IJGPNInternational Journal of General Practice Nursing2981-94152981-9423Bio-Byword Scientific Publishing Pty. Ltd.10.26689/ijgpn.v3iSpecial.13914ArticleA Nomogram for Predicting the Risk of Chronic Kidney Disease among Chinese Adultshttps://artdesignp.com/journal/IJGPN/3/4/10.26689/ijgpn.v3iSpecial.13914HeXiubang,NieJingwen,GuXinyu,HuangXinyue,GaoDichen,PengShuzhi2025342025-12-31Objective: By evaluating the associations between some factors and chronic kidney disease (CKD), a predictive nomogram is established to identify the high-risk population of CKD. Methods: A retrospective survey was conducted on the physical examination population in two hospitals in Shanghai and one hospital in Jiangsu Province from October 2022 to April 2023. Variables with P-values < 0.05 in the univariate analyses were selected as independent predictors for a multivariate logistic regression. The nomogram prediction model for the risk of CKD was established and validated using Stata15.0. Results: The prediction model in our study had the best explanatory power. In our prediction model, the influencing factors and hazard ratios were: age 1.85 (1.108–3.088), family history of hypertension 2.057 (1.248–3.392), family history of diabetes 2.675 (1.623–4.407), family history of CKD 4.142 (2.526–6.793), high-salt diet 3.814 (2.343–6.208), creatinine 1.996 (1.225–3.254), glucose 6.874 (4.129–11.443), triglyceride 4.104 (2.464–6.853), C-reactive protein 4.861 (2.817–8.387), and sodium 4.281 (2.617–7.003). The area under the curve values for the performance of the modeling and validation groups for the test nomogram were 0.9225 and 0.9466 and 0.9616, respectively. The Hosmer–Lemeshow test was: modeling set: (χ2 = 2.237, P = 0.973); verification set (1): (χ2 = 5.380, P = 0.716); verification set (2): (χ2 = 6.752, P = 0.564). The nomogram calibration curves predicting the risk showed good consistency between the modeling and verification groups. Conclusion: A nomogram is established and verified internally and externally, which can help identify individuals with increased risk of CKD and provide a reference for the public and decision-makers to formulate primary prevention of CKD.

Liu SY, Huang M, Chen R, et al., 2019, Comparison of Strategies for Setting Intervention Thresholds for Chinese Postmenopausal Women Using the FRAX Model. Endocrine, 65(1): 200–206.

Abeysekera RA, Healy HG, Wang Z, et al., 2021, Heterogeneity in Patterns of Progression of Chronic Kidney Disease. Intern Med J, 51(2): 220–228.

Stevens PE, Levin A, 2013, Evaluation and Management of Chronic Kidney Disease: Synopsis of the Kidney Disease: Improving Global Outcomes 2012 Clinical Practice Guideline. Ann Intern Med, 158(11): 825–830.

Virani SS, Alonso A, Benjamin EJ, et al., 2020, Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association. Circulation, 141(9): e139–e596.

Sung H, Ferlay J, Siegel RL, et al., 2021, Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin, 71(3): 209–249.

Scott T, Ethier I, Hawley C, et al., 2021, Burden of Kidney Failure from Atheroembolic Disease and Association with Survival in People Receiving Dialysis in Australia and New Zealand: A Multi-Centre Registry Study. BMC Nephrol, 22(1): 401.

Saad RA, Qutob HM, 2022, The Relationship Between Anemia and Obesity. Expert Rev Hematol, 15(10): 911–926.

Wang L, Xu X, Zhang M, et al., 2023, Prevalence of Chronic Kidney Disease in China: Results From the Sixth China Chronic Disease and Risk Factor Surveillance. JAMA Intern Med, 183(4): 298–310.

Haim-Pinhas H, Yoskovitz G, Lishner M, et al., 2022, Effect of Aspirin on Primary Prevention of Cardiovascular Disease and Mortality among Patients with Chronic Kidney Disease. Sci Rep, 12(1): 17788.

Gallagher H, Dumbleton J, Maishman T, et al., 2022, Aspirin to Target Arterial Events in Chronic Kidney Disease (ATTACK): Study Protocol for a Multicentre, Prospective, Randomised, Open-Label, Blinded Endpoint, Parallel Group Trial of Low-Dose Aspirin vs. Standard Care for the Primary Prevention of Cardiovascular Disease in People with Chronic Kidney Disease. Trials, 23(1): 331.

Li Y, Li D, Song Y, et al., 2020, Visit-to-Visit Variability in Blood Pressure and the Development of Chronic Kidney Disease in Treated General Hypertensive Patients. Nephrol Dial Transplant, 35(10): 1739–1746.

Yang L, Li J, Wei W, et al., 2023, Blood Pressure Variability and the Progression of Chronic Kidney Disease: A Systematic Review and Meta-Analysis. J Gen Intern Med, 38(5): 1272–1281.

Zhang L, Tang L, Chen S, et al., 2023, A Nomogram for Predicting the 4-Year Risk of Chronic Kidney Disease among Chinese Elderly Adults. Int Urol Nephrol, 55(6): 1609–1617.

Wilson S, Mone P, Jankauskas SS, et al., 2021, Chronic Kidney Disease: Definition, Updated Epidemiology, Staging, and Mechanisms of Increased Cardiovascular Risk. J Clin Hypertens (Greenwich), 23(4): 831–834.

Chatzipetrou V, Begin MJ, Hars M, et al., 2022, Sarcopenia in Chronic Kidney Disease: A Scoping Review of Prevalence, Risk Factors, Association with Outcomes, and Treatment. Calcif Tissue Int, 110(1): 1–31.

Levine KE, Redmon JH, Elledge MF, et al., 2016, Quest to Identify Geochemical Risk Factors Associated with Chronic Kidney Disease of Unknown Etiology (CKDu) in an Endemic Region of Sri Lanka-A Multimedia Laboratory Analysis of Biological, Food, and Environmental Samples. Environ Monit Assess, 188(10): 548.

Pry JM, Jackson W, Rupasinghe R, et al., 2021, A Pilot Case-Control Study Using a One Health Approach to Evaluate Behavioral, Environmental, and Occupational Risk Factors for Chronic Kidney Disease of Unknown Etiology in Sri Lanka. One Health Outlook, 3: 4.

Chang J, Hou W, Li Y, et al., 2022, Prevalence and Associated Factors of Cognitive Frailty in Older Patients with Chronic Kidney Disease: A Cross-Sectional Study. BMC Geriatr, 22(1): 681.

Zhang X, Fang Y, Zou Z, et al., 2022, Risk Factors for Progression of CKD with and without Diabetes. J Diabetes Res, 2022: 9613062.

Guo D, Wang H, Liu J, et al., 2022, Prediction of Chronic Kidney Disease After Orthotopic Liver Transplantation: Development and Validation of a Nomogram Model. BMC Nephrol, 23(1): 33.

Lin Y, Shao H, Fonseca V, et al., 2023, A Prediction Model on Incident Chronic Kidney Disease among Individuals with Type 2 Diabetes in the United States. Diabetes Obes Metab, 2023.

Shen H, van der Kleij R, van der Boog P, et al., 2020, Development and Evaluation of an eHealth Self-Management Intervention for Patients with Chronic Kidney Disease in China: Protocol for a Mixed-Method Hybrid Type 2 Trial. BMC Nephrol, 21(1): 495.

Nerbass FB, Pecoits-Filho R, McIntyre NJ, et al., 2015, High Sodium Intake is Associated with Important Risk Factors in a Large Cohort of Chronic Kidney Disease Patients. Eur J Clin Nutr, 69(7): 786–790.

Al-Shamsi S, Oulhaj A, Regmi D, et al., 2019, Use of Estimated Glomerular Filtration Rate to Predict Incident Chronic Kidney Disease in Patients at Risk of Cardiovascular Disease: A Retrospective Study. BMC Nephrol, 20(1): 325.

Borrelli S, Provenzano M, Gagliardi I, et al., 2020, Sodium Intake and Chronic Kidney Disease. Int J Mol Sci, 21(13): 4744.

Teo BW, Chan GC, Leo C, et al., 2021, Hypertension and Chronic Kidney Disease in Asian Populations. J Clin Hypertens (Greenwich), 23(3): 475–480.

Chang HL, Wu CC, Lee SP, et al., 2019, A Predictive Model for Progression of CKD. Medicine (Baltimore), 98(26): e16186.

Lin Y, Shao H, Fonseca V, et al., 2023, A Prediction Model of CKD Progression among Individuals with Type 2 Diabetes in the United States. J Diabetes Complications, 37(3): 108413.

Hodson EM, Cooper TE, 2023, Altered Dietary Salt Intake for Preventing Diabetic Kidney Disease and Its Progression. Cochrane Database Syst Rev, 1(1): D6763.

Lee SM, Kim SH, Yoon HJ, 2023, Prediction of Incident Chronic Kidney Disease in a Population with Normal Renal Function and Normo-Proteinuria. PLoS One, 18(5): e285102.

Nelson RG, Grams ME, Ballew SH, et al., 2019, Development of Risk Prediction Equations for Incident Chronic Kidney Disease. JAMA, 322(21): 2104–2114.

Suh SH, Song SH, Oh TR, et al., 2021, Association of Urinary Potassium Excretion with Blood Pressure Variability and Cardiovascular Outcomes in Patients with Pre-Dialysis Chronic Kidney Disease. Nutrients, 13(12): 4443.

Li X, Lindholm B, 2022, Cardiovascular Risk Prediction in Chronic Kidney Disease. Am J Nephrol, 53(10): 730–739.

Whitlock RH, Leslie WD, Shaw J, et al., 2019, The Fracture Risk Assessment Tool (FRAX(R)) Predicts Fracture Risk in Patients with Chronic Kidney Disease. Kidney Int, 95(2): 447–454.

Pallikadavath S, Ashton L, Brunskill NJ, et al., 2022, Aspirin for the Primary Prevention of Cardiovascular Disease in Individuals with Chronic Kidney Disease: A Systematic Review and Meta-Analysis. Eur J Prev Cardiol, 28(17): 1953–1960.

Roshanov PS, Garg AX, 2023, TIPS to Decide Whether to Prescribe Aspirin for the Primary Prevention of Cardiovascular Events in Chronic Kidney Disease. Kidney Int, 103(2): 261–263.

Major RW, Burton JO, 2021, “To Take or Not to Take an Aspirin?” The Age-Old Question of Cardiovascular Disease Primary Prevention for People with Chronic Kidney Disease. Kidney Int, 99(2): 308–310.