5.60;8.79] 6.70 [5.50;8.62] 7.50 [5.90;9.55] 0.071 LYM 1.71 (0.58) 1.76 (0.54) 1.59 (0.65) 0.021 Na 139.00 [137.00;140.00] 139.00 [137.00;140.50] 139.00 [136.50;140.00] 0.219 K 3.89 [3.70;4.05] 3.90 [3.70;4.05] 3.87 [3.70;4.06] 0.405 P 1.09 [1.01;1.17] 1.08 [1.01;1.16] 1.09 [1.01;1.18] 0.519 Ca 2.24 [2.17;2.32] 2.24 [2.16;2.33] 2.24 [2.17;2.32] 0.597 Cl 104.00 [102.00;105.75] 104.00 [102.00;105.00] 105.00 [102.00;106.00] 0.006 UA 260.00 [226.25;303.75] 260.00 [226.50;302.50] 260.00 [226.50;315.50] 0.579 Cr 54.50 [49.00;60.00] 55.00 [50.00;60.00] 54.00 [48.00;60.50] 0.408 CRP 20.65 [10.03;36.60] 20.40 [8.90;36.70] 21.30 [12.20;36.50] 0.488 FIB 3.24 [2.87;3.85] 3.20 [2.82;3.64] 3.40 [3.00;4.27] 0.004 PTA 105.00 [97.00;111.00] 105.00 [97.00;111.00] 105.00 [96.50;112.00] 0.840 TT 15.85 [15.30;16.60] 15.80 [15.30;16.60] 15.90 [15.30;16.60] 0.775 PT 12.90 [12.50;13.40] 12.90 [12.50;13.30] 12.90 [12.50;13.40] 0.770 APTT 37.25 [34.60;40.20] 37.20 [34.90;39.90] 37.50 [33.55;40.35] 0.736 D.dimer 0.41 [0.28;0.65] 0.40 [0.27;0.62] 0.43 [0.30;0.68] 0.094 INR 0.97 [0.94;1.02] 0.97 [0.94;1.02] 0.97 [0.94;1.02] 0.912 infertility: < 0.001 0 217 (63.45%) 197 (82.43%) 20 (19.42%) 1 125 (36.55%) 42 (17.57%) 83 (80.58%) Cramps: < 0.001 0 231 (67.54%) 212 (88.70%) 19 (18.45%) 1 111 (32.46%) 27 (11.30%) 84 (81.55%) 2.2 Variable Selection and Identification of Independent Predictors In the training set, LASSO regression was first performed on all variables. As shown in Fig. 1 A, the optimal penalty parameter λ was determined by 10-fold cross-validation, and using lambda.1se as the selection criterion, nine variables with non-zero coefficients were selected: history of dysmenorrhea, infertility, lymphocyte count (LYM), chloride (Cl), neutrophil count (NEUT), total bilirubin (TBIL), indirect bilirubin (IBIL), fibrinogen (FIB), and adenosine deaminase (ADA) (Fig. 1 B). Figure 1 . LASSO regression for variable selection Note (A) Cross-validation plot for tuning parameter λ selection. The left dashed line indicates λ.min, and the right dashed line indicates λ.1se. (B) LASSO coefficient path plot. The vertical line corresponds to λ.1se, where nine variables with non-zero coefficients were selected. These nine variables were included in univariate logistic regression analysis. The results (Table 2 ) showed that history of dysmenorrhea (OR = 39.38, 95%CI: 18.81–88.62, P < 0.001), infertility (OR = 14.91, 95%CI: 7.83–29.77, P < 0.001), FIB (OR = 1.30, 95%CI: 1.01–1.68, P = 0.041), Cl (OR = 1.12, 95%CI: 1.02–1.24, P = 0.023), LYM (OR = 0.56, 95%CI: 0.34–0.89, P = 0.016), TBIL (OR = 0.94, 95%CI: 0.89–0.99, P = 0.033), and IBIL (OR = 0.90, 95%CI: 0.82–0.97, P = 0.009) were significantly associated with OE occurrence. NEUT ( P = 0.073) and ADA ( P = 0.052), although not reaching P < 0.05, were < 0.1 and were included in multivariate analysis. Table 2 Univariate logistic regression analysis of factors associated with OE in the training set 1 Variable B SE OR CI Z P Cramps 3.673 0.39376 39.385 39.38(18.81–88.62) 9.329 0 2 infertility 2.702 0.33968 14.915 14.91(7.827–29.77) 7.956 0 3 FIB 0.263 0.12863 1.301 1.301(1.012–1.683) 2.043 0.041 4 Cl 0.116 0.05098 1.123 1.123(1.017–1.243) 2.272 0.023 5 LYM -0.586 0.24363 0.556 0.556(0.341–0.89) -2.406 0.016 6 NEUT 0.066 0.0369 1.068 1.068(0.994–1.15) 1.792 0.073 7 TBIL -0.061 0.02854 0.941 0.941(0.887–0.992) -2.134 0.033 8 ADA -0.11 0.05647 0.896 0.896(0.798–0.995) -1.94 0.052 9 IBIL -0.109 0.04156 0.897 0.897(0.823–0.969) -2.612 0.009 The nine variables were entered into multivariate logistic regression analysis (forward selection). The results (Table 3 , Fig. 2 ) showed that history of dysmenorrhea (OR = 60.44, 95%CI: 20.95–213.90, P < 0.001), infertility (OR = 13.10, 95%CI: 4.85–40.21, P < 0.001), elevated fibrinogen levels (OR = 1.84, 95%CI: 1.13–3.01, P = 0.014), and decreased lymphocyte count (OR = 0.42, 95%CI: 0.16–0.99, P = 0.047) were independent predictors of OE in patients with EMs. The remaining variables did not reach statistical significance in multivariate analysis( P > 0.05). Table 3 Multivariate logistic regression analysis of factors associated with OE 1 Variable B SE OR CI Z P (Intercept) -9.09 10.30763 0 0.000 (1.320–58718) -0.882 0.378 2 Cramps 4.102 0.58642 60.443 60.44 (20.95–213.9) 6.995 0 3 infertility 2.572 0.53356 13.098 13.09 (4.848–40.21) 4.821 0 4 IBIL -0.642 0.34522 0.526 0.526 (0.253–0.988) -1.86 0.063 5 ADA -0.108 0.10619 0.897 0.897 (0.738–1.107) -1.021 0.307 6 TBIL 0.379 0.24225 1.46 1.460 (0.937–2.431) 1.563 0.118 7 NEUT 0.081 0.07947 1.084 1.084 (0.924–1.262) 1.02 0.308 8 LYM -0.874 0.4593 0.417 0.417 (0.161–0.993) -1.903 0.057 9 Cl 0.056 0.09249 1.058 1.057 (0.881–1.270) 0.608 0.543 10 FIB 0.61 0.24723 1.84 1.839 (1.127–3.014) 2.466 0.014 Note Odds ratios (OR) and 95% confidence intervals (CI) are shown for each predictor. Dysmenorrhea, infertility, fibrinogen, and lymphocyte count were identified as independent predictors. 2.3 Construction of the Nomogram Prediction Model Based on the results of multivariate logistic regression analysis, the four independent predictors—history of dysmenorrhea, infertility, fibrinogen, and lymphocyte count—were included to construct a nomogram prediction model for OE risk in patients with EMs using the "rms" package in R software (Fig. 3 ). The nomogram is used as follows: for each patient, the value of each predictor is projected upward to the "Points" scale to obtain the individual score; the individual scores are summed to obtain the total score; the total score is then projected downward to the "OE Risk" scale to obtain the predicted probability of OE for that patient. Note To use the nomogram, locate the patient's status for each predictor, draw a vertical line upward to the "Points" scale to obtain the point value for each predictor, sum all points to obtain the total points, and draw a vertical line downward from the total points scale to obtain the predicted probability of OE. 2.4 Validation of the Prediction Model Discrimination assessment : ROC curve analysis (Fig. 4 ) showed that the nomogram model achieved an AUC of 0.945 (95%CI: 0.916–0.974) in the training set and 0.948 (95%CI: 0.912–0.984) in the validation set, indicating excellent discriminatory ability to distinguish between patients with and without OE. Calibration assessment Calibration curves (Fig. 5 ) demonstrated good agreement between the predicted probabilities of OE and the observed OE probabilities in both the training and validation sets. The bias-corrected curves after 1000 bootstrap resamples were closely aligned with the ideal reference line, indicating good model calibration and high predictive accuracy. Note (A) Training set; (B) Validation set. Apparent = raw model calibration results; Bias-corrected=curve after 1000 bootstrap resamples; Ideal=ideal reference line. 2.5 Clinical Utility Assessment of the Model Decision curve analysis (DCA) results (Fig. 6 ) showed that the nomogram model demonstrated positive clinical net benefit across a wide range of threshold probabilities in both the training and validation sets. Compared with the two extreme strategies of "intervene in all patients" or "intervene in none," using this model for risk stratification and decision-making provided greater clinical benefit to patients, indicating good clinical application potential. Note (A) Training set; (B) Validation set. Dashed line=prediction model; solid black line=assume none have OE; solid gray line=assume all have OE. 3. Discussion 3.1 Summary of Main Findings In this study of 342 patients with pathologically confirmed endometriosis (EMs), we identified four independent predictors of ovarian endometrioma (OE) using LASSO regression combined with multivariate logistic analysis: a history of dysmenorrhea (OR = 60.44), infertility (OR = 13.10), elevated fibrinogen levels (OR = 1.84), and a decreased lymphocyte count (OR = 0.42). A nomogram integrating these factors was constructed and validated, demonstrating excellent discrimination in both the training and validation cohorts (AUC = 0.945 and 0.948, respectively), robust calibration, and a positive clinical net benefit. This model provides a simple and practical tool for the early identification and stratified management of patients with EMs who are at high risk for developing OE. 3.2 Clinical Significance and Underlying Mechanisms of the Predictors In this study, a history of dysmenorrhea emerged as the strongest predictor of OE (OR = 60.44), a finding consistent with previous research. Wu et al. [ 15 ] , in a 12-year retrospective cohort study of 334 patients with ovarian endometriosis, reported that those with dysmenorrhea had significantly higher rates of deep infiltrating endometriosis (DIE) and adenomyosis, higher infertility rates, more advanced rAFS scores, and increased postoperative recurrence. This suggests that dysmenorrhea is not merely a clinical symptom but may be a marker of a more aggressive disease phenotype. The mechanisms linking dysmenorrhea to an increased risk of OE are likely multifactorial. First, severe dysmenorrhea is often associated with hyperperistalsis and dysperistalsis of the uterus, which can significantly promote retrograde menstruation and increase the likelihood of endometrial tissue implantation on the ovarian cortex [ 16 ] . Second, the intense local inflammatory response associated with dysmenorrhea, characterized by the release of prostaglandins and nerve growth factors, may create a favorable microenvironment for the survival and proliferation of ectopic endometrial lesions [ 17 ] . Third, patients with severe dysmenorrhea often present with more extensive pelvic pathology, such as DIE and dense adhesions, which can distort the pelvic anatomy and further facilitate OE formation [ 15 ] . Therefore, the clinical significance of dysmenorrhea as the strongest predictor in our model is well-founded. Infertility was identified as the second strongest predictor of OE (OR = 13.10), underscoring a close and bidirectional relationship. On one hand, OE directly compromises ovarian function and diminishes fertility. Tan et al. [ 18 ] described how endometriomas accelerate follicular depletion through mass effect, oxidative stress from iron and reactive oxygen species in the cyst fluid, and the infiltration of surrounding tissues by local inflammatory factors. Rangi et al. [ 19 ] quantified this damage, reporting that serum anti-Müllerian hormone (AMH) levels decrease by 30% after unilateral and up to 44% after bilateral OE cystectomy. On the other hand, infertility may also reflect the common pathological basis for OE, namely a disturbed pelvic immune microenvironment and a chronic inflammatory state. As noted by Bonavina et al. [ 20 ] , this inflammatory environment can interfere with folliculogenesis, oocyte quality, fertilization, and implantation. These same factors can also promote the survival of ectopic endometrial cells and the subsequent formation of OE. Thus, the inclusion of infertility in our model captures both a consequence and a contributing cause of OE. Elevated fibrinogen levels were identified as an independent predictor of OE (OR = 1.84), aligning with recent evidence of a hypercoagulable state in patients with EMs. Wang et al. [ 21 ] demonstrated that patients with stage IV endometriosis have significantly elevated plasma fibrinogen levels, which were positively correlated with the neutrophil-to-lymphocyte ratio (NLR), suggesting an interactive activation between the inflammatory and coagulation systems. Fini et al. [ 22 ] provided a molecular basis for this observation, showing that increased oxidative stress in EMs induces structural changes in fibrinogen, impairing its polymerization while enhancing its resistance to plasmin, thereby creating a prothrombotic state. Several mechanisms may explain how fibrinogen promotes OE formation: (1) As an acute-phase reactant, elevated fibrinogen reflects a systemic inflammatory state conducive to the implantation and growth of ectopic endometrium [ 21 ] ; (2) A hypercoagulable state may facilitate the adhesion and implantation of endometrial fragments on the ovarian surface and support established lesions through enhanced angiogenesis [ 22 ] ; and (3) Fibrinogen and its degradation products can promote cell migration and invasion via integrin signaling pathways, contributing to the progression of OE [ 23 ] . Therefore, elevated fibrinogen in patients with EMs may signify the activation of an inflammation-coagulation axis that actively promotes OE pathogenesis. A decreased lymphocyte count was found to be a protective factor for OE (OR = 0.42, meaning a higher count is protective), highlighting the critical role of immune surveillance. Recent work by Kedzierska et al. [ 24 ] showed significantly increased proportions of IL-24-producing regulatory T cells (Tregs) and plasmablasts in patients with EMs, which may enhance immunosuppressive activity and help ectopic endometrium evade immune clearance. Sobstyl et al. [ 25 ] found increased expression of Toll-like receptors (TLR) on peripheral blood lymphocyte subpopulations in EMs patients, suggesting aberrant activation of the innate immune system. Taken together, a decreased total lymphocyte count may reflect several underlying issues: (1) an imbalance in specific lymphocyte subpopulations (e.g., Tregs) leading to an overall quantitative decrease that masks a qualitative functional disturbance [ 24 ] ; (2) increased lymphocyte apoptosis or abnormal redistribution driven by chronic inflammation; and (3) an overall state of lymphopenia that impairs effective immune surveillance, allowing ectopic endometrial cells to colonize the ovarian cortex and form cysts [ 26 ] . Consequently, the routine lymphocyte count can serve as a simple, cost-effective screening indicator for OE risk, with a decreased count warranting closer clinical monitoring. 3.3 Clinical Value and Potential Applications of the Prediction Model The nomogram developed in this study demonstrated excellent discrimination (AUC > 0.94), outperforming previously published models for predicting EMs recurrence or diagnosis [ 27 ] . Its strong calibration and positive net benefit across a wide range of threshold probabilities, as confirmed by DCA, underscore its significant clinical potential. This model offers several practical advantages: (1) Ease of use and accessibility—the predictors are simple clinical data (dysmenorrhea and infertility history) and routine laboratory tests (fibrinogen and lymphocyte count), available at essentially no additional cost in any clinical setting; (2) Interpretability—the nomogram format provides an intuitive visual tool for clinicians to rapidly calculate an individual patient's OE risk; and (3) Actionable guidance—the risk score enables the stratification of patients into low-, intermediate-, and high-risk groups, facilitating personalized monitoring and intervention strategies. For patients identified as high-risk (e.g., predicted probability > 50%), several proactive interventions could be considered: (1) Enhanced imaging surveillance: more frequent pelvic ultrasound examinations (e.g., every 6–12 months) for early cyst detection [ 28 ] ; (2) Lifestyle modifications: an anti-inflammatory diet and regular exercise to help modulate systemic inflammation; (3) Prophylactic medication: long-term oral contraceptives for patients without immediate fertility desires to suppress ovulation and menstrual cycling [ 29 ] , or a short course of GnRH-a for those with near-term fertility plans to create a favorable window for conception; (4) Fertility preservation counseling: for young, high-risk women planning to delay childbearing, early referral for discussion of oocyte or embryo cryopreservation [ 19 ] ; and (5) Elective surgical intervention: for those with small (< 3 cm) but progressing cysts, early laparoscopic cystectomy may be considered after a multidisciplinary discussion to prevent further damage to healthy ovarian cortex [ 30 ] . 3.4 Conclusions and Future Directions This study successfully developed and internally validated a nomogram based on a history of dysmenorrhea, infertility, fibrinogen level, and lymphocyte count for predicting the risk of OE in patients with EMs. By integrating simple clinical symptoms with routine laboratory parameters, this model provides a practical and effective tool for identifying high-risk individuals, enabling risk-stratified management, and informing personalized intervention strategies. Implementing this model clinically could shift the focus toward the prevention of OE, with the ultimate goal of preserving ovarian function and improving long-term fertility outcomes for patients with EMs. This study has several limitations. First, its single-center, retrospective design is susceptible to selection and information bias, necessitating validation in large, multicenter prospective cohorts. Second, the lack of an independent external validation cohort limits the immediate generalizability of our findings to other populations and settings. Third, we could not include potentially valuable predictors like serum CA-125 levels, detailed ultrasound features, or validated pain scores due to incomplete data; future studies should explore whether these factors add incremental predictive value. Fourth, while we used absolute lymphocyte counts, more detailed functional assessments of lymphocyte subsets (e.g., CD4⁺, CD8⁺ T cells, Tregs) could provide deeper mechanistic insights, though their routine clinical application remains limited. Finally, the cross-sectional nature of our study precludes establishing causality between the identified predictors and OE formation; prospective cohort studies are needed to confirm these associations and clarify the temporal sequence of events. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi University of Chinese Medicine (Approval No: YJS2023-048). The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The requirement for informed consent was waived by the Ethics Committee of the First Affiliated Hospital of Guangxi University of Chinese Medicine due to the retrospective nature of the study. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Natural Science Foundation of China (grant numbers 82460948 and 81960884), the Innovation Project of Guangxi Graduate Education of GXUCM (grant number YCBXJ2025030), and the Guangxi Natural Science Foundation (grant number 2023GXNSFAA026223). Author Contribution LS, LZ and LWH conceived and designed the study. LS, LZ, MFY, BR, TZY and GP collected and assembled the data. LS, LZ, HJM, HSP and XGZ analyzed and interpreted the data. LS and LZ drafted the manuscript. LWH critically revised the manuscript for important intellectual content. All authors reviewed approved the final manuscript. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Becker CM, Bokor A, Heikinheimo O, et al. ESHRE guideline: endometriosis. Hum Reprod Open. 2022;2022(2):hoac009. Chapron C, Marcellin L, Borghese B, Santulli P. Rethinking mechanisms, diagnosis and management of endometriosis. Nat Rev Endocrinol. 2019;15(11):666–82. Zondervan KT, Becker CM, Koga K, Missmer SA, Taylor RN. Viganò P. Endometriosis. Nat Rev Dis Primers. 2018;4(1):9. Sanchez AM, Viganò P, Somigliana E, Panina-Bordignon P, Vercellini P, Candiani M. The distinguishing cellular and molecular features of the endometriotic ovarian cyst: from pathophysiology to the potential endometrioma-mediated damage to the ovary. Hum Reprod Update. 2014;20(2):217–30. Cranney R, Condous G, Reid S. An update on the diagnosis, surgical management, and fertility outcomes for women with endometrioma. Acta Obstet Gynecol Scand. 2017;96(6):633–43. Taylor HS, Kotlyar AM, Flores VA. Endometriosis is a chronic systemic disease: clinical challenges and novel innovations. Lancet. 2021;397(10276):839–52. Özcan P, Varlı B, Sarıdoğan E, et al. Mechanisms of Endometrioma-Mediated Ovarian Damage: Myths and Facts. J Clin Med. 2025;14(7):2147. Zhang Y, Zhang S, Zhao Z, Wang C, Xu S, Wang F. Impact of cystectomy versus ablation for endometrioma on ovarian reserve: a systematic review and meta-analysis. Fertil Steril. 2022;118(6):1172–82. Muzii L, Galati G, Mattei G, et al. Expectant, Medical, and Surgical Management of Ovarian Endometriomas. J Clin Med. 2023;12(5):1858. Practice Committee of the American Society for Reproductive Medicine. Endometriosis and infertility: a committee opinion. Fertil Steril. 2012;98(3):591–8. Vercellini P, Viganò P, Bandini V, Buggio L, Berlanda N, Somigliana E. Association of endometriosis and adenomyosis with pregnancy and infertility. Fertil Steril. 2023;119(5):727–40. Horne AW, Missmer SA. Pathophysiology, diagnosis, and management of endometriosis. BMJ. 2022;379:e070750. Liang Y, Yang X, Lan Y, Lei L, Li Y, Wang S. Effect of Endometrioma cystectomy on cytokines of follicular fluid and IVF outcomes. J Ovarian Res. 2019;12(1):98. Lin KR, Li PX, Zhu XH, et al. Peripheral immune characteristics and subset disorder in reproductive females with endometriosis. Front Immunol. 2024;15:1431175. Wu Y, Li X, Dai Y, et al. Does Dysmenorrhea Affect Clinical Features and Long-Term Surgical Outcomes of Patients With Ovarian Endometriosis? A 12-Year Retrospective Observational Cohort Study. Front Med (Lausanne). 2022;9:905688. Griffiths MJ, Horne AW, Gibson DA, Roberts N, Saunders PTK. Endometriosis: recent advances that could accelerate diagnosis and improve care. Trends Mol Med. 2024;30(9):875–89. Saunders PTK, Horne AW. Endometriosis: Etiology, pathobiology, and therapeutic prospects. Cell. 2021;184(11):2807–24. Tan Z, Gong X, Wang CC, Zhang T, Huang J. Diminished Ovarian Reserve in Endometriosis: Insights from In Vitro, In Vivo, and Human Studies-A Systematic Review. Int J Mol Sci. 2023;24(21):15967. La Marca A, Semprini M, Mastellari E, et al. Fertility preservation in women with endometriosis. Hum Reprod Open. 2025;2025(2):hoaf012. Bonavina G, Taylor HS. Endometriosis-associated infertility: From pathophysiology to tailored treatment. Front Endocrinol (Lausanne). 2022;13:1020827. Wang L, Ling J, Zhu X, et al. The coagulation status in women of endometriosis with stage IV. BMC Womens Health. 2024;24(1):386. Fini E, Argento FR, Borghi S, et al. Fibrinogen Structural Changes and Their Potential Role in Endometriosis-Related Thrombosis. Antioxid (Basel). 2024;13(12):1456. Kobayashi H, Imanaka S, Yoshimoto C, Matsubara S, Shigetomi H. Rethinking the pathogenesis of endometriosis: Complex interactions of genomic, epigenetic, and environmental factors. J Obstet Gynaecol Res. 2024;50(10):1771–84. Kedzierska AE, Lorek D, Slawek A, et al. IL-24 producing regulatory T and B lymphocytes in endometriosis. Front Immunol. 2025;16:1582762. Sobstyl A, Mertowska P, Mertowski S, et al. Expression of Toll-like Receptors on Lymphocyte Subpopulations and Their Soluble Forms in Serum and Urine of Women with Endometriosis. Cells. 2025;14(16):1273. Symons LK, Miller JE, Kay VR, et al. The Immunopathophysiology of Endometriosis. Trends Mol Med. 2018;24(9):748–62. Gu Z, Li X, Shi J, et al. The Development of Predictive Nomogram of Recurrence for Patients With Endometrioma After Cystectomy Who Were Younger Than 45 Years Old and Received Postoperative Therapy. Front Med (Lausanne). 2022;9:872481. Allaire C, Bedaiwy MA, Yong PJ. Diagnosis and management of endometriosis. CMAJ. 2023;195(10):E363–71. Yan H, Shi J, Li X, et al. Oral gonadotropin-releasing hormone antagonists for treating endometriosis-associated pain: a systematic review and network meta-analysis. Fertil Steril. 2022;118(6):1102–16. Zhang C, Li X, Dai Y, et al. Risk factors associated with changes in serum anti-Müllerian hormone levels before and after laparoscopic cystectomy for endometrioma. Front Endocrinol (Lausanne). 2024;15:1359649. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Jun, 2026 Reviews received at journal 26 Jun, 2026 Reviews received at journal 25 Jun, 2026 Reviews received at journal 24 Jun, 2026 Reviewers agreed at journal 24 Jun, 2026 Reviews received at journal 23 Jun, 2026 Reviewers agreed at journal 23 Jun, 2026 Reviewers agreed at journal 23 Jun, 2026 Reviews received at journal 21 Jun, 2026 Reviewers agreed at journal 21 Jun, 2026 Reviewers agreed at journal 19 Jun, 2026 Reviewers agreed at journal 18 Jun, 2026 Reviewers invited by journal 18 Jun, 2026 Editor invited by journal 08 Jun, 2026 Editor assigned by journal 03 Jun, 2026 Submission checks completed at journal 03 Jun, 2026 First submitted to journal 26 May, 2026 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. 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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-9823695","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":665029562,"identity":"f8ccb495-0521-4f95-a46e-e3d86952a18a","order_by":0,"name":"Li Shan","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Shan","suffix":""},{"id":665029563,"identity":"9128f878-dd68-4418-9de0-7f6ac41b0908","order_by":1,"name":"Lin Zhong","email":"","orcid":"","institution":"Guangxi Zhuang Autonomous Region Reproductive Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Zhong","suffix":""},{"id":665029564,"identity":"3038dd34-9c49-467c-9c25-696fc21b8ef1","order_by":2,"name":"Li Weihong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYBACPmYgkdgAYh4EkQcYGNgbGx9+wKOFDVMLz+FmYwl8WkAEYwOcD9Qikd4mwINPCzuP4YOHO2zy5B0PN3748OeOnMHNh20MEgx2croNOLQw8xgbJJ5JKzY8cLBZcmbbM2OD24ltDwoYko3NDuDSwrtNIrHtcOLGhoMN0rwNhxM33E5sN5BgOJC4DbeW7T+gWpp/8/wBarl5sE2CB7+WbQwgLfMZDrZJ87ABtdxgJKSF/zPQYWmJG4BaLGe2HTaWPJMIDGQD3H7h5z+W+PFnm03i/BnHH9/48OewHN/x4w8ffqiwk8OlBQ4MbqCoMCCgHATk+xuIUDUKRsEoGAUjEgAAfx1qUkM0AiEAAAAASUVORK5CYII=","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Weihong","suffix":""},{"id":665029565,"identity":"e15fc611-3ca3-437d-a535-3e3ba888e301","order_by":3,"name":"Meng Fengyun","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Fengyun","suffix":""},{"id":665029566,"identity":"d9eb357c-aa82-4dca-8f3c-09ba8a57488c","order_by":4,"name":"Bai Rui","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bai","middleName":"","lastName":"Rui","suffix":""},{"id":665029568,"identity":"d2f04281-7cd6-4ebf-8594-8851e393576c","order_by":5,"name":"Tang Zhenyu","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tang","middleName":"","lastName":"Zhenyu","suffix":""},{"id":665029569,"identity":"f14e838e-b67d-4162-90e9-faab3dd15f64","order_by":6,"name":"Guo Pei","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Guo","middleName":"","lastName":"Pei","suffix":""},{"id":665029571,"identity":"9b2df516-c388-4cd9-8070-28443a9654e1","order_by":7,"name":"Huang Junming","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Huang","middleName":"","lastName":"Junming","suffix":""},{"id":665029572,"identity":"ed8ff2c5-932d-4da2-bfa8-8af91bc043b5","order_by":8,"name":"Huang Shuping","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Huang","middleName":"","lastName":"Shuping","suffix":""},{"id":665029573,"identity":"a1c2e8eb-44c3-484b-bff1-0be15cd72bb1","order_by":9,"name":"Xie Ganzhen","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xie","middleName":"","lastName":"Ganzhen","suffix":""}],"badges":[],"createdAt":"2026-05-26 09:39:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9823695/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9823695/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":113406319,"identity":"37073b14-2e13-4f2e-8c2d-43e346279eca","added_by":"auto","created_at":"2026-06-30 03:40:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLASSO regression for variable selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: \u003c/strong\u003e(A) Cross-validation plot for tuning parameter λ selection. The left dashed line indicates λ.min, and the right dashed line indicates λ.1se. (B) LASSO coefficient path plot. The vertical line corresponds to λ.1se, where nine variables with non-zero coefficients were selected.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9823695/v1/0d6511abd67898ca98239968.jpg"},{"id":113258040,"identity":"68e8b5fb-9dae-42f2-9731-bc22b67b2fe1","added_by":"auto","created_at":"2026-06-28 16:53:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of multivariate analysis for ovarian endometrioma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Odds ratios (OR) and 95% confidence intervals (CI) are shown for each predictor. Dysmenorrhea, infertility, fibrinogen, and lymphocyte count were identified as independent predictors.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9823695/v1/8dba12826c57c487fa0d9c3e.jpg"},{"id":113257932,"identity":"10956462-d68c-4730-ba14-58542ce8e176","added_by":"auto","created_at":"2026-06-28 16:51:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9927,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for predicting ovarian endometrioma risk in patients with endometriosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: To use the nomogram, locate the patient's status for each predictor, draw a vertical line upward to the \"Points\" scale to obtain the point value for each predictor, sum all points to obtain the total points, and draw a vertical line downward from the total points scale to obtain the predicted probability of OE.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9823695/v1/5189b531535270a7f13babb4.jpg"},{"id":113258135,"identity":"6c83b6cd-f5de-4053-be49-20d8aa9edcbf","added_by":"auto","created_at":"2026-06-28 16:55:26","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66401,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves of the nomogram model in the training and validation sets. (A) Training set; (B) Validation set.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9823695/v1/9a0fa858ffa216324834a3ad.jpg"},{"id":113258038,"identity":"1de740d7-b413-4db0-805f-4473e8290f61","added_by":"auto","created_at":"2026-06-28 16:53:49","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":80281,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curves of the nomogram model in the training and validation sets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e (A) Training set; (B) Validation set. Apparent=raw model calibration results; Bias-corrected=curve after 1000 bootstrap resamples; Ideal=ideal reference line.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9823695/v1/a3f578a1da50a8454c1e7c41.jpg"},{"id":113258131,"identity":"66934d5f-4ca5-4dd9-9f8e-163f53b7f9a4","added_by":"auto","created_at":"2026-06-28 16:55:19","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":54710,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curve analysis of the nomogram model in the training and validation sets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e (A) Training set; (B) Validation set. Dashed line=prediction model; solid black line=assume none have OE; solid gray line=assume all have OE.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9823695/v1/3342c9b79fd22539f82b3250.jpg"},{"id":113409638,"identity":"55630d77-f48f-458f-a9c4-32bd7016536c","added_by":"auto","created_at":"2026-06-30 05:51:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":733404,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9823695/v1/a64b2e49-6078-4b0d-be6d-d085a0706d61.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eA nomogram prediction model for ovarian endometrioma in patients with endometriosis: a retrospective study based on clinical indicators \u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eEndometriosis (EMs) is a condition characterized by the presence, growth, and infiltration of active endometrial tissue outside the uterine cavity, accompanied by recurrent bleeding and varying degrees of inflammatory response\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. As a common disease in women of reproductive age, the prevalence of EMs is approximately 5%-10%, and can reach as high as 30%-50% in women with infertility\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Based on the site of involvement and depth of infiltration, EMs can be classified into peritoneal, ovarian, and deep infiltrating subtypes, each with distinct clinical manifestations, impacts on fertility, and clinical management strategies\u003csup\u003e[1\u003c/sup\u003e].\u003c/p\u003e \u003cp\u003eOvarian endometrioma (OE), commonly known as \"chocolate cyst,\" is one of the most frequent types of EMs, accounting for approximately 17%-44% of patients with EMs\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Compared with other subtypes, OE causes the most direct and progressive damage to ovarian reserve function\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The underlying pathological mechanisms include: mass effect of the cyst compressing the normal ovarian cortex, reducing the volume of functional ovarian tissue; and leakage of free iron, reactive oxygen species, inflammatory factors, and proteolytic enzymes from the old hemorrhagic fluid within the cyst cavity into the surrounding cortex, inducing oxidative stress and fibrosis, and leading to accelerated follicular depletion\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Recent prospective studies have shown that even with expectant management, ovarian reserve indicators\u0026mdash;anti-M\u0026uuml;llerian hormone (AMH)\u0026mdash;decline in patients with OE over 6 months\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Although cystectomy can remove the lesion, the surgery itself may cause incidental loss of normal ovarian tissue, leading to further decline in postoperative AMH levels\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Therefore, the formation of OE not only marks disease progression but also signifies irreversible damage to ovarian function, representing a critical turning point affecting the long-term fertility outcomes of patients with EMs.\u003c/p\u003e \u003cp\u003eBased on the above understanding, early identification of individuals at high risk of progressing to OE within the EMs patient population, followed by proactive intervention, may delay or prevent the formation of OE, thereby preserving ovarian function and improving fertility prognosis. Potential intervention strategies for high-risk OE populations include: ① enhanced imaging surveillance (e.g., pelvic ultrasound follow-up every 6\u0026ndash;12 months); ② lifestyle interventions (anti-inflammatory diet, regular exercise); ③ prophylactic medication (oral contraceptives, progestins, or GnRH-a, individualized according to fertility needs); ④ fertility preservation counseling (oocyte or embryo cryopreservation); and ⑤ elective surgical intervention (for those with small cysts showing progression tendency) \u003csup\u003e[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The effective implementation of these strategies depends on accurate prediction of OE risk.\u003c/p\u003e \u003cp\u003eAlthough studies have explored the association of serum CA-125, neutrophil-to-lymphocyte ratio (NLR) and other indicators with OE \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, most research has focused on the diagnosis of OE or prediction of postoperative recurrence. No study has specifically developed a quantitative tool for predicting OE risk in patients with established EMs. In clinical practice, historical information such as dysmenorrhea and infertility, as well as routine laboratory tests including blood counts and coagulation function, are readily accessible at all levels of medical institutions. If these simple indicators could be used to construct a prediction model, the accessibility of screening for high-risk OE populations would be greatly enhanced. Therefore, based on data from a single-center retrospective cohort of patients with pathologically confirmed EMs, this study aimed to: ① identify independent risk factors for OE in patients with EMs; ② construct a nomogram prediction model integrating these factors; and ③ evaluate the model's discrimination, calibration, and clinical utility through internal validation, thereby providing evidence-based support for early identification and stratified management of high-risk OE populations.\u003c/p\u003e"},{"header":"1 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Study Design and Population\u003c/h2\u003e \u003cp\u003eThis was a single-center retrospective study. Using the hospital electronic medical record system, patients who were hospitalized at the First Affiliated Hospital of Guangxi University of Chinese Medicine from January 2021 to December 2025 and diagnosed with endometriosis (EMs) by laparoscopic surgery and postoperative pathology were consecutively enrolled. Based on intraoperative findings and pathological reports, patients were divided into two groups:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOvarian endometrioma group (OE group)\u003c/strong\u003e \u003cp\u003ePresence of ovarian endometrioma, defined as a cyst located within the ovarian cortex containing chocolate-like old hemorrhagic fluid, with pathological confirmation of endometrial glands and stroma in the cyst wall.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNon-ovarian endometrioma group (non-OE group)\u003c/strong\u003e \u003cp\u003ePatients with other types of EMs, including peritoneal and deep infiltrating subtypes, without OE.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInclusion criteria\u003c/strong\u003e \u003cp\u003e① age 18\u0026ndash;50 years; ② regular menstrual cycles (21\u0026ndash;35 days); ③ complete clinical medical records.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExclusion criteria\u003c/strong\u003e \u003cp\u003e① complicated by immune system diseases, hematological disorders, active infections, or malignancies; ② history of hormonal therapy (including oral contraceptives, progestins, gonadotropin-releasing hormone agonists, etc.) or anticoagulant therapy within the past month; ③ severe hepatic, renal, or cardiac insufficiency; ④ pregnancy or lactation; ⑤ missing key variables in medical records.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi University of Chinese Medicine (Approval No: YJS2023-048) with waiver of informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Data Collection\u003c/h2\u003e \u003cp\u003eThe following data were collected from the electronic medical record system:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDemographic characteristics\u003c/strong\u003e \u003cp\u003eAge, body mass index (BMI).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eClinical symptoms:History of dysmenorrhea\u003c/b\u003e: defined as periodic menstrual lower abdominal pain requiring medication or affecting daily life (yes/no);Infertility: defined as failure to conceive after \u0026ge;\u0026thinsp;1 year of regular unprotected intercourse (yes/no), including primary and secondary infertility.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLaboratory indicators\u003c/b\u003e: Fasting venous blood samples were collected on the morning after admission for testing of the following indicators:lymphocyte count (LYM), neutrophil count (NEUT), white blood cell count, hemoglobin, platelet count, etc.;Liver function: total bilirubin (TBIL), indirect bilirubin (IBIL), adenosine deaminase (ADA), alanine aminotransferase, aspartate aminotransferase, etc.;Coagulation function: fibrinogen (FIB), prothrombin time, activated partial thromboplastin time, D-dimer, etc.;Electrolytes: chloride (Cl), sodium, potassium, etc.;Inflammatory markers: C-reactive protein (CRP).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Data Grouping and Preprocessing\u003c/h2\u003e \u003cp\u003eAll 342 patients were randomly divided into training and validation sets at a 7:3 ratio. To ensure consistent distribution of outcome events between the two datasets, stratified random sampling was used: first, patients in the OE group (103 cases) were randomly assigned 7:3, yielding 72 cases in the training set and 31 cases in the validation set; then, patients in the non-OE group (239 cases) were randomly assigned 7:3, yielding 167 cases in the training set and 72 cases in the validation set. After combination, the training set comprised 239 cases (72 OE, 167 non-OE), and the validation set comprised 103 cases (31 OE, 72 non-OE).\u003c/p\u003e \u003cp\u003eContinuous variables were tested for normality using the Shapiro-Wilk test. Normally distributed data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared between groups using independent samples \u003cem\u003et\u003c/em\u003e-test; non-normally distributed data were expressed as median (interquartile range) and compared using Mann-Whitney U test. Categorical variables were expressed as number of cases (percentage) [n (%)] and compared using χ\u0026sup2; test or Fisher's exact test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Variable Selection and Model Construction\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eVariable selection\u003c/strong\u003e \u003cp\u003eIn the training set, least absolute shrinkage and selection operator (LASSO) regression was used for feature selection. The optimal penalty parameter λ was determined by 10-fold cross-validation, and variables with non-zero coefficients at the λ value corresponding to the maximum λ within one standard error of the minimum mean squared error (lambda.1se) were selected as candidate variables significantly associated with OE.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eModel construction\u003c/strong\u003e \u003cp\u003eCandidate variables selected by LASSO were included in univariate logistic regression analysis to calculate odds ratios (OR) and their 95% confidence intervals (CI). Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in univariate analysis were further entered into multivariate logistic regression analysis (forward selection method), with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant, to identify independent predictors of OE. Based on the partial regression coefficients from multivariate analysis, a nomogram prediction model was constructed using the \"rms\" package in R software.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e1.5 Model Validation and Evaluation\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eDiscrimination assessment\u003c/strong\u003e \u003cp\u003eReceiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) with its 95% confidence interval was calculated. An AUC value closer to 1 indicates better discriminatory ability of the model (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.75 is generally considered to indicate good discrimination).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCalibration assessment\u003c/strong\u003e \u003cp\u003eCalibration curves were plotted, and bootstrap resampling (1000 repetitions) was used for internal validation of the model to assess the consistency between predicted probabilities and observed outcomes. A calibration curve closer to the diagonal (ideal line) indicates better model calibration.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical utility assessment\u003c/strong\u003e \u003cp\u003eDecision curve analysis (DCA) was performed to quantify the clinical net benefit of the model at different threshold probabilities, evaluating its clinical application value. If the DCA curve shows positive net benefit across a range of threshold probabilities compared to the two extreme strategies of \"intervene in all patients\" and \"intervene in none,\" the model is considered to have positive clinical utility.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1.6 Statistical Software\u003c/h2\u003e \u003cp\u003eData analysis was performed using R software (version 4.3.1, R Foundation for Statistical Computing, Vienna, Austria) and SPSS 26.0 (IBM Corp., Armonk, NY, USA). All statistical tests were two-sided, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"2 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Baseline Characteristics of Patients\u003c/h2\u003e \u003cp\u003eA total of 342 patients with pathologically confirmed endometriosis (EMs) were included in this study, comprising 103 patients with ovarian endometrioma (OE group, 30.1%) and 239 patients with other types of EMs (non-OE group, 69.9%). The baseline clinical characteristics of the two groups are compared in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eCompared with the non-OE group, patients in the OE group had significantly higher proportions of dysmenorrhea (81.6% vs. 11.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), infertility (80.6% vs. 17.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher fibrinogen levels [3.40 (3.00, 4.27) g/L vs. 3.20 (2.82, 3.64) g/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004], and lower lymphocyte counts (1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65 vs. 1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) and total bilirubin levels [10.80 (8.35, 14.85) \u0026micro;mol/L vs. 12.50 (9.60, 16.00) \u0026micro;mol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016]. No statistically significant differences were observed in the remaining indicators between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eUsing stratified random sampling at a 7:3 ratio, all 342 patients were divided into training set (239 cases) and validation set (103 cases). The training set contained 72 OE cases (30.1%) and 167 non-OE cases (69.9%); the validation set contained 31 OE cases (30.1%) and 72 non-OE cases (69.9%). Comparison of baseline characteristics between the two sets (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) showed no statistically significant differences in any indicator (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating balanced randomization and comparability.\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 clinical characteristics of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal EMs (N\u0026thinsp;=\u0026thinsp;342)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set (N\u0026thinsp;=\u0026thinsp;239)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation Set (N\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.00 [31.00;43.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.00 [31.00;42.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.00 [30.00;44.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7874.50 [6962.00;9061.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7931.00 [7109.50;9099.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7684.00 [6650.00;9019.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.50 [40.00;44.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.50 [40.25;44.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.10 [39.20;44.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.50 [6.62;11.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.10 [7.00;11.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.60 [6.10;10.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.00 [11.00;19.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.00 [12.00;19.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.00 [11.00;21.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.00 [6.00;9.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.00 [7.00;10.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.00 [5.00;8.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003ePAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e215.00 [192.00;241.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e213.00 [193.00;238.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e217.00 [181.50;245.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.70 [67.00;73.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.90 [67.45;73.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.70 [65.10;74.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.50 [2.50;4.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.60 [2.65;4.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.30 [2.10;4.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.00 [50.00;66.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.00 [50.00;64.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.00 [48.50;71.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.00 [15.00;22.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.00 [15.00;22.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.00 [15.00;22.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA.G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.50 [1.40;1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.50 [1.40;1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.50 [1.40;1.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.10 [9.40;15.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.50 [9.60;16.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.80 [8.35;14.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.05 [26.20;30.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.30 [26.45;30.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.70 [25.45;29.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.60 [1.50;4.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.60 [1.50;4.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.50 [1.55;4.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.00 [19.00;26.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.00 [20.00;26.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.00 [16.00;25.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.00 [9.00;18.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.00 [9.00;17.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.00 [10.00;21.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120.00 [110.00;130.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123.00 [112.50;130.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118.00 [104.00;131.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEUT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.47 [3.42;6.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.30 [3.40;6.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.71 [3.51;7.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.34 [4.04;4.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.37 [4.07;4.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.32 [3.99;4.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281.50 [236.25;332.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e281.00 [238.00;336.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e284.00 [220.50;320.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.91 [5.60;8.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.70 [5.50;8.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.50 [5.90;9.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.71 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.76 (0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.59 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139.00 [137.00;140.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139.00 [137.00;140.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e139.00 [136.50;140.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.89 [3.70;4.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.90 [3.70;4.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.87 [3.70;4.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 [1.01;1.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08 [1.01;1.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09 [1.01;1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.24 [2.17;2.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.24 [2.16;2.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.24 [2.17;2.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104.00 [102.00;105.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104.00 [102.00;105.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105.00 [102.00;106.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e260.00 [226.25;303.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e260.00 [226.50;302.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e260.00 [226.50;315.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.50 [49.00;60.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.00 [50.00;60.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.00 [48.00;60.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.65 [10.03;36.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.40 [8.90;36.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.30 [12.20;36.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.24 [2.87;3.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.20 [2.82;3.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.40 [3.00;4.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105.00 [97.00;111.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105.00 [97.00;111.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105.00 [96.50;112.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.85 [15.30;16.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.80 [15.30;16.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.90 [15.30;16.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.90 [12.50;13.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.90 [12.50;13.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.90 [12.50;13.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.25 [34.60;40.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.20 [34.90;39.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.50 [33.55;40.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.dimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41 [0.28;0.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40 [0.27;0.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43 [0.30;0.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 [0.94;1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97 [0.94;1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97 [0.94;1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einfertility:\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e217 (63.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e197 (82.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (19.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125 (36.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (17.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83 (80.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCramps:\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e231 (67.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e212 (88.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (18.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111 (32.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (11.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84 (81.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Variable Selection and Identification of Independent Predictors\u003c/h2\u003e \u003cp\u003eIn the training set, LASSO regression was first performed on all variables. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, the optimal penalty parameter λ was determined by 10-fold cross-validation, and using lambda.1se as the selection criterion, nine variables with non-zero coefficients were selected: history of dysmenorrhea, infertility, lymphocyte count (LYM), chloride (Cl), neutrophil count (NEUT), total bilirubin (TBIL), indirect bilirubin (IBIL), fibrinogen (FIB), and adenosine deaminase (ADA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003eLASSO regression for variable selection\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003e(A) Cross-validation plot for tuning parameter λ selection. The left dashed line indicates λ.min, and the right dashed line indicates λ.1se. (B) LASSO coefficient path plot. The vertical line corresponds to λ.1se, where nine variables with non-zero coefficients were selected.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThese nine variables were included in univariate logistic regression analysis. The results (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed that history of dysmenorrhea (OR\u0026thinsp;=\u0026thinsp;39.38, 95%CI: 18.81\u0026ndash;88.62, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), infertility (OR\u0026thinsp;=\u0026thinsp;14.91, 95%CI: 7.83\u0026ndash;29.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FIB (OR\u0026thinsp;=\u0026thinsp;1.30, 95%CI: 1.01\u0026ndash;1.68, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041), Cl (OR\u0026thinsp;=\u0026thinsp;1.12, 95%CI: 1.02\u0026ndash;1.24, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023), LYM (OR\u0026thinsp;=\u0026thinsp;0.56, 95%CI: 0.34\u0026ndash;0.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), TBIL (OR\u0026thinsp;=\u0026thinsp;0.94, 95%CI: 0.89\u0026ndash;0.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), and IBIL (OR\u0026thinsp;=\u0026thinsp;0.90, 95%CI: 0.82\u0026ndash;0.97, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) were significantly associated with OE occurrence. NEUT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.073) and ADA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.052), although not reaching \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, were \u0026lt;\u0026thinsp;0.1 and were included in multivariate analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate logistic regression analysis of factors associated with OE in the training set\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCramps\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.673\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39376\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.385\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.38(18.81\u0026ndash;88.62)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.329\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003einfertility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.91(7.827\u0026ndash;29.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.301(1.012\u0026ndash;1.683)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.123(1.017\u0026ndash;1.243)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLYM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.556(0.341\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNEUT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.068(0.994\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.941(0.887\u0026ndash;0.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.896(0.798\u0026ndash;0.995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.897(0.823\u0026ndash;0.969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe nine variables were entered into multivariate logistic regression analysis (forward selection). The results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed that history of dysmenorrhea (OR\u0026thinsp;=\u0026thinsp;60.44, 95%CI: 20.95\u0026ndash;213.90, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), infertility (OR\u0026thinsp;=\u0026thinsp;13.10, 95%CI: 4.85\u0026ndash;40.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), elevated fibrinogen levels (OR\u0026thinsp;=\u0026thinsp;1.84, 95%CI: 1.13\u0026ndash;3.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), and decreased lymphocyte count (OR\u0026thinsp;=\u0026thinsp;0.42, 95%CI: 0.16\u0026ndash;0.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047) were independent predictors of OE in patients with EMs. The remaining variables did not reach statistical significance in multivariate analysis(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eMultivariate logistic regression analysis of factors associated with OE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.09\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.30763\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000 (1.320\u0026ndash;58718)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.882\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCramps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.44 (20.95\u0026ndash;213.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003einfertility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.09 (4.848\u0026ndash;40.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.526 (0.253\u0026ndash;0.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.897 (0.738\u0026ndash;1.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.460 (0.937\u0026ndash;2.431)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNEUT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.084 (0.924\u0026ndash;1.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLYM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.417 (0.161\u0026ndash;0.993)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.057 (0.881\u0026ndash;1.270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.839 (1.127\u0026ndash;3.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eOdds ratios (OR) and 95% confidence intervals (CI) are shown for each predictor. Dysmenorrhea, infertility, fibrinogen, and lymphocyte count were identified as independent predictors.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Construction of the Nomogram Prediction Model\u003c/h2\u003e \u003cp\u003eBased on the results of multivariate logistic regression analysis, the four independent predictors\u0026mdash;history of dysmenorrhea, infertility, fibrinogen, and lymphocyte count\u0026mdash;were included to construct a nomogram prediction model for OE risk in patients with EMs using the \"rms\" package in R software (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The nomogram is used as follows: for each patient, the value of each predictor is projected upward to the \"Points\" scale to obtain the individual score; the individual scores are summed to obtain the total score; the total score is then projected downward to the \"OE Risk\" scale to obtain the predicted probability of OE for that patient.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eTo use the nomogram, locate the patient's status for each predictor, draw a vertical line upward to the \"Points\" scale to obtain the point value for each predictor, sum all points to obtain the total points, and draw a vertical line downward from the total points scale to obtain the predicted probability of OE.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Validation of the Prediction Model\u003c/h2\u003e \u003cp\u003e \u003cb\u003eDiscrimination assessment\u003c/b\u003e: ROC curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed that the nomogram model achieved an AUC of 0.945 (95%CI: 0.916\u0026ndash;0.974) in the training set and 0.948 (95%CI: 0.912\u0026ndash;0.984) in the validation set, indicating excellent discriminatory ability to distinguish between patients with and without OE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCalibration assessment\u003c/strong\u003e \u003cp\u003eCalibration curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) demonstrated good agreement between the predicted probabilities of OE and the observed OE probabilities in both the training and validation sets. The bias-corrected curves after 1000 bootstrap resamples were closely aligned with the ideal reference line, indicating good model calibration and high predictive accuracy.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003e(A) Training set; (B) Validation set. Apparent\u0026thinsp;=\u0026thinsp;raw model calibration results; Bias-corrected=curve after 1000 bootstrap resamples; Ideal=ideal reference line.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Clinical Utility Assessment of the Model\u003c/h2\u003e \u003cp\u003eDecision curve analysis (DCA) results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) showed that the nomogram model demonstrated positive clinical net benefit across a wide range of threshold probabilities in both the training and validation sets. Compared with the two extreme strategies of \"intervene in all patients\" or \"intervene in none,\" using this model for risk stratification and decision-making provided greater clinical benefit to patients, indicating good clinical application potential.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003e(A) Training set; (B) Validation set. Dashed line=prediction model; solid black line=assume none have OE; solid gray line=assume all have OE.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Summary of Main Findings\u003c/h2\u003e \u003cp\u003eIn this study of 342 patients with pathologically confirmed endometriosis (EMs), we identified four independent predictors of ovarian endometrioma (OE) using LASSO regression combined with multivariate logistic analysis: a history of dysmenorrhea (OR\u0026thinsp;=\u0026thinsp;60.44), infertility (OR\u0026thinsp;=\u0026thinsp;13.10), elevated fibrinogen levels (OR\u0026thinsp;=\u0026thinsp;1.84), and a decreased lymphocyte count (OR\u0026thinsp;=\u0026thinsp;0.42). A nomogram integrating these factors was constructed and validated, demonstrating excellent discrimination in both the training and validation cohorts (AUC\u0026thinsp;=\u0026thinsp;0.945 and 0.948, respectively), robust calibration, and a positive clinical net benefit. This model provides a simple and practical tool for the early identification and stratified management of patients with EMs who are at high risk for developing OE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Clinical Significance and Underlying Mechanisms of the Predictors\u003c/h2\u003e \u003cp\u003eIn this study, a history of dysmenorrhea emerged as the strongest predictor of OE (OR\u0026thinsp;=\u0026thinsp;60.44), a finding consistent with previous research. Wu et al.\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, in a 12-year retrospective cohort study of 334 patients with ovarian endometriosis, reported that those with dysmenorrhea had significantly higher rates of deep infiltrating endometriosis (DIE) and adenomyosis, higher infertility rates, more advanced rAFS scores, and increased postoperative recurrence. This suggests that dysmenorrhea is not merely a clinical symptom but may be a marker of a more aggressive disease phenotype.\u003c/p\u003e \u003cp\u003eThe mechanisms linking dysmenorrhea to an increased risk of OE are likely multifactorial. First, severe dysmenorrhea is often associated with hyperperistalsis and dysperistalsis of the uterus, which can significantly promote retrograde menstruation and increase the likelihood of endometrial tissue implantation on the ovarian cortex\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Second, the intense local inflammatory response associated with dysmenorrhea, characterized by the release of prostaglandins and nerve growth factors, may create a favorable microenvironment for the survival and proliferation of ectopic endometrial lesions\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Third, patients with severe dysmenorrhea often present with more extensive pelvic pathology, such as DIE and dense adhesions, which can distort the pelvic anatomy and further facilitate OE formation\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Therefore, the clinical significance of dysmenorrhea as the strongest predictor in our model is well-founded.\u003c/p\u003e \u003cp\u003eInfertility was identified as the second strongest predictor of OE (OR\u0026thinsp;=\u0026thinsp;13.10), underscoring a close and bidirectional relationship. On one hand, OE directly compromises ovarian function and diminishes fertility. Tan et al.\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e described how endometriomas accelerate follicular depletion through mass effect, oxidative stress from iron and reactive oxygen species in the cyst fluid, and the infiltration of surrounding tissues by local inflammatory factors. Rangi et al.\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e quantified this damage, reporting that serum anti-M\u0026uuml;llerian hormone (AMH) levels decrease by 30% after unilateral and up to 44% after bilateral OE cystectomy. On the other hand, infertility may also reflect the common pathological basis for OE, namely a disturbed pelvic immune microenvironment and a chronic inflammatory state. As noted by Bonavina et al.\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, this inflammatory environment can interfere with folliculogenesis, oocyte quality, fertilization, and implantation. These same factors can also promote the survival of ectopic endometrial cells and the subsequent formation of OE. Thus, the inclusion of infertility in our model captures both a consequence and a contributing cause of OE.\u003c/p\u003e \u003cp\u003eElevated fibrinogen levels were identified as an independent predictor of OE (OR\u0026thinsp;=\u0026thinsp;1.84), aligning with recent evidence of a hypercoagulable state in patients with EMs. Wang et al.\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e demonstrated that patients with stage IV endometriosis have significantly elevated plasma fibrinogen levels, which were positively correlated with the neutrophil-to-lymphocyte ratio (NLR), suggesting an interactive activation between the inflammatory and coagulation systems. Fini et al.\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e provided a molecular basis for this observation, showing that increased oxidative stress in EMs induces structural changes in fibrinogen, impairing its polymerization while enhancing its resistance to plasmin, thereby creating a prothrombotic state.\u003c/p\u003e \u003cp\u003eSeveral mechanisms may explain how fibrinogen promotes OE formation: (1) As an acute-phase reactant, elevated fibrinogen reflects a systemic inflammatory state conducive to the implantation and growth of ectopic endometrium\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e; (2) A hypercoagulable state may facilitate the adhesion and implantation of endometrial fragments on the ovarian surface and support established lesions through enhanced angiogenesis \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e; and (3) Fibrinogen and its degradation products can promote cell migration and invasion via integrin signaling pathways, contributing to the progression of OE\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Therefore, elevated fibrinogen in patients with EMs may signify the activation of an inflammation-coagulation axis that actively promotes OE pathogenesis.\u003c/p\u003e \u003cp\u003eA decreased lymphocyte count was found to be a protective factor for OE (OR\u0026thinsp;=\u0026thinsp;0.42, meaning a higher count is protective), highlighting the critical role of immune surveillance. Recent work by Kedzierska et al.\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e showed significantly increased proportions of IL-24-producing regulatory T cells (Tregs) and plasmablasts in patients with EMs, which may enhance immunosuppressive activity and help ectopic endometrium evade immune clearance. Sobstyl et al.\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e found increased expression of Toll-like receptors (TLR) on peripheral blood lymphocyte subpopulations in EMs patients, suggesting aberrant activation of the innate immune system.\u003c/p\u003e \u003cp\u003eTaken together, a decreased total lymphocyte count may reflect several underlying issues: (1) an imbalance in specific lymphocyte subpopulations (e.g., Tregs) leading to an overall quantitative decrease that masks a qualitative functional disturbance\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e; (2) increased lymphocyte apoptosis or abnormal redistribution driven by chronic inflammation; and (3) an overall state of lymphopenia that impairs effective immune surveillance, allowing ectopic endometrial cells to colonize the ovarian cortex and form cysts\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Consequently, the routine lymphocyte count can serve as a simple, cost-effective screening indicator for OE risk, with a decreased count warranting closer clinical monitoring.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Clinical Value and Potential Applications of the Prediction Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe nomogram developed in this study demonstrated excellent discrimination (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.94), outperforming previously published models for predicting EMs recurrence or diagnosis\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Its strong calibration and positive net benefit across a wide range of threshold probabilities, as confirmed by DCA, underscore its significant clinical potential.\u003c/p\u003e \u003cp\u003eThis model offers several practical advantages: (1) Ease of use and accessibility\u0026mdash;the predictors are simple clinical data (dysmenorrhea and infertility history) and routine laboratory tests (fibrinogen and lymphocyte count), available at essentially no additional cost in any clinical setting; (2) Interpretability\u0026mdash;the nomogram format provides an intuitive visual tool for clinicians to rapidly calculate an individual patient's OE risk; and (3) Actionable guidance\u0026mdash;the risk score enables the stratification of patients into low-, intermediate-, and high-risk groups, facilitating personalized monitoring and intervention strategies.\u003c/p\u003e \u003cp\u003eFor patients identified as high-risk (e.g., predicted probability\u0026thinsp;\u0026gt;\u0026thinsp;50%), several proactive interventions could be considered: (1) Enhanced imaging surveillance: more frequent pelvic ultrasound examinations (e.g., every 6\u0026ndash;12 months) for early cyst detection\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e; (2) Lifestyle modifications: an anti-inflammatory diet and regular exercise to help modulate systemic inflammation; (3) Prophylactic medication: long-term oral contraceptives for patients without immediate fertility desires to suppress ovulation and menstrual cycling\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, or a short course of GnRH-a for those with near-term fertility plans to create a favorable window for conception; (4) Fertility preservation counseling: for young, high-risk women planning to delay childbearing, early referral for discussion of oocyte or embryo cryopreservation\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e; and (5) Elective surgical intervention: for those with small (\u0026lt;\u0026thinsp;3 cm) but progressing cysts, early laparoscopic cystectomy may be considered after a multidisciplinary discussion to prevent further damage to healthy ovarian cortex\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Conclusions and Future Directions\u003c/h2\u003e \u003cp\u003eThis study successfully developed and internally validated a nomogram based on a history of dysmenorrhea, infertility, fibrinogen level, and lymphocyte count for predicting the risk of OE in patients with EMs. By integrating simple clinical symptoms with routine laboratory parameters, this model provides a practical and effective tool for identifying high-risk individuals, enabling risk-stratified management, and informing personalized intervention strategies. Implementing this model clinically could shift the focus toward the prevention of OE, with the ultimate goal of preserving ovarian function and improving long-term fertility outcomes for patients with EMs.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, its single-center, retrospective design is susceptible to selection and information bias, necessitating validation in large, multicenter prospective cohorts. Second, the lack of an independent external validation cohort limits the immediate generalizability of our findings to other populations and settings. Third, we could not include potentially valuable predictors like serum CA-125 levels, detailed ultrasound features, or validated pain scores due to incomplete data; future studies should explore whether these factors add incremental predictive value. Fourth, while we used absolute lymphocyte counts, more detailed functional assessments of lymphocyte subsets (e.g., CD4⁺, CD8⁺ T cells, Tregs) could provide deeper mechanistic insights, though their routine clinical application remains limited. Finally, the cross-sectional nature of our study precludes establishing causality between the identified predictors and OE formation; prospective cohort studies are needed to confirm these associations and clarify the temporal sequence of events.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi University of Chinese Medicine (Approval No: YJS2023-048). The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The requirement for informed consent was waived by the Ethics Committee of the First Affiliated Hospital of Guangxi University of Chinese Medicine due to the retrospective nature of the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (grant numbers 82460948 and 81960884), the Innovation Project of Guangxi Graduate Education of GXUCM (grant number YCBXJ2025030), and the Guangxi Natural Science Foundation (grant number 2023GXNSFAA026223).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLS, LZ and LWH conceived and designed the study. LS, LZ, MFY, BR, TZY and GP collected and assembled the data. LS, LZ, HJM, HSP and XGZ analyzed and interpreted the data. LS and LZ drafted the manuscript. LWH critically revised the manuscript for important intellectual content. All authors reviewed approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBecker CM, Bokor A, Heikinheimo O, et al. ESHRE guideline: endometriosis. Hum Reprod Open. 2022;2022(2):hoac009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapron C, Marcellin L, Borghese B, Santulli P. Rethinking mechanisms, diagnosis and management of endometriosis. Nat Rev Endocrinol. 2019;15(11):666\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZondervan KT, Becker CM, Koga K, Missmer SA, Taylor RN. Vigan\u0026ograve; P. Endometriosis. Nat Rev Dis Primers. 2018;4(1):9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanchez AM, Vigan\u0026ograve; P, Somigliana E, Panina-Bordignon P, Vercellini P, Candiani M. The distinguishing cellular and molecular features of the endometriotic ovarian cyst: from pathophysiology to the potential endometrioma-mediated damage to the ovary. Hum Reprod Update. 2014;20(2):217\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCranney R, Condous G, Reid S. An update on the diagnosis, surgical management, and fertility outcomes for women with endometrioma. Acta Obstet Gynecol Scand. 2017;96(6):633\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor HS, Kotlyar AM, Flores VA. Endometriosis is a chronic systemic disease: clinical challenges and novel innovations. Lancet. 2021;397(10276):839\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ouml;zcan P, Varlı B, Sarıdoğan E, et al. Mechanisms of Endometrioma-Mediated Ovarian Damage: Myths and Facts. J Clin Med. 2025;14(7):2147.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Zhang S, Zhao Z, Wang C, Xu S, Wang F. Impact of cystectomy versus ablation for endometrioma on ovarian reserve: a systematic review and meta-analysis. Fertil Steril. 2022;118(6):1172\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuzii L, Galati G, Mattei G, et al. Expectant, Medical, and Surgical Management of Ovarian Endometriomas. J Clin Med. 2023;12(5):1858.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePractice Committee of the American Society for Reproductive Medicine. Endometriosis and infertility: a committee opinion. Fertil Steril. 2012;98(3):591\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVercellini P, Vigan\u0026ograve; P, Bandini V, Buggio L, Berlanda N, Somigliana E. Association of endometriosis and adenomyosis with pregnancy and infertility. Fertil Steril. 2023;119(5):727\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorne AW, Missmer SA. Pathophysiology, diagnosis, and management of endometriosis. BMJ. 2022;379:e070750.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang Y, Yang X, Lan Y, Lei L, Li Y, Wang S. Effect of Endometrioma cystectomy on cytokines of follicular fluid and IVF outcomes. J Ovarian Res. 2019;12(1):98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin KR, Li PX, Zhu XH, et al. Peripheral immune characteristics and subset disorder in reproductive females with endometriosis. Front Immunol. 2024;15:1431175.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Li X, Dai Y, et al. Does Dysmenorrhea Affect Clinical Features and Long-Term Surgical Outcomes of Patients With Ovarian Endometriosis? A 12-Year Retrospective Observational Cohort Study. Front Med (Lausanne). 2022;9:905688.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffiths MJ, Horne AW, Gibson DA, Roberts N, Saunders PTK. Endometriosis: recent advances that could accelerate diagnosis and improve care. Trends Mol Med. 2024;30(9):875\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaunders PTK, Horne AW. Endometriosis: Etiology, pathobiology, and therapeutic prospects. Cell. 2021;184(11):2807\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan Z, Gong X, Wang CC, Zhang T, Huang J. Diminished Ovarian Reserve in Endometriosis: Insights from In Vitro, In Vivo, and Human Studies-A Systematic Review. Int J Mol Sci. 2023;24(21):15967.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLa Marca A, Semprini M, Mastellari E, et al. Fertility preservation in women with endometriosis. Hum Reprod Open. 2025;2025(2):hoaf012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonavina G, Taylor HS. Endometriosis-associated infertility: From pathophysiology to tailored treatment. Front Endocrinol (Lausanne). 2022;13:1020827.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Ling J, Zhu X, et al. The coagulation status in women of endometriosis with stage IV. BMC Womens Health. 2024;24(1):386.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFini E, Argento FR, Borghi S, et al. Fibrinogen Structural Changes and Their Potential Role in Endometriosis-Related Thrombosis. Antioxid (Basel). 2024;13(12):1456.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobayashi H, Imanaka S, Yoshimoto C, Matsubara S, Shigetomi H. Rethinking the pathogenesis of endometriosis: Complex interactions of genomic, epigenetic, and environmental factors. J Obstet Gynaecol Res. 2024;50(10):1771\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKedzierska AE, Lorek D, Slawek A, et al. IL-24 producing regulatory T and B lymphocytes in endometriosis. Front Immunol. 2025;16:1582762.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSobstyl A, Mertowska P, Mertowski S, et al. Expression of Toll-like Receptors on Lymphocyte Subpopulations and Their Soluble Forms in Serum and Urine of Women with Endometriosis. Cells. 2025;14(16):1273.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSymons LK, Miller JE, Kay VR, et al. The Immunopathophysiology of Endometriosis. Trends Mol Med. 2018;24(9):748\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu Z, Li X, Shi J, et al. The Development of Predictive Nomogram of Recurrence for Patients With Endometrioma After Cystectomy Who Were Younger Than 45 Years Old and Received Postoperative Therapy. Front Med (Lausanne). 2022;9:872481.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllaire C, Bedaiwy MA, Yong PJ. Diagnosis and management of endometriosis. CMAJ. 2023;195(10):E363\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan H, Shi J, Li X, et al. Oral gonadotropin-releasing hormone antagonists for treating endometriosis-associated pain: a systematic review and network meta-analysis. Fertil Steril. 2022;118(6):1102\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang C, Li X, Dai Y, et al. Risk factors associated with changes in serum anti-M\u0026uuml;llerian hormone levels before and after laparoscopic cystectomy for endometrioma. Front Endocrinol (Lausanne). 2024;15:1359649.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Endometriosis, Ovarian endometrioma, Nomogram, Dysmenorrhea, Fibrinogen","lastPublishedDoi":"10.21203/rs.3.rs-9823695/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9823695/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAmong the subtypes of endometriosis (EMs), ovarian endometrioma (OE) causes the most direct and progressive damage to ovarian reserve function. This study aimed to identify independent risk factors for OE in patients with EMs, and to develop and validate a clinical prediction model to support early identification and stratified intervention in high-risk patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective study was conducted on 342 patients with pathologically confirmed EMs admitted to the First Affiliated Hospital of Guangxi University of Chinese Medicine from January 2021 to December 2025. Among them, 103 patients had OE (OE group) and 239 had other types of EMs (non-OE group). Patients were randomly divided into training and validation sets at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression was used for preliminary feature selection, followed by univariate and multivariate logistic regression analyses to identify independent predictors of OE. A nomogram prediction model was subsequently constructed. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC), calibration was evaluated using calibration curves comparing predicted probabilities with observed outcomes, and clinical utility was assessed using decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMultivariate logistic regression analysis revealed that history of dysmenorrhea (OR\u0026thinsp;=\u0026thinsp;60.44, 95%CI: 20.95\u0026ndash;213.90), infertility (OR\u0026thinsp;=\u0026thinsp;13.10, 95%CI: 4.85\u0026ndash;40.21), elevated fibrinogen levels (OR\u0026thinsp;=\u0026thinsp;1.84, 95%CI: 1.13\u0026ndash;3.01), and decreased lymphocyte count (OR\u0026thinsp;=\u0026thinsp;0.42, 95%CI: 0.16\u0026ndash;0.99) were independent predictors of OE in patients with EMs (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The nomogram model constructed based on these factors demonstrated excellent discrimination in both the training and validation sets (AUC\u0026thinsp;=\u0026thinsp;0.945 and 0.948, respectively), good calibration, and positive clinical net benefit across a wide range of threshold probabilities as shown by DCA.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study successfully developed and validated a nomogram model integrating clinical symptoms (history of dysmenorrhea, infertility) and routine laboratory indicators (fibrinogen, lymphocyte count), which can effectively predict the individualized risk of OE in patients with EMs. This model provides a simple and practical quantitative tool for clinicians to identify high-risk OE populations, implement risk-stratified management, and formulate individualized intervention strategies.\u003c/p\u003e","manuscriptTitle":"A nomogram prediction model for ovarian endometrioma in patients with endometriosis: a retrospective 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