Chronic Kidney Disease Prediction in Different Populations Using Routine Urine Test: A Multi-Center Study

Chronic Kidney Disease Prediction in Different Populations Using Routine Urine Test: A Multi-Center Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Chronic Kidney Disease Prediction in Different Populations Using Routine Urine Test: A Multi-Center Study Qingyuan Zheng, Chi Wang, Yong He, Wan He, Yuan Zhong, Rulin Zhang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8963275/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 Objectives This study aimed to assess the application value of routine urine tests for predicting chronic kidney disease (CKD) across diverse populations. By developing and validating a predictive model using samples from a multi-center dataset, the study aimed to improve early CKD screening. Methods Urine samples were collected from 3,000 patients at West China Hospital and an external multicenter cohort of 3,856 individuals. Routine dipstick and microscopic urine tests were conducted, and key predictors were identified using LASSO regression. A multivariate logistic regression model was developed, and its performance was assessed through receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis. Results The study identified 11 significant predictors of CKD: RBCs, Mucus threads, UBG, KET, BLD, PRO, LEU, PH, MALB, CA, and Osmolality. The predictive model demonstrated excellent performance with an AUC of 0.849, indicating high discriminatory power. Validation results confirmed the model's robustness and generalizability across diverse populations with the AUC = 0.854 for internal validation cohorts and AUC = 0.848 for external validation cohorts. Conclusion The developed predictive model provides a reliable, non-invasive tool for CKD screening using routine urine tests. Its high accuracy and scalability make it suitable for integration into clinical workflows, facilitating early intervention and improving patient outcomes. Chronic Kidney Disease (CKD) Predictive Model Routine Urine Tests Multicenter Study Multivariate Logistic Regression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Chronic Kidney Disease (CKD) represents a significant and escalating public health concern, affecting approximately 10% of the global population[ 1 ]. The disease is a major contributor to morbidity and mortality, imposing an increasing burden on healthcare systems worldwide[ 2 ]. In China, CKD prevalence has risen markedly over recent decades, fueled by an aging population and the growing incidence of risk factors such as hypertension, diabetes, obesity, and metabolic syndrome[ 3 ]. This trend carries profound socioeconomic consequences, including elevated healthcare costs, reduced productivity, and diminished quality of life for affected individuals[ 4 ]. Early detection and timely intervention are critical to slowing disease progression and alleviating its societal and healthcare impacts[ 5 ]. The estimated glomerular filtration rate (eGFR) serves as a cornerstone for diagnosing and staging CKD[ 2 ], providing an assessment of kidney function based on the filtration capacity of the glomeruli. However, eGFR measurement typically depends on serum creatinine or cystatin C levels, necessitating blood sampling and access to specialized laboratory facilities[ 6 , 7 ]. This reliance on invasive procedures and resource-intensive testing may restrict the feasibility of frequent or widespread CKD screening, particularly in resource-limited settings or specific populations requiring long-term monitoring[ 8 ]. Consequently, there is a pressing need for non-invasive, accessible diagnostic alternatives to complement existing methods and enhance screening efforts. Routine urine tests, encompassing chemical analysis[ 9 ], sediment analysis[ 10 ], and microscopic examination[ 11 ], offer valuable insights into renal function and urinary system health[ 12 ]. Importantly, these indicators often reveal abnormalities preceding measurable declines in eGFR[ 13 ]. As a non-invasive, cost-effective, and widely available tool, routine urine testing holds significant potential for large-scale CKD screening[ 14 ]. Its widespread implementation in clinical practice ensures accessibility for nearly all patient populations. Despite these advantages, the predictive value of routine urine test indicators for CKD diagnosis remains underexplored. A thorough evaluation of these indicators, especially in high-risk groups, could enhance early diagnosis and facilitate timely intervention, thereby improving patient outcomes[ 15 ]. This multicenter study draws on data from a diverse range of hospitals to evaluate the predictive utility of routine urine tests in detecting CKD. Specifically, it aims to develop and validate a predictive model based on urine test parameters, enabling effective differentiation between CKD and other renal or urinary conditions. By focusing on high-risk cohorts—such as individuals with preexisting urinary or renal disorders—this model aspires to enhance early screening and diagnostic accuracy. Ultimately, the study seeks to establish a robust, practical framework for CKD detection that can be integrated into routine clinical workflows, benefiting both general and at-risk populations. 2. Materials and Methods 2.1 Data Collection The training dataset comprised 3,000 urine samples collected from patients at West China Hospital, Sichuan University, Chengdu, China, between October 2023 and October 2024. After excluding samples with incomplete patient data or insufficient serum volume, a total of 1,733 samples were assigned to the training cohort, while 742 samples were allocated to the internal validation cohort. These samples represented a diverse patient population, including a significant proportion of individuals at risk for kidney or urinary system injury. For external validation, samples were obtained from a multicenter urine dataset coordinated by West China Hospital, with contributions from: Shanghai Fengxian District Central Hospital, Shanghai General Hospital, The First Hospital of Jilin University, University-Town Affiliated Hospital of Chongqing Medical University, and The First Affiliated Hospital of Henan University of CM. The external validation cohort comprised 3,856 cases, evenly distributed across these institutions. To ensure data reliability, samples with incomplete patient records or inadequate urine test results were excluded from the analysis. All qualified samples underwent routine urine tests, including dipstick analysis and microscopic examination. The CA-500 Automatic Urine Analyzer and MA-500 Automated Urine Sediment Analyzer (MEDICSIDE Corp, China) were used for automated analysis. In cases where the automated system yielded inconclusive results, manual microscopic examination was performed by experienced laboratory professionals to determine white blood cell (WBC) count, red blood cell (RBC) count, epithelial cells (ECs), casts, and crystals. In addition to urine analysis, serum comprehensive metabolic panel (CMP) results were recorded, including: Creatinine (Cr), Blood Urea Nitrogen (BUN), Uric Acid (UA), Total Bilirubin (TB), Albumin (Alb), Alanine amino Transferase (ALT), Aspartate Aminotransferase (AST), Glucose, Sodium (Na), Potassium (K), Chloride (Cl), Triglyceride (TG), Total cholesterol (TC). Additionally, demographic data—including age, sex, weight, and medical history—were recorded for all participants. Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. The CMP results and demographic data were extracted from each patient’s most recent historical medical records prior to urine sample collection. The flow of sample processing, from initial testing to confirmatory assays, is depicted in Fig. 1 . 2.2 Data preprocessing To maintain confidentiality, patient personal information was anonymized and replaced with unique digital codes before analysis. A general description of the dataset was performed prior to statistical analysis to ensure data integrity and identify potential outliers. The Kolmogorov–Smirnov test[ 16 ] was used to evaluate the normality of data distributions. For variables that conformed to a normal distribution, mean values and standard deviations were calculated. The Levene test was applied to assess the homogeneity of variance between groups. If the assumption of homogeneity was met, parametric tests were employed for comparisons. In cases where variance homogeneity was violated, non-parametric tests, such as the Kolmogorov–Smirnov Z-rank test, were used to analyze the data. Statistical analyses were conducted using IBM SPSS Statistics version 21.0 (SPSS Corporation, Chicago, IL, USA). A significance threshold of P < 0.05 was applied to all tests, ensuring statistical rigor. 2.3 Model Development and Validation To begin, predictors were preliminarily selected using LASSO (Least Absolute Shrinkage and Selection Operator) regression[ 17 ] within the development set. Following this, a multivariate logistic regression analysis[ 18 ] was used to build the prediction model in the training set. Each predictor's score was calculated, and the model was visualized through a nomogram. To evaluate the performance of the developed models, receiver operating characteristic (ROC) curve analysis was conducted, with the area under the curve (AUC) as the primary performance metric[ 19 ]. Calibration curves were then employed to assess the model's calibration, followed by goodness-of-fit testing[ 20 ]. Additionally, decision curve analysis (DCA) was performed to evaluate the clinical utility of the models by estimating the net benefits at different threshold probabilities for both the training and testing groups[ 21 ]. All statistical analyses were carried out using R software (version 4.1.0), with two-sided tests. A P-value of < 0.05 was considered statistically significant. 3. Results 3.1 Descriptive Analysis of the clinical samples and datasets. The baseline clinicopathologic characteristics of the study cohorts are presented in Table 1 . 1,733 and 742 clinical samples were included in the training and internal verification dataset, comprising 1,905 non-CKD cases and 570 pre-diagnosed CKD patients recruited from West China Hospital. For external validation, a multicenter dataset was used, consisting of 3,712 non-CKD cases (eGFR range 587 − 76) and 144 CKD patients (eGFR range 69 − 2) collected from five hospitals across China. For each urine sample, comprehensive clinical information and routine urine test variables were recorded. These included parameters obtained through dipstick urine test—such as Urobilinogen (UBG), Bilirubin (BIL), Ketones (KET), Blood (BLD), Protein (PRO), Nitrite (NIT), Leukocyte esterase (LEU), Glucose (GLU), PH, Vitamin C (VC), Microalbumin (MALB), Creatinine (Cr), Calcium (CA), Albumin/Creatinine ratio (A:C), Conductivity, Osmolality and through urinary microscopic examination—such as Red blood cells (RBCs), White blood cells (WBCs), Squamous epithelial cells, Small round epithelial cells, Hyaline casts, Granular casts, Waxy casts, Broad casts, Bacilli, Candida, Calcium oxalate crystals, Uric acid crystals, Magnesium ammonium phosphate crystals, Sperm, Mucus threads, Cocci. In Table 1 , parameters exhibited consistent distributions between the training and validation cohorts (p < 0.05) were shown, indicating strong concordance and demonstrating the representativeness of the datasets for model development and evaluation. Table 1 Baseline characteristics of datasets Variable Overall Training Validation P value N = 5,589 N = 1,733 N = 3,856 RBCs 3.000 (2.000–7.000) 5.000 (3.000–11.000) 3.000 (2.000–5.000) < 0.001 WBCs 3.000 (1.000–8.000) 4.000 (2.000–9.000) 2.000 (1.000–7.000) < 0.001 Squamous epithelial cells 1.000 (0.000–2.000) 1.000 (0.000–2.000) 1.000 (0.000–2.000) < 0.001 Small round epithelial cells 0.000 (0.000–1.000) 0.000 (0.000–1.000) 0.000 (0.000–1.000) 0.05 Hyaline casts 0.000 (0.000–0.000) 0.000 (0.000–1.000) 0.000 (0.000–0.000) < 0.001 Granular casts 0.000 (0.000–0.000) 0.000 (0.000–1.000) 0.000 (0.000–0.000) < 0.001 Waxy casts 0.000 (0.000–0.000) 0.000 (0.000–0.000) 0.000 (0.000–0.000) < 0.001 Calcium oxalate crystals 0.000 (0.000–0.000) 0.000 (0.000–0.000) 0.000 (0.000–0.000) < 0.001 Mucus threads 1.000 (0.000–6.000) 3.000 (1.000–13.000) 1.000 (0.000–4.000) < 0.001 Cocci 0.000 (0.000–0.000) 0.000 (0.000–86.000) 0.000 (0.000–0.000) < 0.001 Conductivity 17.000 (12.000–21.000) 14.000 (11.000–20.000) 17.500 (13.000–22.000) < 0.001 Osmolality 510.000 (404.000–620.000) 456.000 (369.000–578.000) 530.000 (428.000–639.000) < 0.001 UBG, n (%) < 0.001 - 5,250 (94) 1,545 (89) 3,705 (96) + 334 (6.0) 183 (11) 151 (3.9) ++ 4 (< 0.1) 4 (0.2) 0 (0) +++ 1 (< 0.1) 1 (< 0.1) 0 (0) BIL, n (%) 0.347 - 5,541 (99) 1,715 (99) 3,826 (99) + 38 (0.7) 13 (0.8) 25 (0.6) ++ 9 (0.2) 5 (0.3) 4 (0.1) +++ 1 (< 0.1) 0 (0) 1 (< 0.1) KET, n (%) < 0.001 - 5,268 (94) 1,611 (93) 3,657 (95) + 221 (4.0) 108 (6.2) 113 (2.9) ++ 88 (1.6) 13 (0.8) 75 (1.9) +++ 8 (0.1) 1 (< 0.1) 7 (0.2) ++++ 4 (< 0.1) 0 (0) 4 (0.1) BLD, n (%) < 0.001 - 4,105 (73) 976 (56) 3,129 (81) + 709 (13) 477 (28) 232 (6.0) ++ 232 (4.2) 95 (5.5) 137 (3.6) +++ 543 (9.7) 185 (11) 358 (9.3) PRO, n (%) < 0.001 - 3,680 (66) 635 (37) 3,045 (79) + 1,065 (19) 548 (32) 517 (13) ++ 568 (10) 366 (21) 202 (5.2) +++ 276 (4.9) 184 (11) 92 (2.4) NIT, n (%) 0.692 - 5,318 (95) 1,643 (95) 3,675 (95) + 23 (0.4) 7 (0.4) 16 (0.4) ++ 248 (4.4) 83 (4.8) 165 (4.3) LEU, n (%) < 0.001 - 4,255 (76) 1,172 (68) 3,083 (80) + 669 (12) 385 (22) 284 (7.4) ++ 342 (6.1) 99 (5.7) 243 (6.3) +++ 323 (5.8) 77 (4.4) 246 (6.4) GLU, n (%) < 0.001 - 4,542 (81) 1,373 (79) 3,169 (82) + 210 (3.8) 131 (7.6) 79 (2.0) ++ 127 (2.3) 34 (2.0) 93 (2.4) +++ 98 (1.8) 26 (1.5) 72 (1.9) ++++ 612 (11) 169 (9.8) 443 (11) PH, n (%) =8.0 169 (3.0) 29 (1.7) 140 (3.6) VC, n (%) < 0.001 - 2,784 (50) 7 (0.4) 2,777 (72) + 2,514 (45) 1,639 (95) 875 (23) ++ 143 (2.6) 48 (2.8) 95 (2.5) +++ 148 (2.6) 39 (2.3) 109 (2.8) MAB, n (%) < 0.001 - 2,344 (42) 430 (25) 1,914 (50) + 383 (6.9) 196 (11) 187 (4.8) ++ 2,862 (51) 1,107 (64) 1,755 (46) Cr, n (%) < 0.001 - 462 (8.3) 111 (6.4) 351 (9.1) + 1,357 (24) 262 (15) 1,095 (28) ++ 1,809 (32) 532 (31) 1,277 (33) +++ 1,290 (23) 490 (28) 800 (21) ++++ 671 (12) 338 (20) 333 (8.6) CA, n (%) < 0.001 - 1,751 (31) 587 (34) 1,164 (30) + 1,670 (30) 608 (35) 1,062 (28) ++ 1,966 (35) 515 (30) 1,451 (38) +++ 202 (3.6) 23 (1.3) 179 (4.6) A:C, n (%) < 0.001 - 2,434 (44) 573 (33) 1,861 (48) + 2,596 (46) 964 (56) 1,632 (42) ++ 559 (10) 196 (11) 363 (9.4) CKD, n (%) < 0.001 Yes 5,046 (90) 1,334 (77) 3,712 (96) No 543 (9.7) 399 (23) 144 (3.7) 3.2 Predictor selection. To identify clinical indicators significantly associated with CKD, regression analysis was performed on the 32 recorded features. Using least absolute shrinkage and selection operator (LASSO) regression (Figs. 2 and 3 ), 11 predictors were identified as being independently associated with CKD: RBCs, Mucus threads, UBG, KET, BLD, PRO, LEU, PH, MALB, CA, and Osmolality. These variables were subsequently incorporated into a multivariate logistic regression model, confirming their significance as independent predictors for distinguishing CKD from other urinary or renal conditions. 3.3 Nomogram construction and verification A nomogram was constructed based on the 11 identified predictors to facilitate the diagnosis of CKD in clinical settings. The nomogram assigned scores to each variable, with risk scores calculated from the coefficients of the logistic regression model. The total score for a patient was obtained by summing the contributions of all variables, enabling the calculation of an individualized probability of CKD (Fig. 4 ). The performance of the nomogram was evaluated using a receiver operating characteristic (ROC) curve, which yielded an area under the curve (AUC) of 0.849 (95% CI: 0.828–0.869, Fig. 5 ). This indicates excellent discriminatory power. Calibration curves (Fig. 5 ) demonstrated strong agreement between predicted and observed probabilities, with minimal deviation. Additionally, decision curve analysis (DCA) showed that the net clinical benefit of interventions based on the nomogram outperformed strategies relying on either full or no intervention (Fig. 6 ). The calibration curve of the model ((Fig. 7 ) showed an agreement between the predicted and observed probabilities of the nomogram without undesirable deviations. These findings highlight the utility of the nomogram in enhancing CKD screening and informing timely clinical interventions. The validation results confirmed that the nomogram effectively stratifies patients for CKD risk, providing a practical tool for clinical decision-making. 3.4 Validation and Comparison of Models Across Cohorts The performance of the predictive model was validated using both internal and external cohorts to assess its robustness and generalizability. Internal validation was conducted using k-fold cross-validation. The internal validation cohort consisted of 742 samples randomly selected from an in-house dataset. The model's ability to predict chronic kidney disease (CKD) risk in this cohort was evaluated using a receiver operating characteristic (ROC) curve, which resulted in an area under the curve (AUC) of 0.854 (95% CI: 0.822–0.885), demonstrating the model's strong predictive capacity (Fig. 8 ). External validation was conducted using a multicenter dataset of 3,856 samples, selected based on the inclusion and exclusion criteria outlined in the Methods section. Each sample was assessed using the predictive model, and a risk score was calculated based on routine urine test results, following the nomogram. A higher score indicates a greater risk of CKD. Based on the predictive score, validation samples were divided into two groups with a threshold score of ≥ 345.4. Group 1, with a score > 345.4, represented a higher risk of CKD, while Group 2, with a score ≤ 345.4, represented a lower risk. The estimated glomerular filtration rate (eGFR) values for these groups were analyzed to assess the correlation between the risk score and eGFR values. The mean and median eGFR vales of Group 1 were 41.6 and 25 while the mean and median eGFR vales of Group 2 were 50 and 32. A t-test revealed a statistically significant difference (p < 0.05), confirming that an increased risk score correlates with a lower eGFR. To further validate the model, the clinical diagnoses of the samples were reviewed. The ROC curve for the external validation cohort (Fig. 9 ) yielded an AUC of 0.848 (95% CI: 0.814–0.833), reinforcing the model’s excellent transferability and generalizability across diverse populations. 4. Discussion Chronic Kidney Disease (CKD) remains a significant global health challenge, affecting approximately 10% of the population and contributing to high morbidity, mortality, and healthcare costs[ 22 , 23 ]. Early detection and timely intervention of CKD is critical for slowing disease progression and preventing complications such as end-stage renal disease (ESRD) and the need for dialysis[ 24 ]. Routine urine tests provide a non-invasive, widely accessible, and effective screening method, capable of detecting abnormalities that precede significant changes in glomerular filtration rate (GFR). Given these advantages, urine tests are a valuable tool for early CKD detection in clinical practice. In this study, we developed a predictive model into a comprehensive risk-assessment tool. The model’s strong predictive capability highlights its clinical utility, providing a scalable solution for early CKD detection and improving patient outcomes through timely intervention. To enhance CKD risk prediction, we constructed a predictive nomogram using 11 factors, including red blood cells (RBCs), Mucus threads, Urobilinogen (UBG), Ketones (KET), Blood (BLD), Protein (PRO), Leukocytes (LEU), pH, Microalbumin (MALB), Calcium (CA), and Osmolality. The model was developed using LASSO regression and multivariate logistic regression, allowing for the selection of independent predictors that contribute most significantly to CKD risk. Our findings contribute to the growing body of evidence supporting the diagnostic value of routine urine testing in CKD prediction. Previous studies have focused on individual urine biomarkers, such as proteinuria and hematuria[ 25 , 26 ], to predict kidney dysfunction. By contrast, our study employs a multivariate approach, integrating multiple urine test parameters through LASSO regression and multivariate logistic regression, offering a more comprehensive and accurate tool for disease risk prediction. The model was validated using both internal (AUC: 0.854, 95% CI: 0.822–0.885) and external (AUC: 0.848, 95% CI: 0.814–0.833) cohorts. The internal and external validation results collectively underscore the exceptional performance of the predictive model. Both cohorts demonstrated that the model is highly effective in predicting CKD risk, with the ability to accurately assess the risk of CKD incidences across diverse populations. The consistent results from both internal and external validations confirm that the model not only performs robustly within a single dataset but also exhibits excellent transferability to different clinical settings and patient groups. This highlights the model’s potential for reliable CKD risk prediction and its broad applicability in real-world clinical practice. Calibration curves and decision curve analysis (DCA) further supported its clinical applicability, demonstrating a net benefit for patient care. The predictive nomogram provides a practical tool for early identification of high-risk CKD patients. By integrating multiple urine test parameters into a single, visual tool, clinicians can quantify a patient’s CKD risk, facilitating personalized decision-making in routine practice. The results demonstrated that risk scores correlate with eGFR decline, reinforcing the model’s potential for early CKD screening. Beyond early prediction, our model has significant potential as an early advising tool, with potential to guide clinical management through grading recommendations. When the predictive model generates a high-risk score, it can serve as a prompt for clinicians to conduct additional tests, such as comprehensive metabolic panels and ultrasound imaging studies, to further assess kidney function and structure. This stepwise approach allows for timely diagnosis, enabling interventions like lifestyle modifications, pharmacological treatments, and close monitoring, all of which can slow the progression of kidney damage. By providing early and graded recommendations for further testing, the model facilitates a more structured and proactive approach to CKD management. Given the non-invasive, cost-effective, and widely available nature of routine urine tests, our model offers a scalable solution for CKD screening in both resource-rich and resource-limited settings. The nomogram allows clinicians to better manage patient care by providing actionable recommendations at multiple stages of CKD detection, based on the risk score generated from routine urine analysis. The multicenter design of this study further strengthens the generalizability and external validity of the results. Including diverse patient populations from multiple geographic regions and healthcare settings enhances the applicability of the model across different clinical environments. Despite its strengths, this study has several limitations that warrant further investigation. While multicenter validation enhances the model’s applicability, its performance in populations with different demographics, ethnic backgrounds, and geographic locations requires further validation. Future studies should assess the model’s performance in global and diverse populations to ensure its robustness across varied clinical settings. Additionally, the current study is cross-sectional, limiting its ability to predict CKD progression over time. Future longitudinal studies should evaluate how well the model predicts long-term disease outcomes and renal function decline. Whether the predictive risk score can be used for long-term disease monitoring and chronic disease management remains uncertain. Future research should investigate its role in tracking CKD progression and guiding treatment adjustments. This study demonstrates the feasibility and clinical value of using routine urine tests for CKD risk prediction. By incorporating LASSO regression and multivariate logistic regression, we developed a robust, user-friendly nomogram that enhances CKD screening and early intervention strategies. With further validation and refinement using larger multicenter datasets, this model has the potential for widespread clinical adoption, particularly in high-risk populations. The nomogram-based approach provides an effective tool for CKD risk prediction, personalized risk assessment, guiding clinical decision-making. Ultimately, this model represents a significant step forward in the application of routine urine tests for CKD risk assessment, offering a cost-effective and scalable screening solution for diverse healthcare settings. Declarations Ethics approval and consent to participate This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of the West China Hospital, Sichuan University. The committee waived the requirement for informed consent. All patient information was anonymized and processed with strict confidentiality to protect individual privacy and to ensure the absence of any potential commercial conflicts. Acknowledgments The authors are thankful for the participation of all hospitals participating in the multicenter study. Funding This work was supported by Natural Science Foundation of Sichuan Province (2024YFFK0144, 2025ZNSFSC1560) CRediT authorship contribution statement Qingyuan Zheng: Writing – original draft, Methodology, Formal analysis, Data curation. Chi Wang: Writing – review & editing , Supervision, Validation. Yong He: Supervision, Writing – review & editing. Wan He: Investigation, Data curation, Methodology. Yuan Zhong: Visualization, Investigation. Rulin Zhang, Hu Xiao, Yang Cao, Jianjiang Xue, Zan Zhang: Investigation, Data curation. Chunying Zhang and Hong Jiang: Supervision, Writing – original draft, Conceptualization Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability The raw data can be obtained on request from the corresponding author. References George C, Mogueo A, Okpechi I, Echouffo-Tcheugui JB, Kengne AP. Chronic kidney disease in low-income to middle-income countries: the case for increased screening. BMJ Glob Health. 2017;2(2):e000256. Wilson S, Mone P, Jankauskas SS, Gambardella J, Santulli G. Chronic kidney disease: Definition, updated epidemiology, staging, and mechanisms of increased cardiovascular risk. J Clin Hypertens (Greenwich). 2021;23(4):831–4. Wang L, Xu X, Zhang M, Hu C, Zhang X, Li C, Nie S, Huang Z, Zhao Z, Hou FF, Zhou M. Prevalence of Chronic Kidney Disease in China: Results From the Sixth China Chronic Disease and Risk Factor Surveillance. JAMA Intern Med. 2023;183(4):298–310. Yang C, Wang H, Zhao X, Matsushita K, Coresh J, Zhang L, Zhao MH. CKD in China: Evolving Spectrum and Public Health Implications. Am J Kidney Dis. 2020;76(2):258–64. KDIGO. 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease, Kidney Int 105(4s) (2024) S117-s314. Inker LA, Titan S. Measurement and Estimation of GFR for Use in Clinical Practice: Core Curriculum 2021. Am J Kidney Dis. 2021;78(5):736–49. Levey AS, Titan SM, Powe NR, Coresh J, Inker LA. Kidney Disease, Race, and GFR Estimation. Clin J Am Soc Nephrol. 2020;15(8):1203–12. Webster AC, Nagler EV, Morton RL, Masson P. Chronic Kidney Disease Lancet. 2017;389(10075):1238–52. Kavuru V, Vu T, Karageorge L, Choudhury D, Senger R, Robertson J. Dipstick analysis of urine chemistry: benefits and limitations of dry chemistry-based assays. Postgrad Med. 2020;132(3):225–33. Bunjevac A, Gabaj NN, Miler M, Horvat A. Preanalytics of urine sediment examination: effect of relative centrifugal force, tube type, volume of sample and supernatant removal. Biochem Med (Zagreb). 2018;28(1):010707. Oyaert M, Delanghe J. Progress in Automated Urinalysis. Ann Lab Med. 2019;39(1):15–22. Mayo S, Acevedo D, Quiñones-Torrelo C, Canós I, Sancho M. Clinical laboratory automated urinalysis: comparison among automated microscopy, flow cytometry, two test strips analyzers, and manual microscopic examination of the urine sediments. J Clin Lab Anal. 2008;22(4):262–70. Uchida D, Kawarazaki H, Shibagaki Y, Yasuda T, Tominaga N, Watanabe T, Asahi K, Iseki K, Iseki C, Tsuruya K, Yamagata K, Moriyama T, Narita I, Fujimoto S, Konta T, Kondo M, Kasahara M, Kimura K. Underestimating chronic kidney disease by urine dipstick without serum creatinine as a screening tool in the general Japanese population. Clin Exp Nephrol 19(3) (2015) 474 – 80. Sumida K, Nadkarni GN, Grams ME, Sang Y, Ballew SH, Coresh J, Matsushita K, Surapaneni A, Brunskill N, Chadban SJ, Chang AR, Cirillo M, Daratha KB, Gansevoort RT, Garg AX, Iacoviello L, Kayama T, Konta T, Kovesdy CP, Lash J, Lee BJ, Major RW, Metzger M, Miura K, Naimark DMJ, Nelson RG, Sawhney S, Stempniewicz N, Tang M, Townsend RR, Traynor JP, Valdivielso JM, Wetzels J, Polkinghorne KR, Heerspink HJL. Conversion of Urine Protein-Creatinine Ratio or Urine Dipstick Protein to Urine Albumin-Creatinine Ratio for Use in Chronic Kidney Disease Screening and Prognosis: An Individual Participant-Based Meta-analysis. Ann Intern Med. 2020;173(6):426–35. Jang EC, Park YM, Han HW, Lee CS, Kang ES, Lee YH, Nam SM. Machine-learning enhancement of urine dipstick tests for chronic kidney disease detection. J Am Med Inf Assoc. 2023;30(6):1114–24. Lilliefors HW. On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. J Am Stat Assoc. 1967;62(318):399–402. Tibshirani R. Regression Shrinkage and Selection Via the Lasso. J Roy Stat Soc: Ser B (Methodol). 2018;58(1):267–88. Hidalgo B, Goodman M. Multivariate or multivariable regression? Am J Public Health. 2013;103(1):39–40. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29–36. Steyerberg EW, Harrell FE Jr., Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54(8):774–81. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak. 2006;26(6):565–74. Global regional, national burden of chronic kidney disease. 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709–33. Wang V, Vilme H, Maciejewski ML, Boulware LE. The Economic Burden of Chronic Kidney Disease and End-Stage Renal Disease. Semin Nephrol. 2016;36(4):319–30. Wouters OJ, O'Donoghue DJ, Ritchie J, Kanavos PG, Narva AS. Early chronic kidney disease: diagnosis, management and models of care. Nat Rev Nephrol. 2015;11(8):491–502. Chebotareva N, Vinogradov A, McDonnell V, Zakharova NV, Indeykina MI, Moiseev S, Nikolaev EN, Kononikhin AS. Urinary Protein and Peptide Markers in Chronic Kidney Disease. Int J Mol Sci 22(22) (2021). Cravedi P, Remuzzi G. Pathophysiology of proteinuria and its value as an outcome measure in chronic kidney disease. Br J Clin Pharmacol. 2013;76(4):516–23. Additional Declarations No competing interests reported. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8963275","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605766970,"identity":"f8566b87-26de-47c2-b7c0-6ac3846edf70","order_by":0,"name":"Qingyuan Zheng","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Qingyuan","middleName":"","lastName":"Zheng","suffix":""},{"id":605766971,"identity":"3542fbdd-1ad7-46fa-a769-d2612316c2c9","order_by":1,"name":"Chi Wang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Chi","middleName":"","lastName":"Wang","suffix":""},{"id":605766972,"identity":"738e1a12-d71f-40ff-854c-911b6fbf7ee8","order_by":2,"name":"Yong He","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"He","suffix":""},{"id":605766973,"identity":"96db0ca1-7ffb-470a-8aaa-14d8e2940bfa","order_by":3,"name":"Wan He","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Wan","middleName":"","lastName":"He","suffix":""},{"id":605766974,"identity":"fbbcf407-e49a-48f4-9e8b-af3b9746bea0","order_by":4,"name":"Yuan Zhong","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Zhong","suffix":""},{"id":605766975,"identity":"64600365-7917-45b1-83b2-2c819f8144af","order_by":5,"name":"Rulin Zhang","email":"","orcid":"","institution":"Shanghai General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rulin","middleName":"","lastName":"Zhang","suffix":""},{"id":605766976,"identity":"0a0895fd-88d4-4215-a0d0-31bae34971f5","order_by":6,"name":"Hu Xiao","email":"","orcid":"","institution":"Shanghai Fengxian District Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hu","middleName":"","lastName":"Xiao","suffix":""},{"id":605766977,"identity":"3d2760a2-6745-4aa4-8eec-a9c272617070","order_by":7,"name":"Yang Cao","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Cao","suffix":""},{"id":605766978,"identity":"d66ce21f-6d34-49f0-b255-ee73dcec5f4d","order_by":8,"name":"Jianjiang Xue","email":"","orcid":"","institution":"University-Town Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianjiang","middleName":"","lastName":"Xue","suffix":""},{"id":605766979,"identity":"fa3b72b3-b85a-4aac-b841-4336979dde7d","order_by":9,"name":"Zan Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Henan University of CM","correspondingAuthor":false,"prefix":"","firstName":"Zan","middleName":"","lastName":"Zhang","suffix":""},{"id":605766980,"identity":"dbea7e23-43e8-4d60-9d63-5aae4e33ee9a","order_by":10,"name":"Chunying Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3PsQrCMBCA4atCuhx2jRTqK1wJVAfRV2kp1KWIIDhbCp18AEHBV3Byl6CTD+DgoAi6ODg6CGoH1zajYL7hIHA/RwA07RfxfBAAM5PkeKe2o57UUKbudBAJxeTD4b3MxrsMSgtrNvHOODh0MghS0aaqD6bcLAuPHHZNgXQJM1gn55hYHzCK9kUJ8dizkWTIjCQVMeEQOHqKSdXI7BbxYKyadBj7JEBUnvB9NKrPSfoMjdSdkC9Y2V+sabjit6fsNhbX0/HxfDmWKbeFSa6CAMH4+2Jl6znjAdBVWdQ0TftTb3NNRMMIzYhUAAAAAElFTkSuQmCC","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Chunying","middleName":"","lastName":"Zhang","suffix":""},{"id":605766981,"identity":"2253cbea-b1eb-4b76-a103-8717eee9fb9d","order_by":11,"name":"Hong Jiang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2026-02-25 05:09:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8963275/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8963275/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104874209,"identity":"43f67c4d-a026-499b-b9c3-a5a09cec1f2c","added_by":"auto","created_at":"2026-03-18 08:29:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79560,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow diagram of sample collection and data processing.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8963275/v1/9bc878c02602092616091353.png"},{"id":104874217,"identity":"ce04de8e-c9e7-45ad-a792-b15033f8d347","added_by":"auto","created_at":"2026-03-18 08:29:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52293,"visible":true,"origin":"","legend":"\u003cp\u003ePlot for coefficient distribution of variables in LASSO regression. The horizontal coordinate indicates the logarithm of the penalty parameter l, the vertical coordinate represents the coefficient of potential predictor, and colored curves represents the coefficients changing trajectories of the predictors as the penalty parameter l increasing.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8963275/v1/9e9af4a060811c3ebe92b586.png"},{"id":104874171,"identity":"26b6f686-cf3a-435e-ad00-1be81b46e38b","added_by":"auto","created_at":"2026-03-18 08:29:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43824,"visible":true,"origin":"","legend":"\u003cp\u003eCross-validation results for the penalty parameter in LASSO regression. The left dashed line corresponded to the value of the parameter log(l) and the number of variables with the minimum variable loss error.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8963275/v1/53a39fdff9ce15573aac4559.png"},{"id":104874170,"identity":"905ce37c-8298-4fab-ae54-dd25827fc1c8","added_by":"auto","created_at":"2026-03-18 08:29:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68949,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram to distinguish CKD of patients suspected with urinary or renal disease by calculating the sum of the corresponding scores for each indicator.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8963275/v1/082369fd4be13669d8fcd4ba.png"},{"id":104874153,"identity":"342872dd-cd98-4ba7-8a5f-018080e9cfab","added_by":"auto","created_at":"2026-03-18 08:29:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":56419,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC and AUC of model calculated by training dataset.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8963275/v1/00c5fd095b58628e7a53d1fb.png"},{"id":104874114,"identity":"bdc6fa48-480e-4594-b619-7d3e813a21eb","added_by":"auto","created_at":"2026-03-18 08:29:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":44556,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis (DCA) of the nomogram. The horizontal coordinate represents the threshold probability, above which the predicted outcome was a true positive; the clinical intervention at this point would benefit, there are drawbacks conversely, and the vertical coordinate represented the net benefit after the benefit minus the drawback.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8963275/v1/8defa0b51a5f5e377ff314a9.png"},{"id":104874215,"identity":"55803114-e7da-4074-aa04-340327531ebe","added_by":"auto","created_at":"2026-03-18 08:29:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":123049,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the nomogram. The horizontal and vertical coordinated represent the predicted probability of CKD by the nomogram and actual probability of CKD, the diagonal line is a reference line representing the predicted probability equaled to the actual probability, and the black line indicated the degree of matching between the predicted and actual results of this nomogram.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8963275/v1/bb06af974602b49fb21e795f.png"},{"id":104874167,"identity":"4f966091-21e3-4133-bdd7-b4b7d5b72510","added_by":"auto","created_at":"2026-03-18 08:29:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":48167,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC and AUC of the models validated in internal validation cohorts.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8963275/v1/b0f40b4dfaa98d45bc40f572.png"},{"id":104874218,"identity":"fc1fa274-ff74-47ff-a2f4-f87b64d706e3","added_by":"auto","created_at":"2026-03-18 08:29:31","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":44369,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC and AUC of the models validated in external multi-center validation cohorts.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8963275/v1/d60511036fecfa6424aa4d76.png"},{"id":109453352,"identity":"77503559-b9b4-4cc0-bf66-64a2918352fd","added_by":"auto","created_at":"2026-05-18 09:26:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":806906,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8963275/v1/352dd253-42b1-402e-83be-b012e4ea5d6c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Chronic Kidney Disease Prediction in Different Populations Using Routine Urine Test: A Multi-Center Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChronic Kidney Disease (CKD) represents a significant and escalating public health concern, affecting approximately 10% of the global population[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The disease is a major contributor to morbidity and mortality, imposing an increasing burden on healthcare systems worldwide[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In China, CKD prevalence has risen markedly over recent decades, fueled by an aging population and the growing incidence of risk factors such as hypertension, diabetes, obesity, and metabolic syndrome[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This trend carries profound socioeconomic consequences, including elevated healthcare costs, reduced productivity, and diminished quality of life for affected individuals[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Early detection and timely intervention are critical to slowing disease progression and alleviating its societal and healthcare impacts[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe estimated glomerular filtration rate (eGFR) serves as a cornerstone for diagnosing and staging CKD[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], providing an assessment of kidney function based on the filtration capacity of the glomeruli. However, eGFR measurement typically depends on serum creatinine or cystatin C levels, necessitating blood sampling and access to specialized laboratory facilities[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This reliance on invasive procedures and resource-intensive testing may restrict the feasibility of frequent or widespread CKD screening, particularly in resource-limited settings or specific populations requiring long-term monitoring[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Consequently, there is a pressing need for non-invasive, accessible diagnostic alternatives to complement existing methods and enhance screening efforts.\u003c/p\u003e \u003cp\u003eRoutine urine tests, encompassing chemical analysis[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], sediment analysis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and microscopic examination[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], offer valuable insights into renal function and urinary system health[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Importantly, these indicators often reveal abnormalities preceding measurable declines in eGFR[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As a non-invasive, cost-effective, and widely available tool, routine urine testing holds significant potential for large-scale CKD screening[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Its widespread implementation in clinical practice ensures accessibility for nearly all patient populations. Despite these advantages, the predictive value of routine urine test indicators for CKD diagnosis remains underexplored. A thorough evaluation of these indicators, especially in high-risk groups, could enhance early diagnosis and facilitate timely intervention, thereby improving patient outcomes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis multicenter study draws on data from a diverse range of hospitals to evaluate the predictive utility of routine urine tests in detecting CKD. Specifically, it aims to develop and validate a predictive model based on urine test parameters, enabling effective differentiation between CKD and other renal or urinary conditions. By focusing on high-risk cohorts\u0026mdash;such as individuals with preexisting urinary or renal disorders\u0026mdash;this model aspires to enhance early screening and diagnostic accuracy. Ultimately, the study seeks to establish a robust, practical framework for CKD detection that can be integrated into routine clinical workflows, benefiting both general and at-risk populations.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Collection\u003c/h2\u003e \u003cp\u003eThe training dataset comprised 3,000 urine samples collected from patients at West China Hospital, Sichuan University, Chengdu, China, between October 2023 and October 2024. After excluding samples with incomplete patient data or insufficient serum volume, a total of 1,733 samples were assigned to the training cohort, while 742 samples were allocated to the internal validation cohort. These samples represented a diverse patient population, including a significant proportion of individuals at risk for kidney or urinary system injury. For external validation, samples were obtained from a multicenter urine dataset coordinated by West China Hospital, with contributions from: Shanghai Fengxian District Central Hospital, Shanghai General Hospital, The First Hospital of Jilin University, University-Town Affiliated Hospital of Chongqing Medical University, and The First Affiliated Hospital of Henan University of CM. The external validation cohort comprised 3,856 cases, evenly distributed across these institutions. To ensure data reliability, samples with incomplete patient records or inadequate urine test results were excluded from the analysis.\u003c/p\u003e \u003cp\u003eAll qualified samples underwent routine urine tests, including dipstick analysis and microscopic examination. The CA-500 Automatic Urine Analyzer and MA-500 Automated Urine Sediment Analyzer (MEDICSIDE Corp, China) were used for automated analysis. In cases where the automated system yielded inconclusive results, manual microscopic examination was performed by experienced laboratory professionals to determine white blood cell (WBC) count, red blood cell (RBC) count, epithelial cells (ECs), casts, and crystals.\u003c/p\u003e \u003cp\u003eIn addition to urine analysis, serum comprehensive metabolic panel (CMP) results were recorded, including: Creatinine (Cr), Blood Urea Nitrogen (BUN), Uric Acid (UA), Total Bilirubin (TB), Albumin (Alb), Alanine amino Transferase (ALT), Aspartate Aminotransferase (AST), Glucose, Sodium (Na), Potassium (K), Chloride (Cl), Triglyceride (TG), Total cholesterol (TC). Additionally, demographic data\u0026mdash;including age, sex, weight, and medical history\u0026mdash;were recorded for all participants. Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. The CMP results and demographic data were extracted from each patient\u0026rsquo;s most recent historical medical records prior to urine sample collection. The flow of sample processing, from initial testing to confirmatory assays, is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data preprocessing\u003c/h2\u003e \u003cp\u003eTo maintain confidentiality, patient personal information was anonymized and replaced with unique digital codes before analysis. A general description of the dataset was performed prior to statistical analysis to ensure data integrity and identify potential outliers. The Kolmogorov\u0026ndash;Smirnov test[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] was used to evaluate the normality of data distributions. For variables that conformed to a normal distribution, mean values and standard deviations were calculated. The Levene test was applied to assess the homogeneity of variance between groups. If the assumption of homogeneity was met, parametric tests were employed for comparisons. In cases where variance homogeneity was violated, non-parametric tests, such as the Kolmogorov\u0026ndash;Smirnov Z-rank test, were used to analyze the data.\u003c/p\u003e \u003cp\u003eStatistical analyses were conducted using IBM SPSS Statistics version 21.0 (SPSS Corporation, Chicago, IL, USA). A significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied to all tests, ensuring statistical rigor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Model Development and Validation\u003c/h2\u003e \u003cp\u003eTo begin, predictors were preliminarily selected using LASSO (Least Absolute Shrinkage and Selection Operator) regression[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] within the development set. Following this, a multivariate logistic regression analysis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] was used to build the prediction model in the training set. Each predictor's score was calculated, and the model was visualized through a nomogram. To evaluate the performance of the developed models, receiver operating characteristic (ROC) curve analysis was conducted, with the area under the curve (AUC) as the primary performance metric[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Calibration curves were then employed to assess the model's calibration, followed by goodness-of-fit testing[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, decision curve analysis (DCA) was performed to evaluate the clinical utility of the models by estimating the net benefits at different threshold probabilities for both the training and testing groups[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. All statistical analyses were carried out using R software (version 4.1.0), with two-sided tests. A P-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive Analysis of the clinical samples and datasets.\u003c/h2\u003e \u003cp\u003eThe baseline clinicopathologic characteristics of the study cohorts are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. 1,733 and 742 clinical samples were included in the training and internal verification dataset, comprising 1,905 non-CKD cases and 570 pre-diagnosed CKD patients recruited from West China Hospital. For external validation, a multicenter dataset was used, consisting of 3,712 non-CKD cases (eGFR range 587\u0026thinsp;\u0026minus;\u0026thinsp;76) and 144 CKD patients (eGFR range 69\u0026thinsp;\u0026minus;\u0026thinsp;2) collected from five hospitals across China.\u003c/p\u003e \u003cp\u003eFor each urine sample, comprehensive clinical information and routine urine test variables were recorded. These included parameters obtained through dipstick urine test\u0026mdash;such as Urobilinogen (UBG), Bilirubin (BIL), Ketones (KET), Blood (BLD), Protein (PRO), Nitrite (NIT), Leukocyte esterase (LEU), Glucose (GLU), PH, Vitamin C (VC), Microalbumin (MALB), Creatinine (Cr), Calcium (CA), Albumin/Creatinine ratio (A:C), Conductivity, Osmolality and through urinary microscopic examination\u0026mdash;such as Red blood cells (RBCs), White blood cells (WBCs), Squamous epithelial cells, Small round epithelial cells, Hyaline casts, Granular casts, Waxy casts, Broad casts, Bacilli, Candida, Calcium oxalate crystals, Uric acid crystals, Magnesium ammonium phosphate crystals, Sperm, Mucus threads, Cocci. In Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, parameters exhibited consistent distributions between the training and validation cohorts (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were shown, indicating strong concordance and demonstrating the representativeness of the datasets for model development and evaluation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of datasets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;5,589\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,733\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;3,856\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.000 (2.000\u0026ndash;7.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.000 (3.000\u0026ndash;11.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000 (2.000\u0026ndash;5.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.000 (1.000\u0026ndash;8.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.000 (2.000\u0026ndash;9.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.000 (1.000\u0026ndash;7.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous epithelial cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000 (0.000\u0026ndash;2.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000 (0.000\u0026ndash;2.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000 (0.000\u0026ndash;2.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall round epithelial cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyaline casts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGranular casts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaxy casts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium oxalate crystals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucus threads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000 (0.000\u0026ndash;6.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.000 (1.000\u0026ndash;13.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000 (0.000\u0026ndash;4.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCocci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;86.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000 (0.000\u0026ndash;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.000 (12.000\u0026ndash;21.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.000 (11.000\u0026ndash;20.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.500 (13.000\u0026ndash;22.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsmolality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e510.000 (404.000\u0026ndash;620.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e456.000 (369.000\u0026ndash;578.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e530.000 (428.000\u0026ndash;639.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUBG, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,250 (94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,545 (89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,705 (96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151 (3.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (\u0026lt;\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIL, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,541 (99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,715 (99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,826 (99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKET, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,268 (94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,611 (93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,657 (95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113 (2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (0.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (\u0026lt;\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,105 (73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e976 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,129 (81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e709 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e477 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232 (6.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137 (3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e543 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e358 (9.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRO, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,680 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e635 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,045 (79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,065 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e548 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e517 (13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e568 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e366 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e202 (5.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIT, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,318 (95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,643 (95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,675 (95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEU, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,255 (76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,172 (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,083 (80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e669 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e385 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e284 (7.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e342 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243 (6.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e323 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246 (6.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,542 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,373 (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,169 (82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e612 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e443 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.0\u0026ndash;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,291 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e438 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e853 (22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.0\u0026ndash;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,101 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,155 (67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,946 (50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.0\u0026ndash;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,028 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e917 (24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140 (3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVC, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,784 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,777 (72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,514 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,639 (95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e875 (23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (2.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (2.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAB, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,344 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e430 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,914 (50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e383 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187 (4.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,862 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,107 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,755 (46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e462 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e351 (9.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,357 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,095 (28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,809 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e532 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,277 (33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,290 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e490 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e800 (21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e671 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e338 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e333 (8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,751 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e587 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,164 (30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,670 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e608 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,062 (28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,966 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e515 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,451 (38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA:C, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,434 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e573 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,861 (48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,596 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e964 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,632 (42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e559 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e363 (9.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,046 (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,334 (77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,712 (96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e543 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e399 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144 (3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Predictor selection.\u003c/h2\u003e \u003cp\u003eTo identify clinical indicators significantly associated with CKD, regression analysis was performed on the 32 recorded features. Using least absolute shrinkage and selection operator (LASSO) regression (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), 11 predictors were identified as being independently associated with CKD: RBCs, Mucus threads, UBG, KET, BLD, PRO, LEU, PH, MALB, CA, and Osmolality. These variables were subsequently incorporated into a multivariate logistic regression model, confirming their significance as independent predictors for distinguishing CKD from other urinary or renal conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Nomogram construction and verification\u003c/h2\u003e \u003cp\u003eA nomogram was constructed based on the 11 identified predictors to facilitate the diagnosis of CKD in clinical settings. The nomogram assigned scores to each variable, with risk scores calculated from the coefficients of the logistic regression model. The total score for a patient was obtained by summing the contributions of all variables, enabling the calculation of an individualized probability of CKD (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe performance of the nomogram was evaluated using a receiver operating characteristic (ROC) curve, which yielded an area under the curve (AUC) of 0.849 (95% CI: 0.828\u0026ndash;0.869, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This indicates excellent discriminatory power. Calibration curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) demonstrated strong agreement between predicted and observed probabilities, with minimal deviation. Additionally, decision curve analysis (DCA) showed that the net clinical benefit of interventions based on the nomogram outperformed strategies relying on either full or no intervention (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The calibration curve of the model ((Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) showed an agreement between the predicted and observed probabilities of the nomogram without undesirable deviations. These findings highlight the utility of the nomogram in enhancing CKD screening and informing timely clinical interventions.\u003c/p\u003e \u003cp\u003eThe validation results confirmed that the nomogram effectively stratifies patients for CKD risk, providing a practical tool for clinical decision-making.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Validation and Comparison of Models Across Cohorts\u003c/h2\u003e \u003cp\u003eThe performance of the predictive model was validated using both internal and external cohorts to assess its robustness and generalizability.\u003c/p\u003e \u003cp\u003eInternal validation was conducted using k-fold cross-validation. The internal validation cohort consisted of 742 samples randomly selected from an in-house dataset. The model's ability to predict chronic kidney disease (CKD) risk in this cohort was evaluated using a receiver operating characteristic (ROC) curve, which resulted in an area under the curve (AUC) of 0.854 (95% CI: 0.822\u0026ndash;0.885), demonstrating the model's strong predictive capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExternal validation was conducted using a multicenter dataset of 3,856 samples, selected based on the inclusion and exclusion criteria outlined in the Methods section. Each sample was assessed using the predictive model, and a risk score was calculated based on routine urine test results, following the nomogram. A higher score indicates a greater risk of CKD.\u003c/p\u003e \u003cp\u003eBased on the predictive score, validation samples were divided into two groups with a threshold score of \u0026ge;\u0026thinsp;345.4. Group 1, with a score\u0026thinsp;\u0026gt;\u0026thinsp;345.4, represented a higher risk of CKD, while Group 2, with a score\u0026thinsp;\u0026le;\u0026thinsp;345.4, represented a lower risk. The estimated glomerular filtration rate (eGFR) values for these groups were analyzed to assess the correlation between the risk score and eGFR values. The mean and median eGFR vales of Group 1 were 41.6 and 25 while the mean and median eGFR vales of Group 2 were 50 and 32. A t-test revealed a statistically significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), confirming that an increased risk score correlates with a lower eGFR. To further validate the model, the clinical diagnoses of the samples were reviewed. The ROC curve for the external validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) yielded an AUC of 0.848 (95% CI: 0.814\u0026ndash;0.833), reinforcing the model\u0026rsquo;s excellent transferability and generalizability across diverse populations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eChronic Kidney Disease (CKD) remains a significant global health challenge, affecting approximately 10% of the population and contributing to high morbidity, mortality, and healthcare costs[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Early detection and timely intervention of CKD is critical for slowing disease progression and preventing complications such as end-stage renal disease (ESRD) and the need for dialysis[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Routine urine tests provide a non-invasive, widely accessible, and effective screening method, capable of detecting abnormalities that precede significant changes in glomerular filtration rate (GFR). Given these advantages, urine tests are a valuable tool for early CKD detection in clinical practice.\u003c/p\u003e \u003cp\u003eIn this study, we developed a predictive model into a comprehensive risk-assessment tool. The model\u0026rsquo;s strong predictive capability highlights its clinical utility, providing a scalable solution for early CKD detection and improving patient outcomes through timely intervention.\u003c/p\u003e \u003cp\u003eTo enhance CKD risk prediction, we constructed a predictive nomogram using 11 factors, including red blood cells (RBCs), Mucus threads, Urobilinogen (UBG), Ketones (KET), Blood (BLD), Protein (PRO), Leukocytes (LEU), pH, Microalbumin (MALB), Calcium (CA), and Osmolality. The model was developed using LASSO regression and multivariate logistic regression, allowing for the selection of independent predictors that contribute most significantly to CKD risk.\u003c/p\u003e \u003cp\u003eOur findings contribute to the growing body of evidence supporting the diagnostic value of routine urine testing in CKD prediction. Previous studies have focused on individual urine biomarkers, such as proteinuria and hematuria[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], to predict kidney dysfunction. By contrast, our study employs a multivariate approach, integrating multiple urine test parameters through LASSO regression and multivariate logistic regression, offering a more comprehensive and accurate tool for disease risk prediction.\u003c/p\u003e \u003cp\u003eThe model was validated using both internal (AUC: 0.854, 95% CI: 0.822\u0026ndash;0.885) and external (AUC: 0.848, 95% CI: 0.814\u0026ndash;0.833) cohorts. The internal and external validation results collectively underscore the exceptional performance of the predictive model. Both cohorts demonstrated that the model is highly effective in predicting CKD risk, with the ability to accurately assess the risk of CKD incidences across diverse populations. The consistent results from both internal and external validations confirm that the model not only performs robustly within a single dataset but also exhibits excellent transferability to different clinical settings and patient groups. This highlights the model\u0026rsquo;s potential for reliable CKD risk prediction and its broad applicability in real-world clinical practice. Calibration curves and decision curve analysis (DCA) further supported its clinical applicability, demonstrating a net benefit for patient care.\u003c/p\u003e \u003cp\u003eThe predictive nomogram provides a practical tool for early identification of high-risk CKD patients. By integrating multiple urine test parameters into a single, visual tool, clinicians can quantify a patient\u0026rsquo;s CKD risk, facilitating personalized decision-making in routine practice. The results demonstrated that risk scores correlate with eGFR decline, reinforcing the model\u0026rsquo;s potential for early CKD screening.\u003c/p\u003e \u003cp\u003eBeyond early prediction, our model has significant potential as an early advising tool, with potential to guide clinical management through grading recommendations. When the predictive model generates a high-risk score, it can serve as a prompt for clinicians to conduct additional tests, such as comprehensive metabolic panels and ultrasound imaging studies, to further assess kidney function and structure. This stepwise approach allows for timely diagnosis, enabling interventions like lifestyle modifications, pharmacological treatments, and close monitoring, all of which can slow the progression of kidney damage. By providing early and graded recommendations for further testing, the model facilitates a more structured and proactive approach to CKD management. Given the non-invasive, cost-effective, and widely available nature of routine urine tests, our model offers a scalable solution for CKD screening in both resource-rich and resource-limited settings. The nomogram allows clinicians to better manage patient care by providing actionable recommendations at multiple stages of CKD detection, based on the risk score generated from routine urine analysis.\u003c/p\u003e \u003cp\u003eThe multicenter design of this study further strengthens the generalizability and external validity of the results. Including diverse patient populations from multiple geographic regions and healthcare settings enhances the applicability of the model across different clinical environments.\u003c/p\u003e \u003cp\u003eDespite its strengths, this study has several limitations that warrant further investigation. While multicenter validation enhances the model\u0026rsquo;s applicability, its performance in populations with different demographics, ethnic backgrounds, and geographic locations requires further validation. Future studies should assess the model\u0026rsquo;s performance in global and diverse populations to ensure its robustness across varied clinical settings. Additionally, the current study is cross-sectional, limiting its ability to predict CKD progression over time. Future longitudinal studies should evaluate how well the model predicts long-term disease outcomes and renal function decline. Whether the predictive risk score can be used for long-term disease monitoring and chronic disease management remains uncertain. Future research should investigate its role in tracking CKD progression and guiding treatment adjustments.\u003c/p\u003e \u003cp\u003eThis study demonstrates the feasibility and clinical value of using routine urine tests for CKD risk prediction. By incorporating LASSO regression and multivariate logistic regression, we developed a robust, user-friendly nomogram that enhances CKD screening and early intervention strategies. With further validation and refinement using larger multicenter datasets, this model has the potential for widespread clinical adoption, particularly in high-risk populations. The nomogram-based approach provides an effective tool for CKD risk prediction, personalized risk assessment, guiding clinical decision-making. Ultimately, this model represents a significant step forward in the application of routine urine tests for CKD risk assessment, offering a cost-effective and scalable screening solution for diverse healthcare settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of the West China Hospital, Sichuan University.\u0026nbsp;The committee waived the requirement for informed consent. All patient information was anonymized and processed with strict confidentiality to protect individual privacy and to ensure the absence of any potential commercial conflicts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful for the participation of all hospitals participating in the multicenter study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Natural Science Foundation of Sichuan Province (2024YFFK0144,\u0026nbsp;2025ZNSFSC1560)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQingyuan Zheng:\u003c/strong\u003e Writing – original draft, Methodology, Formal analysis, Data curation. \u003cstrong\u003eChi Wang:\u003c/strong\u003e Writing – review \u0026amp; editing\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eSupervision, Validation.\u003cstrong\u003e\u0026nbsp;Yong He:\u003c/strong\u003e Supervision, Writing – review \u0026amp; editing. \u003cstrong\u003eWan He:\u003c/strong\u003e Investigation, Data curation, Methodology.\u003cstrong\u003e\u0026nbsp;Yuan Zhong:\u003c/strong\u003e Visualization, Investigation. \u003cstrong\u003eRulin\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Zhang, Hu Xiao, Yang Cao, Jianjiang Xue, Zan Zhang:\u003c/strong\u003eInvestigation, Data curation.\u003cstrong\u003e\u0026nbsp;Chunying Zhang and Hong Jiang:\u003c/strong\u003e Supervision, Writing – original draft, Conceptualization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data can be obtained on request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGeorge C, Mogueo A, Okpechi I, Echouffo-Tcheugui JB, Kengne AP. Chronic kidney disease in low-income to middle-income countries: the case for increased screening. BMJ Glob Health. 2017;2(2):e000256.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson S, Mone P, Jankauskas SS, Gambardella J, Santulli G. Chronic kidney disease: Definition, updated epidemiology, staging, and mechanisms of increased cardiovascular risk. J Clin Hypertens (Greenwich). 2021;23(4):831\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Xu X, Zhang M, Hu C, Zhang X, Li C, Nie S, Huang Z, Zhao Z, Hou FF, Zhou M. Prevalence of Chronic Kidney Disease in China: Results From the Sixth China Chronic Disease and Risk Factor Surveillance. JAMA Intern Med. 2023;183(4):298\u0026ndash;310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang C, Wang H, Zhao X, Matsushita K, Coresh J, Zhang L, Zhao MH. CKD in China: Evolving Spectrum and Public Health Implications. Am J Kidney Dis. 2020;76(2):258\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKDIGO. 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease, Kidney Int 105(4s) (2024) S117-s314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInker LA, Titan S. Measurement and Estimation of GFR for Use in Clinical Practice: Core Curriculum 2021. Am J Kidney Dis. 2021;78(5):736\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevey AS, Titan SM, Powe NR, Coresh J, Inker LA. Kidney Disease, Race, and GFR Estimation. Clin J Am Soc Nephrol. 2020;15(8):1203\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWebster AC, Nagler EV, Morton RL, Masson P. Chronic Kidney Disease Lancet. 2017;389(10075):1238\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKavuru V, Vu T, Karageorge L, Choudhury D, Senger R, Robertson J. Dipstick analysis of urine chemistry: benefits and limitations of dry chemistry-based assays. Postgrad Med. 2020;132(3):225\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBunjevac A, Gabaj NN, Miler M, Horvat A. Preanalytics of urine sediment examination: effect of relative centrifugal force, tube type, volume of sample and supernatant removal. Biochem Med (Zagreb). 2018;28(1):010707.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOyaert M, Delanghe J. Progress in Automated Urinalysis. Ann Lab Med. 2019;39(1):15\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMayo S, Acevedo D, Qui\u0026ntilde;ones-Torrelo C, Can\u0026oacute;s I, Sancho M. Clinical laboratory automated urinalysis: comparison among automated microscopy, flow cytometry, two test strips analyzers, and manual microscopic examination of the urine sediments. J Clin Lab Anal. 2008;22(4):262\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUchida D, Kawarazaki H, Shibagaki Y, Yasuda T, Tominaga N, Watanabe T, Asahi K, Iseki K, Iseki C, Tsuruya K, Yamagata K, Moriyama T, Narita I, Fujimoto S, Konta T, Kondo M, Kasahara M, Kimura K. Underestimating chronic kidney disease by urine dipstick without serum creatinine as a screening tool in the general Japanese population. Clin Exp Nephrol 19(3) (2015) 474\u0026thinsp;\u0026ndash;\u0026thinsp;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSumida K, Nadkarni GN, Grams ME, Sang Y, Ballew SH, Coresh J, Matsushita K, Surapaneni A, Brunskill N, Chadban SJ, Chang AR, Cirillo M, Daratha KB, Gansevoort RT, Garg AX, Iacoviello L, Kayama T, Konta T, Kovesdy CP, Lash J, Lee BJ, Major RW, Metzger M, Miura K, Naimark DMJ, Nelson RG, Sawhney S, Stempniewicz N, Tang M, Townsend RR, Traynor JP, Valdivielso JM, Wetzels J, Polkinghorne KR, Heerspink HJL. Conversion of Urine Protein-Creatinine Ratio or Urine Dipstick Protein to Urine Albumin-Creatinine Ratio for Use in Chronic Kidney Disease Screening and Prognosis: An Individual Participant-Based Meta-analysis. Ann Intern Med. 2020;173(6):426\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJang EC, Park YM, Han HW, Lee CS, Kang ES, Lee YH, Nam SM. Machine-learning enhancement of urine dipstick tests for chronic kidney disease detection. J Am Med Inf Assoc. 2023;30(6):1114\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLilliefors HW. On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. J Am Stat Assoc. 1967;62(318):399\u0026ndash;402.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTibshirani R. Regression Shrinkage and Selection Via the Lasso. J Roy Stat Soc: Ser B (Methodol). 2018;58(1):267\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHidalgo B, Goodman M. Multivariate or multivariable regression? Am J Public Health. 2013;103(1):39\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteyerberg EW, Harrell FE Jr., Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54(8):774\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak. 2006;26(6):565\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal regional, national burden of chronic kidney disease. 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang V, Vilme H, Maciejewski ML, Boulware LE. The Economic Burden of Chronic Kidney Disease and End-Stage Renal Disease. Semin Nephrol. 2016;36(4):319\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWouters OJ, O'Donoghue DJ, Ritchie J, Kanavos PG, Narva AS. Early chronic kidney disease: diagnosis, management and models of care. Nat Rev Nephrol. 2015;11(8):491\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChebotareva N, Vinogradov A, McDonnell V, Zakharova NV, Indeykina MI, Moiseev S, Nikolaev EN, Kononikhin AS. Urinary Protein and Peptide Markers in Chronic Kidney Disease. Int J Mol Sci 22(22) (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCravedi P, Remuzzi G. Pathophysiology of proteinuria and its value as an outcome measure in chronic kidney disease. Br J Clin Pharmacol. 2013;76(4):516\u0026ndash;23.\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":"Chronic Kidney Disease (CKD), Predictive Model, Routine Urine Tests, Multicenter Study, Multivariate Logistic Regression","lastPublishedDoi":"10.21203/rs.3.rs-8963275/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8963275/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study aimed to assess the application value of routine urine tests for predicting chronic kidney disease (CKD) across diverse populations. By developing and validating a predictive model using samples from a multi-center dataset, the study aimed to improve early CKD screening.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUrine samples were collected from 3,000 patients at West China Hospital and an external multicenter cohort of 3,856 individuals. Routine dipstick and microscopic urine tests were conducted, and key predictors were identified using LASSO regression. A multivariate logistic regression model was developed, and its performance was assessed through receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study identified 11 significant predictors of CKD: RBCs, Mucus threads, UBG, KET, BLD, PRO, LEU, PH, MALB, CA, and Osmolality. The predictive model demonstrated excellent performance with an AUC of 0.849, indicating high discriminatory power. Validation results confirmed the model's robustness and generalizability across diverse populations with the AUC\u0026thinsp;=\u0026thinsp;0.854 for internal validation cohorts and AUC\u0026thinsp;=\u0026thinsp;0.848 for external validation cohorts.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe developed predictive model provides a reliable, non-invasive tool for CKD screening using routine urine tests. Its high accuracy and scalability make it suitable for integration into clinical workflows, facilitating early intervention and improving patient outcomes.\u003c/p\u003e","manuscriptTitle":"Chronic Kidney Disease Prediction in Different Populations Using Routine Urine Test: A Multi-Center Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 08:27:57","doi":"10.21203/rs.3.rs-8963275/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":"9c846a7e-9168-41ec-9830-b80ed629356c","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-18T09:12:56+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T09:25:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 08:27:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8963275","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8963275","identity":"rs-8963275","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}