{"paper_id":"db7511f6-289a-4ad6-983b-170f6a1da5be","body_text":"Article  1 \nValidation of an in vitro diagnostic test for endometriosis: impact of confounding medical 2 \nconditions and lesion location 3 \nElza Daoud 1, David F. Archer 2 , Bárbara Herranz-Blanco1,* 4 \n1 Chemo Research, Madrid, Spain,  5 \n2 Department of Obstetrics and Gynecology, Eastern Virginia Medical School, Norfolk, Virginia, USA. 6 \n* Correspondence: barbara.herranz@exeltis.com 7 \nAbstract: With the aim to shorten the time for diagnosis and accelerate access to correct manage- 8 \nment, a non-invasive diagnostic test for endometriosis was developed and validated. The IVD test 9 \ncombines an ELISA test kit to quantify CA125 and BDNF concentrations in serum and a data treat- 10 \nment algorithm hosted in medical software processing results from the ELISA test and responses to 11 \nsix clinical variables. Serum samples and clinical variables extracted from psychometric question- 12 \nnaires from 77 patients were collected from the Oxford Endometriosis CaRe Centre biobank (UK). 13 \nCase/control classification was performed based on laparoscopy and histological verification of the 14 \nexcised lesions. Biomarkers serum concentrations and clinical variables were introduced to the soft- 15 \nware, which generates the qualitative diagnostic result (“positive” or “negative”). This test allowed 16 \nthe detection of 32% of cases with superficial endometriosis, which is an added value given the 17 \nlimited efficacy of existing imaging techniques. Even in the presence of various confounding medi- 18 \ncal conditions, the test maintained a specificity of 100%, supporting its suitability for use in patients 19 \nwith underlying medical conditions.  20 \nKeywords: In vitro diagnostic test; endometriosis; validation; lesion location; superficial endome- 21 \ntriosis; confounding conditions. 22 \n 23 \n1. Introduction 24 \nEndometriosis is a progressive, estrogen-dependent disease that affects approxi- 25 \nmately 10% of women of reproductive age [1]. It is characterized by the presence of en- 26 \ndometrial-like tissue outside the uterus, commonly affecting the pelvic cavity, ovaries, 27 \nfallopian tubes, and other surrounding structures[2]. These lesions result in a chronic 28 \ninflammatory response, which can lead to the formation of scar tissue and adhesions[3].   29 \nThe clinical presentation of endometriosis can be very diverse, with a wide range of 30 \nsymptoms, including chronic non-menstrual pelvic pain, dysmenorrhea, dysuria, infer- 31 \ntility, and many others; with the onset of symptoms usually occurring during adoles- 32 \ncence[1,4]. The severity and manifestation of symptoms can be influenced by various 33 \nfactors, including the location and extent of the endometrial implants, hormonal fluctua- 34 \ntions, and individual pain thresholds[5,6]. Also, symptoms often overlap with those of 35 \nvarious other conditions [7,8]. Although imaging techniques such as transvaginal ultra- 36 \nsound (TVUS) and magnetic resonance imaging (MRI) have been shown to accurately 37 \ndiagnose some endometriosis cases, these are usually limited to more severe stages of 38 \nthe disease[9,10]. Laparoscopy, with or without histological confirmation, remains the 39 \ngold standard for diagnosing endometriosis, but its invasive nature contributes to diag- 40 \nnostic delays [1–3,11]. Therefore, despite its high prevalence, accurately diagnosing en- 41 \ndometriosis can be challenging, with an initial misdiagnosis in up to 65% of women and 42 \na diagnostic delay of 4-11 years [7,12].This delay hinders the identification of early 43 \n \nCopyright: © 2024 by the  authors. \nSubmitted for possible open access \npublication under the terms and \nconditions of the Creative Commons \nAttribution (CC BY) license \n(https://creativecommons.org/license\ns/by/4.0/). \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted April 19, 2024. ; https://doi.org/10.1101/2024.04.17.24305952doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n 2 of 14  \n \nstages, allowing the condition to progress, leading to increased severity, fibrosis, and 44 \npotential infertility [13,14]. Developing a non-invasive diagnostic test for endometriosis 45 \nbecomes crucial in order to obviate the delay in diagnosis [15,16].  46 \nPrior studies have delved into an extensive array of biomarkers, highlighting the 47 \ncomplexity of understanding endometriosis. CA125, a widely recognized glycoprotein, 48 \nhas been a focal point in research due to its association with various gynecological con- 49 \nditions, including endometriosis [17–19]. Despite its usefulness, the lack of specificity 50 \nand limited sensitivity as a standalone marker, along with its utility being limited to late 51 \nstages of the disease, has underscored the need for complementary biomarkers. Brain - 52 \nderived neurotrophic factor (BDNF), known for its involvement in neuroplasticity and 53 \nneuronal survival, has emerged as a promising candidate, with studies demonstrating 54 \nelevated levels in patients with endometriosis compared to healthy controls [11,20,21]. 55 \nHowever, the lack of specificity among individual biomarkers emphasizes the necessity 56 \nof a comprehensive diagnostic approach integrating multiple markers to enhance accu- 57 \nracy and reliability in endometriosis detection. 58 \nRecently, we have developed a diagnostic treatment algorithm that combines 59 \nCA125 and BDNF measurements with six pertinent clinical variables: patient's surgical 60 \nhistory related to endometriosis, the manifestation of painful periods as a leading symp- 61 \ntom for endometriosis referral, the intensity of menstrual pain during the previous cycle, 62 \nthe age at the onset of intercourse-related pain, the age at the initiation of regular pain- 63 \nkiller usage, and the age at the initial diagnosis of an ovarian cyst. CA125, BDNF, and 64 \nthe six clinical factors were integrated into the final logistic regression model, achieving 65 \nan AUC of 0.867, sensitivity of 51.5%, and specificity of 95.6% [22].  66 \nThe influence of confounding conditions on the final diagnosis of endometriosis 67 \nusing this test was challenged. This is because multiple conditions, gynecological (for 68 \ninstance, adenomyosis [23–26], pelvic inflammatory disease (PID) [27–29], uterine fi- 69 \nbroids [29–31] and ovarian cysts [29,32]) and non-gynecological (for instance, inflamma- 70 \ntory bowel disease (IBD) [33] or rheumatoid arthritis [34–36], asthma [37], anxiety and 71 \ndepression [38–40]) could affect the levels of CA125 and BDNF.  72 \nThe primary aim of this study was to validate the diagnostic performance of the test in 73 \nendometriosis patients while also discerning the specific subgroup of patients in which 74 \nthe test demonstrates superior performance. The secondary aim was to further investi- 75 \ngate how confounding conditions influence CA125 and BDNF and whether or not the 76 \nperformance of the test is affected.  77 \n2. Results 78 \n2.1. Diagnostic performance by endometriosis lesion type 79 \nOne hundred percent of controls from the validation dataset were correctly diagnosed 80 \n(negative) with the IVD test, based on the threshold established in the development da- 81 \ntaset. With this, a sensitivity (after weighing for disease stages) of 46.2% (95% CI: 25.5 - 82 \n66.8%) and a specificity of 100% (95% CI: 86.7-100%) was obtained. The accuracy was 83 \n64.1% (95% CI: 50.4-77.8%) and the AUC was 0.758 (95% CI: 0.650-0.867). To understand 84 \nin which subgroup of endometriosis patients the test works best, i.e., is capable of detect- 85 \ning the highest number of cases, patients were separated in subgroups by lesion types. 86 \nFirst, the association between the stages of endometriosis and the types of endometriosis 87 \nlesions was examined using Pearson's chi-squared test. The analysis revealed a signifi- 88 \ncant association (χ² = 765.76, df = 25, p < 0.001), indicating a strong relationship between 89 \nthe rASRM stages classification of endometriosis and classification by types of lesions. 90 \nThe contingency table (Table 1) provides insight on how lesion types are distributed by 91 \nendometriosis rASRM stage for patients of pooled development and validation datasets. 92 \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted April 19, 2024. ; https://doi.org/10.1101/2024.04.17.24305952doi: medRxiv preprint \n\n 3 of 14  \n \nSuperficial lesions are observed mostly in stage I (81.5%). Extended lesions (endometri- 93 \noma+DIE) are as expected mostly observed in stage IV. 94 \n 95 \nrASRM stage \nlesion type \nStage I  \n(n=81) \nStage II \n(n=30) \nStage III \n(n=34) \nStage IV \n(n=41) \nUnclassified \n(n=2) \nC-section 0 0 0 0 2 \nDIE 7 17 7 9 0 \nEndometrioma 5 0 16 7 0 \nEndometrioma \n+ DIE \n0 3 10 20 0 \nSuperficial 66 10 1 2 0 \nUnclassified 3 0 0 3 0 \n 96 \nTable 1. Contingency table for the distribution of lesion types by endometriosis rASRM stages. 97 \n 98 \nSensitivity was investigated by lesion type. Results (as reported in table 2) indicate that 99 \nthe IVD test successfully identified around half of the cases of DIE and endometrioma. 100 \nFurthermore, with a sensitivity of 69.70%, the IVD test demonstrate that the test works 101 \nbest in identifying cases of DIE+endometrioma. Interestingly, 32% of cases of superficial 102 \nendometriosis were correctly identified with the test. As expected, the two cases of endo- 103 \nmetriosis located within c -section scars could not be identified with the test (different 104 \npathophysiology, as described above). 105 \n 106 \n 107 \nTable 2. Distribution of cases, number of true positive and sensitivity by lesion type in both devel- 108 \nopment and validation datasets. 109 \n 110 \nAn ANOVA was conducted to examine the differences in CA125 values among various 111 \ntypes of endometriosis lesions in the pooled datasets (development and validation da- 112 \ntasets, figure 1 ). The results revealed a significant effect of lesion type on CA125 levels 113 \n(F(5, 275) = 26.162, p < 0.001). Post hoc analyses indicated that the differences were statis- 114 \ntically significant (p < 0.001) across the various lesion types. The Tukey multiple compar- 115 \nison of means at a 95% family -wise confidence level revealed several significant differ- 116 \nences between the types of lesions in terms of CA125 levels: comparing endometrioma to 117 \nDIE, there was a statistically significant difference (p<0.01). Additionally, the mean CA125 118 \nlevel (56.05 IU/mL, SD=39.35) were higher for endometrioma than for DIE (32.28 IU/mL, 119 \nSD=32.69) (p=0.01). Moreover, the mean CA125 level for endometrioma + DIE (67.69 120 \nGynecological \n Condition \nNumber of controls in \ndevelopment data \n(n=68) \nNumber of  \ncontrols in valida-\ntion data (n=25) \nTotal number of \ncontrols \n(n=93) \nOvarian cysts 28 11 39 \nUterine fibroids 7 3 10 \nAdenomyosis 0 1 1 \nPCOS 16 8 24 \nPelvic inflammatory \ndisease 4 2 6 \nAt least one condition 40 19 55 \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted April 19, 2024. ; https://doi.org/10.1101/2024.04.17.24305952doi: medRxiv preprint \n\n 4 of 14  \n \nIU/mL, SD=45.49) was higher than the mean CA125 level for DIE (mean=32.28 IU/mL, 121 \nSD=32.69) (p<0.001). Lower CA125 levels were observed for superficial lesions 122 \n(mean=19.55 IU/mL, SD=24.74)   than for endometrioma (p<0.001), DIE (p=0.02) and endo- 123 \nmetrioma + DIE (p<0.001). An ANOVA conducted on BDNF values across different lesion 124 \ntypes did not show any significant differences of BDNF across different types of lesions 125 \n(p=0.094). This suggests that the improved sensitivity for DIE+endometrioma lesions is 126 \nlikely to be due to higher levels of CA125 in those lesions, contributing to a higher rate of 127 \ntrue positive results in cases with those lesions.   128 \n 129 \n 130 \nFigure 1. Comparison of CA125 levels between lesion types. 131 \n2.2. Interference of potentially confounding medical conditions 132 \nAs shown in table 3, despite 76% (19 out of 25 controls) of controls in the validation da- 133 \ntaset having at least one condition that could elevate CA125, the specificity of the diag- 134 \nnostic test was 100%. 135 \n 136 \nGynecological Condi-\ntion \nNumber of controls \nin development \ndata (n=68) \nNumber of controls in \nvalidation data (n=25) \nTotal number of \ncontrols \n(n=93) \nOvarian cysts 28 11 39 \nUterine fibroids 7 3 10 \nAdenomyosis 0 1 1 \nPCOS 16 8 24 \nPelvic inflammatory \ndisease 4 2 6 \nAt least one condition 40 19 55 \n 137 \nTable 3. Distribution of gynecological conditions known to elevate CA125 across controls. 138 \n 139 \nTwo-way ANOVA with EndoState (Cases/controls) and each confounding condition as 140 \npredictors was run on CA125 levels in the pooled datasets. For ovarian cysts, the ANOVA 141 \nrevealed the main effect of EndoState (F = 32.97, p<0.001) and Ovarian cyst condition (F = 142 \n22.65, p<0.001) on CA125 levels. Individuals with ovarian cysts had higher CA125 values 143 \nthan individuals without ovarian cysts (p<0.001). No interaction between both predictors 144 \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted April 19, 2024. ; https://doi.org/10.1101/2024.04.17.24305952doi: medRxiv preprint \n\n 5 of 14  \n \nwas reported. For uterine fibroids (UF), a main effect for condition on CA125 was ob- 145 \nserved (F=11.22, p<0.001) as well as an expected main effect for EndoState (F=15.30, 146 \np<0.001). No interaction between both predictors was reported. Individuals with uterine 147 \nfibroids had higher CA125 values than individuals without uterine fibroids (p<0.001).  148 \nTwo-way analysis of variance (ANOVA) with EndoState (Cases/controls) and each con- 149 \nfounding condition as predictors was run on BDNF in pooled datasets. For Chronic fa- 150 \ntigue only, a main effect was observed for EndoState (F=5.75, p=0.017) and an interaction 151 \nbetween EndoState and the condition (F=4.20, p=0.04). Pairwise comparisons revealed 152 \nthat cases without chronic fatigue have higher BDNF values than controls with chronic 153 \nfatigue (mean difference=6.06, p=0.04). 154 \nThe performance of the diagnostic test was determined in the validation dataset exclud- 155 \ning each confounding condition at a time. Results, as shown in Table 4, indicate that the 156 \nsensitivity values when excluding conditions stay within the 95% CI of the original sen- 157 \nsitivity (all conditions included) between 34.3 and 62.9, meaning that no condition criti- 158 \ncally affects the ability of the test of detecting cases.  159 \n 160 \n 161 \nTable 4. Performance of the IVD test (validation dataset) excluding each medical condi- 162 \ntion at a time. 163 \n 164 \n 165 \nLeft out condition Number of subjects by \ncondition \nSensi-\ntivity \n95% CI lower \nlimit \n95% CI upper \nlimit \nAll (no data left out) 0 48,5 34,3 62,9 \nOvarian cyst 27 50,8 34,1 67,4 \nUterine fibroids 7 45,8 31,3 61 \nAdenomyosis 2 46,1 32 60,8 \nInflammatory Bowel Disease 2 46,1 32 60,8 \nDepression requiring medication or ther-\napy \n29 34,3 18,9 53,4 \nAnxiety requiring medication or therapy 20 37,9 22,6 55,8 \nPelvic Inflammatory Disease 6 49,8 35,8 63,9 \nEczema 16 55,6 39,6 70,5 \nPolycystic Ovary Syndrome 17 48,6 33,1 64,3 \nInterstitial cystitis 7 48,9 33,9 64 \nAsthma 23 48,8 32,1 65,7 \nChronic fatigue syndrome - Myalgic en-\ncephalomyelitis \n1 47,6 33,4 62,2 \nFibromyalgia 1 47,6 33,4 62,2 \nIrritable Bowel Syndrome  17 42,7 27,2 59,6 \nMigraine 22 42 26 59,6 \nGlandular fever 5 47,9 33,5 62,6 \nUlcerative colitis 2 46,1 32 60,8 \nHigh blood pressure 4 47,9 33,5 62,6 \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted April 19, 2024. ; https://doi.org/10.1101/2024.04.17.24305952doi: medRxiv preprint \n\n 6 of 14  \n \n3. Discussion 166 \nThe newly developed test for endometriosis demonstrates a high specificity of 100%, 167 \nsuggesting its potential use as a rule -in test in clinical practice. This diagnostic test could 168 \nsignificantly contribute to the initial diagnostic workup, effectively confirming the pres- 169 \nence of endometriosis and providing clinicians with a reliable tool for early detection and 170 \nintervention. Moreover, the test demonstrates an encouraging ability to identify superfi- 171 \ncial lesions of endometriosis, as evidenced by the reported sensitivity of 32 %. This feature 172 \nis of particular significance considering the constraints associated with the ability of alter- 173 \nnative diagnostic methods to detect superficial lesions: superficial endometriosis, charac- 174 \nterized by its subtle and less invasive nature, presents unique challenges for detection 175 \nusing ultrasound or MRI. Peritoneal implants invading less than 5 mm of depth from the 176 \nperitoneal surface are often invisible on MRI [10]. These imaging techniques may struggle 177 \nto capture the nuanced characteristics of these lesions due to their limited ability to visu- 178 \nalize subtle changes in the peritoneum and pelvic surfaces [42]. Additionally, the lack of 179 \nspecific imaging markers or distinguishing features that differentiate superficial lesions 180 \nfrom surrounding healthy tissue makes it difficult to accurately identify these lesions us- 181 \ning standard imaging modalities. The intricate anatomical location of superficial lesions, 182 \noften nestled within complex pelvic structures, further contributes to the complexity of 183 \ntheir detection, as these areas may be challenging to access and visualize accurately using 184 \ntraditional imaging approaches [43]. By enabling the identification of superficial lesions, 185 \nthe test offers clinicians an essential means of identifying cases that would otherwise have 186 \ngone undetected, thereby facilitating a more comprehensive and accurate patient man- 187 \nagement.  188 \nAlso, the test demonstrated a relatively high sensitivity of 69.70% in detecting endo- 189 \nmetrioma+ DIE lesions, possibly correlated to patients with those lesions having the high- 190 \nest level of CA125 compared to other types of lesions. Endometrioma, an endometriosis - 191 \nrelated ovarian cyst, often exhibits elevated CA125 levels due to its involvement of the 192 \novaries and resulting inflammatory processes. The higher mean CA125 level observed in 193 \nthis group aligns with prior studies [44]. The observed higher mean CA125 level in the 194 \nendometrioma + DIE lesions compared to the DIE alone, along with the lowest CA125 195 \nlevels in the superficial endometriosis, suggest that CA125 expression increases with the 196 \nextent of the disease (i.e., the extent of tissue involvement and disease spread).  197 \nEven in the presence of various confounding medical conditions, the test maintains 198 \nits robustness and reliability, emphasizing its independence from potential confounding 199 \nfactors with 100% of the controls being negative. This characteristic supports its suitability 200 \nfor use in various clinical settings, irrespective of the patient's medical history, thereby 201 \nensuring its applicability without contraindications. 202 \n4. Materials and Methods 203 \n4.1. Patients’ characteristics and classification 204 \nThe current report is a prospective analysis study using biobank samples. A total of 205 \n281 samples extracted from the renowned Oxford Endometriosis CaRe Centre biobank in 206 \nthe UK were included for the development (I) and external validation (II) studies. The 207 \nbiobank's repository comprised meticulously curated serum samples and comprehensive 208 \nclinical information derived from pre-surgical assessments and post-operative procedures 209 \nof patients within reproductive age (18–50 years old) undergoing laparoscopy because of 210 \na suspicion of endometriosis. Patients were classified as cases or controls based on lapa- 211 \nroscopy and thorough evaluation of histological findings. After undergoing laparoscopy, 212 \npatients diagnosed with endometriosis were categorized into stages according to the re- 213 \nvised American Society of Reproductive Medicine (rASRM) classification. Patients who 214 \nhad not used hormones in the 3 months prior to surgery were selected.  215 \n136 endometriosis cases and 68 controls were included in the development study 216 \n(n=204). For the validation study (n=77), 52 cases and 25 controls were included. The 217 \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted April 19, 2024. ; https://doi.org/10.1101/2024.04.17.24305952doi: medRxiv preprint \n\n 7 of 14  \n \ndemographic characteristics of those patients are available in Table 5. The experimental 218 \nprocedures received approval from the Ethics Committee of CEIm HM Hospitales (codes: 219 \n19.05.1411-GHM and 22.03.2001-GHM). 220 \n 221 \n Development study (I) Validation study (II) \n Controls \nN=68 \nCases \nN=136 \nControls \nN=25 \nCases \nN=52 \nAge years (mean ± SD) 33.5 (5.96) 35.6 (6.42) 35 (6.44) 35 (6.47) \nBMI (mean ± SD) 25.38 (4.63) 26.46 (5.32) 26 (5.23) 26 (5.14) \nrASRM classification \nI–II \nIII–IV \nMissing information \n \n- \n- \n \n68 (50%) \n68 (50%) \n- \n \n- \n- \n- \n \n42 (81%) \n7 (13%) \n3 (6%) \n 222 \nTable 5. Demographic characteristics and rASRM classification of the patients in the 223 \ndevelopment (I) and validation (II) studies. 224 \n 225 \n4.2. Lesion location and subtyping  226 \nImaging findings and surgical examinations have been reported for each subject in- 227 \ncluded in the study. Endometriosis lesions were investigated by location. From these find- 228 \nings, endometriosis lesions were classified into subgroups according to their location in 229 \nthe ovaries and the peritoneal cavity: superficial (< 5 mm depth), endometrioma, and/or 230 \ndeep infiltrative endometriosis (DIE). Specifically, the designation \"superficial\" was as- 231 \nsigned when only superficial endometriosis lesions were identified in the ovaries or peri- 232 \ntoneal cavity. The classification of \"endometrioma\" was used when endometriomas were 233 \ndetected in the ovaries, either with or without accompanying superficial endometriosis. 234 \nIn cases where infiltrative lesions were observed in the peritoneal cavity, with or without 235 \nassociated superficial endometriosis, lesions were classified as \"DIE\". Moreover, the \"en- 236 \ndometrioma + DIE\" classification was assigned when both DIE and endometriomas were 237 \nfound in the peritoneal cavity, with or without superficial endometriosis. While endome- 238 \ntriosis is thought to be caused by retrograde menstruation, the most likely cause of cae- 239 \nsarean section (c-section) scar endometriosis is iatrogenic implantation. Due to this differ- 240 \nent aetiology, 2 patients with c -section scar endometriosis were misclassified as they 241 \nshould fall under a different category than endometriosis with spontaneous implantation. 242 \nThe distributions of cases of the development and validation studies by lesions type are 243 \ndescribed in Table 6. 244 \n  245 \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted April 19, 2024. ; https://doi.org/10.1101/2024.04.17.24305952doi: medRxiv preprint \n\n 8 of 14  \n \n 246 \n 247 \nEndometriosis \nClassification \n \nDevelopment study (I) \nN=136 \nValidation study (II) \n N=52 \nSuperficial 54 (39.7%) 24 (46.2%) \nEndometrioma 25 (18.4%) 3 (5.8%) \nDIE 28 (20.6%) 13 (25%) \nDIE + endometri-\noma \n25 (18.4%) 8 (15.4%) \nUnclassified 4 (2.9%) 2(3.8%) \nC-section scar 0 2 (3.8%) \n 248 \nTable 6. Classification of endometriosis cases according to lesion location. 249 \n 250 \n4.3. Confounding disease screening 251 \nPatients were asked to fill out a presurgical survey including a question to indicate 252 \nthe absence/presence of confounding medical conditions from a list. They were asked: 253 \nplease mark whether you have had any of the following medical conditions, and at what 254 \nage you were first diagnosed by a doctor (please tick all that apply)” and were given the 255 \nlist of medical conditions. Patients were also asked to indicate whether they were affected 256 \nby other unlisted medical conditions. This survey was administered to patients in one of 257 \nits 3 versions: version #1 did not list 3 medical conditions: Anxiety (1), cardiovascular dis- 258 \nease (2) and high blood pressure (3). These conditions were only listed in questionnaires 259 \n#2 and #3. Versions #2 and #3 were responded by 141 out of 190 patients included in the 260 \ndevelopment study (14 patients did not answer to this question out of 204) and 64 out of 261 \n77 patients included in the validation study. For completeness, imaging and surgical find- 262 \nings were used to further identify patients with gynaecological conditions.  263 \nTable 7 depicts the prevalence of the confounding conditions in patients included in 264 \nthe development and validation studies.  265 \n 266 \nConfounding condition Prevalence in devel-\nopment study \nPrevalence in validation \nstudy \nAnxiety requiring medication or ther-\napy \n39/141 (28%) 20/64 (31%) \nAsthma 42/190 (22%) 23/77 (30%) \nAdenomyosis 7/190 (3.7%) 2/77 (2.6%) \nCardiovascular disease 0 0 \nCrohn’s disease 0 0 \nChronic fatigue syndrome - Myalgic \nencephalomyelitis  \n10/190 (5.2%) 1/77 (1.3%) \nDepression requiring medication or \ntherapy \n68/190 (35.8%) 29/77 (38%) \nDiabetes requiring diet control 3/190 (1.6%) 0 \nDiabetes requiring insulin or tablets 1/190 (0.5%) 0 \nEczema 32/190 (16.8%) 16/77 (21%) \nUterine fibroids 28/190 (9.5%) 7/77 (9.1%) \nFibromyalgia 4/190 (2%) 1/77 (1.3%) \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted April 19, 2024. ; https://doi.org/10.1101/2024.04.17.24305952doi: medRxiv preprint \n\n 9 of 14  \n \nGlandular fever 17/190 (8.9%) 5/77 (6.5%) \nGraves’s disease 0 0 \nHashimoto’s disease 0 0 \nHigh blood pressure 9/141 (6%) 4/64 (6.2%) \nIrritable bowel syndrome 43/190 (23%) 17/77 (22%) \nInterstitial Cystitis 12/190 (62.5%) 7/77 (9%) \nMigraine 51/190 (27%) 22/77 (28.6%) \nMitral valve prolapse 2/190 (1%) 0 \nMultiple sclerosis 2/190 (1%) 0 \nOvarian cysts 93/190 (49%) 27/77 (35.1%) \nPelvic inflammatory disease 13/190 (6.84%) 6/77 (7.8%) \nPolycystic ovarian syndrome 32/190 (16.8%) 17 (22.1%) \nRheumatoid arthritis 0 0 \nSjogren’s syndrome 0 1/77 (1.3%) \nSystemic lupus erythematosus 0 0 \nThyroid disease 3/190 (1.6%) 0 \nUlcerative colitis 1/190 (0.5%) 2/77 (2.6%) \n 267 \n 268 \nTable 7. Prevalence of confounding conditions in the development and validation 269 \ndatasets. 270 \n 271 \n 272 \n 273 \n 274 \n4.4. Blood sample collection and biomarkers measurement 275 \nThe specimens were gathered and managed with explicit patient consent, following 276 \nthe guidelines outlined in the Standard Operating procedures of the World Endometriosis 277 \nResearch Foundation [41]. Before the collection of blood, patients were instructed to main- 278 \ntain a minimum fasting period of 10 hours. The serum samples were then preserved in 279 \nthe biobank at temperatures as low as -80 ºC for a duration of up to 5 years, after which 280 \nthey were transferred to the designated laboratory for analysis. The ELISA utilized in this 281 \nin vitro diagnostic test functions as a solid-phase sandwich enzyme-immunoassay for the 282 \nprecise determination of BDNF and CA125 levels within human serum [22].  283 \n 284 \n4.5. Data treatment algorithm 285 \nAll the necessary input parameters, including serum CA125, serum BDNF, and clin- 286 \nical variables were gathered. Subsequently, laboratory technicians input this data into the 287 \nIVD test diagnostic medical software, which houses the data treatment algorithm. The 288 \nalgorithm processed the input and generated outcomes, classifying them as either positive 289 \nor negative based on whether the value exceeded or fell below the predetermined thresh- 290 \nold value, respectively. 291 \n 292 \n4.6. Statistical analysis  293 \nStatistical analysis was conducted utilizing R software, version 4.1.3, provided by the 294 \nR Foundation for Statistical Computing in Vienna, Austria. The statistical significance 295 \nlevel was set at p < 0.05, indicating a threshold below which results were considered sta- 296 \ntistically significant. In the validation study, the IVD test software was utilized to compute 297 \nalgorithm scores and their corresponding outcomes. These outcomes were delineated as 298 \npositive diagnosis when the score surpassed the defined cut -off, and negative diagnosis 299 \nwhen the score fell below the defined cut -off. Specifically, the validation study's 300 \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted April 19, 2024. ; https://doi.org/10.1101/2024.04.17.24305952doi: medRxiv preprint \n\n 10 of 14  \n \nsensitivity and specificity were expected to align with or exceed the lower limits of the 301 \nsensitivity and specificity 95% confidence intervals outlined in the algorithm development 302 \nstudy: an AUC of 0.867 with a sensitivity of 51.5% (42.8 - 60.1) at a specificity of 95.6% 303 \n(86.8 - 98.9%) as reported by Herranz et al. To assess the IVD test clinical performance, the 304 \nresults of the primary performance parameters (sensitivity and specificity) were con- 305 \ntrasted with the acceptance criteria values established during the development study. To 306 \nensure equitable representation of both the low -stage and high -stage groups, the out- 307 \ncomes in the validation were appropriately weighted.  308 \nTo further elucidate the performance of the IVD test, the sensitivity for each endome- 309 \ntriosis classification, with the values specified alongside their respective 95% CI are re- 310 \nported for the distinct subgroups based on lesion types. For a more comprehensive assess- 311 \nment of test’s efficacy over a larger sample size, development and validation datasets were 312 \npooled. Analysis was run on pooled dataset. BDNF values in pooled datasets followed a 313 \nnormal distribution and CA125 values were arithmetically transformed to follow a normal 314 \ndistribution. To investigate the effect of confounding diseases on biomarkers levels and 315 \nthe performance of the test, only conditions with >1% prevalence in both datasets were 316 \nconsidered. A two -way ANOVA analysis was conducted to assess the effect of medical 317 \nconditions and EndoState (Cases/controls) on CA125 and BDNF, respectively, including 318 \nan interaction term. Only conditions showing significant main effects or interaction will 319 \nbe reported. Furthermore, the performance of the algorithm on validation data was eval- 320 \nuated after excluding each specific conditions, one at a time. 321 \n 322 \n5. Conclusions 323 \nOverall, the high specificity of the test, coupled with its independence from potential 324 \nconfounding medical conditions, position it as a valuable and reliable tool for the accurate 325 \nand timely diagnosis of endometriosis. 326 \n6. Patents 327 \nThere is a patent resulting from the work reported in this manuscript. 328 \n 329 \nAuthor Contributions: For research articles with several authors, a short paragraph specifying their 330 \nindividual contributions must be provided. The following statements should be used “Conceptual- 331 \nization, E.D. and B-H.-B; methodology, E.D. and B-H.-B; software, E.D. and B-H.-B; validation, E.D. 332 \nand B-H.-B.; formal analysis, E.D. and B-H.-B; investigation, E.D. and B-H.-B; resources, E.D and B- 333 \nH.-B.; data curation, E.D. and B-H.-B.; writing—original draft preparation, E.D.; writing—review 334 \nand editing, E.D., B.H.-B, D.A.; visualization, E.D..; supervision, B.H.; project administration, E.D. 335 \nand B-H.-B.; funding acquisition, E.D. and B -H.-B. All authors have read and agreed to the pub- 336 \nlished version of the manuscript.”  Please turn to the CRediT taxonomy for the term explanation. 337 \nAuthorship must be limited to those who have contributed substantially to the work reported. 338 \nFunding: Exeltis (represented by Chemo Research S.L.) has fully sponsored the studies. 339 \nInstitutional Review Board Statement: The study was conducted in accordance with the Declara- 340 \ntion of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of CEIm 341 \nHM Hospitales (codes: 19.05.1411-GHM and 22.03.2001-GHM and date of approval: April 12th, 2022 342 \nInformed Consent Statement: Informed consent was obtained from all subjects involved in the 343 \nstudy. The experimental procedures received approval from the Ethics Committee of CEIm HM 344 \nHospitales (codes: 19.05.1411-GHM and 22.03.2001-GHM). 345 \n 346 \nConflicts of Interest: The authors E.D. and B.H.-B. were employed by the company Exeltis (repre- 347 \nsented by Chemo Research S.L.). 348 \n 349 \n 350 \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted April 19, 2024. ; https://doi.org/10.1101/2024.04.17.24305952doi: medRxiv preprint \n\n 11 of 14  \n \nReferences 351 \n 352 \n[1] Zondervan KT, Becker CM, Missmer SA. Endometriosis. New England Journal of Medicine 353 \n2020;382:1244–56. https://doi.org/10.1056/NEJMra1810764. 354 \n[2] Bulun SE, Yilmaz BD, Sison C, Miyazaki K, Bernardi L, Liu S, et al. Endometriosis. 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No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted April 19, 2024. ; https://doi.org/10.1101/2024.04.17.24305952doi: medRxiv preprint","source_license":"CC0","license_restricted":false}