Combination of Serological Biomarkers and Clinical Features to predict Mucosal Healing in Crohn’s Disease: A Multicenter Cohort Study

Combination of Serological Biomarkers and Clinical Features to predict Mucosal Healing in Crohn’s Disease: A Multicenter Cohort Study | 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 Combination of Serological Biomarkers and Clinical Features to predict Mucosal Healing in Crohn’s Disease: A Multicenter Cohort Study Nana Tang, Han Chen, Ruidong Chen, Wen Tang, Hongjie Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-985096/v3 This work is licensed under a CC BY 4.0 License Status: Under Review Version 3 posted 8 You are reading this latest preprint version Show more versions Abstract Purpose Mucosal healing (MH) has become the treatment goal of patients with Crohn’s disease (CD). This study aims to develop a noninvasive and reliable clinical tool for individual evaluation of mucosal healing in patients with Crohn’s disease. Methods A multicenter retrospective cohort was established. Clinical and serological variables were collected. Separate risk factors were incorporated into a binary logistic regression model. A primary model and a simple model were established, respectively. The model performance was evaluated with C-index, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy. Internal validation was performed in patients with small intestinal lesions. Results A total of 348 consecutive patients diagnosed with CD who underwent endoscopic examination and review after treatment from January 2010 to June 2021 were composed in the derivation cohort, and 112 patients with small intestinal lesions were included in the validation cohort. The following variables were independently associated with the MH and were subsequently included into the primary prediction model: PLR (platelet to lymphocyte ratio), CAR (C-reactive protein to albumin ratio), ESR (erythrocyte sedimentation rate), HBI (Harvey-Bradshaw Index) score and infliximab treatment. The simple model only included factors of PLR, CAR and ESR. The primary model performed better than the simple one in C-index (87.5% vs 83.0%, p = 0.004). There was no statistical significance between these two models in sensitivity (70.43% vs 62.61%, p = 0.467), specificity (87.12% vs 80.69%, p = 0.448), PPV (72.97% vs 61.54%, p = 0.292), NPV (85.65% vs 81.39%, p = 0.614), and accuracy (81.61% vs 74.71%, p = 0.303). The primary model had good calibration and high levels of explained variation and discrimination in validation cohort. Conclusions This model can be used to predict MH in post-treatment patients with CD. It can also be used as an indication of endoscopic surveillance to evaluate mucosal healing in patients with CD after treatment. Crohn’s disease mucosal healing nomogram PLR endoscopic Figures Figure 1 Figure 2 Figure 3 Background Crohn’s disease (CD) is a life-long and progressive inflammatory disease with symptoms evolving in a remitting and relapsing manner 1 . Most patients develop bowel damage and disability including strictures, fistulas, abscesses and so on in several years after diagnosis, resulting in surgery. 2 Current therapeutic strategies in CD aim for deep and prolonged remission, with the goal of preventing complications and therefore improve prognosis as well as quality of life. 3 Treatment strategies aimed solely at resolution of clinical symptoms does not eliminate long-term bowel damage in patients with CD. 4 Mucosal healing (MH) is confirmed lead to a lower rate of relapse, hospitalization and surgical resection. 5 In recent years, MH is preferred over clinical remission as straightforward goal of clinical treatment in CD. 6 Endoscopy is still the golden standard for evaluation of disease activity. However, frequently endoscopy for disease monitoring in long-term follow-up is limited by considerations of invasiveness, high cost and patient acceptance. Alternative noninvasive methods are necessary for assessment of CD-related mucosal inflammation. 7 , 8 It is reported that clinical characteristics such as mild clinical manifestation and early introduction of biologicals associated with MH in patients with CD. 9 – 11 In addition, some systemic inflammatory markers obtained from the serological examination including neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) 12 , C-reactive protein to Albumin ratio (CAR) 13 , combination of fecal calprotectin (FC), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and Albumin (ALB) 14 have been explored as diagnostic and predictive indicators of CD. Clinically, there is still lack of accurate instrument with high sensitivity and specificity using serological parameters and clinical characteristics to predict MH in CD. In this study, we aimed to address the predictive role of serum inflammatory index and clinical features in patients with CD who diagnosed and treated in two tertiary hospitals in China. We analyzed the pre-treatment and post-treatment data individually and explored their relationship with MH after treatment in patients with CD. Subsequently, we used hematological data with or without clinical features to construct assessment models for MH prediction. Model with superior performance is recommended for clinical use. Methods Patients and Data source This was a retrospective, multi-center observational cohort study of consecutive patients with CD from Inflammatory Bowel Disease Center of The First Affiliated Hospital of Nanjing Medical University and the Second Affiliated Hospital of Soochow University, China, between 2010 and 2021. Diagnoses of CD were determined according to standard clinical, laboratory, radiological, endoscopic, and histopathologic findings. 15 Data regarding patients’ demographics, laboratory values and endoscopic characteristics were retrospectively reviewed through hospital medical database records and endoscopic image system. Harvey-Bradshaw Index (HBI) consists of five descriptors: general well-being, abdominal pain, number of liquid stools for the previous day, abdominal mass and complications. 16 Inclusion criteria were listed as follows: (1) Patients underwent at least twice endoscopic procedures and serological examination both pre-treatment and post-treatment during the study period. (2) Corticosteroids had been discontinued for more than 12 weeks. Exclusion criteria: (1) Acute or chronic infections during the inspection; (2) Previous medical history of hematologic or rheumatic autoimmune disease; (3) Acute or chronic renal failure, heart diseases, cirrhosis or cancer; (4) A previous history of taking aspirin or warfarin; (5) Missing complete blood count, ALB, ESR or CRP data. (6) Any other conditions that affect the blood routine results or inflammatory markers. The clinical, endoscopic features and laboratory data of the study population are summarized in Tables 1 and 2. Ethical approval for the study was approved by Clinical Research Ethics Committee of The First Affiliated Hospital of Nanjing Medical University (ref: 2021-SR-235), in compliance with the Declaration of Helsinki. All patients in the study gave their informed consent for reviewing their clinical data. Blood assessment and Endoscopic documentation Baseline blood values had been collected at the time of CD diagnosis when patients were admitted to hospital before administration of any treatment. Post-treatment hematology was completed within one week of the patient's endoscopic review. Venous blood specimens were drawn into sterile standard tubes containing ethylene diamine tetraacetic acid (EDTA) as an anticoagulant and evaluated within 1h after venipuncture using a Beckman Coulter UniCel DxH800 hematology analyzer. The Beckman Coulter UniCel® DxH 800 was used for analyzing ESR and routine blood markers including White Blood Cell (WBC), neutrophils (NE), monocytes (MO), lymphocytes (LY), Eosinophils (EO), Basophilic (BA), Hemoglobin (HGB), platelet (PLT) and hematocrit (HCT). The Beckman Coulter AU5800 Clinical Chemistry Analyzer was used for assessing ALB and CRP. Inflammatory markers of NLR, Monocyte-to-Lymphocyte Ratio (MLR), PLR, CRP-ALB Ratio (CAR) and Platelet-ALB Ratio (PAR) were calculated subsequently. Patients underwent at least twice endoscopic examination during the study, before treatment and approximately one year after treatment (10-14 months), respectively. Endoscopic procedures were performed with the standard protocol and the static endoscopic images were reassessed retrospectively by two experienced gastroenterologists. MH was defined as a mucosal activity of gastrointestinal tract as remission or mild inflammatory activity, without ulcer. 17 Disease phenotype was established according to Montreal Classification. 18,19 Statistical Analysis The statistical analyses were performed by using SPSS 26.0 software (SPSS, Chicago, IL, USA). Normality test were applied by Shapiro-Wilk test. Data with normal distribution are presented as mean with Standard deviation (SD), and data with non-normal distribution are presented as median with Interquartile Range (Q). The t test (2-tailed) was applied for data with normal distribution while Mann-Whitney U test were performed in data with abnormal distribution. Chi-square tests or Fisher's exact test were used to compare the nonparametric categorical data between groups. Univariate and multivariant analyses were applied in SPSS. R software (version 3.3.2) was used to build the nomogram and evaluation of model performance (“rms” package). Parameters inclusive of the interaction terms and of clinical significance were included in a full multivariate model subsequently. The model performance was evaluated with C-index, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy. To simplify the logistic regression results and create a practical tool, the coefficients derived from the multivariate analysis were used as weights to elaborate a nomogram, which facilitates the practical application of the model for evaluating probability of MH expected for a given patient. Internal validation was performed in patients with small intestinal lesions. P value less than 0.05 was considered statistically significant. Results Demographic and Clinical Characteristics of Patients A total of 348 patients with CD were enrolled into present study and 115 patients achieved MH. Baseline demographic and clinical characteristics are shown in Table 1. The median diagnosis age of the included patients was 28.0 years (IQR:21-39years) and median disease course of all the individuals was 12months (IQR:4-36months). Median HBI score of the patients was 7 (IQR:5-9). Shapiro-Wilk test showed that data of diagnosis age, disease course and HBI score were with abnormal distributions (all p<0.001). Thus, Mann-Whitney U test was performed and identified that levels of HBI ( p =0.006) and age at diagnosis ( p =0.003) were significantly associate with MH, while the disease course was not associated with MH ( p =0.893). In addition, chi-square analyze showed that patients without lumen stenosis( p =0.005) and treatment with infliximab( p <0.001) are associated with MH. TABLE 1. Demographic and Clinical Characteristics of Patients Mucosal healing N (%) Non-Mucosal healing N (%) P-value Number of patients Gender Male Female 115(33) 85(35.7) 30(27.3) 233(67) 153(64.3) 80(72.7) 0.120 Smoking 0.701 Non-smoker Smoker 99(32.7) 16(35.6) 204(67.3) 29(64.4) Family history of IBD No Yes 113(33.1) 2(28.6) 228(66.9) 5(71.4) 1.000 Surgical history No Yes 95(32.8) 20(34.5) 195(67.2) 38(65.5) 0.799 Disease location 0.055 L1 Ileal 41(36.6) 71(63.4) L2 Colonic 13(20.3) 51(79.7) L3 Ileocolonic 61(35.5) 111(64.5) Upper digestive tract involved No Yes 92(32.4) 24(37.5) 192(67.6) 40(62.5) 0.412 Stenosis 0.005 No Yes Penetrating 97(37.2) 18(20.7) 164(62.8) 69(79.3) 0.074 No Yes 225(68.0) 8(47.1) 106(32.0) 9(52.9) Perianal lesion 0.626 No 59(31.9) 126(68.1) Yes 56(34.4) 107(65.6) Medication treatment Corticosteroids No Yes 98(33.1) 17(32.7) 198(66.9) 35(67.3) 0.953 Immunomodulators No Yes 95(32.6) 20(35.1) 196(67.4) 37(64.9) 0.72 Infliximab No Yes 39(17.4) 76(61.3) 185(82.6) 48(38.7) ＜ 0.001 Pre-treatment hematological parameters and Mucosal Healing Pre-treatment laboratory blood parameters of patients with CD were summarized in Table 2. Shapiro-Wilk test showed that all the data of blood test were with abnormal distributions (all p<0.001). Thus Mann-Whitney U test was performed and identified that the levels of Eosinophils ( p =0.021), MLR ( p =0.020), PLR ( p =0.015) and CAR ( p =0.044) were significantly associate with MH (Table 2). Furthermore, these significant factors were selected to further perform multivariate regression analysis and only PLR was associated with MH after treatment ( p =0.037). However, the ROC curve analysis showed that AUC of PLR were only 0.58 (95% CI: 0.515-0.644, P =0.015) and specificity was only 0.313, lacking of clinical application significance. TABLE 2. Logistic regression for hematological parameters evaluation of MH Pre-treatment Post-treatment Blood tests Non-MH M (Q) MH M (Q) P-value Non-MH M (Q) MH M (Q) P- value WBC 7.02(3.43) 7.23(3.60) 0.276 6.27(2.70) 5.86(2.23) 0.205 NE 5.19(2.82) 5.44(2.74) 0.985 4.17(2.18) 3.39(1.75) ＜ 0.001 MO 0.54(0.35) 0.52(0.28) 0.790 0.97(0.26) 0.42(0.20) 0.001 EO 0.20(0.12) 0.26(0.17) 0.021 0.21(0.13) 0.12(0.08) 0.209 BA 0.03(0.02) 0.03(0.02) 0.687 0.04(0.03) 0.02(0.02) 0.239 HGB HCT PLT 117.9(34.8) 42.29(9.33) 304.9(128) 121.8(31.0) 37.29(9.1) 300.57(155) 0.161 0.582 0.943 125.1(34) 38.5(8.45) 275.9(120) 134.1(23) 40.4(6.7) 230.3(79) ＜ 0.001 0.003 ＜ 0.001 CRP ESR NLR MLR PLR CAR 25.87(31.7) 29.52(34.0) 4.41(2.45) 0.42(0.30) 242(154.2) 0.80(0.98) 24.65(30.76) 27.09(35.0) 6.52(2.56) 0.53(0.24) 475.8(148) 0.72(0.82) 0.086 0.139 0.112 0.020 0.015 0.044 17.2(17.33) 25.9(30.4) 3.69(2.29) 0.67(0.25) 228.6(124) 0.49(0.48) 3.41(2.15) 9.54(11.0) 2.03(1.07) 0.25(0.15) 137.1(77.4) 0.09(0.06) ＜ 0.001 ＜ 0.001 ＜ 0.001 ＜ 0.001 ＜ 0.001 ＜ 0.001 PAR 8.89(5.24) 8.20(5.39) 0.209 7.46(3.44) 5.52(2.11) ＜ 0.001 Abbreviations: MH, mucosal healing; WBC, White Blood Cell; NE, Neutrophils; MO, Monocyte; EO, Eosinophils; BA, Basophils; HGB, Hemoglobin; HCT, hematocrit; PLT, platelet; CRP, C reactive protein; ESR, erythrocyte sedimentation rate; NLR, Neutrophil-Lymphocyte Ratio; MLR, Monocyte-Lymphocyte Ratio; PLR, Platelet-Lymphocyte Ratio; CAR, C reactive protein-Albumin Ratio; PAR, Platelet- Albumin Ratio; Post-treatment hematological parameters and Mucosal Healing Shapiro-Wilk test showed that all the data of blood test after treatment of 54 weeks were with abnormal distributions (all p<0.001). Mann-Whitney U test identified that the levels of NE( p <0.001), MO( p =0.001), HGB( p <0.001), HCT( p =0.003), PLT( p <0.001), CRP( p <0.001), ESR( p <0.001), NLR( p <0.001), MLR( p <0.001), PLR( p <0.001), CAR( p <0.001), PAR( p <0.001) were significantly associate with MH (Table 2). In the multivariate regression, we identified the following three variables as the independently associated factors with MH: PLR, CAR and ESR. Model establishment We established two models: a simple model and a primary model. The simple model (model-1) only contains serum biomarkers including PLR, CAR and ESR. The primary model (model-2) was consisted of the following variables: PLR, CAR, ESR, HBI score and treatment with infliximab. Variables included in the simple and primary models are showed in Table 3. TABLE 3. Multivariate logistic regression of models for Mucosal healing evaluation Simple Model (model-1) Primary Model (model-2) OR [95%CI] p -Value OR [95%CI] p -Value HGB 0.996[0.971-1.021] 0.754 0.986[0.952-1.022] 0.437 HCT NE 0.968[0.877-1.068] 0.909[0.754-1.096] 0.519 0.317 0.978[0.861-1.112] 0.847[0.676-1.060] 0.734 0.147 MO CAR PLR 0.950[0.756-1.194] 0.022[0.002-0.219] 0.993[0.989-0.997] 0.661 0.001 0.001 0.848[0.120-5.984] 0.036 [0.004-0.320] 0.995[0.990-0.999] 0.848 0.003 0.014 ESR Age HBI Stenosis 0.955[0.928-0.982] NA NA NA 0.001 NA NA NA 0.951[0.922-0.981] 0.993[0.967-1.021] 0.907[0.824-0.999] 0.599[0.289-1.241] 0.002 0.682 0.047 0.168 Infliximab NA NA 6.346[3.324-12.117] ＜ 0.001 Comparisons between simple model and primary model Diagnostic value was compared between the two models. The golden standard is whether MH has been achieved under endoscopy. Table 4 shows the classification of the two models. The C-index of simple model was 0.830 (95% CI: 0.79-0.87, P<0.001) (Fig.1A). The sensitivity and specificity were 0.626 and 0.807, respectively (Table.4). Primary model showed a perfect capacity for predicting MH, with a C-index of 0.875 (95% CI: 0.84–0.91, P<0.001) (Fig.1A). Sensitivity of primary model was 0.704 and specificity was 0.871 (Table 4). According to DeLong’s test, there is significant difference of C-index between primary model and simple model (Z=2.8519, P=0.0043). Primary model was superior to simple model in C-index (87.5% vs 83.0 %, p =0.004). There was no statistical significance between primary model and simple model in sensitivity (70.43% vs 62.61%, p =0.467), specificity (87.12% vs 80.69%, p =0.448), PPV (72.97% vs 61.54%, p =0.292), NPV (85.65% vs 81.39%, p =0.614), and accuracy (81.61% vs 74.71%, p =0.303) (Table 4). TABLE 4. Comparison of simple model and primary model Diagnostic Index Simple Model (%, 95% CI) Primary Model (%, 95% CI) P-value C-index 0.830 (0.79-0.87) 0.875 (0.84-0.91) 0.004 * Sensitivity 62.61% (53.10-71.45%) 70.43% (61.21- 78.58%) 0.467 Specificity 80.69% (75.02-85.55%) 87.12% (82.13- 91.14%) 0.448 PPV 61.54% (54.29-68.31%) 72.97% (65.45-79.37%) 0.292 NPV 81.39% (77.39-84.81%) 85.65% (81.76- 88.83%) 0.614 Accuracy 74.71% (69.80-79.20%) 81.61% (77.13-85.54%) 0.303 Note: * Statistically significant with a p-value less than 0.05. Abbreviations: Simple Model: model constructed from PLR, CAR and ESR; Primary Model: model constructed from PLR, CAR, ESR, HBI and IFX treatment; PPV, positive predictive value; NPV, negative predictive value. Evaluation and Validation of the Primary Nomogram A nomogram was established based on the variables in the primary model (Fig.2). Model performance was evaluated by discrimination and calibration. This model had a high C-index (0.88) as mentioned above. The calibration curve also showed satisfactory performance (Fig. 3A). The internal validation was performed in 112 patients with small bowel involvement. After validation, the C-index of the model was 0.834 (95% CI: 0.76-0.91, P <0.001) (Fig.1B). The calibration curve in validation group is shown in Fig.3B. The internal validation also performed good in discrimination and calibration. Discussion In the present study, the most important findings include: (1) Serum biomarkers such as PLR, CAR and ESR after approximately 1 year treatment was independently associated with MH. (2) Patients with lower HBI score and the use of infliximab are more likely to achieve MH. Based on this, we developed prediction model with the above significant variables to evaluate MH in patients with CD. The routine blood test is the most fundamental and accessible examination, which has long been proposed as an essential assistant tool for disease assessment. 20 In our study, there was no statistical significance between pre-treatment serum inflammatory indexes and MH. Thus, we were unable to develop a pre-treatment model. However, in the post-treatment data, we found the combination use of PLR, CAR and ESR can effectively evaluate MH in patients with CD. Platelet count can be affected by cytokines released in acute inflammation. Thrombocytosis and high ESR level are common feature of acute inflammation. Lymphocytes is the basic component of the adaptive and innate immune system. It is demonstrated that PLR increased significantly in endoscopically active ulcerative colitis. 21 CRP is the most widely used serological indicators in clinical evaluation of disease activity in CD. 22,23 Serum ALB is an indicator of nutrition, synthesis rate of which directly affected by the severity of acute infection. CAR was initially used to identify critical patients in emergency ward and predict disease progression in Takayasu arteritis and cancer in recent years. 24,25 It is reported that CAR is useful biomarker of disease activity and histological activity in CD. 26 Consistent with previous research, we included the above three variables including PLR, CAR and ESR into our model. Some clinical characteristics can also predict MH in patients with CD. In is reported that early introduction of tumor necrosis factor (TNF) antagonists, particularly in combination with immunosuppressives associate with MH in patients with CD. 27 In our study, patients received infliximab was significantly associate with an increased rate of MH, consistent with previous reports. HBI was derived to simplify calculation of the Crohn's disease activity index (CDAI). We newly found that HBI was associated with MH. In univariate logistic regression analyses, diagnose age and lumen stenosis were found associate with MH in the present data. Unfortunately, neither of them was included in the final multivariate logistic regression model. A simple model and a primary model were established in the study. The simple model is simpler, easier to operate clinically, and with favorable accuracy. However, taking into account of the C-index and the calibration plots, primary model showed better discrimination ability. Reliable nomogram based on aforementioned factors was constructed and showed excellent evaluation abilities for MH among patients with CD. Parameters in the monogram are easy to obtain, which increases the clinical practicality. This nomogram can predict MH probability in patients with CD after one year of treatment and provide reference for doctors to perform endoscopic review. If the prediction results indicate low probability of MH, doctors could temporarily eliminate endoscopy and adjust treatment regimen, avoiding repeated and unnecessary invasive endoscopy. Fecal calprotectin (FC) has been widely clarified for the correlation with endoscopically proven CD activity. 28-30 However, FC is still not commonly used in clinical practice because of detection results may vary from different kits of calprotectin. In addition, some researchers pointed out that PPV of FC for MH was not high enough and FC was not sensitive to assess CD activity with small intestine involvement. 31,32 For these reasons, we did not include FC in present study. Nevertheless, we validated primary model in patients with CD with small intestine involvement, indicating a good evaluation effect. Our study has some limitations. Firstly, although the patients we included from two tertiary hospitals in Eastern China, the results may not represent the general population of patients with CD. Secondly, only internal validation was performed in the present study. Results of present study still need to be verified by external large-scale clinical studies with follow-up study. In summary, this study provides comprehensive insights into serum inflammatory index and clinical information to evaluate MH after treatment in patients with CD. We conducted a nomogram, providing a portable decision tool for early MH screening and clinical decision of endoscopic review time. More prospective studies in the future are warrant to perform. Declarations Ethics approval and consent to participate Ethical approval for the study was approved by Clinical Research Ethics Committee of The First Affiliated Hospital of Nanjing Medical University, China(ref:2021-SR-235), in compliance with the Declaration of Helsinki. All patients in the study gave their informed consent for reviewing their clinical data. Consent for publication Not applicable Availability of data and materials We share our raw data by providing it in a supplementary file. Competing interests and Declaration of financial interests: There is no competing interests in the article. The authors have nothing to declare regarding the work under consideration for publication. Funding This study was supported by grants from the National Natural Science Foundation of China, No. 81770553 and 82070568. Authors' contributions N.T. and H.C. wrote the manuscript. N.T. and R.C. contributed to data accumulation. H.C. and N.T. performed statistical analysis. H.Z. and W.T. have participated in the study design. H.Z. critically revised the manuscript. Acknowledgements Nana Tang, Han Chen and Ruidong Chen have contributed equally to this manuscript. Authors' information (optional) Nana Tang 1 , Han Chen 1 , Ruidong Chen 2 , Wen Tang 2 , Hongjie Zhang 1 1. Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University，China 2. Department of Gastroenterology, The Second Affiliated Hospital of Soochow University, China References Hanzel J. A Novel Endoscopic Score for Postoperative Recurrence of Crohn's Disease: More Information Needed. Am J Gastroenterol. 2021;116(1):217-218. Ho GT, Cartwright JA, Thompson EJ, Bain CC, Rossi AG. Resolution of Inflammation and Gut Repair in IBD: Translational Steps Towards Complete Mucosal Healing. Inflamm Bowel Dis. 2020;26(8):1131-1143. Nunez FP, Mahadevan U, Quera R, Bay C, Ibanez P. Treat-to-target approach in the management of inflammatory Bowel disease. Gastroenterol Hepatol. 2021;44(4):312-319. Lafeuille P, Hordonneau C, Vignette J, et al. Transmural healing and MRI healing are associated with lower risk of bowel damage progression than endoscopic mucosal healing in Crohn's disease. Aliment Pharmacol Ther. 2021;53(5):577-586. Dal Buono A, Roda G, Argollo M, Zacharopoulou E, Peyrin-Biroulet L, Danese S. Treat to target or 'treat to clear' in inflammatory bowel diseases: one step further? Expert Rev Gastroenterol Hepatol. 2020;14(9):807-817. Cucchiara S, D'Arcangelo G, Isoldi S, Aloi M, Stronati L. Mucosal healing in Crohn's disease: new insights. Expert Rev Gastroenterol Hepatol. 2020;14(5):335-345. Zhou FS, Gao N, Sun X, et al. C-reactive protein/abumin ratio is a useful biomarker for predicting the mucosal healing in the Crohn disease: A retrospective study. Medicine (Baltimore). 2021;100(10):e24925. FGC EP, Rosa RM, da Cunha PFS, de Souza SCS, de Abreu Ferrari ML. Faecal calprotectin is the biomarker that best distinguishes remission from different degrees of endoscopic activity in Crohn's disease. BMC Gastroenterol. 2020;20(1):35. Colombel JF, Adedokun OJ, Gasink C, et al. Combination Therapy With Infliximab and Azathioprine Improves Infliximab Pharmacokinetic Features and Efficacy: A Post Hoc Analysis. Clin Gastroenterol Hepatol. 2019;17(8):1525-1532 e1521. Dulai PS, Boland BS, Singh S, et al. Development and Validation of a Scoring System to Predict Outcomes of Vedolizumab Treatment in Patients With Crohn's Disease. Gastroenterology. 2018;155(3):687-695 e610. Mao R, Qiu Y, Chen BL, et al. Factors associated with the achievement of mucosal healing in Crohn's disease: the benefit of endoscopic monitoring in treating to target. Therap Adv Gastroenterol. 2017;10(6):453-463. Feng JR, Qiu X, Wang F, et al. Diagnostic Value of Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio in Crohn's Disease. Gastroenterol Res Pract. 2017;2017:3526460. Chen YH, Wang L, Feng SY, Cai WM, Chen XF, Huang ZM. The Relationship between C-Reactive Protein/Albumin Ratio and Disease Activity in Patients with Inflammatory Bowel Disease. Gastroenterol Res Pract. 2020;2020:3467419. Daniluk U, Daniluk J, Krasnodebska M, Lotowska JM, Sobaniec-Lotowska ME, Lebensztejn DM. The combination of fecal calprotectin with ESR, CRP and albumin discriminates more accurately children with Crohn's disease. Adv Med Sci. 2019;64(1):9-14. Alkhatry M, Al-Rifai A, Annese V, et al. First United Arab Emirates consensus on diagnosis and management of inflammatory bowel diseases: A 2020 Delphi consensus. World J Gastroenterol. 2020;26(43):6710-6769. Levesque BG, Sandborn WJ, Ruel J, Feagan BG, Sands BE, Colombel JF. Converging goals of treatment of inflammatory bowel disease from clinical trials and practice. Gastroenterology. 2015;148(1):37-51 e31. af Bjorkesten CG, Nieminen U, Turunen U, Arkkila PE, Sipponen T, Farkkila MA. Endoscopic monitoring of infliximab therapy in Crohn's disease. Inflamm Bowel Dis. 2011;17(4):947-953. Park Y, Cheon JH, Park YL, et al. Development of a Novel Predictive Model for the Clinical Course of Crohn's Disease: Results from the CONNECT Study. Inflamm Bowel Dis. 2017;23(7):1071-1079. Satsangi J, Silverberg MS, Vermeire S, Colombel JF. The Montreal classification of inflammatory bowel disease: controversies, consensus, and implications. Gut. 2006;55(6):749-753. Sun S, Karsdal MA, Mortensen JH, et al. Serological Assessment of the Quality of Wound Healing Processes in Crohn's Disease. J Gastrointestin Liver Dis. 2019;28:175-182. Akpinar MY, Ozin YO, Kaplan M, et al. Platelet-to-lymphocyte Ratio and Neutrophil-to-lymphocyte Ratio Predict Mucosal Disease Severity in Ulcerative Colitis. J Med Biochem. 2018;37(2):155-162. Lin X, Qiu Y, Feng R, et al. Normalization of C-Reactive Protein Predicts Better Outcome in Patients With Crohn's Disease With Mucosal Healing and Deep Remission. Clin Transl Gastroenterol. 2020;11(2):e00135. Ma C, Battat R, Khanna R, Parker CE, Feagan BG, Jairath V. What is the role of C-reactive protein and fecal calprotectin in evaluating Crohn's disease activity? Best Pract Res Clin Gastroenterol. 2019;38-39:101602. Seringec Akkececi N, Yildirim Cetin G, Gogebakan H, Acipayam C. The C-Reactive Protein/Albumin Ratio and Complete Blood Count Parameters as Indicators of Disease Activity in Patients with Takayasu Arteritis. Med Sci Monit. 2019;25:1401-1409. Wu J, Tan W, Chen L, Huang Z, Mai S. Clinicopathologic and prognostic significance of C-reactive protein/albumin ratio in patients with solid tumors: an updated systemic review and meta-analysis. Oncotarget. 2018;9(17):13934-13947. Nassri A, Muftah M, Nassri R, et al. Novel Inflammatory-Nutritional Biomarkers as Predictors of Histological Activity in Crohn's Disease. Clin Lab. 2020;66(7). Vasudevan A, Raghunath A, Anthony S, et al. Higher Mucosal Healing with Tumor Necrosis Factor Inhibitors in Combination with Thiopurines Compared to Methotrexate in Crohn's Disease. Dig Dis Sci. 2019;64(6):1622-1631. Leach ST, Day AS, Messenger R, et al. Fecal Markers of Inflammation and Disease Activity in Pediatric Crohn Disease: Results from the ImageKids Study. J Pediatr Gastroenterol Nutr. 2020;70(5):580-585. Kawashima K, Ishihara S, Yuki T, et al. Fecal Calprotectin More Accurately Predicts Endoscopic Remission of Crohn's Disease than Serological Biomarkers Evaluated Using Balloon-assisted Enteroscopy. Inflamm Bowel Dis. 2017;23(11):2027-2034. Kennedy NA, Jones GR, Plevris N, Patenden R, Arnott ID, Lees CW. Association Between Level of Fecal Calprotectin and Progression of Crohn's Disease. Clin Gastroenterol Hepatol. 2019;17(11):2269-2276 e2264. Costa F, Mumolo MG, Ceccarelli L, et al. Calprotectin is a stronger predictive marker of relapse in ulcerative colitis than in Crohn's disease. Gut. 2005;54(3):364-368. Verdejo C, Hervias D, Roncero O, et al. Fecal calprotectin is not superior to serum C-reactive protein or the Harvey-Bradshaw index in predicting postoperative endoscopic recurrence in Crohn's disease. Eur J Gastroenterol Hepatol. 2018;30(12):1521-1527. Additional Declarations No competing interests reported. Supplementary Files rawdataEnglish.xlsx Cite Share Download PDF Status: Under Review Version 3 posted Editorial decision: Major revision 07 Apr, 2022 Reviews received at journal 22 Mar, 2022 Reviewers agreed at journal 09 Mar, 2022 Reviewers invited by journal 03 Mar, 2022 Editor assigned by journal 03 Mar, 2022 Editor invited by journal 02 Mar, 2022 Submission checks completed at journal 02 Mar, 2022 First submitted to journal 17 Feb, 2022 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-985096","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[{"code":1,"date":"2021-11-02 14:08:47","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":2,"date":"2022-02-21 20:45:40","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":89790205,"identity":"bef15abb-2852-4d92-9dc3-7ef655c43006","order_by":0,"name":"Nana Tang","email":"","orcid":"","institution":"The First Affiliated Hospital of Nanjing Medical University，","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nana","middleName":"","lastName":"Tang","suffix":""},{"id":89790206,"identity":"cd325a92-ceed-4a8b-b856-ad4a3f8b11ff","order_by":1,"name":"Han Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Nanjing Medical University，","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Chen","suffix":""},{"id":89790207,"identity":"396ad0cf-3e3f-4f07-a7a2-577e5b6170e4","order_by":2,"name":"Ruidong Chen","email":"","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruidong","middleName":"","lastName":"Chen","suffix":""},{"id":89790208,"identity":"eab4a96b-997d-4f94-9387-7d29bef43883","order_by":3,"name":"Wen Tang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Tang","suffix":""},{"id":89790209,"identity":"63015b78-19ae-4522-8d8c-49e48dbbbca9","order_by":4,"name":"Hongjie Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACA4YDbCCah5+9sfHhB1K0yEn2HG42liBOCwNYi7HBjPQ2AR5itJgzHn/24OeO2sQNkg/bGCQY7OR0GwhosWw4kG7Ye+Z44nbpxLYHBQzJxmYHCDnswIFjErxtxxJ3zk5sN5BgOJC4jbCWg22Sf4FaNtw82CbBQ5yWw2zSvG01xgY3GInWcoxNWrbtADCQE4GBbECMX24cfyb5tq0OGJXHHz78UGEnR1ALgwRYxWGYCYSUgwB/A4isI0bpKBgFo2AUjFQAABqWSmjEgpkiAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Nanjing Medical University，","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hongjie","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2021-10-17 07:41:56","currentVersionCode":3,"declarations":"","doi":"10.21203/rs.3.rs-985096/v3","doiUrl":"https://doi.org/10.21203/rs.3.rs-985096/v3","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":19125769,"identity":"c46a8876-fcf6-4e5b-b743-b6e6ebbf740e","added_by":"auto","created_at":"2022-03-11 15:17:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1229896,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of simple model (model-1) and primary model (model-2) in training group(A); ROC curve analysis of primary model in validation group (B).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-985096/v3/0f9976cc1e041868cdbe26b0.png"},{"id":19126242,"identity":"be802b3e-c2b2-4549-a48a-4fa20fb83113","added_by":"auto","created_at":"2022-03-11 15:20:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":693634,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for evaluation of MH rate in a given patient, constructed using as weights the coefficients derived from multivariate analysis. To calculate the probability of MH, we first obtained the value of each evaluator by drawing a vertical line straight upward from that factor to the points’ axis, then summed the points achieved for each factor and located this sum on the total points’ axis of the nomogram, where the probability of MH can be located by drawing a vertical line downward. Abbreviations: MH, mucosal healing\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-985096/v3/43f5d0c8b8d3b78608cb707a.png"},{"id":19125770,"identity":"385ce407-986f-4846-8a2a-6fb016b4b84f","added_by":"auto","created_at":"2022-03-11 15:17:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1130573,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves for primary model in (A) training cohort and (B) validation cohort. The x-axis represents the predicted MH while y-axis represents actual MH rate. The 45-degree dotted lines represent a perfect prediction. The solid line represents the performance of the evaluation models. The closer solid line fits to the dotted line, the better accuracy of the model shows. Abbreviations: MH, mucosal healing\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-985096/v3/cb7481c346212bff7069253b.png"},{"id":19126243,"identity":"64da5983-1f46-49cc-9af4-bcbded97a9b6","added_by":"auto","created_at":"2022-03-11 15:20:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":393794,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-985096/v3/50159d5a-925a-4ff7-aa65-0066964d423f.pdf"},{"id":19125772,"identity":"9365ec15-bcc0-42d6-b82c-614c05814e2b","added_by":"auto","created_at":"2022-03-11 15:17:30","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":164485,"visible":true,"origin":"","legend":"","description":"","filename":"rawdataEnglish.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-985096/v3/4df3c7464b0b6009de3d0eb4.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combination of Serological Biomarkers and Clinical Features to predict Mucosal Healing in Crohn’s Disease: A Multicenter Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eCrohn\u0026rsquo;s disease (CD) is a life-long and progressive inflammatory disease with symptoms evolving in a remitting and relapsing manner\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Most patients develop bowel damage and disability including strictures, fistulas, abscesses and so on in several years after diagnosis, resulting in surgery.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Current therapeutic strategies in CD aim for deep and prolonged remission, with the goal of preventing complications and therefore improve prognosis as well as quality of life.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Treatment strategies aimed solely at resolution of clinical symptoms does not eliminate long-term bowel damage in patients with CD.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMucosal healing (MH) is confirmed lead to a lower rate of relapse, hospitalization and surgical resection.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e In recent years, MH is preferred over clinical remission as straightforward goal of clinical treatment in CD.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Endoscopy is still the golden standard for evaluation of disease activity. However, frequently endoscopy for disease monitoring in long-term follow-up is limited by considerations of invasiveness, high cost and patient acceptance. Alternative noninvasive methods are necessary for assessment of CD-related mucosal inflammation.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIt is reported that clinical characteristics such as mild clinical manifestation and early introduction of biologicals associated with MH in patients with CD.\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e In addition, some systemic inflammatory markers obtained from the serological examination including neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, C-reactive protein to Albumin ratio (CAR)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, combination of fecal calprotectin (FC), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and Albumin (ALB)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e have been explored as diagnostic and predictive indicators of CD. Clinically, there is still lack of accurate instrument with high sensitivity and specificity using serological parameters and clinical characteristics to predict MH in CD.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to address the predictive role of serum inflammatory index and clinical features in patients with CD who diagnosed and treated in two tertiary hospitals in China. We analyzed the pre-treatment and post-treatment data individually and explored their relationship with MH after treatment in patients with CD. Subsequently, we used hematological data with or without clinical features to construct assessment models for MH prediction. Model with superior performance is recommended for clinical use.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePatients and Data source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a retrospective, multi-center observational cohort study of consecutive\u0026nbsp;patients with CD\u0026nbsp;from Inflammatory Bowel Disease Center of The First Affiliated Hospital of Nanjing Medical University and the Second Affiliated Hospital of Soochow University, China, between 2010 and 2021. Diagnoses of CD were determined according to standard clinical, laboratory, radiological, endoscopic, and histopathologic findings.\u003csup\u003e15\u003c/sup\u003e Data regarding patients\u0026rsquo; demographics, laboratory values and endoscopic characteristics were retrospectively reviewed through hospital medical database records and endoscopic image system. Harvey-Bradshaw Index (HBI) consists of five descriptors: general well-being, abdominal pain, number of liquid stools for the previous day, abdominal mass and complications.\u003csup\u003e16\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInclusion criteria were listed as follows: (1) Patients underwent at least twice endoscopic procedures and serological examination both pre-treatment and post-treatment during the study period. (2) Corticosteroids\u0026nbsp;had been discontinued for more than 12 weeks. Exclusion criteria: (1) Acute or chronic infections during the inspection; (2) Previous medical history of hematologic or rheumatic autoimmune disease; (3) Acute or chronic renal failure, heart diseases, cirrhosis or cancer; (4) A previous history of taking aspirin or warfarin; (5) Missing complete blood count, ALB, ESR or CRP data.\u0026nbsp;(6) Any other conditions that affect the blood routine results or inflammatory markers. The clinical, endoscopic features and laboratory data of the study population are summarized in Tables 1 and 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was approved by Clinical Research Ethics Committee of The First Affiliated Hospital of Nanjing Medical University (ref: 2021-SR-235), in compliance with the Declaration of Helsinki. All patients in the study gave their informed consent for reviewing their clinical data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBlood assessment and Endoscopic documentation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline blood values had been collected at the time of CD diagnosis when patients were admitted to hospital before administration of any treatment. Post-treatment hematology was completed within one week of the patient\u0026apos;s endoscopic review. Venous blood specimens were drawn into sterile standard tubes containing ethylene diamine tetraacetic acid (EDTA) as an anticoagulant and evaluated within 1h after venipuncture using a Beckman Coulter UniCel DxH800 hematology analyzer. The Beckman Coulter UniCel\u0026reg; DxH 800 was used for analyzing ESR and routine blood markers including White Blood Cell (WBC), neutrophils (NE), monocytes (MO), lymphocytes (LY), Eosinophils (EO), Basophilic (BA), Hemoglobin (HGB), platelet (PLT) and hematocrit (HCT). The Beckman Coulter AU5800 Clinical Chemistry Analyzer was used for assessing ALB and CRP. Inflammatory markers of NLR, Monocyte-to-Lymphocyte Ratio (MLR), PLR, CRP-ALB Ratio (CAR) and Platelet-ALB Ratio (PAR) were calculated subsequently. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatients underwent at least twice endoscopic examination during the study, before treatment and approximately one year after treatment (10-14 months), respectively. Endoscopic procedures were performed with the standard protocol and the static endoscopic images were reassessed retrospectively by two experienced gastroenterologists. MH was defined as a mucosal activity of gastrointestinal tract as remission or mild inflammatory activity, without ulcer.\u003csup\u003e17\u003c/sup\u003e Disease phenotype was established according to Montreal Classification.\u003csup\u003e18,19\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical analyses were performed by using SPSS 26.0 software (SPSS, Chicago, IL, USA). Normality test were applied by Shapiro-Wilk\u0026nbsp;test. Data with normal distribution are presented as mean with Standard deviation (SD), and data with non-normal distribution are presented as median with Interquartile Range (Q). The t test (2-tailed) was applied for data with normal distribution while Mann-Whitney U test were performed in data with abnormal distribution. Chi-square tests or Fisher\u0026apos;s exact test were used to compare the nonparametric categorical data between groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnivariate and multivariant analyses were applied in SPSS. R software (version 3.3.2) was used to build the nomogram and evaluation of model performance (\u0026ldquo;rms\u0026rdquo; package). Parameters inclusive of the interaction terms and of clinical significance were included in a full multivariate model subsequently. The model performance was evaluated with C-index, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy. To simplify the logistic regression results and create a practical tool, the coefficients derived from the multivariate analysis were used as weights to elaborate a nomogram, which facilitates the practical application of the model for evaluating probability of MH expected for a given patient. Internal validation was performed in patients with small intestinal lesions. P value less than 0.05 was considered statistically significant.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic and Clinical Characteristics of Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 348 patients with CD were enrolled into present study and 115 patients achieved MH. Baseline demographic and clinical characteristics are shown in Table 1. The median diagnosis age of the included patients was 28.0 years (IQR:21-39years) and median disease course of all the individuals was 12months (IQR:4-36months). Median HBI score of the patients was 7 (IQR:5-9). Shapiro-Wilk test showed that data of diagnosis age, disease course and HBI score were with abnormal distributions (all p\u0026lt;0.001). Thus, Mann-Whitney U test was performed and identified that levels of HBI (\u003cem\u003ep\u003c/em\u003e=0.006) and age at diagnosis (\u003cem\u003ep\u003c/em\u003e=0.003) were significantly associate with MH, while the disease course was not associated with MH (\u003cem\u003ep\u003c/em\u003e=0.893). In addition, chi-square analyze showed that patients without lumen stenosis(\u003cem\u003ep\u003c/em\u003e=0.005) and treatment with infliximab(\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) are associated with MH.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTABLE 1. Demographic and Clinical Characteristics of Patients\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.62043795620438%\"\u003e\n \u003cp\u003eMucosal healing\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.912408759124087%\"\u003e\n \u003cp\u003eNon-Mucosal healing\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eNumber of patients\u003c/p\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e115(33)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e85(35.7)\u003c/p\u003e\n \u003cp\u003e30(27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e233(67)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e153(64.3)\u003c/p\u003e\n \u003cp\u003e80(72.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eSmoking \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Non-smoker\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e99(32.7)\u003c/p\u003e\n \u003cp\u003e16(35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e204(67.3)\u003c/p\u003e\n \u003cp\u003e29(64.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eFamily history of IBD \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e113(33.1)\u003c/p\u003e\n \u003cp\u003e2(28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e228(66.9)\u003c/p\u003e\n \u003cp\u003e5(71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eSurgical history\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e95(32.8)\u003c/p\u003e\n \u003cp\u003e20(34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e195(67.2)\u003c/p\u003e\n \u003cp\u003e38(65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eDisease location\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eL1 Ileal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e\u0026nbsp;41(36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e71(63.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eL2 Colonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e\u0026nbsp;13(20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e51(79.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eL3 Ileocolonic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e\u0026nbsp;61(35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e111(64.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eUpper digestive tract involved\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e92(32.4)\u003c/p\u003e\n \u003cp\u003e24(37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e192(67.6)\u003c/p\u003e\n \u003cp\u003e40(62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eStenosis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003ePenetrating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e97(37.2)\u003c/p\u003e\n \u003cp\u003e18(20.7)\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e164(62.8)\u003c/p\u003e\n \u003cp\u003e69(79.3)\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e225(68.0)\u003c/p\u003e\n \u003cp\u003e8(47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e106(32.0)\u003c/p\u003e\n \u003cp\u003e9(52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003ePerianal lesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e59(31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e126(68.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e56(34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e107(65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eMedication treatment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eCorticosteroids \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e98(33.1)\u003c/p\u003e\n \u003cp\u003e17(32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e198(66.9)\u003c/p\u003e\n \u003cp\u003e35(67.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eImmunomodulators\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e95(32.6)\u003c/p\u003e\n \u003cp\u003e20(35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e196(67.4)\u003c/p\u003e\n \u003cp\u003e37(64.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.956204379562045%\"\u003e\n \u003cp\u003eInfliximab\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.153284671532848%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39(17.4)\u003c/p\u003e\n \u003cp\u003e76(61.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.37956204379562%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e185(82.6)\u003c/p\u003e\n \u003cp\u003e48(38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.510948905109489%\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePre-treatment hematological parameters and Mucosal Healing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePre-treatment laboratory blood parameters of\u0026nbsp;patients with CD\u0026nbsp;were summarized in Table 2. Shapiro-Wilk test showed that all the data of blood test were with abnormal distributions (all p\u0026lt;0.001). Thus Mann-Whitney U test was performed and identified that the levels of Eosinophils (\u003cem\u003ep\u003c/em\u003e=0.021), MLR (\u003cem\u003ep\u003c/em\u003e=0.020), PLR (\u003cem\u003ep\u003c/em\u003e=0.015) and CAR (\u003cem\u003ep\u003c/em\u003e=0.044) were significantly associate with MH (Table 2). Furthermore, these significant factors were selected to further perform multivariate regression analysis and only PLR was associated with MH after treatment (\u003cem\u003ep\u003c/em\u003e=0.037). However, the ROC curve analysis showed that AUC of PLR were only 0.58 (95% CI: 0.515-0.644, \u003cem\u003eP\u003c/em\u003e=0.015) and specificity was only 0.313, lacking of clinical application significance. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTABLE 2.\u0026nbsp;Logistic regression for hematological parameters evaluation of MH\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.8234%;\" valign=\"top\" width=\"10.23339317773788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 31.1638%;\" valign=\"top\" width=\"45.24236983842011%\"\u003e\n \u003cp\u003ePre-treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 28.9232%;\" valign=\"top\" width=\"40.93357271095152%\"\u003e\n \u003cp\u003ePost-treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.8234%;\" valign=\"top\" width=\"10.23339317773788%\"\u003e\n \u003cp\u003eBlood\u0026nbsp;\u003c/p\u003e\n \u003cp\u003etests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4248%;\" valign=\"top\" width=\"18.671454219030522%\"\u003e\n \u003cp\u003eNon-MH\u003c/p\u003e\n \u003cp\u003eM (Q)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6285%;\" valign=\"top\" width=\"17.414721723518852%\"\u003e\n \u003cp\u003eMH\u003c/p\u003e\n \u003cp\u003eM (Q)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1105%;\" valign=\"top\" width=\"9.156193895870736%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4064%;\" valign=\"top\" width=\"14.901256732495511%\"\u003e\n \u003cp\u003eNon-MH\u003c/p\u003e\n \u003cp\u003eM (Q)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 10.7953%;\" valign=\"top\" width=\"15.978456014362656%\"\u003e\n \u003cp\u003eMH\u003c/p\u003e\n \u003cp\u003eM (Q)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 6.7216%;\" valign=\"top\" width=\"10.053859964093357%\"\u003e\n \u003cp\u003eP-\u003c/p\u003e\n \u003cp\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.8234%;\" valign=\"top\" width=\"10.21505376344086%\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4248%;\" valign=\"top\" width=\"18.63799283154122%\"\u003e\n \u003cp\u003e7.02(3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6285%;\" valign=\"top\" width=\"17.38351254480287%\"\u003e\n \u003cp\u003e7.23(3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1105%;\" valign=\"top\" width=\"9.13978494623656%\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5997%;\" valign=\"top\" width=\"14.695340501792115%\"\u003e\n \u003cp\u003e6.27(2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4064%;\" valign=\"top\" width=\"15.232974910394265%\"\u003e\n \u003cp\u003e5.86(2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.6382%;\" valign=\"top\" width=\"11.827956989247312%\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.8234%;\" valign=\"top\" width=\"10.21505376344086%\"\u003e\n \u003cp\u003eNE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4248%;\" valign=\"top\" width=\"18.63799283154122%\"\u003e\n \u003cp\u003e5.19(2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6285%;\" valign=\"top\" width=\"17.38351254480287%\"\u003e\n \u003cp\u003e5.44(2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1105%;\" valign=\"top\" width=\"9.13978494623656%\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5997%;\" valign=\"top\" width=\"14.695340501792115%\"\u003e\n \u003cp\u003e4.17(2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4064%;\" valign=\"top\" width=\"15.232974910394265%\"\u003e\n \u003cp\u003e3.39(1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.6382%;\" valign=\"top\" width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.8234%;\" valign=\"top\" width=\"10.21505376344086%\"\u003e\n \u003cp\u003eMO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4248%;\" valign=\"top\" width=\"18.63799283154122%\"\u003e\n \u003cp\u003e0.54(0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6285%;\" valign=\"top\" width=\"17.38351254480287%\"\u003e\n \u003cp\u003e0.52(0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1105%;\" valign=\"top\" width=\"9.13978494623656%\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5997%;\" valign=\"top\" width=\"14.695340501792115%\"\u003e\n \u003cp\u003e0.97(0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4064%;\" valign=\"top\" width=\"15.232974910394265%\"\u003e\n \u003cp\u003e0.42(0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.6382%;\" valign=\"top\" width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.8234%;\" valign=\"top\" width=\"10.21505376344086%\"\u003e\n \u003cp\u003eEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4248%;\" valign=\"top\" width=\"18.63799283154122%\"\u003e\n \u003cp\u003e0.20(0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6285%;\" valign=\"top\" width=\"17.38351254480287%\"\u003e\n \u003cp\u003e0.26(0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1105%;\" valign=\"top\" width=\"9.13978494623656%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5997%;\" valign=\"top\" width=\"14.695340501792115%\"\u003e\n \u003cp\u003e0.21(0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4064%;\" valign=\"top\" width=\"15.232974910394265%\"\u003e\n \u003cp\u003e0.12(0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.6382%;\" valign=\"top\" width=\"11.827956989247312%\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.8234%;\" valign=\"top\" width=\"10.21505376344086%\"\u003e\n \u003cp\u003eBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4248%;\" valign=\"top\" width=\"18.63799283154122%\"\u003e\n \u003cp\u003e0.03(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6285%;\" valign=\"top\" width=\"17.38351254480287%\"\u003e\n \u003cp\u003e0.03(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1105%;\" valign=\"top\" width=\"9.13978494623656%\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5997%;\" valign=\"top\" width=\"14.695340501792115%\"\u003e\n \u003cp\u003e0.04(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4064%;\" valign=\"top\" width=\"15.232974910394265%\"\u003e\n \u003cp\u003e0.02(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.6382%;\" valign=\"top\" width=\"11.827956989247312%\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.8234%;\" valign=\"top\" width=\"10.21505376344086%\"\u003e\n \u003cp\u003eHGB\u003c/p\u003e\n \u003cp\u003eHCT\u003c/p\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4248%;\" valign=\"top\" width=\"18.63799283154122%\"\u003e\n \u003cp\u003e117.9(34.8)\u003c/p\u003e\n \u003cp\u003e42.29(9.33)\u003c/p\u003e\n \u003cp\u003e304.9(128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6285%;\" valign=\"top\" width=\"17.38351254480287%\"\u003e\n \u003cp\u003e121.8(31.0)\u003c/p\u003e\n \u003cp\u003e37.29(9.1)\u003c/p\u003e\n \u003cp\u003e300.57(155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1105%;\" valign=\"top\" width=\"9.13978494623656%\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5997%;\" valign=\"top\" width=\"14.695340501792115%\"\u003e\n \u003cp\u003e125.1(34)\u003c/p\u003e\n \u003cp\u003e38.5(8.45)\u003c/p\u003e\n \u003cp\u003e275.9(120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4064%;\" valign=\"top\" width=\"15.232974910394265%\"\u003e\n \u003cp\u003e134.1(23)\u003c/p\u003e\n \u003cp\u003e40.4(6.7)\u003c/p\u003e\n \u003cp\u003e230.3(79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.6382%;\" valign=\"top\" width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.8234%;\" valign=\"top\" width=\"10.21505376344086%\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003cp\u003eESR\u003c/p\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003cp\u003eMLR\u003c/p\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003cp\u003eCAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4248%;\" valign=\"top\" width=\"18.63799283154122%\"\u003e\n \u003cp\u003e25.87(31.7)\u003c/p\u003e\n \u003cp\u003e29.52(34.0)\u003c/p\u003e\n \u003cp\u003e4.41(2.45)\u003c/p\u003e\n \u003cp\u003e0.42(0.30)\u003c/p\u003e\n \u003cp\u003e242(154.2)\u003c/p\u003e\n \u003cp\u003e0.80(0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6285%;\" valign=\"top\" width=\"17.38351254480287%\"\u003e\n \u003cp\u003e24.65(30.76)\u003c/p\u003e\n \u003cp\u003e27.09(35.0)\u003c/p\u003e\n \u003cp\u003e6.52(2.56)\u003c/p\u003e\n \u003cp\u003e0.53(0.24)\u003c/p\u003e\n \u003cp\u003e475.8(148)\u003c/p\u003e\n \u003cp\u003e0.72(0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1105%;\" valign=\"top\" width=\"9.13978494623656%\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5997%;\" valign=\"top\" width=\"14.695340501792115%\"\u003e\n \u003cp\u003e17.2(17.33)\u003c/p\u003e\n \u003cp\u003e25.9(30.4)\u003c/p\u003e\n \u003cp\u003e3.69(2.29)\u003c/p\u003e\n \u003cp\u003e0.67(0.25)\u003c/p\u003e\n \u003cp\u003e228.6(124)\u003c/p\u003e\n \u003cp\u003e0.49(0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4064%;\" valign=\"top\" width=\"15.232974910394265%\"\u003e\n \u003cp\u003e3.41(2.15)\u003c/p\u003e\n \u003cp\u003e9.54(11.0)\u003c/p\u003e\n \u003cp\u003e2.03(1.07)\u003c/p\u003e\n \u003cp\u003e0.25(0.15)\u003c/p\u003e\n \u003cp\u003e137.1(77.4)\u003c/p\u003e\n \u003cp\u003e0.09(0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.6382%;\" valign=\"top\" width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.8234%;\" valign=\"top\" width=\"10.21505376344086%\"\u003e\n \u003cp\u003ePAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4248%;\" valign=\"top\" width=\"18.63799283154122%\"\u003e\n \u003cp\u003e8.89(5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6285%;\" valign=\"top\" width=\"17.38351254480287%\"\u003e\n \u003cp\u003e8.20(5.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1105%;\" valign=\"top\" width=\"9.13978494623656%\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5997%;\" valign=\"top\" width=\"14.695340501792115%\"\u003e\n \u003cp\u003e7.46(3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.4064%;\" valign=\"top\" width=\"15.232974910394265%\"\u003e\n \u003cp\u003e5.52(2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 7.6382%;\" valign=\"top\" width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e MH, mucosal healing; WBC, White Blood Cell; NE, Neutrophils; MO, Monocyte; EO, Eosinophils; BA, Basophils; HGB, Hemoglobin; HCT, hematocrit; PLT, platelet; CRP, C reactive protein; ESR, erythrocyte sedimentation rate; NLR, Neutrophil-Lymphocyte Ratio; MLR, Monocyte-Lymphocyte Ratio; PLR, Platelet-Lymphocyte Ratio; CAR, C reactive protein-Albumin Ratio; PAR, Platelet- Albumin Ratio;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePost-treatment\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;hematological parameters and Mucosal Healing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShapiro-Wilk test showed that all the data of blood test after treatment of 54 weeks were with abnormal distributions (all p\u0026lt;0.001). Mann-Whitney U test identified that the levels of NE(\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), MO(\u003cem\u003ep\u003c/em\u003e=0.001), HGB(\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001),\u0026nbsp;HCT(\u003cem\u003ep\u003c/em\u003e=0.003), PLT(\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), CRP(\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), ESR(\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), NLR(\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), MLR(\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), PLR(\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), CAR(\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), PAR(\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) were significantly associate with MH (Table 2). In the multivariate regression, we identified the following three variables as the independently associated factors with MH: PLR, CAR and ESR. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel establishment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe established two models: a simple model and a primary model. The simple model (model-1) only contains serum biomarkers including PLR, CAR and ESR. The primary model (model-2) was consisted of the following variables: PLR, CAR, ESR, HBI score and treatment with infliximab. Variables included in the simple and primary models are showed in Table 3.\u003c/p\u003e\n\u003cp\u003eTABLE 3. Multivariate logistic regression of models for Mucosal healing evaluation\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.1689%;\" valign=\"top\" width=\"12.824956672443674%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 35.8229%;\" valign=\"top\" width=\"37.954939341421145%\"\u003e\n \u003cp\u003eSimple Model (model-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 29.465%;\" valign=\"top\" width=\"46.447140381282495%\"\u003e\n \u003cp\u003ePrimary Model (model-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.1689%;\" valign=\"top\" width=\"12.824956672443674%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.7399%;\" valign=\"top\" width=\"23.223570190641247%\"\u003e\n \u003cp\u003e\u0026nbsp;OR [95%CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0831%;\" valign=\"top\" width=\"14.731369150779896%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40.1487%;\" valign=\"top\" width=\"31.7157712305026%\"\u003e\n \u003cp\u003e\u0026nbsp;OR [95%CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9463%;\" valign=\"top\" width=\"14.731369150779896%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.1689%;\" valign=\"top\" width=\"12.847222222222221%\"\u003e\n \u003cp\u003eHGB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.7399%;\" valign=\"top\" width=\"23.26388888888889%\"\u003e\n \u003cp\u003e0.996[0.971-1.021]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0831%;\" valign=\"top\" width=\"14.756944444444445%\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.6192%;\" valign=\"top\" width=\"29.51388888888889%\"\u003e\n \u003cp\u003e0.986[0.952-1.022]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9463%;\" valign=\"top\" width=\"14.756944444444445%\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.1689%;\" valign=\"top\" width=\"12.847222222222221%\"\u003e\n \u003cp\u003eHCT\u003c/p\u003e\n \u003cp\u003eNE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.7399%;\" valign=\"top\" width=\"23.26388888888889%\"\u003e\n \u003cp\u003e0.968[0.877-1.068]\u003c/p\u003e\n \u003cp\u003e0.909[0.754-1.096]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0831%;\" valign=\"top\" width=\"14.756944444444445%\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.6192%;\" valign=\"top\" width=\"29.51388888888889%\"\u003e\n \u003cp\u003e0.978[0.861-1.112]\u003c/p\u003e\n \u003cp\u003e0.847[0.676-1.060]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9463%;\" valign=\"top\" width=\"14.756944444444445%\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.1689%;\" valign=\"top\" width=\"12.847222222222221%\"\u003e\n \u003cp\u003eMO\u003c/p\u003e\n \u003cp\u003eCAR\u003c/p\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.7399%;\" valign=\"top\" width=\"23.26388888888889%\"\u003e\n \u003cp\u003e0.950[0.756-1.194]\u003c/p\u003e\n \u003cp\u003e0.022[0.002-0.219]\u003c/p\u003e\n \u003cp\u003e0.993[0.989-0.997]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0831%;\" valign=\"top\" width=\"14.756944444444445%\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.6192%;\" valign=\"top\" width=\"29.51388888888889%\"\u003e\n \u003cp\u003e0.848[0.120-5.984]\u003c/p\u003e\n \u003cp\u003e0.036 [0.004-0.320]\u003c/p\u003e\n \u003cp\u003e0.995[0.990-0.999]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9463%;\" valign=\"top\" width=\"14.756944444444445%\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.1689%;\" valign=\"top\" width=\"12.847222222222221%\"\u003e\n \u003cp\u003eESR\u003c/p\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003eHBI\u003c/p\u003e\n \u003cp\u003eStenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.7399%;\" valign=\"top\" width=\"23.26388888888889%\"\u003e\n \u003cp\u003e0.955[0.928-0.982]\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0831%;\" valign=\"top\" width=\"14.756944444444445%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.6192%;\" valign=\"top\" width=\"29.51388888888889%\"\u003e\n \u003cp\u003e0.951[0.922-0.981]\u003c/p\u003e\n \u003cp\u003e0.993[0.967-1.021]\u003c/p\u003e\n \u003cp\u003e0.907[0.824-0.999]\u003c/p\u003e\n \u003cp\u003e0.599[0.289-1.241]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9463%;\" valign=\"top\" width=\"14.756944444444445%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.1689%;\" valign=\"top\" width=\"12.847222222222221%\"\u003e\n \u003cp\u003eInfliximab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.7399%;\" valign=\"top\" width=\"23.26388888888889%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0831%;\" valign=\"top\" width=\"14.756944444444445%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.6192%;\" valign=\"top\" width=\"29.51388888888889%\"\u003e\n \u003cp\u003e6.346[3.324-12.117]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9463%;\" valign=\"top\" width=\"14.756944444444445%\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eComparisons between simple model and primary model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiagnostic value was compared between the two models. The golden standard is whether MH has been achieved under endoscopy. Table 4 shows the classification of the two models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe C-index of simple model was 0.830 (95% CI: 0.79-0.87, P\u0026lt;0.001) (Fig.1A). The sensitivity and specificity were 0.626 and 0.807, respectively (Table.4). Primary model showed a perfect capacity for predicting MH, with a C-index of 0.875 (95% CI: 0.84\u0026ndash;0.91, P\u0026lt;0.001) (Fig.1A). Sensitivity of primary model was 0.704 and specificity was 0.871 (Table 4). According to DeLong\u0026rsquo;s test, there is significant difference of C-index between primary model and simple model (Z=2.8519, P=0.0043). Primary model was superior to simple model in C-index (87.5% vs 83.0 %, \u003cem\u003ep\u003c/em\u003e=0.004). There was no statistical significance between primary model and simple model in sensitivity (70.43% vs 62.61%, \u003cem\u003ep\u003c/em\u003e=0.467), specificity (87.12% vs 80.69%, \u003cem\u003ep\u003c/em\u003e=0.448), PPV (72.97% vs 61.54%, \u003cem\u003ep\u003c/em\u003e=0.292), NPV (85.65% vs 81.39%, \u003cem\u003ep\u003c/em\u003e=0.614), and accuracy (81.61% vs 74.71%, \u003cem\u003ep\u003c/em\u003e=0.303) (Table 4).\u003c/p\u003e\n\u003cp\u003eTABLE 4.\u0026nbsp;Comparison of simple model and primary model\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.24231464737794%\"\u003e\n \u003cp\u003eDiagnostic Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.31645569620253%\"\u003e\n \u003cp\u003eSimple Model\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(%, 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.94394213381555%\"\u003e\n \u003cp\u003ePrimary Model\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(%, 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.49728752260398%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.24231464737794%\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.31645569620253%\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003cp\u003e(0.79-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.94394213381555%\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003cp\u003e(0.84-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.49728752260398%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.24231464737794%\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.31645569620253%\"\u003e\n \u003cp\u003e62.61%\u003c/p\u003e\n \u003cp\u003e(53.10-71.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.94394213381555%\"\u003e\n \u003cp\u003e70.43%\u003c/p\u003e\n \u003cp\u003e(61.21- 78.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.49728752260398%\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.24231464737794%\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.31645569620253%\"\u003e\n \u003cp\u003e80.69%\u003c/p\u003e\n \u003cp\u003e(75.02-85.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.94394213381555%\"\u003e\n \u003cp\u003e87.12%\u003c/p\u003e\n \u003cp\u003e(82.13- 91.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.49728752260398%\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.24231464737794%\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.31645569620253%\"\u003e\n \u003cp\u003e61.54%\u003c/p\u003e\n \u003cp\u003e(54.29-68.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.94394213381555%\"\u003e\n \u003cp\u003e72.97%\u003c/p\u003e\n \u003cp\u003e(65.45-79.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.49728752260398%\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.24231464737794%\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.31645569620253%\"\u003e\n \u003cp\u003e81.39%\u003c/p\u003e\n \u003cp\u003e(77.39-84.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.94394213381555%\"\u003e\n \u003cp\u003e85.65%\u003c/p\u003e\n \u003cp\u003e(81.76- 88.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.49728752260398%\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.24231464737794%\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.31645569620253%\"\u003e\n \u003cp\u003e74.71%\u003c/p\u003e\n \u003cp\u003e(69.80-79.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.94394213381555%\"\u003e\n \u003cp\u003e81.61%\u003c/p\u003e\n \u003cp\u003e(77.13-85.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.49728752260398%\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote: *\u003c/strong\u003eStatistically significant with a p-value less than 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e Simple Model: model constructed from PLR, CAR and ESR; Primary Model: model constructed from PLR, CAR, ESR, HBI and IFX treatment; PPV, positive predictive value; NPV, negative predictive value.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation and Validation of the Primary Nomogram\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA nomogram was established based on the variables in the primary model (Fig.2). Model performance was evaluated by discrimination and calibration. This model had a high C-index (0.88) as mentioned above. The calibration curve also showed satisfactory performance (Fig. 3A). The internal validation was performed in 112 patients with small bowel involvement. After validation, the C-index of the model was 0.834 (95% CI: 0.76-0.91, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) (Fig.1B). The calibration curve in validation group is shown in Fig.3B. The internal validation also performed good in discrimination and calibration.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, the most important findings include: (1) Serum biomarkers such as PLR, CAR and ESR after approximately 1 year treatment was independently associated with MH. (2) Patients with lower HBI score and the use of infliximab are more likely to achieve MH. Based on this, we developed prediction model with the above significant variables to evaluate MH in patients with CD.\u003c/p\u003e\n\u003cp\u003eThe routine blood test is the most fundamental and accessible examination, which has long been proposed as an essential assistant tool for disease assessment.\u003csup\u003e20\u003c/sup\u003e In our study, there was no statistical significance between pre-treatment serum inflammatory indexes and MH. Thus, we were unable to develop a pre-treatment model. However, in the post-treatment data, we found the combination use of\u0026nbsp;PLR, CAR and ESR\u0026nbsp;can effectively evaluate MH in patients with CD.\u0026nbsp;Platelet count can be affected by cytokines released in acute inflammation. Thrombocytosis and high ESR level are common feature of acute inflammation. Lymphocytes is the basic component of the adaptive and innate immune system. It is demonstrated that PLR increased significantly in endoscopically active ulcerative colitis.\u003csup\u003e21\u003c/sup\u003e CRP is the most widely used serological indicators in clinical evaluation of disease activity in CD.\u003csup\u003e22,23\u003c/sup\u003e Serum ALB is an indicator of nutrition, synthesis rate of which directly affected by the severity of acute infection.\u0026nbsp;CAR was initially used to identify critical patients in emergency ward and predict disease progression in Takayasu arteritis and cancer in recent years.\u003csup\u003e24,25\u003c/sup\u003e It is reported that CAR is useful biomarker of disease activity and histological activity in CD.\u003csup\u003e26\u003c/sup\u003e Consistent with previous research, we included the above three variables including PLR, CAR and ESR into our model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSome clinical\u0026nbsp;characteristics can also predict MH in\u0026nbsp;patients with CD. In\u0026nbsp;is reported that early introduction of tumor necrosis factor (TNF) antagonists, particularly in combination with immunosuppressives associate with MH in patients with CD.\u0026nbsp;\u003csup\u003e27\u003c/sup\u003e In our study, patients received infliximab was significantly associate with an increased rate of MH, consistent with previous reports. HBI was derived to simplify calculation of the Crohn\u0026apos;s disease activity index (CDAI). We newly found that HBI was associated with MH. In univariate logistic regression analyses, diagnose age and lumen stenosis were found associate with MH in the present data. Unfortunately, neither of them was included in the final multivariate logistic regression model.\u003c/p\u003e\n\u003cp\u003eA simple model and a primary model were established in the study. The\u0026nbsp;simple model is simpler, easier to operate clinically, and with favorable accuracy.\u0026nbsp;However, taking\u0026nbsp;into account of the C-index and the calibration plots, primary model showed better discrimination ability. Reliable nomogram based on aforementioned factors was constructed and\u0026nbsp;showed excellent evaluation abilities for MH among patients with CD. Parameters in the monogram are easy to obtain, which increases the clinical practicality. This nomogram can predict MH probability in patients with CD after one year of treatment and provide reference for doctors to perform endoscopic review. If the prediction results indicate low probability of MH, doctors could temporarily eliminate endoscopy and adjust treatment regimen, avoiding repeated and unnecessary invasive endoscopy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFecal calprotectin (FC) has been widely clarified for the correlation with endoscopically proven CD activity.\u003csup\u003e28-30\u003c/sup\u003e However, FC is still not commonly used in clinical practice because of detection results may vary from different kits of calprotectin. In addition, some researchers pointed out that PPV of FC for MH was not high enough and FC was not sensitive to assess CD activity with small intestine involvement.\u003csup\u003e31,32\u003c/sup\u003e For these reasons, we did not include FC in present study.\u0026nbsp;Nevertheless, we validated primary model in\u0026nbsp;patients with CD\u0026nbsp;with small intestine involvement, indicating a good evaluation effect.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study has some limitations. Firstly, although the patients we included from two tertiary hospitals in Eastern China, the results may not represent the general population of\u0026nbsp;patients with CD. Secondly, only internal validation was performed in the present study. Results of present study still need to be verified by external large-scale clinical studies with follow-up study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, this study provides comprehensive insights into serum inflammatory index and clinical information to evaluate MH after treatment in patients with CD. We conducted a nomogram, providing a portable decision tool for early MH screening and clinical decision of endoscopic review time. More prospective studies in the future are warrant to perform.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was approved by Clinical Research Ethics Committee of The First Affiliated Hospital of Nanjing Medical University, China(ref:2021-SR-235), in compliance with the Declaration of Helsinki. All patients in the study gave their informed consent for reviewing their clinical data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe share our raw data by providing it in a supplementary file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests and Declaration of financial interests:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no competing interests in the article. The authors have nothing to declare regarding the work under consideration for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the National Natural Science Foundation of China, No. 81770553 and 82070568.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eN.T. and H.C. wrote the manuscript. N.T. and R.C. contributed to data accumulation. H.C. and N.T. performed statistical analysis. H.Z. and W.T. have participated in the study design. H.Z. critically revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNana Tang, Han Chen and Ruidong Chen have contributed equally to this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information (optional)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNana Tang\u003csup\u003e1\u003c/sup\u003e, Han Chen\u003csup\u003e1\u003c/sup\u003e, Ruidong Chen\u003csup\u003e2\u003c/sup\u003e, Wen Tang\u003csup\u003e2\u003c/sup\u003e, Hongjie Zhang\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e1. Department of Gastroenterology,\u0026nbsp;The First Affiliated Hospital of Nanjing Medical University，China\u003c/p\u003e\n\u003cp\u003e2. Department of Gastroenterology, The Second Affiliated Hospital of Soochow University, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHanzel J. A Novel Endoscopic Score for Postoperative Recurrence of Crohn\u0026apos;s Disease: More Information Needed. \u003cem\u003eAm J Gastroenterol. \u003c/em\u003e2021;116(1):217-218.\u003c/li\u003e\n\u003cli\u003eHo GT, Cartwright JA, Thompson EJ, Bain CC, Rossi AG. Resolution of Inflammation and Gut Repair in IBD: Translational Steps Towards Complete Mucosal Healing. \u003cem\u003eInflamm Bowel Dis. \u003c/em\u003e2020;26(8):1131-1143.\u003c/li\u003e\n\u003cli\u003eNunez FP, Mahadevan U, Quera R, Bay C, Ibanez P. Treat-to-target approach in the management of inflammatory Bowel disease. \u003cem\u003eGastroenterol Hepatol. \u003c/em\u003e2021;44(4):312-319.\u003c/li\u003e\n\u003cli\u003eLafeuille P, Hordonneau C, Vignette J, et al. Transmural healing and MRI healing are associated with lower risk of bowel damage progression than endoscopic mucosal healing in Crohn\u0026apos;s disease. \u003cem\u003eAliment Pharmacol Ther. \u003c/em\u003e2021;53(5):577-586.\u003c/li\u003e\n\u003cli\u003eDal Buono A, Roda G, Argollo M, Zacharopoulou E, Peyrin-Biroulet L, Danese S. Treat to target or \u0026apos;treat to clear\u0026apos; in inflammatory bowel diseases: one step further? \u003cem\u003eExpert Rev Gastroenterol Hepatol. \u003c/em\u003e2020;14(9):807-817.\u003c/li\u003e\n\u003cli\u003eCucchiara S, D\u0026apos;Arcangelo G, Isoldi S, Aloi M, Stronati L. Mucosal healing in Crohn\u0026apos;s disease: new insights. \u003cem\u003eExpert Rev Gastroenterol Hepatol. \u003c/em\u003e2020;14(5):335-345.\u003c/li\u003e\n\u003cli\u003eZhou FS, Gao N, Sun X, et al. C-reactive protein/abumin ratio is a useful biomarker for predicting the mucosal healing in the Crohn disease: A retrospective study. \u003cem\u003eMedicine (Baltimore). \u003c/em\u003e2021;100(10):e24925.\u003c/li\u003e\n\u003cli\u003eFGC EP, Rosa RM, da Cunha PFS, de Souza SCS, de Abreu Ferrari ML. Faecal calprotectin is the biomarker that best distinguishes remission from different degrees of endoscopic activity in Crohn\u0026apos;s disease. \u003cem\u003eBMC Gastroenterol. \u003c/em\u003e2020;20(1):35.\u003c/li\u003e\n\u003cli\u003eColombel JF, Adedokun OJ, Gasink C, et al. Combination Therapy With Infliximab and Azathioprine Improves Infliximab Pharmacokinetic Features and Efficacy: A Post Hoc Analysis. \u003cem\u003eClin Gastroenterol Hepatol. \u003c/em\u003e2019;17(8):1525-1532 e1521.\u003c/li\u003e\n\u003cli\u003eDulai PS, Boland BS, Singh S, et al. Development and Validation of a Scoring System to Predict Outcomes of Vedolizumab Treatment in Patients With Crohn\u0026apos;s Disease. \u003cem\u003eGastroenterology. \u003c/em\u003e2018;155(3):687-695 e610.\u003c/li\u003e\n\u003cli\u003eMao R, Qiu Y, Chen BL, et al. Factors associated with the achievement of mucosal healing in Crohn\u0026apos;s disease: the benefit of endoscopic monitoring in treating to target. \u003cem\u003eTherap Adv Gastroenterol. \u003c/em\u003e2017;10(6):453-463.\u003c/li\u003e\n\u003cli\u003eFeng JR, Qiu X, Wang F, et al. Diagnostic Value of Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio in Crohn\u0026apos;s Disease. \u003cem\u003eGastroenterol Res Pract. \u003c/em\u003e2017;2017:3526460.\u003c/li\u003e\n\u003cli\u003eChen YH, Wang L, Feng SY, Cai WM, Chen XF, Huang ZM. The Relationship between C-Reactive Protein/Albumin Ratio and Disease Activity in Patients with Inflammatory Bowel Disease. \u003cem\u003eGastroenterol Res Pract. \u003c/em\u003e2020;2020:3467419.\u003c/li\u003e\n\u003cli\u003eDaniluk U, Daniluk J, Krasnodebska M, Lotowska JM, Sobaniec-Lotowska ME, Lebensztejn DM. The combination of fecal calprotectin with ESR, CRP and albumin discriminates more accurately children with Crohn\u0026apos;s disease. \u003cem\u003eAdv Med Sci. \u003c/em\u003e2019;64(1):9-14.\u003c/li\u003e\n\u003cli\u003eAlkhatry M, Al-Rifai A, Annese V, et al. First United Arab Emirates consensus on diagnosis and management of inflammatory bowel diseases: A 2020 Delphi consensus. \u003cem\u003eWorld J Gastroenterol. \u003c/em\u003e2020;26(43):6710-6769.\u003c/li\u003e\n\u003cli\u003eLevesque BG, Sandborn WJ, Ruel J, Feagan BG, Sands BE, Colombel JF. Converging goals of treatment of inflammatory bowel disease from clinical trials and practice. \u003cem\u003eGastroenterology. \u003c/em\u003e2015;148(1):37-51 e31.\u003c/li\u003e\n\u003cli\u003eaf Bjorkesten CG, Nieminen U, Turunen U, Arkkila PE, Sipponen T, Farkkila MA. Endoscopic monitoring of infliximab therapy in Crohn\u0026apos;s disease. \u003cem\u003eInflamm Bowel Dis. \u003c/em\u003e2011;17(4):947-953.\u003c/li\u003e\n\u003cli\u003ePark Y, Cheon JH, Park YL, et al. Development of a Novel Predictive Model for the Clinical Course of Crohn\u0026apos;s Disease: Results from the CONNECT Study. \u003cem\u003eInflamm Bowel Dis. \u003c/em\u003e2017;23(7):1071-1079.\u003c/li\u003e\n\u003cli\u003eSatsangi J, Silverberg MS, Vermeire S, Colombel JF. The Montreal classification of inflammatory bowel disease: controversies, consensus, and implications. \u003cem\u003eGut. \u003c/em\u003e2006;55(6):749-753.\u003c/li\u003e\n\u003cli\u003eSun S, Karsdal MA, Mortensen JH, et al. Serological Assessment of the Quality of Wound Healing Processes in Crohn\u0026apos;s Disease. \u003cem\u003eJ Gastrointestin Liver Dis. \u003c/em\u003e2019;28:175-182.\u003c/li\u003e\n\u003cli\u003eAkpinar MY, Ozin YO, Kaplan M, et al. Platelet-to-lymphocyte Ratio and Neutrophil-to-lymphocyte Ratio Predict Mucosal Disease Severity in Ulcerative Colitis. \u003cem\u003eJ Med Biochem. \u003c/em\u003e2018;37(2):155-162.\u003c/li\u003e\n\u003cli\u003eLin X, Qiu Y, Feng R, et al. Normalization of C-Reactive Protein Predicts Better Outcome in Patients With Crohn\u0026apos;s Disease With Mucosal Healing and Deep Remission. \u003cem\u003eClin Transl Gastroenterol. \u003c/em\u003e2020;11(2):e00135.\u003c/li\u003e\n\u003cli\u003eMa C, Battat R, Khanna R, Parker CE, Feagan BG, Jairath V. What is the role of C-reactive protein and fecal calprotectin in evaluating Crohn\u0026apos;s disease activity? \u003cem\u003eBest Pract Res Clin Gastroenterol. \u003c/em\u003e2019;38-39:101602.\u003c/li\u003e\n\u003cli\u003eSeringec Akkececi N, Yildirim Cetin G, Gogebakan H, Acipayam C. The C-Reactive Protein/Albumin Ratio and Complete Blood Count Parameters as Indicators of Disease Activity in Patients with Takayasu Arteritis. \u003cem\u003eMed Sci Monit. \u003c/em\u003e2019;25:1401-1409.\u003c/li\u003e\n\u003cli\u003eWu J, Tan W, Chen L, Huang Z, Mai S. Clinicopathologic and prognostic significance of C-reactive protein/albumin ratio in patients with solid tumors: an updated systemic review and meta-analysis. \u003cem\u003eOncotarget. \u003c/em\u003e2018;9(17):13934-13947.\u003c/li\u003e\n\u003cli\u003eNassri A, Muftah M, Nassri R, et al. Novel Inflammatory-Nutritional Biomarkers as Predictors of Histological Activity in Crohn\u0026apos;s Disease. \u003cem\u003eClin Lab. \u003c/em\u003e2020;66(7).\u003c/li\u003e\n\u003cli\u003eVasudevan A, Raghunath A, Anthony S, et al. Higher Mucosal Healing with Tumor Necrosis Factor Inhibitors in Combination with Thiopurines Compared to Methotrexate in Crohn\u0026apos;s Disease. \u003cem\u003eDig Dis Sci. \u003c/em\u003e2019;64(6):1622-1631.\u003c/li\u003e\n\u003cli\u003eLeach ST, Day AS, Messenger R, et al. Fecal Markers of Inflammation and Disease Activity in Pediatric Crohn Disease: Results from the ImageKids Study. \u003cem\u003eJ Pediatr Gastroenterol Nutr. \u003c/em\u003e2020;70(5):580-585.\u003c/li\u003e\n\u003cli\u003eKawashima K, Ishihara S, Yuki T, et al. Fecal Calprotectin More Accurately Predicts Endoscopic Remission of Crohn\u0026apos;s Disease than Serological Biomarkers Evaluated Using Balloon-assisted Enteroscopy. \u003cem\u003eInflamm Bowel Dis. \u003c/em\u003e2017;23(11):2027-2034.\u003c/li\u003e\n\u003cli\u003eKennedy NA, Jones GR, Plevris N, Patenden R, Arnott ID, Lees CW. Association Between Level of Fecal Calprotectin and Progression of Crohn\u0026apos;s Disease. \u003cem\u003eClin Gastroenterol Hepatol. \u003c/em\u003e2019;17(11):2269-2276 e2264.\u003c/li\u003e\n\u003cli\u003eCosta F, Mumolo MG, Ceccarelli L, et al. Calprotectin is a stronger predictive marker of relapse in ulcerative colitis than in Crohn\u0026apos;s disease. \u003cem\u003eGut. \u003c/em\u003e2005;54(3):364-368.\u003c/li\u003e\n\u003cli\u003eVerdejo C, Hervias D, Roncero O, et al. Fecal calprotectin is not superior to serum C-reactive protein or the Harvey-Bradshaw index in predicting postoperative endoscopic recurrence in Crohn\u0026apos;s disease. \u003cem\u003eEur J Gastroenterol Hepatol. \u003c/em\u003e2018;30(12):1521-1527.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Crohn’s disease, mucosal healing, nomogram, PLR, endoscopic","lastPublishedDoi":"10.21203/rs.3.rs-985096/v3","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-985096/v3","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eMucosal healing (MH) has become the treatment goal of patients with Crohn\u0026rsquo;s disease (CD). This study aims to develop a noninvasive and reliable clinical tool for individual evaluation of mucosal healing in patients with Crohn\u0026rsquo;s disease.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA multicenter retrospective cohort was established. Clinical and serological variables were collected. Separate risk factors were incorporated into a binary logistic regression model. A primary model and a simple model were established, respectively. The model performance was evaluated with C-index, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy. Internal validation was performed in patients with small intestinal lesions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 348 consecutive patients diagnosed with CD who underwent endoscopic examination and review after treatment from January 2010 to June 2021 were composed in the derivation cohort, and 112 patients with small intestinal lesions were included in the validation cohort. The following variables were independently associated with the MH and were subsequently included into the primary prediction model: PLR (platelet to lymphocyte ratio), CAR (C-reactive protein to albumin ratio), ESR (erythrocyte sedimentation rate), HBI (Harvey-Bradshaw Index) score and infliximab treatment. The simple model only included factors of PLR, CAR and ESR. The primary model performed better than the simple one in C-index (87.5% vs 83.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). There was no statistical significance between these two models in sensitivity (70.43% vs 62.61%, p\u0026thinsp;=\u0026thinsp;0.467), specificity (87.12% vs 80.69%, p\u0026thinsp;=\u0026thinsp;0.448), PPV (72.97% vs 61.54%, p\u0026thinsp;=\u0026thinsp;0.292), NPV (85.65% vs 81.39%, p\u0026thinsp;=\u0026thinsp;0.614), and accuracy (81.61% vs 74.71%, p\u0026thinsp;=\u0026thinsp;0.303). The primary model had good calibration and high levels of explained variation and discrimination in validation cohort.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis model can be used to predict MH in post-treatment patients with CD. It can also be used as an indication of endoscopic surveillance to evaluate mucosal healing in patients with CD after treatment.\u003c/p\u003e","manuscriptTitle":"Combination of Serological Biomarkers and Clinical Features to predict Mucosal Healing in Crohn’s Disease: A Multicenter Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":3,"date":"2022-03-11 15:17:28","doi":"10.21203/rs.3.rs-985096/v3","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-04-07T10:07:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-03-23T02:31:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"cadce357-495f-4163-87dc-176f470f15c9","date":"2022-03-10T03:42:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-03-03T08:30:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-03-03T08:25:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-03-02T10:08:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-03-02T10:05:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2022-02-18T01:58:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"48bd6c6f-3c01-4fa4-8f5e-63f57640b34e","owner":[],"postedDate":"March 11th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-04-27T06:44:20+00:00","versionOfRecord":[],"versionCreatedAt":"2022-03-11 15:17:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v3","identity":"rs-985096","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-985096","identity":"rs-985096","version":["v3"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}