Development and Validation of a Modified, Simplified Prediction Model Based on the Kyoto Classification of Gastritis for Current Helicobacter Pylori Infection

Development and Validation of a Modified, Simplified Prediction Model Based on the Kyoto Classification of Gastritis for Current Helicobacter Pylori Infection | 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 Development and Validation of a Modified, Simplified Prediction Model Based on the Kyoto Classification of Gastritis for Current Helicobacter Pylori Infection Mei Yang, Xiaomei Ma, Yujie Wang, Yu Long, Jin Shan, Tianxu Chen, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8827948/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective This study aims to validate the application value of the endoscopic Kyoto Classification of Gastritis for determining the current status of Helicobacter pylori (H. pylori) infection in a population, and to construct a modified simplified prediction model based on this scoring system. Methods Data were collected from 313 patients who underwent gastroscopy and a 13C- or 14C-breath test (UBT) at the Digestive Endoscopy Center of Chengdu Third People’s Hospital between June 2022 and July 2023, and from 175 patients at Qionglai Second People’s Hospital between April 2022 and July 2023. The dataset from our hospital was used as the development set to construct a simplified prediction model for the current H. pylori infection, while the external dataset was used for validation. The model was developed using binary logistic regression and clinical expertise, and ROC curve analysis and the DeLong test were employed to compare diagnostic performance. Results The Kyoto Classification showed area under the curve (AUC) values of 0.862 (95%, CI: 0.822–0.902) and 0.850 (95%, CI: 0.775–0.925) in the development and validation sets, respectively. The modified model incorporating the absence of a regular arrangement of collecting venules (RAC), mucosal swelling, and diffuse/spotty redness achieved higher AUC values: 0.922 (95% CI: 0.888–0.956) and 0.914 (95% CI: 0.864–0.964) (p < 0.05). Accuracy rates were 87.2% and 89.1% in the development and validation sets, respectively. Conclusion The modified, simplified prediction model demonstrated superior diagnostic performance to the Kyoto Classification. This makes it a practical tool for endoscopists to use when assessing current H. pylori infection. Kyoto Classification of Gastritis Helicobacter pylori digestive endoscopy Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Helicobacter pylori (H. pylori) is a spiral-shaped, microaerophilic, flagellated, Gram-negative bacillus that can switch between spiral and spherical forms. Its spiral morphology enables it to survive and move around the human gastrointestinal tract, while its spherical form helps it to colonise gastric epithelial cells [ 1 ]. H. pylori infection is now recognized as a primary cause of gastric carcinogenesis [ 2 ]. The International Agency for Research on Cancer has classified H. pylori infection as a Group 1 carcinogen. It is implicated in approximately 89% of gastric cancer cases, establishing it as a critical target for the prevention and control of gastric cancer [ 3 ]. Studies of Chinese populations demonstrate that the eradication of H. pylori significantly reduces the incidence of gastric cancer, with earlier intervention yielding greater reductions [ 4 – 6 ], and the benefits increasing with longer follow-up after eradication. In addition to gastrointestinal disorders, H. pylori infection has been linked to extra-digestive conditions, including autoimmune diseases, iron-deficiency anaemia, idiopathic thrombocytopenic purpura and cardiovascular/cerebrovascular diseases [ 7 – 10 ]. Therefore, early detection and eradication of H. pylori are pivotal for reducing morbidity across multiple systems. Current clinical diagnostics for H. pylori comprise both invasive and non-invasive methods. Non-invasive techniques include the urea breath test (UBT), stool antigen testing and serology. Advances in endoscopy now enable real-time assessment of H. pylori infection during procedures. At the 85th Annual Meeting of the Japanese Gastroenterological Endoscopy Society in Kyoto in 2013, the Kyoto Classification of Gastritis was formalised[ 11 ]. The Kyoto Classification of Gastritis focuses on five key features: atrophy, intestinal metaplasia, fold enlargement, nodularity and diffuse redness. A score of ≥ 2 indicates H. pylori infection, and international validation supports its clinical utility [ 12 ]. Chinese studies reported high diagnostic accuracy: Zhang et al. [ 13 ] showed 82.9% overall accuracy using endoscopic mucosal features, while Zhao et al. [ 14 ] confirmed diffuse redness and mucosal swelling were found to be robust markers of active H. pylori infection, whereas atrophy and intestinal metaplasia were found to be of limited utility. A modified, simplified prediction model, such as Wang et al’s seven-feature model (atrophy, fold enlargement, nodularity, diffuse redness, sticky mucus, spotty redness and fundic gland polyps), demonstrates improved performance [ 15 ]. However, despite these advances, the Kyoto Classification of Gastritis has not yet been fully popularized in China, mainly due to its multiple observation indicators, the difficulty in distinguishing some endoscopic mucosal manifestations and the insufficient diagnostic value of certain mucosal findings for current H. pylori infection. Therefore, there is an urgent need for a simpler diagnostic model that endoscopists can easily master. Adoption of the Kyoto Classification of Gastritis in China remains limited due to complex criteria, variability in the interpretation of mucosal features by different observers, and suboptimal diagnostic value of certain features. Simplified models are urgently needed for clinical translation. The Kyoto Gastritis Score has been successfully streamlined through overseas studies: Kim et al. [ 16 ] developed a model with four features (RAC, nodularity, diffuse redness and spotty redness), achieving 98% accuracy in identifying active H. pylori infection at scores of 2 or above. However, nodules predominantly occur in young patients and are rarely observed in elderly patients. Spotty erythema can also be detected in patients following eradication therapy, and the assessment of both spotty and diffuse erythema is subjective. Meanwhile, simplified Kyoto classification models of gastritis remain underreported in China. Therefore, the aims of this study are to: (1) validate the Kyoto Classification of Gastritis as a predictor of active H. pylori infection in Chinese populations; (2) evaluate its diagnostic accuracy and reliability; (3) develop a simplified H. pylori prediction model tailored to Chinese clinical practice and population characteristics, which is also endoscopist-friendly and based on statistical and clinical optimization. Materials and Methods Patient sets We retrospectively collected endoscopic mucosal features and 13C-UBT results from 313 patients who underwent upper gastrointestinal endoscopy and 13C-UBT at the Digestive Endoscopy Center of Chengdu Third People’s Hospital between June 2022 and July 2023. These data were used to develop a simplified endoscopic diagnostic model for H. pylori infection, which is based on the Kyoto Classification of Gastritis. Additionally, 175 patients who underwent upper gastrointestinal endoscopy and 14C-UBT at Qionglai Second People’s Hospital between April 2022 and July 2023 were included for validation, with endoscopic mucosal features and 14C-UBT results analyzed. Human Ethics and Consent to Participate This study was reviewed and approved by the Ethics Committee of the Third People's Hospital of Chengdu (2022-S-98) and carried out in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study and the use of anonymized data, the requirement for informed consent was waived by the aforementioned Ethics Committee. Clinical trial number: not applicable. Inclusion and Exclusion Criteria Inclusion criteria: Patients who underwent standardized 13C- or 14C-UBT. Patients with confirmed absence of prior H. pylori eradication therapy and exclusion of factors predisposing to false-positive/false-negative UBT results (via medical history review). Patients with complete clinical records and endoscopic reports. Exclusion criteria Prior gastric/duodenal surgery. Use of antibiotics, bacteriostatic traditional Chinese medicine, bismuth agents, proton pump inhibitors, or potassium-competitive acid blockers within 1–2 months before the procedure. Advanced gastric cancer, active/history of upper gastrointestinal bleeding, autoimmune diseases, or gastric varices. Suspected autoimmune gastritis. Incomplete endoscopic examination. Pregnant individuals or children. Endoscopic Procedures All patients provided informed consent before undergoing painless white-light gastroscopy. The procedures were performed by senior endoscopists with ≥ 5 years of experience and ≥ 1,000 independent operations. Pre-procedure preparation included oral administration of dimethicone (20 minutes prior) and positioning guidance. An endoscopist with ≥ 15 years of experience, who was blinded to the results of the UBT, interpreted the endoscopic mucosal features, which were classified as present or absent. Discrepancies in ambiguous cases were resolved by a second senior endoscopist via consensus. All patients underwent 14C- or 13C-UBT. H. pylori Infection Status 13C-UBT: Conducted using the HY-IREXBplus 13C-UBT (Guangzhou Huayou Mingkang Optoelectronic Technology Co., Ltd.). A delta over baseline (DOB) value > 6 dpm indicated active H. pylori infection, while a value of ≤ 6 dpm indicated no infection. 14C-UBT: Performed using a 14C-UBT. A value > 150 dmp/mmol CO₂ indicated active infection; a value of ≤ 150 dmp/mmol CO₂ indicated no infection. Endoscopic Mucosal Features and the Kyoto Classification of Gastritis The sixteenth endoscopic mucosal features were evaluated according to the Kyoto Gastritis Classification. These features include atrophy, intestinal metaplasia, fold enlargement, nodularity, diffuse redness, sticky mucus, fundic gland polyps, map-like redness, mucosal swelling, spotty redness, xanthoma, hyperplastic polyps, old bleeding spots, red streak, multiple white and flat elevated lesions and RAC (Fig. 1 ). In the Kyoto Classification of Gastritis, mucosal swelling (thickened, soft gastric mucosa with widened areas of inflammation/oedema), diffuse redness (continuous uniform redness of the non-atrophic corpus mucosa, a basic sign of H. pylori gastritis, aided by RAC status for scoring), and spotty redness (irregular small red spots or patches without unevenness, often on a background of diffuse redness from the corpus to the fundus) are all core endoscopic features that are specific to, or linked to, a current H. pylori infection. The Kyoto Gastritis Score (0–8 points) is calculated based on five features: Atrophy (0–2 points, Intestinal metaplasia, fold enlargement (0–1 point), nodularity (0–1 point) and diffuse redness (0–2 points). Modified Simplified Prediction Model for Active H. pylori Infection Patients were divided into two groups according to their testing dates: an in-house development group (June 2022 - August 2023) and an external validation group (April 2022 - July 2023). Due to previous reports of ambiguous boundaries between spotty and diffuse redness [ 16 ], these features were combined into a single “erythema” category. Chi-square or Fisher’s exact tests were used to identify endoscopic mucosal features associated with active H. pylori infection (p < 0.05). Selected features (based on statistical significance, prior literature, and clinical expertise) were then subjected to binary logistic regression in order to construct a modified, simplified prediction model. Statistical Analysis The data were analysed using SPSS 26.0. Continuous variables that were normally distributed were reported as mean ± standard deviation (x ± s), and those that were not normally distributed were reported as median (interquartile range) [M (Q1, Q3)]. Categorical data were presented as counts (%), and comparisons were made using chi-squared or Fisher’s exact tests. Statistically significant mucosal features identified in the univariate analysis (p < 0.05) were selected for binary logistic regression via literature/clinical review. A modified model was then developed using regression coefficients and clinical judgement. The diagnostic performance of the Kyoto Gastritis Score and the modified model was evaluated using sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve (AUC). DeLong’s test was used to compare the ROC curves of the models. An AUC > 0.75 indicated a high diagnostic value and a p-value < 0.05 denoted statistical significance. Results The development set comprised 313 patients, of whom 142 (45.37%) were H. pylori-positive and 171 were H. pylori-negative. Of these, there were 145 males and 168 females with a mean age of 59.71 ± 14.36 years. The validation set (n = 175) comprised 50 H. pylori-positive patients (28.6%) and 125 H. pylori-negative patients. There were 60 males and 115 females, with an average age of 52.03 ± 11.62 years. Figure 2 shows the flowchart of patients tested for H. pylori infection. Development Group Results The different endoscopic mucosal features based on H. pylori infection status in H. pylori-positive and H. pylori-negative patients were shown in Table 1 . Several presentations in the H. pylori-positive group were considerably higher than those in the H. pylori-negative group, including atrophy (65.5% vs. 26.3%, p < 0.001), intestinal metaplasia (59.9% vs. 26.3%, p < 0.001), fold enlargement (51.4% vs. 0%, p < 0.001), diffuse redness (78.2% vs. 5.8%, p < 0.001), and spotty redness (7.7% vs. 2.3%, p = 0.026). Significantly higher rates were observed for the following presentations in the H. pylori-negative group, including RAC (94.2% vs. 3.3%, p < 0.001), fundic gland polyps (2.1% vs. 14.0%, p < 0.001), multiple flat elevations (12.9% vs. 0.7%, p < 0.001) and old bleeding spots (4.7% vs. 0.7%, p < 0.001). No significant differences were observed in nodularity (0.7% vs. 0%, p = 0.454) or map-like redness (14% vs. 2.9%, p = 0.462), xanthoma (2.8% vs. 2.3%, p = 0.790), hyperplastic polyps (1.4% vs. 0%, p = 0.205) or linear erythema (0% vs. 2.3%, p = 0.126). Table 1 Differences in gastric mucosal endoscopic manifestations among patients with different H. pylori infection statuses in the development center Endoscopic mucosal presentation Hp(+) [n(%)] Hp(-) [n(%)] χ2 p value Total 142 171 RAC 33(23.2) 161(94.2) 165.549 <0.001 Atrophy 93(65.5) 45(26.3) 48.303 <0.001 Intestinal metaplasia 85(59.9) 45(26.3) 35.946 <0.001 Fold enlargement 73(51.4) 0 114.647 <0.001 Nodularity 1(0.7) 0 1.208 0.454 Diffuse redness 111(78.2) 10(5.8) 171.109 <0.001 Spotty redness 11(7.7) 4(2.3) 4.971 0.026 Mucosal swelling 141(99.3) 10(5.8) 271.316 <0.001 Map-like redness 2(1.4) 5(2.9) 0.815 0.462 Diffuse redness or Spotty redness 106(74.6) 8(4.7) 164.017 <0.001 Sticky mucus 48(33.8) 3(1.8) 58.421 <0.001 Fundic gland polyp 3(2.1) 24(14.0) 13.990 <0.001 Xanthoma 4(2.8) 4(2.3) 0.071 0.790 hyperplastic polyps 2(1.4) 0 2.424 0.205 Old bleeding spots 1(0.7) 8(4.7) 4.378 0.044 Red streak 0 4(2.3) 3.365 0.129 Multiple white and flat elevated lesions 1(0.7) 22(12.9) 16.852 <0.001 Diagnostic Value of the Kyoto Gastritis Score for Active H. pylori Infection in the Development Group ROC curve analysis revealed the following area under the curve (AUC) values for individual Kyoto Gastritis Score features in diagnosing active H. pylori infection: atrophy (0.696, 95% confidence interval (CI) = 0.636–0.755), intestinal metaplasia (0.668, 95% CI = 0.607–0.729), fold enlargement (0.757, 95% CI = 0.700-0.814), nodularity (0.504, 95% CI = 0.439–0.568) and diffuse redness (0.862, 95% CI = 0.816–0.907). Atrophy, intestinal metaplasia, fold enlargement and diffuse redness demonstrated good diagnostic performance, whereas nodularity lacked independent predictive value. The composite Kyoto Gastritis Score achieved an AUC of 0.862 (95% CI = 0.822–0.902) (Table 2 ), indicating optimal diagnostic efficacy. At a threshold score of ≥ 2, the diagnostic accuracy was 78.9%, with a sensitivity of 83.8%, a specificity of 74.9%, a positive predictive value (PPV) of 84.8%, and a negative predictive value (NPV) of 92.8%. Table 2 The value of Kyoto Gastritis Score in determining current Hp infection in the development center Endoscopic mucosal presentation AUC Se p value 95%CI Atrophy 0.696 0.030 <0.001 0.636 ~ 0.755 Intestinal metaplasia 0.668 0.031 <0.001 0.607 ~ 0.729 Fold enlargement 0.757 0.029 <0.001 0.700 ~ 0.814 Nodularity 0.504 0.033 0.915 0.439 ~ 0.568 Diffuse redness 0.862 0.023 <0.001 0.816 ~ 0.907 Kyoto Gastritis Score 0.862 0.020 <0.001 0.822 ~ 0.902 Development and Diagnostic Value of the Modified Simplified Prediction Model in the Development Group Endoscopic mucosal manifestations that were statistically different from H. pylori infection in the univariate analysis were screened again for entry into binary logistic regression analysis. Some endoscopic mucosal manifestations in Table 1 may be affected by age or other factors. For example, fold enlargement and atrophy may progress with age, which could lead to errors in diagnosing H. pylori infection [ 17 – 19 ]. At the same time, previous studies have shown that, following H. pylori eradication treatment, symptoms such as atrophy and bowelisation of the mucosa do not improve completely. This results in relatively low specificity for diagnosing H. pylori infection [ 20 ]. A Japanese study [ 21 ] showed that sticky mucus had a specificity of 97.1% for diagnosing H. pylori infection, but its low sensitivity may be related to patients routinely taking antifoaming agents before gastroscopy. Therefore, we excluded these indicators. Diffuse and spotty redness are considered strong predictors of H. pylori infection and can differentiate between previous and current infection. To further facilitate the clinical application of the scoring model, we performed a binary logistic regression analysis of three submucosal manifestations: RAC, mucosal swelling and combined indicators (spotty or diffuse redness). The results, shown in Table 3 , indicated that RAC suggested a low risk of current H. pylori infection, whereas mucosal swelling and combined indicators suggested a high risk. Table 3 Binary Logistic Regression Analysis of Endoscopic Manifestations in Patients with Different Hp Infection States Endoscopic mucosal presentation β SE Wald χ2 OR 95%CI p value RAC -1.485 0.707 4.414 0.227 0.057 ~ 0.905 0.036 Mucosal swelling 2.562 0.398 41.441 12.964 5.942 ~ 28.284 <0.001 Map-like redness and Spotty redness 1.704 0.714 1.367 5.496 1.298 ~ 23.178 0.021 The improved, simplified predictive model was constructed based on binary logistic regression analysis coefficients, clinical experience constructs and ease of clinical use. The following scores were assigned: absence of RAC, mucosal swelling and spotty or diffuse redness each counted as 1 point, with a total possible score of 3 (Table 4 ). We used the modified simplified predictive model to reassess the mucosal manifestations of endoscopic patients. The scores were 2–3 and 0–1 for H. pylori-infected and H. pylori-uninfected patients, respectively; this difference was statistically significant (p < 0.001). The results of the ROC curve analysis indicated that the absence of RAC and mucosal swelling, as well as the combined indicator (spotty or diffuse redness), were important in validating the presenting infection of H. pylori. The AUC values for the centralised determination of the infection were 0.978 (95% CI = 0.959–0.997), 0.865 (95% CI = 0.821–0.910) and 0.850 (95% CI = 0.803–0.897), with sensitivities of 76.8%, 82.4% and 74.6%, and specificities of 94.2%, 91.6% and 95.6% and 95.2%, respectively. The sensitivity was 76.8%. The respective sensitivities were 82.4% and 74.6%, while the respective specificities were 94.2%, 91.6% and 95.3%. The area under the curve (AUC) of the improved simplified prediction model for diagnosing H. pylori infection was 0.922 (95% confidence interval (CI) = 0.888–0.956), which was higher than the Kyoto gastritis score (0.862, 95% CI = 0.822–0.902) (p < 0.05). The optimal threshold for the modified simplified prediction model for diagnosing H. pylori infection was two points, with an accuracy of 87.2%. The sensitivity and specificity were 78.2% and 94.7% respectively, and the positive predictive value (PPV) and negative predictive value (NPV) were 92.5% and 84.0% (Table 5 and Fig. 3 ). Table 4 Modified simplified prediction model Endoscopic mucosal presentation Score RAC Presence 0 Absense 1 Mucosal swelling Presence 1 Absense 0 Diffuse redness or Spotty redness Presence 1 Absense 0 Modified simplified prediction model 0 ~ 3 Table 5 Value of developing centrally improved simplified prediction models for determining presenting Hp infection Endoscopic mucosal presentation AUC Se p value 95%CI RAC Absense 0.978 0.010 <0.001 0.959 ~ 0.997 Mucosal swelling 0.865 0.023 <0.001 0.821 ~ 0.910 Diffuse redness or Spotty redness 0.850 0.024 <0.001 0.803 ~ 0.897 Modified simplified prediction model 0.922 0.017 <0.001 0.888 ~ 0.956 Validation Set Results Endoscopic Mucosal Features Based on H. pylori Infection Status In the validation set (n = 175), patients positive for H. pylori (n = 50) showed a significantly higher prevalence of atrophy (74% vs. 13.6%, p < 0.001), intestinal metaplasia (32.0% vs. 4.0%, p < 0.001), fold enlargement (30.0% vs. 0%, p < 0.001), diffuse redness (60.0% vs. 2.4%, p < 0.001), mucosal swelling (85.7% vs. 11.2%, p < 0.001), sticky mucus (18% vs. 0%, p < 0.001), merged erythema (64% vs. 4.8%, p < 0.001) and xanthoma (8.0%% vs. 0.8%, p = 0.010). H. pylori-negative patients (n = 125) had a higher prevalence of RAC (63.2% vs. 6.0%, p < 0.001). No significant differences were observed for nodularity, map-like redness, fundic gland polyps, hyperplastic polyps, linear erythema or multiple flat elevations (Table 6 ). Table 6 Differences in endoscopic manifestations of gastric mucosa in patients with different H. pylori infection status in the validation set Endoscopic mucosal presentation Hp(+)[n(%)] Hp(-)[n(%)] χ2 p value Total 50 125 RAC 3(6) 79(63.2) 46.926 <0.001 Atrophy 37(74.0) 17(13.6) 61.068 <0.001 Intestinal metaplasia 16(32.0) 5(4.0) 26.515 <0.001 Fold enlargement 15(30.0) 0 41.016 <0.001 Nodularity 1(2.0) 0 2.514 0.113 Diffuse redness 30(60.0) 3(2.4) 77.439 <0.001 Mucosal swelling 42(85.7) 14(11.2) 86.985 <0.001 Map-like redness 0 2(1.6) 0.809 0.368 Diffuse redness or Spotty redness 32(64.0) 6(4.8) 73.630 <0.001 Sticky mucus 9(18.0) 0 23.720 <0.001 Fundic gland polyp 4(8.0) 18(14.4) 1.331 0.249 Xanthoma 4(8.0) 1(0.8) 6.671 0.010 hyperplastic polyps 0 0 - - Old bleeding spots 0 7(5.6) 2.917 0.088 Red streak 3(6.0) 6(4.8) 0.105 1.000 Multiple white and flat elevated lesions 1(2.0) 13(9.6) 3.000 0.083 Diagnostic Value of Kyoto Gastritis Score in the Validation set The AUC for the diagnosis of Hp infection according to the Kyoto gastritis score criteria was 0.850 (95%, CI = 0.775 ~ 0.925) (Table 7 ), with a score of 2 being its optimal cut-off value. When the score of Kyoto gastritis was at or beyond 2, the accuracy for assessing Hp presenting infection reached 88.0%, and the test was also found to have good sensitivity and specificity, which were as high as 74.0% and 93.6%, respectively, with PPV and NPV were 93.6% and 92.5%. Table 7 Value of Kyoto Gastritis Score for determining presenting Hp infection in the validation set Endoscopic mucosal presentation AUC Se p value 95%CI Atrophy 0.802 0.041 <0.001 0.723 ~ 0.881 Intestinal metaplasia 0.640 0.050 0.004 0.542 ~ 0.783 Fold enlargement 0.650 0.051 0.002 0.551 ~ 0.749 Nodularity 0.510 0.049 0.836 0.414 ~ 0.606 Diffuse redness 0.788 0.045 <0.001 0.700 ~ 0.876 Kyoto Gastritis Score 0.850 0.038 <0.001 0.775 ~ 0.925 Diagnostic Value of the Modified Model in the Validation Set After scoring the endoscopic submucosal findings of patients in the validation set using a modified scoring model, the area under the curve (AUC) values for determining H. pylori infection using ROC curve analysis were as follows: RAC deficiency, 0.786 (95% CI: 0.145–0.283) and Mucosal swelling, 0.864 (95% CI: 0.797–0.931). Combined indicators (spotty redness or diffuse redness) were 0.796 (95% CI: 0.711–0.881). The sensitivities were: 94.0%, 84.0% and 0.796 (95% CI: 0.711–0.881), respectively. Sensitivity was 94.0%, 84.0% and 64.0% respectively, and specificity was 63.2%, 88.8% and 95.2% respectively. The area under the curve (AUC) of the improved simplified prediction model for H. pylori infection in the validation set was 0.914 (95% CI: 0.864–0.964), which was higher than the AUC of 0.850 (95% CI: 0.775–0.925) for the Kyoto gastritis score (p < 0.05). The optimal threshold was two points, achieving an accuracy of 89.1%, with a sensitivity and specificity of 82.0% and 92.0% respectively (Table 8 and Fig. 4 ). Table 8 Validation of the value of the centralized modified simplified model for determining presenting Hp infection Endoscopic mucosal presentation AUC Se p value 95%CI RACAbsense 0.786 0.035 <0.001 0.145 ~ 0.283 Mucosal swelling 0.864 0.034 <0.001 0.797 ~ 0.931 Diffuse redness or Spotty redness 0.796 0.044 <0.001 0.711 ~ 0.881 Modified simplified prediction model 0.914 0.026 <0.001 0.864 ~ 0.964 Discussion Clinical Utility of the Kyoto Gastritis Score for Active H. pylori Infection In 2014, the Japan Gastroenterological Endoscopy Society provided a comprehensive summary of the endoscopic manifestations of chronic gastritis across gastric mucosal phenotypes, thereby establishing the Kyoto Classification of Gastritis as the first standardised diagnostic framework. Current evidence further links atrophy and intestinal metaplasia to gastric carcinogenesis [ 22 ], while diffuse redness has been found to correlate with the active status of H. pylori infection [ 23 ]. Our ROC analysis revealed that the Kyoto gastritis score achieved an area under the curve (AUC) value of 0.862 (95% confidence interval (CI) 0.822–0.902) and 0.850 (95% CI 0.775–0.925) in the training and validation sets, respectively, for detecting active H. pylori infection in Chinese populations, indicating its robust diagnostic utility. Toyoshima et al. [ 24 ] reported 90% accuracy in predicting H. pylori infection in Japanese patients with Kyoto scores of at least 2, with scores of at least 4 suggesting an elevated risk of gastric cancer. Similar findings were reported by Wang et al. [ 25 ], who demonstrated 88.14% accuracy at Kyoto scores ≥ 2. Our study showed comparable results, with 78.9% and 88.0% diagnostic accuracy in the training and validation sets, respectively, at this threshold. Although H. pylori-induced atrophy can progress to intestinal metaplasia without eradication therapy [ 26 ], advanced atrophy and metaplasia are actually associated with a decline in H. pylori prevalence, which is likely due to microenvironmental incompatibility [ 26 ]. Therefore, these features alone cannot confirm active infection. Kato et al. [ 27 ] reported 58.5% sensitivity and 79.5% specificity for enlarged folds in H. pylori diagnosis, while Yoshii et al. [ 21 ] confirmed high specificity but low sensitivity in a study of 498 patients. Due to age-related fold enlargement and diagnostic variability, we excluded this parameter from our model. Nodularity, characterised by 3–5 mm antral nodules resulting from lymphoid hyperplasia [ 28 – 30 ], showed no independent predictive value in our cohorts. This is consistent with the low endoscopic detection rates observed in Chinese adults and may reflect the older mean age of our study participants (training: 59.71 ± 14.36 years; validation: 52.03 ± 11.62 years). Map-like redness, a post-eradication feature pathologically linked to extensive pre-existing metaplasia [ 31 ], similarly lacked predictive value (training: 1.4% vs. validation: 2.9%, p = 0.462). Although H. pylori elevates gastrin levels [ 32 ] and hypergastrinemia promotes hyperplastic polyps via glandular proliferation [ 33 ], these lesions showed no diagnostic relevance here. This is consistent with their low prevalence (5% overall; 20–30% hyperplastic subtype) [ 34 ]. Superiority of the Modified Simplified Prediction Model for Active H. pylori Infection In the present study, we constructed a modified, simplified prediction model based on binary logistic regression coefficients and the relevant clinical experience of endoscopists. The model incorporated three mucosal manifestations of RAC: mucosal swelling and combined indicators (e.g. diffuse and spotty redness). Analysis of the ROC curves showed that the area under the curve (AUC) values for diagnosing H. pylori infection in the modified simplified prediction model were higher than those of the Kyoto Gastritis Score in both the development set and the external validation set. 0.922 (95% CI: 0.888–0.956) vs. 0.862 (95% CI: 0.822–0.902) (p < 0.05) and the external validation set: 0.914 (95% CI: 0.864–0.964) vs. 0.850 (95% CI: 0.775–0.925) (p < 0.05). Accuracy (89.1% vs. 88.0%) and sensitivity (82.0% vs. 74.0%) were higher in the external validation set than in the Kyoto Gastritis Score. The modified simplified prediction model was found to be more valuable for diagnosing H. pylori infection. In the validation set, AUC of RAC deficiency, mucosal swelling, and diffuse redness or spotty redness were higher than for H. pylori infection. These three indicators have a high single diagnostic value for H. pylori infection. A bacterial infection triggers an inflammatory response, leading to vasodilation and congestion of the gastric mucosa and resulting in diffuse redness [ 35 ]. Spotty reddening is usually associated with vasodilation, the infiltration of inflammatory cells and mucosal damage and repair [ 36 ]. It has previously been shown that a combination of spotty reddening, mucosal swelling and diffuse redness and atrophy can increase the area under the curve (AUC) for diagnosing H. pylori infection while maintaining high sensitivity and specificity [ 37 ]. One study [ 38 ] showed no significant difference in the incidence of spotty reddening among H. pylori-infected individuals of different ages. This suggests that spotty reddening is an ideal indicator for the endoscopic diagnosis of H. pylori infection. In our study, we combined diffuse and spotty redness into one indicator, which demonstrated significant diagnostic value in determining H. pylori infection in both the development and external validation sets, with respective AUC values of 0.850 (95% CI = 0.803–0.897) and 0.796 (95% CI = 0.711–0.881). This could inform future approaches to the parsimonious diagnosis of H. pylori infection. Mucosal swelling is an important endoscopic manifestation of H. pylori infection in the gastric mucosa [ 39 ], characterised by softness and thickening of the mucosal area. Sometimes, the surface of the mucosa appears enlarged and uneven. A previous study by Zhao et al. [ 37 ] found that mucosal swelling could predict H. pylori infection with the following sensitivities and specificities: 68.2% and 80.9% respectively. It was also found to be a good predictor of H. pylori infection in the current out-of-hospital validation study. In this study, the sensitivity and specificity of mucosal swelling in predicting H. pylori infection were 84.0% and 88.8%, respectively, indicating higher sensitivity. RAC (regular arrangement of collecting veins) is a reliable endoscopic manifestation of the H. pylori-uninfected state. Yagi [ 40 ] and others demonstrated that the RAC observed endoscopically has a sensitivity of 100% and a specificity of 90%. In both the development and validation sets, the area under the ROC curve for diagnosing H. pylori infection in subjects with RAC deletion was categorised as 0.978 (95% CI = 0.959–0.997) and 0.786 (95% CI = 0.145–0.283), respectively. Although both had an AUC greater than 0.75, demonstrating high diagnostic value, there was a significant difference between the two. However, the difference in the AUC values for RAC deletion was significant, which was considered to be due to differences in the total number of patients and the rate of H. pylori infection in the two datasets, as well as differences in the gastrointestinal endoscope models used. In the development set, we defined H. pylori positivity as a positive 13C breath test. For the validation set, we defined H. pylori positivity as a positive 14C breath test. However, our modified, simplified prediction model still demonstrated excellent diagnostic ability, regardless of whether the data were used for development or validation. This may explain why the sensitivity and specificity of positive 13C and 14C breath tests for diagnosing H. pylori infection were higher [ 41 , 42 ]. In the side-by-side comparison of the development and validation sets, the Kyoto Gastritis Score performed well; however, there was still a small difference in the area under the curve (AUC) for determining H. pylori infection. The AUCs for the development and validation sets were 0.862 (95% CI = 0.822–0.902) and 0.850 (95% CI = 0.775–0.925), respectively. This difference was mainly considered to be due to the endoscopic examinations performed in the two centers. There were also some differences in equipment, and the sensitivity and specificity of the positive 13C and 14C breath tests still differed. Nevertheless, our study has several limitations that warrant consideration. Firstly, while 13C- or 14C-UBT are widely accepted for their high sensitivity and specificity, they may still diverge from histopathological confirmation, the gold standard for H. pylori detection. Secondly, the single-centre design restricted participant recruitment, resulting in a relatively small sample size that may compromise the generalisability of the findings. Furthermore, the absence of standardised training for endoscopists and observers in assessing mucosal features introduces the risk of interobserver variability and subjective interpretations, which could bias the identification of endoscopic markers. Although external validation was performed, the limited sample size of the validation set (smaller than the training set) and discrepancies in endoscopist expertise between centres may affect reproducibility. Future multicentre studies involving larger and more diverse populations are essential to validate the robustness of our simplified Kyoto gastritis scoring system. Based on the above findings, this study confirms the clinical utility and reliability of the Kyoto Gastritis Score in identifying active Helicobacter pylori infection. Compared to the traditional Kyoto Gastritis Score, our simplified predictive model — incorporating loss of regular arrangement of collecting venules (RAC), mucosal swelling and combined erythema (diffuse or spotty redness) — demonstrates enhanced diagnostic efficiency and accuracy in identifying active H. pylori infection. This streamlined approach offers clinicians a more intuitive and practical tool for the real-time endoscopic assessment of H. pylori infection status, thereby facilitating timely clinical decision-making. Nevertheless, our refined model streamlines the endoscopic diagnosis of H. pylori infection by prioritising three easily interpretable features, its clinical adoption requires further refinement and validation. Addressing these limitations through rigorous multicentre trials will be critical to advancing its utility in real-world settings. Declarations Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Supplementary Material N.A. Funding This study was supported by grant Foundation of China and the Medical Research Project of Chengdu Municipal Health Commission (Grant No. 2023176). Author Contribution Mei Yang: Writing - original draft, Formal analysis, Writing - review & editing. Xiaomei Ma, Yujie Wang and Yu Long: Data curation, Investigation. Jin Shan, Tianxu Chen, Jinlin Li and Yuanyuan Chen: Methodology, Writing - review & editing. Jin Shan, Liu Liu and Li Liu: Software, Writing - review & editing. Xiaobin Sun: Supervision, Writing - review & editing, Conceptualization, Project administration, Formal analysis. Data Availability The original contributions presented in this study are included in this article/supplementary material, further inquiries can be directed to the corresponding author. References Al-Fakhrany OM, Elekhnawy E. Helicobacter pylori in the post-antibiotics era: from virulence factors to new drug targets and therapeutic agents [J]. Arch Microbiol. 2023;205(9):301. Rasool KH, Mahmood Alubadi AE, Al-Bayati IFI. The role of Serum Interleukin-4 and Interleukin-6 in Helicobacter pylori-infected patients [J]. Microb Pathog. 2022;162:105362. Du Y, Zhu H, Liu J, et al. Consensus on eradication of Helicobacter pylori and prevention and control of gastric cancer in China (2019, Shanghai). 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J Gastroenterol Hepatol [J] Gastroenterol Hepatol [J]. 2022;31(09):961–4. Zhao J, Xu S, Gao Y, et al. Accuracy of Endoscopic Diagnosis of Helicobacter pylori Based on the Kyoto Classification of Gastritis: A Multicenter Study [J]. Front Oncol. 2020;10:599218. Wang K, Zhao J, Jin H, et al. Establishment of a modified Kyoto classification scoring model and its significance in the diagnosis of Helicobacter pylori current infection [J]. Gastrointest Endosc. 2023;97(4):684–93. Seo JY, Ahn JY, Kim S, et al. Predicting Helicobacter pylori infection from endoscopic features [J]. Korean J Intern Med. 2024;39(3):439–47. Garcés-Durán R, García-Rodríguez A, Córdova H, et al. Association between a regular arrangement of collecting venules and absence of Helicobacter pylori infection in a European population [J]. Gastrointest Endosc. 2019;90(3):461–6. Gong YN, Li YM, Yang NM, et al. Centralized isolation of Helicobacter pylori from multiple centers and transport condition influences [J]. 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Diffuse redness in linked color imaging is useful for diagnosing current Helicobacter pylori infection in the stomach [J]. J Gen family Med. 2018;19(5):176–7. Toyoshima O, Nishizawa T, Koike K. Endoscopic Kyoto classification of Helicobacter pylori infection and gastric cancer risk diagnosis [J]. World J Gastroenterol. 2020;26(5):466–77. Kaijie WANG, Jing ZHAO, Yanlin ZHOU, et al. Value and significance of Kyoto Gastritis Score for endoscopic prediction of Helicobacter pylori infection [J]. Chin J Gastroenterol. 2021;41(10):654–9. Watari J, Chen N, Amenta PS, et al. Helicobacter pylori associated chronic gastritis, clinical syndromes, precancerous lesions, and pathogenesis of gastric cancer development [J]. World J Gastroenterol. 2014;20(18):5461–73. Kato T, Yagi N, Kamada T, et al. Diagnosis of Helicobacter pylori infection in gastric mucosa by endoscopic features: a multicenter prospective study [J]. Dig endoscopy: official J Japan Gastroenterological Endoscopy Soc. 2013;25(5):508–18. Achyut BR, Moorchung N, Srivastava AN, et al. Risk of lymphoid follicle development in patients with chronic antral gastritis: role of endoscopic features, histopathological parameters, CagA status and interleukin-1 gene polymorphisms [J]. Inflamm research: official J Eur Histamine Res Soc [et al]. 2008;57(2):51–6. Chen XY, Liu WZ, Shi Y, et al. Helicobacter pylori associated gastric diseases and lymphoid tissue hyperplasia in gastric antral mucosa [J]. J Clin Pathol. 2002;55(2):133–7. Nagata T, Ishitake H, Shimamoto F, et al. [Histopathological Study of the Relationship between Lymphoid Follicles and Different Endoscopic Types of Nodular Gastritis] [J]. Rinsho byori Japanese J Clin Pathol. 2014;62(11):1031–9. Matsumoto S, Sugimoto M, Terai T, et al. Map-Like Redness Development After Eradication Therapy for Helicobacter pylori Infection: Prospective Multicenter Observational Study [J]. Helicobacter. 2024;29(6):e13146. Peach HG, Barnett NE. Determinants of basal plasma gastrin levels in the general population [J]. J Gastroenterol Hepatol. 2000;15(11):1267–71. Jeong CY, Kim N, Lee HS, et al. Risk Factors of Multiple Gastric Polyps according to the Histologic Classification: Prospective Observational Cohort Study [J]. Korean J Gastroenterol = Taehan Sohwagi Hakhoe chi. 2019;74(1):17–29. Amarapurkar AD, Kale KM, Naik LP, et al. Histomorphological analysis of gastric polyps [J]. Indian J Pathol Microbiol. 2021;64(Supplement):S69–72. Yamazaki K, Kushima R, Shimizu M. Vonoprazan-associated Diffuse Gastric Mucosal Redness [J]. Gastro hep Adv. 2022;1(3):350–1. Kubo K, Kimura N, Watanabe R, et al. Vonoprazan-Associated Gastric Mucosal Redness in Non-Helicobacter pylori-Infected and Helicobacter pylori-Eradicated Stomach [J]. Case Rep Gastroenterol. 2021;15(2):751–8. Jiandong LIN, Xiao WANG, Guijun ZHAO. Correlation between endoscopic mucosal manifestations and Helicobacter pylori infection.Inner Mongolia Medical Journal [J]. Inner Mongolia Med J [J]. 2024;56(7):937–43. Hojo M, Nagahara A, Kudo T, et al. Endoscopic findings of Helicobacter pylori gastritis in children and young adults based on the Kyoto classification of gastritis and age-associated changes [J]. JGH open: open access J Gastroenterol Hepatol. 2021;5(10):1197–202. Sugimoto M, Murata M, Murakami K, et al. Characteristic endoscopic findings in Helicobacter pylori diagnosis in clinical practice [J]. Expert Rev Gastroenterol Hepatol. 2024;18(8):457–72. Yagi K, Aruga Y, Nakamura A, et al. Regular arrangement of collecting venules (RAC): a characteristic endoscopic feature of Helicobacter pylori-negative normal stomach and its relationship with esophago-gastric adenocarcinoma [J]. J Gastroenterol. 2005;40(5):443–52. Nawacki Ł, Czyż A, Bryk P et al. Can urea breath test (UBT) replace rapid urea test (RUT)? [J]. Polski przeglad chirurgiczny, 2018, 90(5): 44–8. Pathak CM, Kaur B, Khanduja KL. 14C-urea breath test is safe for pediatric patients [J]. Nucl Med Commun. 2010;31(9):830–5. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers invited by journal 17 Mar, 2026 Editor invited by journal 23 Feb, 2026 Editor assigned by journal 23 Feb, 2026 Submission checks completed at journal 23 Feb, 2026 First submitted to journal 09 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8827948","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607609599,"identity":"ea63944e-7d18-4b0d-a8c0-fd328921b1dc","order_by":0,"name":"Mei Yang","email":"","orcid":"","institution":"Chengdu Third People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"Yang","suffix":""},{"id":607609600,"identity":"263a6a78-9628-4b20-8462-12117f6ea37a","order_by":1,"name":"Xiaomei Ma","email":"","orcid":"","institution":"Chengdu Third People’s 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07:48:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3608198,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8827948/v1/eb8f393a9ae08c92a9279679.png"},{"id":105038246,"identity":"192a054e-aacf-4a76-9d63-fa919a96ee3a","added_by":"auto","created_at":"2026-03-20 07:43:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":213908,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8827948/v1/8417cdc9c7b3918bc113ab0f.png"},{"id":105039358,"identity":"c60b3ba8-4315-42f9-ba88-cdc77710b5b0","added_by":"auto","created_at":"2026-03-20 07:46:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":104446,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8827948/v1/4908fc08e2ccd5a711d635eb.png"},{"id":105038380,"identity":"f34e8f35-f066-49b1-918d-a2779714eff8","added_by":"auto","created_at":"2026-03-20 07:43:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":102819,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8827948/v1/7211b89dfa8b959d21f022dc.png"},{"id":105040698,"identity":"7bc2b0e5-ec95-4bef-96cc-afad0a2aff30","added_by":"auto","created_at":"2026-03-20 07:50:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4860101,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8827948/v1/07cd332e-5371-4b08-88cb-959ab27650b9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Modified, Simplified Prediction Model Based on the Kyoto Classification of Gastritis for Current Helicobacter Pylori Infection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHelicobacter pylori (H. pylori) is a spiral-shaped, microaerophilic, flagellated, Gram-negative bacillus that can switch between spiral and spherical forms. Its spiral morphology enables it to survive and move around the human gastrointestinal tract, while its spherical form helps it to colonise gastric epithelial cells [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. H. pylori infection is now recognized as a primary cause of gastric carcinogenesis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The International Agency for Research on Cancer has classified H. pylori infection as a Group 1 carcinogen. It is implicated in approximately 89% of gastric cancer cases, establishing it as a critical target for the prevention and control of gastric cancer [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Studies of Chinese populations demonstrate that the eradication of H. pylori significantly reduces the incidence of gastric cancer, with earlier intervention yielding greater reductions [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and the benefits increasing with longer follow-up after eradication. In addition to gastrointestinal disorders, H. pylori infection has been linked to extra-digestive conditions, including autoimmune diseases, iron-deficiency anaemia, idiopathic thrombocytopenic purpura and cardiovascular/cerebrovascular diseases [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, early detection and eradication of H. pylori are pivotal for reducing morbidity across multiple systems.\u003c/p\u003e \u003cp\u003eCurrent clinical diagnostics for H. pylori comprise both invasive and non-invasive methods. Non-invasive techniques include the urea breath test (UBT), stool antigen testing and serology. Advances in endoscopy now enable real-time assessment of H. pylori infection during procedures. At the 85th Annual Meeting of the Japanese Gastroenterological Endoscopy Society in Kyoto in 2013, the Kyoto Classification of Gastritis was formalised[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The Kyoto Classification of Gastritis focuses on five key features: atrophy, intestinal metaplasia, fold enlargement, nodularity and diffuse redness. A score of \u0026ge;\u0026thinsp;2 indicates H. pylori infection, and international validation supports its clinical utility [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Chinese studies reported high diagnostic accuracy: Zhang et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] showed 82.9% overall accuracy using endoscopic mucosal features, while Zhao et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] confirmed diffuse redness and mucosal swelling were found to be robust markers of active H. pylori infection, whereas atrophy and intestinal metaplasia were found to be of limited utility. A modified, simplified prediction model, such as Wang et al\u0026rsquo;s seven-feature model (atrophy, fold enlargement, nodularity, diffuse redness, sticky mucus, spotty redness and fundic gland polyps), demonstrates improved performance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, despite these advances, the Kyoto Classification of Gastritis has not yet been fully popularized in China, mainly due to its multiple observation indicators, the difficulty in distinguishing some endoscopic mucosal manifestations and the insufficient diagnostic value of certain mucosal findings for current H. pylori infection. Therefore, there is an urgent need for a simpler diagnostic model that endoscopists can easily master. Adoption of the Kyoto Classification of Gastritis in China remains limited due to complex criteria, variability in the interpretation of mucosal features by different observers, and suboptimal diagnostic value of certain features. Simplified models are urgently needed for clinical translation.\u003c/p\u003e \u003cp\u003eThe Kyoto Gastritis Score has been successfully streamlined through overseas studies: Kim et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] developed a model with four features (RAC, nodularity, diffuse redness and spotty redness), achieving 98% accuracy in identifying active H. pylori infection at scores of 2 or above. However, nodules predominantly occur in young patients and are rarely observed in elderly patients. Spotty erythema can also be detected in patients following eradication therapy, and the assessment of both spotty and diffuse erythema is subjective. Meanwhile, simplified Kyoto classification models of gastritis remain underreported in China. Therefore, the aims of this study are to: (1) validate the Kyoto Classification of Gastritis as a predictor of active H. pylori infection in Chinese populations; (2) evaluate its diagnostic accuracy and reliability; (3) develop a simplified H. pylori prediction model tailored to Chinese clinical practice and population characteristics, which is also endoscopist-friendly and based on statistical and clinical optimization.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient sets\u003c/h2\u003e \u003cp\u003eWe retrospectively collected endoscopic mucosal features and 13C-UBT results from 313 patients who underwent upper gastrointestinal endoscopy and 13C-UBT at the Digestive Endoscopy Center of Chengdu Third People\u0026rsquo;s Hospital between June 2022 and July 2023. These data were used to develop a simplified endoscopic diagnostic model for H. pylori infection, which is based on the Kyoto Classification of Gastritis. Additionally, 175 patients who underwent upper gastrointestinal endoscopy and 14C-UBT at Qionglai Second People\u0026rsquo;s Hospital between April 2022 and July 2023 were included for validation, with endoscopic mucosal features and 14C-UBT results analyzed.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHuman Ethics and Consent to Participate\u003c/h3\u003e\n\u003cp\u003e This study was reviewed and approved by the Ethics Committee of the Third People's Hospital of Chengdu (2022-S-98) and carried out in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study and the use of anonymized data, the requirement for informed consent was waived by the aforementioned Ethics Committee. Clinical trial number: not applicable.\u003c/p\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003eInclusion criteria:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePatients who underwent standardized 13C- or 14C-UBT.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePatients with confirmed absence of prior H. pylori eradication therapy and exclusion of factors predisposing to false-positive/false-negative UBT results (via medical history review).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePatients with complete clinical records and endoscopic reports.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eExclusion criteria\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePrior gastric/duodenal surgery.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eUse of antibiotics, bacteriostatic traditional Chinese medicine, bismuth agents, proton pump inhibitors, or potassium-competitive acid blockers within 1\u0026ndash;2 months before the procedure.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAdvanced gastric cancer, active/history of upper gastrointestinal bleeding, autoimmune diseases, or gastric varices.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSuspected autoimmune gastritis.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIncomplete endoscopic examination.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePregnant individuals or children.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\n\u003ch3\u003eEndoscopic Procedures\u003c/h3\u003e\n\u003cp\u003eAll patients provided informed consent before undergoing painless white-light gastroscopy. The procedures were performed by senior endoscopists with \u0026ge;\u0026thinsp;5 years of experience and \u0026ge;\u0026thinsp;1,000 independent operations. Pre-procedure preparation included oral administration of dimethicone (20 minutes prior) and positioning guidance. An endoscopist with \u0026ge;\u0026thinsp;15 years of experience, who was blinded to the results of the UBT, interpreted the endoscopic mucosal features, which were classified as present or absent. Discrepancies in ambiguous cases were resolved by a second senior endoscopist via consensus. All patients underwent 14C- or 13C-UBT.\u003c/p\u003e\n\u003ch3\u003eH. pylori Infection Status\u003c/h3\u003e\n\u003cp\u003e13C-UBT: Conducted using the HY-IREXBplus 13C-UBT (Guangzhou Huayou Mingkang Optoelectronic Technology Co., Ltd.). A delta over baseline (DOB) value\u0026thinsp;\u0026gt;\u0026thinsp;6 dpm indicated active H. pylori infection, while a value of \u0026le;\u0026thinsp;6 dpm indicated no infection. 14C-UBT: Performed using a 14C-UBT. A value\u0026thinsp;\u0026gt;\u0026thinsp;150 dmp/mmol CO₂ indicated active infection; a value of \u0026le;\u0026thinsp;150 dmp/mmol CO₂ indicated no infection.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEndoscopic Mucosal Features and the Kyoto Classification of Gastritis\u003c/h2\u003e \u003cp\u003eThe sixteenth endoscopic mucosal features were evaluated according to the Kyoto Gastritis Classification. These features include atrophy, intestinal metaplasia, fold enlargement, nodularity, diffuse redness, sticky mucus, fundic gland polyps, map-like redness, mucosal swelling, spotty redness, xanthoma, hyperplastic polyps, old bleeding spots, red streak, multiple white and flat elevated lesions and RAC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the Kyoto Classification of Gastritis, mucosal swelling (thickened, soft gastric mucosa with widened areas of inflammation/oedema), diffuse redness (continuous uniform redness of the non-atrophic corpus mucosa, a basic sign of H. pylori gastritis, aided by RAC status for scoring), and spotty redness (irregular small red spots or patches without unevenness, often on a background of diffuse redness from the corpus to the fundus) are all core endoscopic features that are specific to, or linked to, a current H. pylori infection. The Kyoto Gastritis Score (0\u0026ndash;8 points) is calculated based on five features: Atrophy (0\u0026ndash;2 points, Intestinal metaplasia, fold enlargement (0\u0026ndash;1 point), nodularity (0\u0026ndash;1 point) and diffuse redness (0\u0026ndash;2 points).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eModified Simplified Prediction Model for Active H. pylori Infection\u003c/h3\u003e\n\u003cp\u003ePatients were divided into two groups according to their testing dates: an in-house development group (June 2022 - August 2023) and an external validation group (April 2022 - July 2023). Due to previous reports of ambiguous boundaries between spotty and diffuse redness [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], these features were combined into a single \u0026ldquo;erythema\u0026rdquo; category. Chi-square or Fisher\u0026rsquo;s exact tests were used to identify endoscopic mucosal features associated with active H. pylori infection (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Selected features (based on statistical significance, prior literature, and clinical expertise) were then subjected to binary logistic regression in order to construct a modified, simplified prediction model.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe data were analysed using SPSS 26.0. Continuous variables that were normally distributed were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s), and those that were not normally distributed were reported as median (interquartile range) [M (Q1, Q3)]. Categorical data were presented as counts (%), and comparisons were made using chi-squared or Fisher\u0026rsquo;s exact tests. Statistically significant mucosal features identified in the univariate analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were selected for binary logistic regression via literature/clinical review. A modified model was then developed using regression coefficients and clinical judgement.\u003c/p\u003e \u003cp\u003eThe diagnostic performance of the Kyoto Gastritis Score and the modified model was evaluated using sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve (AUC). DeLong\u0026rsquo;s test was used to compare the ROC curves of the models. An AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.75 indicated a high diagnostic value and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 denoted statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe development set comprised 313 patients, of whom 142 (45.37%) were H. pylori-positive and 171 were H. pylori-negative. Of these, there were 145 males and 168 females with a mean age of 59.71\u0026thinsp;\u0026plusmn;\u0026thinsp;14.36 years. The validation set (n\u0026thinsp;=\u0026thinsp;175) comprised 50 H. pylori-positive patients (28.6%) and 125 H. pylori-negative patients. There were 60 males and 115 females, with an average age of 52.03\u0026thinsp;\u0026plusmn;\u0026thinsp;11.62 years. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the flowchart of patients tested for H. pylori infection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment Group Results\u003c/h2\u003e \u003cp\u003eThe different endoscopic mucosal features based on H. pylori infection status in H. pylori-positive and H. pylori-negative patients were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Several presentations in the H. pylori-positive group were considerably higher than those in the H. pylori-negative group, including atrophy (65.5% vs. 26.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), intestinal metaplasia (59.9% vs. 26.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fold enlargement (51.4% vs. 0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), diffuse redness (78.2% vs. 5.8%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and spotty redness (7.7% vs. 2.3%, p\u0026thinsp;=\u0026thinsp;0.026). Significantly higher rates were observed for the following presentations in the H. pylori-negative group, including RAC (94.2% vs. 3.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fundic gland polyps (2.1% vs. 14.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), multiple flat elevations (12.9% vs. 0.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and old bleeding spots (4.7% vs. 0.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were observed in nodularity (0.7% vs. 0%, p\u0026thinsp;=\u0026thinsp;0.454) or map-like redness (14% vs. 2.9%, p\u0026thinsp;=\u0026thinsp;0.462), xanthoma (2.8% vs. 2.3%, p\u0026thinsp;=\u0026thinsp;0.790), hyperplastic polyps (1.4% vs. 0%, p\u0026thinsp;=\u0026thinsp;0.205) or linear erythema (0% vs. 2.3%, p\u0026thinsp;=\u0026thinsp;0.126).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences in gastric mucosal endoscopic manifestations among patients with different H. pylori infection statuses in the development center\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndoscopic mucosal presentation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHp(+) [n(%)]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHp(-) [n(%)]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33(23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161(94.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e165.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93(65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntestinal metaplasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85(59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFold enlargement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73(51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiffuse redness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111(78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpotty redness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucosal swelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141(99.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e271.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMap-like redness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiffuse redness or Spotty redness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106(74.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e164.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSticky mucus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFundic gland polyp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXanthoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehyperplastic polyps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOld bleeding spots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed streak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple white and flat elevated lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDiagnostic Value of the Kyoto Gastritis Score for Active H. pylori Infection in the Development Group\u003c/b\u003e \u003c/p\u003e \u003cp\u003eROC curve analysis revealed the following area under the curve (AUC) values for individual Kyoto Gastritis Score features in diagnosing active H. pylori infection: atrophy (0.696, 95% confidence interval (CI)\u0026thinsp;=\u0026thinsp;0.636\u0026ndash;0.755), intestinal metaplasia (0.668, 95% CI\u0026thinsp;=\u0026thinsp;0.607\u0026ndash;0.729), fold enlargement (0.757, 95% CI\u0026thinsp;=\u0026thinsp;0.700-0.814), nodularity (0.504, 95% CI\u0026thinsp;=\u0026thinsp;0.439\u0026ndash;0.568) and diffuse redness (0.862, 95% CI\u0026thinsp;=\u0026thinsp;0.816\u0026ndash;0.907). Atrophy, intestinal metaplasia, fold enlargement and diffuse redness demonstrated good diagnostic performance, whereas nodularity lacked independent predictive value. The composite Kyoto Gastritis Score achieved an AUC of 0.862 (95% CI\u0026thinsp;=\u0026thinsp;0.822\u0026ndash;0.902) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating optimal diagnostic efficacy. At a threshold score of \u0026ge;\u0026thinsp;2, the diagnostic accuracy was 78.9%, with a sensitivity of 83.8%, a specificity of 74.9%, a positive predictive value (PPV) of 84.8%, and a negative predictive value (NPV) of 92.8%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe value of Kyoto Gastritis Score in determining current Hp infection in the development center\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndoscopic mucosal presentation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.636\u0026thinsp;~\u0026thinsp;0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntestinal metaplasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.607\u0026thinsp;~\u0026thinsp;0.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFold enlargement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.700\u0026thinsp;~\u0026thinsp;0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.439\u0026thinsp;~\u0026thinsp;0.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiffuse redness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.816\u0026thinsp;~\u0026thinsp;0.907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKyoto Gastritis Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.822\u0026thinsp;~\u0026thinsp;0.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and Diagnostic Value of the Modified Simplified Prediction Model in the Development Group\u003c/h2\u003e \u003cp\u003eEndoscopic mucosal manifestations that were statistically different from H. pylori infection in the univariate analysis were screened again for entry into binary logistic regression analysis. Some endoscopic mucosal manifestations in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e may be affected by age or other factors. For example, fold enlargement and atrophy may progress with age, which could lead to errors in diagnosing H. pylori infection [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. At the same time, previous studies have shown that, following H. pylori eradication treatment, symptoms such as atrophy and bowelisation of the mucosa do not improve completely. This results in relatively low specificity for diagnosing H. pylori infection [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A Japanese study [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] showed that sticky mucus had a specificity of 97.1% for diagnosing H. pylori infection, but its low sensitivity may be related to patients routinely taking antifoaming agents before gastroscopy. Therefore, we excluded these indicators.\u003c/p\u003e \u003cp\u003eDiffuse and spotty redness are considered strong predictors of H. pylori infection and can differentiate between previous and current infection. To further facilitate the clinical application of the scoring model, we performed a binary logistic regression analysis of three submucosal manifestations: RAC, mucosal swelling and combined indicators (spotty or diffuse redness). The results, shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, indicated that RAC suggested a low risk of current H. pylori infection, whereas mucosal swelling and combined indicators suggested a high risk.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary Logistic Regression Analysis of Endoscopic Manifestations in Patients with Different Hp Infection States\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndoscopic mucosal presentation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald χ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.057\u0026thinsp;~\u0026thinsp;0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucosal swelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.942\u0026thinsp;~\u0026thinsp;28.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMap-like redness and Spotty redness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.298\u0026thinsp;~\u0026thinsp;23.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe improved, simplified predictive model was constructed based on binary logistic regression analysis coefficients, clinical experience constructs and ease of clinical use. The following scores were assigned: absence of RAC, mucosal swelling and spotty or diffuse redness each counted as 1 point, with a total possible score of 3 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We used the modified simplified predictive model to reassess the mucosal manifestations of endoscopic patients. The scores were 2\u0026ndash;3 and 0\u0026ndash;1 for H. pylori-infected and H. pylori-uninfected patients, respectively; this difference was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results of the ROC curve analysis indicated that the absence of RAC and mucosal swelling, as well as the combined indicator (spotty or diffuse redness), were important in validating the presenting infection of H. pylori. The AUC values for the centralised determination of the infection were 0.978 (95% CI\u0026thinsp;=\u0026thinsp;0.959\u0026ndash;0.997), 0.865 (95% CI\u0026thinsp;=\u0026thinsp;0.821\u0026ndash;0.910) and 0.850 (95% CI\u0026thinsp;=\u0026thinsp;0.803\u0026ndash;0.897), with sensitivities of 76.8%, 82.4% and 74.6%, and specificities of 94.2%, 91.6% and 95.6% and 95.2%, respectively. The sensitivity was 76.8%. The respective sensitivities were 82.4% and 74.6%, while the respective specificities were 94.2%, 91.6% and 95.3%. The area under the curve (AUC) of the improved simplified prediction model for diagnosing H. pylori infection was 0.922 (95% confidence interval (CI)\u0026thinsp;=\u0026thinsp;0.888\u0026ndash;0.956), which was higher than the Kyoto gastritis score (0.862, 95% CI\u0026thinsp;=\u0026thinsp;0.822\u0026ndash;0.902) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The optimal threshold for the modified simplified prediction model for diagnosing H. pylori infection was two points, with an accuracy of 87.2%. The sensitivity and specificity were 78.2% and 94.7% respectively, and the positive predictive value (PPV) and negative predictive value (NPV) were 92.5% and 84.0% (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModified simplified prediction model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndoscopic mucosal presentation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucosal swelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiffuse redness or Spotty redness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModified simplified prediction model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValue of developing centrally improved simplified prediction models for determining presenting Hp infection\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndoscopic mucosal presentation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAC Absense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.959\u0026thinsp;~\u0026thinsp;0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucosal swelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.821\u0026thinsp;~\u0026thinsp;0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiffuse redness or Spotty redness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.803\u0026thinsp;~\u0026thinsp;0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModified simplified prediction model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.888\u0026thinsp;~\u0026thinsp;0.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eValidation Set Results\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eEndoscopic Mucosal Features Based on H. pylori Infection Status\u003c/h2\u003e \u003cp\u003eIn the validation set (n\u0026thinsp;=\u0026thinsp;175), patients positive for H. pylori (n\u0026thinsp;=\u0026thinsp;50) showed a significantly higher prevalence of atrophy (74% vs. 13.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), intestinal metaplasia (32.0% vs. 4.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fold enlargement (30.0% vs. 0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), diffuse redness (60.0% vs. 2.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), mucosal swelling (85.7% vs. 11.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), sticky mucus (18% vs. 0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), merged erythema (64% vs. 4.8%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and xanthoma (8.0%% vs. 0.8%, p\u0026thinsp;=\u0026thinsp;0.010). H. pylori-negative patients (n\u0026thinsp;=\u0026thinsp;125) had a higher prevalence of RAC (63.2% vs. 6.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were observed for nodularity, map-like redness, fundic gland polyps, hyperplastic polyps, linear erythema or multiple flat elevations (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences in endoscopic manifestations of gastric mucosa in patients with different H. pylori infection status in the validation set\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndoscopic mucosal presentation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHp(+)[n(%)]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHp(-)[n(%)]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79(63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(74.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntestinal metaplasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFold enlargement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiffuse redness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucosal swelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMap-like redness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiffuse redness or Spotty redness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSticky mucus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFundic gland polyp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXanthoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehyperplastic polyps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOld bleeding spots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed streak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple white and flat elevated lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Value of Kyoto Gastritis Score in the Validation set\u003c/h2\u003e \u003cp\u003eThe AUC for the diagnosis of Hp infection according to the Kyoto gastritis score criteria was 0.850 (95%, CI\u0026thinsp;=\u0026thinsp;0.775\u0026thinsp;~\u0026thinsp;0.925) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), with a score of 2 being its optimal cut-off value. When the score of Kyoto gastritis was at or beyond 2, the accuracy for assessing Hp presenting infection reached 88.0%, and the test was also found to have good sensitivity and specificity, which were as high as 74.0% and 93.6%, respectively, with PPV and NPV were 93.6% and 92.5%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValue of Kyoto Gastritis Score for determining presenting Hp infection in the validation set\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndoscopic mucosal presentation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.723\u0026thinsp;~\u0026thinsp;0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntestinal metaplasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.542\u0026thinsp;~\u0026thinsp;0.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFold enlargement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.551\u0026thinsp;~\u0026thinsp;0.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.414\u0026thinsp;~\u0026thinsp;0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiffuse redness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.700\u0026thinsp;~\u0026thinsp;0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKyoto Gastritis Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.775\u0026thinsp;~\u0026thinsp;0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Value of the Modified Model in the Validation Set\u003c/h2\u003e \u003cp\u003eAfter scoring the endoscopic submucosal findings of patients in the validation set using a modified scoring model, the area under the curve (AUC) values for determining H. pylori infection using ROC curve analysis were as follows: RAC deficiency, 0.786 (95% CI: 0.145\u0026ndash;0.283) and Mucosal swelling, 0.864 (95% CI: 0.797\u0026ndash;0.931). Combined indicators (spotty redness or diffuse redness) were 0.796 (95% CI: 0.711\u0026ndash;0.881). The sensitivities were: 94.0%, 84.0% and 0.796 (95% CI: 0.711\u0026ndash;0.881), respectively. Sensitivity was 94.0%, 84.0% and 64.0% respectively, and specificity was 63.2%, 88.8% and 95.2% respectively. The area under the curve (AUC) of the improved simplified prediction model for H. pylori infection in the validation set was 0.914 (95% CI: 0.864\u0026ndash;0.964), which was higher than the AUC of 0.850 (95% CI: 0.775\u0026ndash;0.925) for the Kyoto gastritis score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The optimal threshold was two points, achieving an accuracy of 89.1%, with a sensitivity and specificity of 82.0% and 92.0% respectively (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidation of the value of the centralized modified simplified model for determining presenting Hp infection\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndoscopic mucosal presentation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRACAbsense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.145\u0026thinsp;~\u0026thinsp;0.283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucosal swelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.797\u0026thinsp;~\u0026thinsp;0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiffuse redness or Spotty redness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.711\u0026thinsp;~\u0026thinsp;0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModified simplified prediction model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.864\u0026thinsp;~\u0026thinsp;0.964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eClinical Utility of the Kyoto Gastritis Score for Active H. pylori Infection\u003c/h2\u003e \u003cp\u003e In 2014, the Japan Gastroenterological Endoscopy Society provided a comprehensive summary of the endoscopic manifestations of chronic gastritis across gastric mucosal phenotypes, thereby establishing the Kyoto Classification of Gastritis as the first standardised diagnostic framework. Current evidence further links atrophy and intestinal metaplasia to gastric carcinogenesis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], while diffuse redness has been found to correlate with the active status of H. pylori infection [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our ROC analysis revealed that the Kyoto gastritis score achieved an area under the curve (AUC) value of 0.862 (95% confidence interval (CI) 0.822\u0026ndash;0.902) and 0.850 (95% CI 0.775\u0026ndash;0.925) in the training and validation sets, respectively, for detecting active H. pylori infection in Chinese populations, indicating its robust diagnostic utility. Toyoshima et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] reported 90% accuracy in predicting H. pylori infection in Japanese patients with Kyoto scores of at least 2, with scores of at least 4 suggesting an elevated risk of gastric cancer. Similar findings were reported by Wang et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], who demonstrated 88.14% accuracy at Kyoto scores\u0026thinsp;\u0026ge;\u0026thinsp;2. Our study showed comparable results, with 78.9% and 88.0% diagnostic accuracy in the training and validation sets, respectively, at this threshold.\u003c/p\u003e \u003cp\u003eAlthough H. pylori-induced atrophy can progress to intestinal metaplasia without eradication therapy [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], advanced atrophy and metaplasia are actually associated with a decline in H. pylori prevalence, which is likely due to microenvironmental incompatibility [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, these features alone cannot confirm active infection. Kato et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] reported 58.5% sensitivity and 79.5% specificity for enlarged folds in H. pylori diagnosis, while Yoshii et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] confirmed high specificity but low sensitivity in a study of 498 patients. Due to age-related fold enlargement and diagnostic variability, we excluded this parameter from our model.\u003c/p\u003e \u003cp\u003eNodularity, characterised by 3\u0026ndash;5 mm antral nodules resulting from lymphoid hyperplasia [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], showed no independent predictive value in our cohorts. This is consistent with the low endoscopic detection rates observed in Chinese adults and may reflect the older mean age of our study participants (training: 59.71\u0026thinsp;\u0026plusmn;\u0026thinsp;14.36 years; validation: 52.03\u0026thinsp;\u0026plusmn;\u0026thinsp;11.62 years).\u003c/p\u003e \u003cp\u003eMap-like redness, a post-eradication feature pathologically linked to extensive pre-existing metaplasia [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], similarly lacked predictive value (training: 1.4% vs. validation: 2.9%, p\u0026thinsp;=\u0026thinsp;0.462). Although H. pylori elevates gastrin levels [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and hypergastrinemia promotes hyperplastic polyps via glandular proliferation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], these lesions showed no diagnostic relevance here. This is consistent with their low prevalence (5% overall; 20\u0026ndash;30% hyperplastic subtype) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSuperiority of the Modified Simplified Prediction Model for Active H. pylori Infection\u003c/h2\u003e \u003cp\u003eIn the present study, we constructed a modified, simplified prediction model based on binary logistic regression coefficients and the relevant clinical experience of endoscopists. The model incorporated three mucosal manifestations of RAC: mucosal swelling and combined indicators (e.g. diffuse and spotty redness). Analysis of the ROC curves showed that the area under the curve (AUC) values for diagnosing H. pylori infection in the modified simplified prediction model were higher than those of the Kyoto Gastritis Score in both the development set and the external validation set. 0.922 (95% CI: 0.888\u0026ndash;0.956) vs. 0.862 (95% CI: 0.822\u0026ndash;0.902) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and the external validation set: 0.914 (95% CI: 0.864\u0026ndash;0.964) vs. 0.850 (95% CI: 0.775\u0026ndash;0.925) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Accuracy (89.1% vs. 88.0%) and sensitivity (82.0% vs. 74.0%) were higher in the external validation set than in the Kyoto Gastritis Score. The modified simplified prediction model was found to be more valuable for diagnosing H. pylori infection.\u003c/p\u003e \u003cp\u003eIn the validation set, AUC of RAC deficiency, mucosal swelling, and diffuse redness or spotty redness were higher than for H. pylori infection. These three indicators have a high single diagnostic value for H. pylori infection. A bacterial infection triggers an inflammatory response, leading to vasodilation and congestion of the gastric mucosa and resulting in diffuse redness [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Spotty reddening is usually associated with vasodilation, the infiltration of inflammatory cells and mucosal damage and repair [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. It has previously been shown that a combination of spotty reddening, mucosal swelling and diffuse redness and atrophy can increase the area under the curve (AUC) for diagnosing H. pylori infection while maintaining high sensitivity and specificity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. One study [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] showed no significant difference in the incidence of spotty reddening among H. pylori-infected individuals of different ages. This suggests that spotty reddening is an ideal indicator for the endoscopic diagnosis of H. pylori infection.\u003c/p\u003e \u003cp\u003eIn our study, we combined diffuse and spotty redness into one indicator, which demonstrated significant diagnostic value in determining H. pylori infection in both the development and external validation sets, with respective AUC values of 0.850 (95% CI\u0026thinsp;=\u0026thinsp;0.803\u0026ndash;0.897) and 0.796 (95% CI\u0026thinsp;=\u0026thinsp;0.711\u0026ndash;0.881). This could inform future approaches to the parsimonious diagnosis of H. pylori infection. Mucosal swelling is an important endoscopic manifestation of H. pylori infection in the gastric mucosa [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], characterised by softness and thickening of the mucosal area. Sometimes, the surface of the mucosa appears enlarged and uneven. A previous study by Zhao et al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] found that mucosal swelling could predict H. pylori infection with the following sensitivities and specificities: 68.2% and 80.9% respectively. It was also found to be a good predictor of H. pylori infection in the current out-of-hospital validation study. In this study, the sensitivity and specificity of mucosal swelling in predicting H. pylori infection were 84.0% and 88.8%, respectively, indicating higher sensitivity.\u003c/p\u003e \u003cp\u003eRAC (regular arrangement of collecting veins) is a reliable endoscopic manifestation of the H. pylori-uninfected state. Yagi [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and others demonstrated that the RAC observed endoscopically has a sensitivity of 100% and a specificity of 90%. In both the development and validation sets, the area under the ROC curve for diagnosing H. pylori infection in subjects with RAC deletion was categorised as 0.978 (95% CI\u0026thinsp;=\u0026thinsp;0.959\u0026ndash;0.997) and 0.786 (95% CI\u0026thinsp;=\u0026thinsp;0.145\u0026ndash;0.283), respectively. Although both had an AUC greater than 0.75, demonstrating high diagnostic value, there was a significant difference between the two. However, the difference in the AUC values for RAC deletion was significant, which was considered to be due to differences in the total number of patients and the rate of H. pylori infection in the two datasets, as well as differences in the gastrointestinal endoscope models used.\u003c/p\u003e \u003cp\u003eIn the development set, we defined H. pylori positivity as a positive 13C breath test. For the validation set, we defined H. pylori positivity as a positive 14C breath test. However, our modified, simplified prediction model still demonstrated excellent diagnostic ability, regardless of whether the data were used for development or validation. This may explain why the sensitivity and specificity of positive 13C and 14C breath tests for diagnosing H. pylori infection were higher [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In the side-by-side comparison of the development and validation sets, the Kyoto Gastritis Score performed well; however, there was still a small difference in the area under the curve (AUC) for determining H. pylori infection. The AUCs for the development and validation sets were 0.862 (95% CI\u0026thinsp;=\u0026thinsp;0.822\u0026ndash;0.902) and 0.850 (95% CI\u0026thinsp;=\u0026thinsp;0.775\u0026ndash;0.925), respectively. This difference was mainly considered to be due to the endoscopic examinations performed in the two centers. There were also some differences in equipment, and the sensitivity and specificity of the positive 13C and 14C breath tests still differed.\u003c/p\u003e \u003cp\u003eNevertheless, our study has several limitations that warrant consideration. Firstly, while 13C- or 14C-UBT are widely accepted for their high sensitivity and specificity, they may still diverge from histopathological confirmation, the gold standard for H. pylori detection. Secondly, the single-centre design restricted participant recruitment, resulting in a relatively small sample size that may compromise the generalisability of the findings. Furthermore, the absence of standardised training for endoscopists and observers in assessing mucosal features introduces the risk of interobserver variability and subjective interpretations, which could bias the identification of endoscopic markers.\u003c/p\u003e \u003cp\u003eAlthough external validation was performed, the limited sample size of the validation set (smaller than the training set) and discrepancies in endoscopist expertise between centres may affect reproducibility. Future multicentre studies involving larger and more diverse populations are essential to validate the robustness of our simplified Kyoto gastritis scoring system.\u003c/p\u003e \u003cp\u003eBased on the above findings, this study confirms the clinical utility and reliability of the Kyoto Gastritis Score in identifying active Helicobacter pylori infection. Compared to the traditional Kyoto Gastritis Score, our simplified predictive model \u0026mdash; incorporating loss of regular arrangement of collecting venules (RAC), mucosal swelling and combined erythema (diffuse or spotty redness) \u0026mdash; demonstrates enhanced diagnostic efficiency and accuracy in identifying active H. pylori infection. This streamlined approach offers clinicians a more intuitive and practical tool for the real-time endoscopic assessment of H. pylori infection status, thereby facilitating timely clinical decision-making. Nevertheless, our refined model streamlines the endoscopic diagnosis of H. pylori infection by prioritising three easily interpretable features, its clinical adoption requires further refinement and validation. Addressing these limitations through rigorous multicentre trials will be critical to advancing its utility in real-world settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003ePublisher\u0026rsquo;s note\u003c/h2\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.A.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by grant Foundation of China and the Medical Research Project of Chengdu Municipal Health Commission (Grant No. 2023176).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eMei Yang: Writing - original draft, Formal analysis, Writing - review \u0026amp; editing. Xiaomei Ma, Yujie Wang and Yu Long: Data curation, Investigation. Jin Shan, Tianxu Chen, Jinlin Li and Yuanyuan Chen: Methodology, Writing - review \u0026amp; editing. Jin Shan, Liu Liu and Li Liu: Software, Writing - review \u0026amp; editing. Xiaobin Sun: Supervision, Writing - review \u0026amp; editing, Conceptualization, Project administration, Formal analysis.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe original contributions presented in this study are included in this article/supplementary material, further inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl-Fakhrany OM, Elekhnawy E. Helicobacter pylori in the post-antibiotics era: from virulence factors to new drug targets and therapeutic agents [J]. Arch Microbiol. 2023;205(9):301.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRasool KH, Mahmood Alubadi AE, Al-Bayati IFI. The role of Serum Interleukin-4 and Interleukin-6 in Helicobacter pylori-infected patients [J]. Microb Pathog. 2022;162:105362.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu Y, Zhu H, Liu J, et al. 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Kimura-Takemoto Classification: A Tool to Predict Gastric Intestinal Metaplasia Progression to Advanced Gastric Neoplasia [J]. Dig Dis Sci. 2022;67(8):4092\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshii S, Mabe K, Watano K, et al. Validity of endoscopic features for the diagnosis of Helicobacter pylori infection status based on the Kyoto classification of gastritis [J]. Dig endoscopy: official J Japan Gastroenterological Endoscopy Soc. 2020;32(1):74\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDilaghi E, Dottori L, Pivetta G, et al. Incidence and Predictors of Gastric Neoplastic Lesions in Corpus-Restricted Atrophic Gastritis: A Single-Center Cohort Study [J]. Am J Gastroenterol. 2023;118(12):2157\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIwamuro M, Sakae H, Kanzaki H, et al. 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J Clin Pathol. 2002;55(2):133\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagata T, Ishitake H, Shimamoto F, et al. [Histopathological Study of the Relationship between Lymphoid Follicles and Different Endoscopic Types of Nodular Gastritis] [J]. Rinsho byori Japanese J Clin Pathol. 2014;62(11):1031\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsumoto S, Sugimoto M, Terai T, et al. Map-Like Redness Development After Eradication Therapy for Helicobacter pylori Infection: Prospective Multicenter Observational Study [J]. Helicobacter. 2024;29(6):e13146.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeach HG, Barnett NE. Determinants of basal plasma gastrin levels in the general population [J]. J Gastroenterol Hepatol. 2000;15(11):1267\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeong CY, Kim N, Lee HS, et al. 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Expert Rev Gastroenterol Hepatol. 2024;18(8):457\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYagi K, Aruga Y, Nakamura A, et al. Regular arrangement of collecting venules (RAC): a characteristic endoscopic feature of Helicobacter pylori-negative normal stomach and its relationship with esophago-gastric adenocarcinoma [J]. J Gastroenterol. 2005;40(5):443\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNawacki Ł, Czyż A, Bryk P et al. Can urea breath test (UBT) replace rapid urea test (RUT)? [J]. Polski przeglad chirurgiczny, 2018, 90(5): 44\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePathak CM, Kaur B, Khanduja KL. 14C-urea breath test is safe for pediatric patients [J]. Nucl Med Commun. 2010;31(9):830\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-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":"Kyoto Classification of Gastritis, Helicobacter pylori, digestive endoscopy","lastPublishedDoi":"10.21203/rs.3.rs-8827948/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8827948/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aims to validate the application value of the endoscopic Kyoto Classification of Gastritis for determining the current status of Helicobacter pylori (H. pylori) infection in a population, and to construct a modified simplified prediction model based on this scoring system.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were collected from 313 patients who underwent gastroscopy and a 13C- or 14C-breath test (UBT) at the Digestive Endoscopy Center of Chengdu Third People\u0026rsquo;s Hospital between June 2022 and July 2023, and from 175 patients at Qionglai Second People\u0026rsquo;s Hospital between April 2022 and July 2023. The dataset from our hospital was used as the development set to construct a simplified prediction model for the current H. pylori infection, while the external dataset was used for validation. The model was developed using binary logistic regression and clinical expertise, and ROC curve analysis and the DeLong test were employed to compare diagnostic performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe Kyoto Classification showed area under the curve (AUC) values of 0.862 (95%, CI: 0.822\u0026ndash;0.902) and 0.850 (95%, CI: 0.775\u0026ndash;0.925) in the development and validation sets, respectively. The modified model incorporating the absence of a regular arrangement of collecting venules (RAC), mucosal swelling, and diffuse/spotty redness achieved higher AUC values: 0.922 (95% CI: 0.888\u0026ndash;0.956) and 0.914 (95% CI: 0.864\u0026ndash;0.964) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Accuracy rates were 87.2% and 89.1% in the development and validation sets, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe modified, simplified prediction model demonstrated superior diagnostic performance to the Kyoto Classification. This makes it a practical tool for endoscopists to use when assessing current H. pylori infection.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Modified, Simplified Prediction Model Based on the Kyoto Classification of Gastritis for Current Helicobacter Pylori Infection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-20 07:04:07","doi":"10.21203/rs.3.rs-8827948/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-30T11:43:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280739211162416030957300184340266313426","date":"2026-03-17T09:34:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-17T09:00:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-23T07:44:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-23T05:50:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-23T05:48:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2026-02-09T08:20:45+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":"af153ddf-ba49-4112-914e-e563524b72bd","owner":[],"postedDate":"March 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-20T07:04:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-20 07:04:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8827948","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8827948","identity":"rs-8827948","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}