A predictive model for risk stratification of men in west China with benign prostatic hyperplasia
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Abstract Purpose: we sought to develop and validate a model based on machine learning to predict high-risk BPH in Chinese males in physical examination. Materials and Methods: We retrospectively included 1099 Chinese southwest males with or without BPH in West China Hospital and its related medical consortium from November 2017 to November 2022 if they were determined by their physician to be at sufficient risk to warrant the urological ultrasound examination. Pearson correlation analysis was conducted to determine the predictive factors of BPH. And gradient boosting classifier-based (GBC) algorithm based on machine learning was used to design a model to predict individual risk of BPH. Results: A training cohort (n = 659) and validation cohort (n = 220) were randomly selected to build and validate the BPH-risk predictive model, respectively. The model was then tested on an independent cohort (n = 220), identifying men at low-risk of BPH (n = 102) for whom the urological ultrasound examination would be necessary. The highest normalized mean scores across three complementary quality indicators showed that ten factors: age, smoking status (never, former, current), drinking status (none, occasional, regular), surgical history (yes, no), comorbidities (yes, no), red blood cell count, white blood cell count, platelet count, low-density lipoprotein, glycated hemoglobin, free prostate-specific antigen and packed cell volume were significant for predicting high-risk BPH. The best final model based on GBC reached out accuracy of 97.3% with the ten predictive factors. Conclusion: While further external test in an intended-use cohort is needed, our proposed model based on laboratory and physical examination allows us to predict the risk stratification of BPH in Chinese southwest men. And the predictive model offers a promising tool for identifying individuals at high-risk of BPH who are being considered for urological ultrasound examination.
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A predictive model for risk stratification of men in west China with benign prostatic hyperplasia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A predictive model for risk stratification of men in west China with benign prostatic hyperplasia Qiaosen Dong, Wanjing Huang, Yalan Ye, Deyi Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4672871/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: we sought to develop and validate a model based on machine learning to predict high-risk BPH in Chinese males in physical examination. Materials and Methods: We retrospectively included 1099 Chinese southwest males with or without BPH in West China Hospital and its related medical consortium from November 2017 to November 2022 if they were determined by their physician to be at sufficient risk to warrant the urological ultrasound examination. Pearson correlation analysis was conducted to determine the predictive factors of BPH. And gradient boosting classifier-based (GBC) algorithm based on machine learning was used to design a model to predict individual risk of BPH. Results: A training cohort (n = 659) and validation cohort (n = 220) were randomly selected to build and validate the BPH-risk predictive model, respectively. The model was then tested on an independent cohort (n = 220), identifying men at low-risk of BPH (n = 102) for whom the urological ultrasound examination would be necessary. The highest normalized mean scores across three complementary quality indicators showed that ten factors: age, smoking status ( never, former, current ), drinking status ( none, occasional, regular ), surgical history ( yes, no ), comorbidities ( yes, no ), red blood cell count, white blood cell count, platelet count, low-density lipoprotein, glycated hemoglobin, free prostate-specific antigen and packed cell volume were significant for predicting high-risk BPH. The best final model based on GBC reached out accuracy of 97.3% with the ten predictive factors. Conclusion: While further external test in an intended-use cohort is needed, our proposed model based on laboratory and physical examination allows us to predict the risk stratification of BPH in Chinese southwest men. And the predictive model offers a promising tool for identifying individuals at high-risk of BPH who are being considered for urological ultrasound examination. Benign prostatic hyperplasia Risk prediction Machine learning Figures Figure 1 Figure 2 Figure 3 Introduction Approximately one quarter of men worldwide experience symptoms of pain, tenderness, and burning sensation during urination in the lower urinary tract, which in many cases are caused by benign prostatic hyperplasia (BPH) [ 1 ]. Convenient decision support tools for diagnosing BPH are especially important because it is one of the most frequent causes of urinary obstruction in elderly men. Conventional diagnosis requires not only laboratory and physical examinations but also ultrasonography of the prostate. However, there may not easily be ultrasound equipment in some clinics, especially in resource-limited settings. Reports linking clinicodemographic factors and examination findings to risk of BPH [ 2 – 5 ]. Therefore, obviating the need for imaging to make diagnosis easier and more accessible to more individuals is a possible solution. Previous studies have developed some intelligent systems to predict BPH with a small number of samples. For example, 44 participants were conducted to develop fuzzy intelligent systems [ 6 ], and 12 samples were used to train a computer vision model for detecting glandular component hyperplasia of the prostate [ 7 ]. In addition, a study [ 8 ] built a model to classify prostate cancer and BPH from patients with Luts, rather than investigating the risk factors and diagnosis of BPH. To the best of our knowledge, no BPH predictive models used raw data from laboratory and physical examination results collected for routine care, such as electronic medical records, to identify occult high-risk BPH. Recently, machine learning, which has already proven effective at diagnosing other urological disease [ 9 – 10 ], could be used to predict BPH purely on the basis of laboratory and physical examinations. In this study, we sought to develop and validate a model based on machine learning to predict high-risk of BPH in Chinese men through physical examinations. We then tested the predictive model on populations at low or high risk of BPH with the goal of identifying men who should not undergo urological ultrasound examination. This model used machine learning algorithms to risk-stratify Chinese men with BPH who could be candidates for the urological ultrasound examination. Materials and methods Study Design This retrospective and observational study that included 1099 individuals who underwent examinations at West China Hospital and its related medical consortium (Chengdu, China) between November 2017 and November 2022. We divided the samples into 3 sets randomly: 659 samples for the training of a predictive model, 220 samples for a validation set and 220 samples for a test set. The study was approved by the Biomedical Ethics Review Committee of West China hospital (approval #2022 − 1871). Identification of Risk Factors We initially extracted data for 22 variables that we considered relevant to risk of BPH based on the previous literature [ 2 – 5 ] and our surgeons’ experience. But we decided not to retain the following five variables because values were missing > 20% in the individuals: urinary glucose, albumin-to-creatinine ratio, 2-h postprandial blood glucose, fasting blood glucose, and urinary protein. We explored pairwise correlations of the remaining 17 variables and the presence of BPH using Pearson correlation analysis. Furthermore, to assess the strength of associations between a given variable and the outcome of interest, we focused on the subset of the 17 variables that were associated with the average normalized scores across three indicators (Gini index, F score, and mutual information score) [ 11 ] from the presence of benign prostatic hyperplasia (609 individuals diagnosed with BPH). From this subset, we identified and selected the top 10 variables and data missing for the selected variables were imputed using the mean value (Fig. 1 ). Formulas to compute the three indicators are shown in Supplementary Material 1. Modeling Firstly, seven machine learning algorithms were selected to model: Random Forest, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Multilayer Perceptron, XGBoost, and Gradient Boosting Classifier. And as an eighth model, we averaged these seven models together using the mean method to create an ensemble classification model. Parameters used in the various models are shown in Supplementary Material 2. Predictive performance of the eight models was compared in terms of precision, recall, accuracy and specificity (shown in Supplementary Material 3. Finally, based on the performance, the gradient boosting classifier-based (GBC) algorithm was chosen to design a model to predict BPH-risk for a given individuals. Results Based on the demographic, laboratory and physical data of the 1,099 individuals in the study ( Tables 1 – 2 ) , Pearson related heatmap indicated that three laboratory factors, glycosylated hemoglobin (GHb_A1c), free prostate-specific antigen (FPSA) and glucose (GLC) with the other two baseline factors (surgery history, age) were potentially related with BPH (Fig. 2 a). A model was built based on these five factors. GBC performed best among the machine learning algorithms, reaching the highest accuracy (95.0%) and AUC (0.99). To assess the strength of associations between a given variable and the outcome of interest, we chose 10 variables that achieved the highest normalized mean scores across three complementary quality indicators: age, smoking status ( never, former, current ), drinking status ( none, occasional, regular ), surgical history ( yes, no ), comorbidities ( yes, no ), red blood cell count, white blood cell count, platelet count, low-density lipoprotein, glycated hemoglobin, free prostate-specific antigen and packed cell volume (Fig. 2 b ) . Table 1 Demographic data and findings from laboratory and physical examinations used to develop and validate models to predict benign prostatic hyperplasia Variable Set P value 95%CI Training Validation Test Training Validation Test Training Validation Test N 659 220 220 Age (SD) 57.17 (18.15) 60.48 (17.08) 57.14 (17.05) < 0.001 < 0.001 < 0.001 (0.344,0.389) (0.299,0.379) (0.302,0.377) Male 659 (100%) 220 (100%) 220 (100%) Drinking status < 0.001 < 0.001 < 0.001 None 369 (55.99) 137 (62.27) 129 (58.64) Occasional 225 (34.14) 68 (30.91) 69 (31.36) Regular 65 (9.87) 15 (6.82) 22 (10.00) Smoking status < 0.001 < 0.001 < 0.001 Never 436 (66.16) 148 (67.27) 127 (57.73) Former 79 (11.99) 25 (11.36) 31 (14.09) Current 144 (21.85) 47 (21.36) 62 (28.18) Previous surgery < 0.001 < 0.001 < 0.001 Yes 268 (40.67) 113 (51.36) 89 (40.45) No 391 (59.33) 107 (48.64) 131 (59.55) White Blood Cell Count, mean (SD), /uL 56.76 (408.78) 30.70 (123.01) 80.22 (677.69) 0.003 0.022 0.131 (0.004,0.017) (0.002,0.007) (-0.003, 0.034) Packed Cell Volume, mean (SD), 10^9/L 0.50 (1.59) 0.44 (0.058) 0.45 (0.054) 0.636 < 0.001 < 0.001 (-0.004,0.007) (-0.002, -0.001) (-0.001, -0.0008) Cholesterol, mean (SD), mmol/L 4.63 (0.99) 4.63 (0.91) 4.63 (0.87) < 0.001 < 0.001 < 0.001 (-0.073, -0.045) (-0.078, -0.030) (-0.069, -0.025) Red Blood, Cells, mean (SD), 10^12/L 4.86 (0.59) 4.93 (0.69) 4.89 (0.56) < 0.001 < 0.001 < 0.001 (-0.096, -0.073) (-0.089, -0.041) (-0.094, -0.054) Glycated Hemoglobin A1c, mean (SD), % 5.91 (0.84) 5.89 (0.71) 5.91 (0.78) < 0.001 < 0.001 < 0.001 (0.052,0.081) (0.017, 0.062) (0.035,0.084) Free Prostate-Specific Antigen, mean (SD), ng/m 0.81 (2.33) 0.82 (1.97) 0.60 (0.64) < 0.001 0.002 < 0.001 (0.019, 0.039) (0.013,0.038) (0.013,0.038) Low-Density Lipoprotein, mean (SD), mmol/L 2.75 (0.83) 2.78 (0.88) 2.78 (0.81) < 0.001 < 0.001 < 0.001 (-0.112, -0.071) (-0.139, -0.064) (-0.130, -0.062) Benign prostatic hyperplasia 353 (53.57) 138 (62.73) 118 (53.64) Table 2 Demographic data and findings from laboratory and physical examinations used to develop and validate models to predict benign prostatic hyperplasia, stratified by the presence or absence of the condition Variable Unit Median Variance White Blood Cell Count /uL 56.24 464.82 Platelet Count 10^9/L 196.27 62.30 Hematocrit L/L 0.48 1.28 Red Blood Cell Count 10^12/L 4.88 0.68 High-Density Lipoprotein mmol/L 1.23 0.35 Uric Acid umol/L 373.94 93.47 Low-Density Lipoprotein mmol/L 2.76 0.90 Glucose mmol/L 5.65 1.80 Cholesterol mmol/L 4.63 1.07 Triglycerides mmol/L 1.76 1.76 Free Prostate-Specific Antigen ng/m 0.76 2.12 Glycated Hemoglobin A1c % 5.71 0.96 Fasting Blood Sugar mmol/L 6.38 1.97 2-Hour Postprandial Blood Sugar mmol/L 12.35 4.81 Quantitative Urinary Protein g/L 0.095 0.29 Comparing the models built on the top five BPH-high-related factors with the top ten factors, the result showed that the model based on the heatmap had lower accuracy, which were 92.3% on the validation set and 95.0% on the test set. Models built on the top six factors and the top ten factors of the best algorithm (Gradient Boosting Classifier) performed closely. On the validation set, the two models reached out the accuracy of 96.8% and 97.2% respectively, however, they showed the same accuracy of 97.3% on the test set. Therefore, we used the top 10 factors to build our model. Of the eight predictive models, the gradient boosting classifier showed the highest accuracy (97.3%) and AUC (0.996) against the test dataset from 220 individuals (Fig. 3 ) . Thus, this boosting-based algorithms performed best against our validation data. The Gaussian naive Bayes model, conversely, performed the worst in terms of the various indicators. The other models showed intermediate diagnostic performance, including the ensemble model. Discussion This study demonstrates the feasibility of predicting BPH using machine learning based on laboratory and physical data accessible to clinicians even in resource-limited settings, which may obviate the need for expensive ultrasonography of the prostate. Of 22 demographic, laboratory and clinical variables that we screened for inclusion in our models, we identified 10 as quite strongly linked to risk of the condition, and these 10 included drinking. Our results highlight the need to advise men, especially older men, to reduce these behaviors in order to minimize risk of benign prostatic hyperplasia. The median age of our entire sample of 1,099 individuals was 58 years, and 91% of those older than the median had BPH, compared to only 18% of those younger than the median. These results highlight age as a major risk factor for the condition, consistent with the literature [ 12 ]. Further study should explore whether urological ultrasound for BPH should be recommended for all men beyond a certain age. Among the variables that we found to be tightly associated with BPH were levels of glycated hemoglobin and free prostate-specific antigen as well as history of surgery. All these associations have previously been reported [ 13 – 15 ], suggesting that our modeling focused on appropriate variables. Based on these associations, future work should explore whether BPH shares pathogenic pathways with diabetes, another prevalent disease involving elevated levels of glycated hemoglobin, or with prostate cancer, which is also associated with elevated levels of free prostate-specific antigen [ 16 ]. Future work should also explore how surgeries such as appendectomy, cystolithotomy and cholecystectomy influence risk of subsequent BPH. We surveyed a range of algorithms and combined them into an ensemble model, and we found that boosting-based algorithms were particularly effective at detecting the condition, which may reflect their abilities to adapt and learn from their mistakes [ 17 ], as well as to handle complex, non-linear relationships [19]. These models also have the advantage of being interpretable, so clinicians can understand and therefore trust their predictions [ 18 ]. Differences were small between models built from the top 6 or top 10 factors based on three complementary quality indicators, indicating that adapting the 6-factor model is effective to predict the risk of BPH when available patient data are incomplete. The model built from the top 10 factors based on three quality indicators performed better than the model built from the top 5 factors selected from the Pearson heatmap, which indicates the feasibility of using these three machine learning indicators to analyze disease-related indicators. It may be possible to improve the performance of our predictive models by incorporating scores on the International Prognostic Scoring System, which includes evaluation of intermittent urination and narrowing of the urine stream. We did not incorporate these data because they were not routinely recorded at our hospital during the enrollment period. Future work should also aim to reduce the risk of overfitting in our models by defining the minimal set of variables needed for accurate diagnosis. Ultimately our models should be validated in larger, preferably multicenter samples with a prospective design in order to assess the ability of machine learning to predict subsequent BPH. Despite its limitations, the present study strongly demonstrates the potential for reliable detection of BPH using machine learning-based analysis of routine laboratory and physical examinations. This may improve diagnosis of BPH, particularly in resource-limited settings, and it may facilitate large-scale urological ultrasound examination of older men. Conclusion Our study presents valuable findings and provides essential guidance for future research endeavors. Further external testing in an intended-use cohort is needed. Our proposed model based on laboratory and physical examination allows us to predict BPH risk among men in west China, which may make it a promising tool for identifying individuals at high risk of BPH who are being considered for urological ultrasound examination. Abbreviations ACR Albumin-to-creatinine ratio AUC Area under the curve BPH Benign prostatic hyperplasia CHOL Cholesterol FBG Fasting blood glucose FPSA Free prostate-specific antigen GBC Gradient boosting classifier GHb_A1c Glycosylated hemoglobin Glc Glucose GLU Urinary glucose GNB Gaussian naive bayes HDL High-density lipoprotein LDL Low-density lipoprotein LR Logistic regression MA Microalbuminuria MLP Multilayer perceptron PBG 2-Hour postprandial blood glucose Pca Prostate cancer PCV Cell volume PLT Platelet count PRO Urine protein quantification RBCs Red blood cells RF Random forest SD Standard division SVM Support vector machine TAG Triglycerides UA Uric acid WBC White blood cell count XGBoost Extreme gradient boosting Declarations Ethics approval and consent to participate This retrospective use of patient data was approved by the Biomedical Ethics Review Committee of our hospital (approval 2022-1871). Consent for publication Not applicable. Availability of data and materials The datasets analysed during the current study are not publicly available due to the strict regulation of the hospital but are available from the corresponding author on reasonable request. The version of the machine learning algorithm was sklearn 1.2.0. Program codes are available at github.com/hwj20/BPH_prediction. Competing Interests All authors disclosed no relevant relationships Funding None Authors' contributions Qiaosen Dong and Wanjing Huang wrote the main manuscript text and prepared figures and tables. Yalan Ye and Deyi Luo reviewed the manuscript. Acknowledgments This work was supported by the College Student Innovation and Entrepreneurship Training Program (grants 20231170L and C2024129719) and the Health Department Project "Study on risk prediction model of prostate hyperplasia in Chengdu area based on artificial intelligence" (grant GBKT23030). Conflicts of Interest None declared References Lee SWH, Chan EMC, Lai YK. 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Interpretability of machine learning-based prediction models in healthcare[J]. Wiley Interdisciplinary Reviews: Data Min Knowl Discovery. 2020;10(5):e1379. Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYMATERIAL.docx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4672871","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":333333109,"identity":"10b6b1f8-8fa7-4f6b-a6a7-ade86f0c436b","order_by":0,"name":"Qiaosen Dong","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Qiaosen","middleName":"","lastName":"Dong","suffix":""},{"id":333333110,"identity":"ac47e938-d6c9-41ba-aaaa-7aa29333ee48","order_by":1,"name":"Wanjing Huang","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Wanjing","middleName":"","lastName":"Huang","suffix":""},{"id":333333111,"identity":"52e90dd7-5a5f-4126-bd9f-5c997335fc38","order_by":2,"name":"Yalan Ye","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Yalan","middleName":"","lastName":"Ye","suffix":""},{"id":333333112,"identity":"8d433cb4-0ea3-4a56-9254-b2e402add9d3","order_by":3,"name":"Deyi Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACPiCWACIefvb+hw8SKmoIa2GDapGR7DnDbPDgzDGitTDYGNzIYZN82MJMhBaJ5IM3PrZZ8BicOXusIrGBjYG/vTuBgJa0ZMuZbRI8ksf70m4k7pBhkDhzdgMBLTlm0rxALXxnDpjdSDzDxmAgkUuElr9ALQw3EswKEtuYidTCCNQicCPHjIE4LTzPki17zgH90nMsWSLhzDEegn7hZweG2I+yOnt+9uaDH39U1Mjxt/fi1wIGjGwINg9h5WDwh0h1o2AUjIJRMDIBAM+GRVa/nip1AAAAAElFTkSuQmCC","orcid":"","institution":"Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Deyi","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-07-02 08:57:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4672871/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4672871/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63298018,"identity":"b4d2e4c9-df1a-422a-9685-2a494a47e93c","added_by":"auto","created_at":"2024-08-26 15:43:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78698,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of parameters and classification of individuals from West China Hospital and its related medical consortium (Chengdu, China) between November 2017 and November 2022.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4672871/v1/0af341176770cfbbadea011c.png"},{"id":63299179,"identity":"40b49044-5dba-4e11-9f72-a40a5d3ea7b8","added_by":"auto","created_at":"2024-08-26 15:51:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":141598,"visible":true,"origin":"","legend":"\u003cp\u003eAssessment of the predictive factors. \u003cstrong\u003ea \u003c/strong\u003eHeatmap of Pearson coefficients describing correlations of laboratory and physical variables with each other and with the presence of benign prostatic hyperplasia. Positive correlations are shown in dark color; negative correlations, in bright color. Color intensity reflects correlation strength. Data come from the entire sample of 1,099 individuals. \u003cstrong\u003eb\u003c/strong\u003e Values of the Gini index, F score and mutual information score for 17 laboratory and physical examination variables. The 10 variables are selected to train the machine learning-based predictive models. Data come from the entire sample of 1,099 individuals.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4672871/v1/5dd58cf080311bc2a51ebab8.png"},{"id":63299891,"identity":"bdc874cc-0027-490d-9c25-2f6885ed2f5b","added_by":"auto","created_at":"2024-08-26 15:59:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60789,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves to assess the ability of different machine learning-based models to predict\u003cstrong\u003e \u003c/strong\u003ebenign prostatic hyperplasia. \u003cstrong\u003ea\u003c/strong\u003e validation set based on the top 10 factors. \u003cstrong\u003eb\u003c/strong\u003e Test set based on the top 10 factors. (AUC, area under the curve)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4672871/v1/ecde47f30b311af401ea47b4.png"},{"id":84539445,"identity":"1a0930fe-540d-4a16-ad48-d221d4464e31","added_by":"auto","created_at":"2025-06-13 07:53:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1042806,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4672871/v1/57592ce9-ed23-487e-b31c-e8cda60f65da.pdf"},{"id":63298020,"identity":"f5e9b9d1-1f13-4cca-bb94-c79b0b3570b6","added_by":"auto","created_at":"2024-08-26 15:43:38","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17602,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIAL.docx","url":"https://assets-eu.researchsquare.com/files/rs-4672871/v1/c153a8b8e52d6b625046bb31.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A predictive model for risk stratification of men in west China with benign prostatic hyperplasia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eApproximately one quarter of men worldwide experience symptoms of pain, tenderness, and burning sensation during urination in the lower urinary tract, which in many cases are caused by benign prostatic hyperplasia (BPH) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Convenient decision support tools for diagnosing BPH are especially important because it is one of the most frequent causes of urinary obstruction in elderly men. Conventional diagnosis requires not only laboratory and physical examinations but also ultrasonography of the prostate. However, there may not easily be ultrasound equipment in some clinics, especially in resource-limited settings. Reports linking clinicodemographic factors and examination findings to risk of BPH [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, obviating the need for imaging to make diagnosis easier and more accessible to more individuals is a possible solution.\u003c/p\u003e \u003cp\u003ePrevious studies have developed some intelligent systems to predict BPH with a small number of samples. For example, 44 participants were conducted to develop fuzzy intelligent systems [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and 12 samples were used to train a computer vision model for detecting glandular component hyperplasia of the prostate [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In addition, a study [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] built a model to classify prostate cancer and BPH from patients with Luts, rather than investigating the risk factors and diagnosis of BPH. To the best of our knowledge, no BPH predictive models used raw data from laboratory and physical examination results collected for routine care, such as electronic medical records, to identify occult high-risk BPH. Recently, machine learning, which has already proven effective at diagnosing other urological disease [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], could be used to predict BPH purely on the basis of laboratory and physical examinations.\u003c/p\u003e \u003cp\u003eIn this study, we sought to develop and validate a model based on machine learning to predict high-risk of BPH in Chinese men through physical examinations. We then tested the predictive model on populations at low or high risk of BPH with the goal of identifying men who should not undergo urological ultrasound examination. This model used machine learning algorithms to risk-stratify Chinese men with BPH who could be candidates for the urological ultrasound examination.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis retrospective and observational study that included 1099 individuals who underwent examinations at West China Hospital and its related medical consortium (Chengdu, China) between November 2017 and November 2022. We divided the samples into 3 sets randomly: 659 samples for the training of a predictive model, 220 samples for a validation set and 220 samples for a test set. The study was approved by the Biomedical Ethics Review Committee of West China hospital (approval #2022\u0026thinsp;\u0026minus;\u0026thinsp;1871).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Risk Factors\u003c/h2\u003e \u003cp\u003eWe initially extracted data for 22 variables that we considered relevant to risk of BPH based on the previous literature [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and our surgeons\u0026rsquo; experience. But we decided not to retain the following five variables because values were missing\u0026thinsp;\u0026gt;\u0026thinsp;20% in the individuals: urinary glucose, albumin-to-creatinine ratio, 2-h postprandial blood glucose, fasting blood glucose, and urinary protein. We explored pairwise correlations of the remaining 17 variables and the presence of BPH using Pearson correlation analysis. Furthermore, to assess the strength of associations between a given variable and the outcome of interest, we focused on the subset of the 17 variables that were associated with the average normalized scores across three indicators (Gini index, F score, and mutual information score) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] from the presence of benign prostatic hyperplasia (609 individuals diagnosed with BPH). From this subset, we identified and selected the top 10 variables and data missing for the selected variables were imputed using the mean value (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Formulas to compute the three indicators are shown in \u003cb\u003eSupplementary Material 1.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eModeling\u003c/h2\u003e \u003cp\u003eFirstly, seven machine learning algorithms were selected to model: Random Forest, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Multilayer Perceptron, XGBoost, and Gradient Boosting Classifier. And as an eighth model, we averaged these seven models together using the mean method to create an ensemble classification model. Parameters used in the various models are shown in \u003cb\u003eSupplementary Material 2.\u003c/b\u003e Predictive performance of the eight models was compared in terms of precision, recall, accuracy and specificity (shown in \u003cb\u003eSupplementary Material 3.\u003c/b\u003e Finally, based on the performance, the gradient boosting classifier-based (GBC) algorithm was chosen to design a model to predict BPH-risk for a given individuals.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBased on the demographic, laboratory and physical data of the 1,099 individuals in the study \u003cb\u003e(\u003c/b\u003eTables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, Pearson related heatmap indicated that three laboratory factors, glycosylated hemoglobin (GHb_A1c), free prostate-specific antigen (FPSA) and glucose (GLC) with the other two baseline factors (surgery history, age) were potentially related with BPH (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). A model was built based on these five factors. GBC performed best among the machine learning algorithms, reaching the highest accuracy (95.0%) and AUC (0.99). To assess the strength of associations between a given variable and the outcome of interest, we chose 10 variables that achieved the highest normalized mean scores across three complementary quality indicators: age, smoking status (\u003cem\u003enever, former, current\u003c/em\u003e), drinking status (\u003cem\u003enone, occasional, regular\u003c/em\u003e), surgical history (\u003cem\u003eyes, no\u003c/em\u003e), comorbidities (\u003cem\u003eyes, no\u003c/em\u003e), red blood cell count, white blood cell count, platelet count, low-density lipoprotein, glycated hemoglobin, free prostate-specific antigen and packed cell volume (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e.\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\u003eDemographic data and findings from laboratory and physical examinations used to develop and validate models to predict benign prostatic hyperplasia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eSet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eValidation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eValidation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.17\u003c/p\u003e \u003cp\u003e(18.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.48 (17.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.14 (17.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.344,0.389)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.299,0.379)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.302,0.377)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e659\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e220\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDrinking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e369 (55.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137\u003c/p\u003e \u003cp\u003e(62.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129 (58.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccasional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 (34.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003cp\u003e(30.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69 (31.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003cp\u003e(9.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e(6.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e436 (66.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148\u003c/p\u003e \u003cp\u003e(67.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e127 (57.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (11.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003cp\u003e(11.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (14.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (21.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003cp\u003e(21.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62 (28.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrevious surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268 (40.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003cp\u003e(51.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89 (40.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e391 (59.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107\u003c/p\u003e \u003cp\u003e(48.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131 (59.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWhite Blood Cell Count, mean (SD), /uL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.76 (408.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.70\u003c/p\u003e \u003cp\u003e(123.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.22\u003c/p\u003e \u003cp\u003e(677.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.004,0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.002,0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-0.003, 0.034)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePacked Cell Volume,\u003c/p\u003e \u003cp\u003emean (SD), 10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003cp\u003e(1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.004,0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(-0.002, -0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-0.001, -0.0008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCholesterol,\u003c/p\u003e \u003cp\u003emean (SD),\u003c/p\u003e \u003cp\u003emmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003cp\u003e(0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003cp\u003e(0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003cp\u003e(0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.073, -0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(-0.078, -0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-0.069, -0.025)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRed Blood, Cells, mean (SD),\u003c/p\u003e \u003cp\u003e10^12/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003cp\u003e(0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003cp\u003e(0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.89\u003c/p\u003e \u003cp\u003e(0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.096, -0.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(-0.089, -0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-0.094, -0.054)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGlycated Hemoglobin A1c, mean (SD),\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.91\u003c/p\u003e \u003cp\u003e(0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.89\u003c/p\u003e \u003cp\u003e(0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.91\u003c/p\u003e \u003cp\u003e(0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.052,0.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.017, 0.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.035,0.084)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFree Prostate-Specific Antigen, mean (SD), ng/m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003cp\u003e(2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003cp\u003e(1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003cp\u003e(0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.019, 0.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.013,0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.013,0.038)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLow-Density Lipoprotein, mean (SD), mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003cp\u003e(0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003cp\u003e(0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003cp\u003e(0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.112, -0.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(-0.139, -0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-0.130, -0.062)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBenign prostatic hyperplasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e353 (53.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138\u003c/p\u003e \u003cp\u003e(62.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118 (53.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\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=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic data and findings from laboratory and physical examinations used to develop and validate models to predict benign prostatic hyperplasia, stratified by the presence or absence of the condition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite Blood Cell Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/uL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e464.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed Blood Cell Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10^12/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-Density Lipoprotein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eumol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e373.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-Density Lipoprotein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree Prostate-Specific Antigen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eng/m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycated Hemoglobin A1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting Blood Sugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-Hour Postprandial Blood Sugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantitative Urinary Protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparing the models built on the top five BPH-high-related factors with the top ten factors, the result showed that the model based on the heatmap had lower accuracy, which were 92.3% on the validation set and 95.0% on the test set. Models built on the top six factors and the top ten factors of the best algorithm (Gradient Boosting Classifier) performed closely. On the validation set, the two models reached out the accuracy of 96.8% and 97.2% respectively, however, they showed the same accuracy of 97.3% on the test set.\u003c/p\u003e \u003cp\u003eTherefore, we used the top 10 factors to build our model. Of the eight predictive models, the gradient boosting classifier showed the highest accuracy (97.3%) and AUC (0.996) against the test dataset from 220 individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Thus, this boosting-based algorithms performed best against our validation data. The Gaussian naive Bayes model, conversely, performed the worst in terms of the various indicators. The other models showed intermediate diagnostic performance, including the ensemble model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates the feasibility of predicting BPH using machine learning based on laboratory and physical data accessible to clinicians even in resource-limited settings, which may obviate the need for expensive ultrasonography of the prostate. Of 22 demographic, laboratory and clinical variables that we screened for inclusion in our models, we identified 10 as quite strongly linked to risk of the condition, and these 10 included drinking. Our results highlight the need to advise men, especially older men, to reduce these behaviors in order to minimize risk of benign prostatic hyperplasia.\u003c/p\u003e \u003cp\u003eThe median age of our entire sample of 1,099 individuals was 58 years, and 91% of those older than the median had BPH, compared to only 18% of those younger than the median. These results highlight age as a major risk factor for the condition, consistent with the literature [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Further study should explore whether urological ultrasound for BPH should be recommended for all men beyond a certain age.\u003c/p\u003e \u003cp\u003eAmong the variables that we found to be tightly associated with BPH were levels of glycated hemoglobin and free prostate-specific antigen as well as history of surgery. All these associations have previously been reported [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], suggesting that our modeling focused on appropriate variables. Based on these associations, future work should explore whether BPH shares pathogenic pathways with diabetes, another prevalent disease involving elevated levels of glycated hemoglobin, or with prostate cancer, which is also associated with elevated levels of free prostate-specific antigen [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Future work should also explore how surgeries such as appendectomy, cystolithotomy and cholecystectomy influence risk of subsequent BPH.\u003c/p\u003e \u003cp\u003eWe surveyed a range of algorithms and combined them into an ensemble model, and we found that boosting-based algorithms were particularly effective at detecting the condition, which may reflect their abilities to adapt and learn from their mistakes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], as well as to handle complex, non-linear relationships [19]. These models also have the advantage of being interpretable, so clinicians can understand and therefore trust their predictions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDifferences were small between models built from the top 6 or top 10 factors based on three complementary quality indicators, indicating that adapting the 6-factor model is effective to predict the risk of BPH when available patient data are incomplete. The model built from the top 10 factors based on three quality indicators performed better than the model built from the top 5 factors selected from the Pearson heatmap, which indicates the feasibility of using these three machine learning indicators to analyze disease-related indicators.\u003c/p\u003e \u003cp\u003eIt may be possible to improve the performance of our predictive models by incorporating scores on the International Prognostic Scoring System, which includes evaluation of intermittent urination and narrowing of the urine stream. We did not incorporate these data because they were not routinely recorded at our hospital during the enrollment period. Future work should also aim to reduce the risk of overfitting in our models by defining the minimal set of variables needed for accurate diagnosis. Ultimately our models should be validated in larger, preferably multicenter samples with a prospective design in order to assess the ability of machine learning to predict subsequent BPH.\u003c/p\u003e \u003cp\u003eDespite its limitations, the present study strongly demonstrates the potential for reliable detection of BPH using machine learning-based analysis of routine laboratory and physical examinations. This may improve diagnosis of BPH, particularly in resource-limited settings, and it may facilitate large-scale urological ultrasound examination of older men.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study presents valuable findings and provides essential guidance for future research endeavors. Further external testing in an intended-use cohort is needed. Our proposed model based on laboratory and physical examination allows us to predict BPH risk among men in west China, which may make it a promising tool for identifying individuals at high risk of BPH who are being considered for urological ultrasound examination.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlbumin-to-creatinine ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBPH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBenign prostatic hyperplasia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHOL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFBG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFasting blood glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFPSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFree prostate-specific antigen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGradient boosting classifier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGHb_A1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycosylated hemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGlc\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGLU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrinary glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGNB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGaussian naive bayes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLogistic regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicroalbuminuria\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultilayer perceptron\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePBG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e2-Hour postprandial blood glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePca\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstate cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCell volume\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlatelet count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrine protein quantification\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRBCs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRed blood cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandom forest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard division\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSVM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSupport vector machine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUric acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhite blood cell count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eXGBoost\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtreme gradient boosting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective use of patient data was approved by the Biomedical Ethics Review Committee of our hospital (approval 2022-1871).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are not publicly available due to the strict regulation of the hospital but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eThe version of the machine learning algorithm was sklearn 1.2.0. Program codes are available at github.com/hwj20/BPH_prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors disclosed no relevant relationships\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQiaosen Dong and Wanjing Huang wrote the main manuscript text and prepared figures and tables. Yalan Ye and Deyi Luo reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the College Student Innovation and Entrepreneurship Training Program (grants 20231170L and C2024129719) and the Health Department Project \u0026quot;Study on risk prediction model of prostate hyperplasia in Chengdu area based on artificial intelligence\u0026quot; (grant GBKT23030).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLee SWH, Chan EMC, Lai YK. The global burden of lower urinary tract symptoms suggestive of benign prostatic hyperplasia: a systematic review and meta-analysis, Scientific reports, vol. 7, p. 7984, 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoeb S, Kettermann A, Carter HB, Ferrucci L, Metter EJ, Walsh PC. Prostate volume changes over time: results from the Baltimore Longitudinal Study of Aging. J Urol. 2009;182:1458\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlatz EA, Joshu CE, Mondul AM, Peskoe SB, Willett WC, Giovannucci E. Incidence and progression of lower urinary tract symptoms in a large prospective cohort of United States men. J Urol. 2012;188:496\u0026ndash;501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHammarsten J, H\u0026ouml;gstedt B, Holthuis N, Mellstr\u0026ouml;m D. Components of the metabolic syndrome\u0026mdash;risk factors for the development of benign prostatic hyperplasia. Prostate Cancer Prostatic Dis. 1998;1:157\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParsons JK, Carter HB, Partin AW, Windham BG, Metter EJ, Ferrucci L, Landis P, Platz EA. Metabolic factors associated with benign prostatic hyperplasia. J Clin Endocrinol Metabolism. 2006;91:2562\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorshizi AD, Zarandi MHF, Torshizi GD, Eghbali K. A hybrid fuzzy-ontology based intelligent system to determine level of severity and treatment recommendation for Benign Prostatic Hyperplasia, Computer methods and programs in biomedicine, 113, p. 301\u0026ndash;13, 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhalid SU, Syed A, Shah SSH. Machine learning approaches for the histopathological diagnosis of prostatic hyperplasia. Ann Clin Anal Med. 2020;11:425\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Li W, Zhang Z, Xue Y, Liu Y-L, Nie K, Su M-Y, Ye Q. Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics. Volume 61. Medical \u0026amp; Biological Engineering \u0026amp; Computing; 2023. pp. 757\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede la Cruz Mart\u0026iacute;n B, Adot Zurbano JM, Guti\u0026eacute;rrez-M\u0026iacute;nguez E, G\u0026oacute;mez S\u0026aacute;nchez E, Calvo S. Tamayo G\u0026oacute;mez E. Development of a Predictive Model for the Diagnosis of Lower Urinary Tract Obstruction in Men. J Urol. 2022;208(3):668\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Neste L, Henao R, Wojno KJ, Signes J, DeHart J, Busta A, Marriott E, Willing M, Argentini A, Hurley PM, Korman H, Hafron J, Putzi M, Pieczonka CM, Karsh LI, Morris DS, Kassis AI, Kantoff PW. Development and Optimization of a Subtraction-Normalized Immunocyte Profiling Signature for Prostate Cancer Active Surveillance Risk Stratification. J Urol. 2024;211(3):415\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWitten IH, Frank E, Hall MA et al. Practical machine learning tools and techniques[J]. Data Mining. Fourth Edition, Elsevier Publishers, 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouhadana D, Lu XH, Luo JW, Assad A, Deyirmendjian C, Guennoun A, Nguyen D-D, Kwong JCC, Chughtai B. Elterman and others, Clinical Applications of Machine Learning for Urolithiasis and Benign Prostatic Hyperplasia: A Systematic Review. J Endourol. 2023;37:474\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu M, Shu X, Yu G, Wu X, V\u0026auml;lim\u0026auml;ki M, Feng H. A risk prediction model based on machine learning for cognitive impairment among Chinese community-dwelling elderly people with normal cognition: development and validation study. J Med Internet Res. 2021;23:e20298.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei X, Niu X, Zhang X, Li Y. Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays, in 2022 IEEE International Conference on Big Data (Big Data), 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah M, Naik N, Hameed BZ, Paul R, Shetty DK, Ibrahim S, Rai BP, Chlosta P, Rice P, Somani BK. Current Applications of Artificial Intelligence in Benign Prostatic Hyperplasia. Turkish J Urol. 2022;48:262.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuffy MJ. Biomarkers for prostate cancer: prostate-specific antigen and beyond. Clin Chem Lab Med vol. 2020;58:326\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira AJ, Figueiredo MAT. Boosting algorithms: A review of methods, theory, and applications[J]. Ensemble machine learning: Methods and applications, 2012: 35\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStiglic G, Kocbek P, Fijacko N, et al. Interpretability of machine learning-based prediction models in healthcare[J]. Wiley Interdisciplinary Reviews: Data Min Knowl Discovery. 2020;10(5):e1379.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Benign prostatic hyperplasia, Risk prediction, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-4672871/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4672871/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003e\u003c/em\u003ewe sought to develop and validate a model based on machine learning to predict high-risk BPH in Chinese males in physical examination.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMaterials and Methods: \u003c/strong\u003e\u003c/em\u003eWe retrospectively included 1099 Chinese southwest males with or without BPH in West China Hospital and its related medical consortium from November 2017 to November 2022 if they were determined by their physician to be at sufficient risk to warrant the urological ultrasound examination. Pearson correlation analysis was conducted to determine the predictive factors of BPH. And gradient boosting classifier-based (GBC) algorithm based on machine learning was used to design a model to predict individual risk of BPH.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/em\u003e \u0026nbsp;A training cohort (n = 659) and validation cohort (n = 220) were randomly selected to build and validate the BPH-risk predictive model, respectively. The model was then tested on an independent cohort (n = 220), identifying men at low-risk of BPH (n = 102) for whom the urological ultrasound examination would be necessary. The highest normalized mean scores across three complementary quality indicators showed that ten factors: age, smoking status (\u003cem\u003enever, former, current\u003c/em\u003e), drinking status (\u003cem\u003enone, occasional, regular\u003c/em\u003e), surgical history (\u003cem\u003eyes, no\u003c/em\u003e), comorbidities (\u003cem\u003eyes, no\u003c/em\u003e), red blood cell count, white blood cell count, platelet count, low-density lipoprotein, glycated hemoglobin, free prostate-specific antigen and packed cell volume were significant for predicting high-risk BPH. The best final model based on GBC reached out accuracy of 97.3% with the ten predictive factors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion: \u0026nbsp;\u003c/strong\u003e\u003c/em\u003eWhile further external test in an intended-use cohort is needed, our proposed model based on laboratory and physical examination allows us to predict the risk stratification of BPH in Chinese southwest men. And the predictive model offers a promising tool for identifying individuals at high-risk of BPH who are being considered for urological ultrasound examination.\u003c/p\u003e","manuscriptTitle":"A predictive model for risk stratification of men in west China with benign prostatic hyperplasia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-26 15:43:34","doi":"10.21203/rs.3.rs-4672871/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d6893530-a316-4032-be25-b19ea3b39366","owner":[],"postedDate":"August 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-13T07:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-26 15:43:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4672871","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4672871","identity":"rs-4672871","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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