Design of a mortality predictive scale in patients with chronic kidney disease
Keywords:
chronic kidney disease, forecast, mortality.Abstract
Introduction: The prediction of mortality in patients with chronic kidney disease using scales or prognostic indices has real limitations.Objective: Design a mortality predictive scale in patients with chronic kidney disease.
Methods: A prospective observational, analytical, longitudinal study was carried out in 169 patients with chronic kidney disease from January 1, 2022 to December 31, 2022. The research was developed in 2 stages: during the first 6 months of the year, the variables were analyzed for the design of the predictive scale. In the next 6 months, patients were followed to identify the occurrence or not of the dependent variable mortality. The discriminatory capacity of the predictive scale was determined and survival curves were evaluated.
Results: The variables that made up the predictive tool were age > 65 years, cardiovascular disease, albumin < 35 g/L, dyslipidemia, hemoglobin < 10 g/L, and uric acid > 390 mmol/L. The discriminatory power to predict mortality was good, C index: 0.856 (95% CI: 0.783-0.929; p< 0.001). Patients with values less than 4 points had a mean survival of 149.438 ± 7.296 days. In contrast, those with higher values presented a mean survival of 93.128 ± 8.545 days.
Conclusions: The scale contributed to the stratification of the mortality risk of the patients. The variables included are easy to determine and interpret, making it a useful model for medical decision making in the current clinical setting.
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References
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22. Wittbrodt E, James G, Kumar S. Contemporary outcomes of anemia in US patients with chronic kidney disease. Clinical Kidney Journal. 2022;15(2):244-52. DOI: 10.1093/ckj/sfab195
2. Anuario Estadístico de Salud, 2021. Ministerio de Salud Pública. Dirección de Registros médicos y Estadísticas de salud. La Habana, 2022. [acceso: 15/05/2023]. Disponible en: http://bvscuba.sld.cu/anuario-estadistico-de-cuba/
3. Khoo C, Gao F, Choong H. Death and cardiovascular outcomes in end-stage renal failure patients on different modalities of dialysis. Ann Acad Med Singap. 2022 [acceso: 15/05/2023];51(3):136-42. DOI: 10.47102/annals-acadmedsg.20219
4. González-Milán ZC, Escalona-González SO, Ramírez-Fernández A. Factores pronósticos de mortalidad en pacientes con insuficiencia renal crónica terminal en terapia hemodialítica. Revista Electrónica Dr. Zoilo E. Marinello Vidaurreta. 2019 [acceso: 15/05/2023]; 44(6):1-14. Disponible en: http://revzoilomarinello.sld.cu/index.php/zmv/article/view/2041
5. Escalona González SO, González Milán ZC. Inteligencia artificial en la predicción de mortalidad por enfermedad renal crónica. An Acad Cienc Cuba. 2022 [acceso: 15/05/2023];12(3):e1292. Disponible en: http://www.revistaccuba.cu/index.php/revacc/article/view/1292
6. Belur Nagaraj S, Pena MJ, Ju W, The BEAt-DKD Consortium. Machine-learning- based early prediction of end-stage renal disease in patients with diabetic kidney disease using clinical trials data. Diabetes Obes Metab. 2020 [acceso:15/05/2023];22(12):2479-86. DOI: 10.1111/dom.14178
7. Gamboa Graus ME. Estadística aplicada a la investigación educativa. Revista Dilemas Contemporáneos: Educación, política y valores. 2018 [acceso:15/05/2023];5(2):1-32. Disponible en: http://www.dilemascontemporaneoseducacionpoliticayvalores.com/index.php/dile-mas/article/view/427/443
8. Marín Prada MdC, Gutiérrez García F, Martínez Morales MA. Características y causas de muerte de pacientes fallecidos con enfermedad renal crónica. Cuba 2011-2016. Revista Habanera de Ciencias Médicas. 2021 [acceso: 15/05/2023];20(5):e3579. Disponible en: https://revhabanera.sld.cu/index.php/rhab/article/view/3579
9. Ruperto M, Barril G. Nutritional status, body composition, and inflammation profile in older patients with advanced chronic kidney disease stage 4-5: A case control study. Nutrients. 2022 [acceso:15/05/2023];14(17):3650. DOI: 10.3390/nu14173650
10. Matsushita K, Ballew S, Wang A. Epidemiology and risk of cardiovascular disease in populations with chronic kidney disease. Nature Reviews Nephrology. 2022 [acceso: 15/05/2023];18(11):696-707. DOI: 10.1038/s41581-022-00616-6
11. Lee S, Kang S, Joo Y. Smoking, smoking cessation, and progression of chronic kidney disease: results form KNOW-CKD study. Nicotine & Tobacco Research. 2021 [acceso: 15/05/2023];23(1):92-98. DOI: 10. 1093/ntr/ntaa071
12. Liang X, Ye M, Tao M. The association between dyslipidemia and the incidence of chronic kidney disease in the general Zhejiang population: a retrospective study. BMC Nephrol. 2020 [acceso:15/05/2023]; 21(252):1-9. DOI: 10. 1186/s12882-020-01907-5
13. Feng-Ching S, Yi-Wen Ch, Mei-Chuan K. U-shaped association between waist-to-hip ratio and all-cause mortality in stage 3-5 chronic kidney disease patients with body mass index paradox. J Pers Med. 2021 [acceso: 15/05/2023];11(12):1355. DOI: 10.3390/jpm11121355
14. Miao H, Liu L, Wang Y. Chronic kidney disease biomarkers and mortality among older adults: A comparison study of survey samples in China and the United States. Plos One. 2022 [acceso:15/05/2023];17(1):e0260074. DOI: 10.1371/journal.pone.0260074
15. Cornish R, Bartlett J, Macleod J. Complete case logistic regression with a dichotomized continuous outcome led to biased estimates. Journal of Clinical Epidemiology. 2023 [acceso: 15/05/2023];154(2023):33-41. DOI: 10.1016/j.jclinepi.2022.11.022
16. Montalescot L, Dorard G, Speyer G. Patient perspectives on chronic kidney disease and decision-making about treatment. Discourse of participants in the French CKD-REIN cohort study. J. Nephrol. 2022 [acceso: 15/05/2023];35(5):1387-1397. DOI: 10.1007/s40620-022-01345-6
17. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. Reviewing the latest version. Int. J. Surg. Sci. 2014 [acceso: 15/05/2023];1(4):339-46. DOI: 10.32457/IJMSS.2014.042
18. Schmidt RJ, Landry DL, Cohen L. Derivation and validation of a prognostic model to predict mortality in patients with advanced chronic kidney disease. Nephrol Dial Transplant. 2019;34(9):1517-25. DOI: 10.1093/ndt/gfy305
19. Miao H, Liu L, Wang Y. Chronic kidney disease biomarkers and mortality among older adults: A comparison study of survey samples in China and the United States. PLoS ONE. 2022;17(1):e0260074. DOI: 10.1371/journal.pone.0260074
20. Liu H, Wang R. Associations between the serum magnesium and all-cause or cardiovascular mortality in chronic kidney disease and end-stage renal disease patients. A meta-analysis. Medicine. 2021;100(45):e27486. DOI: 10.1097/MD.0000000000027486
21. Zeng YQ, Qin ZA, Guo ZW. Non- linear relationship between basal serum albumin concentration and cardiac arrest in critically ill patients with end-stage renal disease: a cross-sectional study. BMJ Open. 2022;12(2):e051721. DOI: 10.1136/bmjopen-2021-051721
22. Wittbrodt E, James G, Kumar S. Contemporary outcomes of anemia in US patients with chronic kidney disease. Clinical Kidney Journal. 2022;15(2):244-52. DOI: 10.1093/ckj/sfab195
Published
2024-02-01
How to Cite
1.
Escalona González SO, Caballero Mota Y, Rodríguez Alvarez Y, León Acebo M, González Milán ZC, Ricardo Paez B, et al. Design of a mortality predictive scale in patients with chronic kidney disease. Rev Cubana Med Milit [Internet]. 2024 Feb. 1 [cited 2025 Mar. 31];53(1):e024017622. Available from: https://revmedmilitar.sld.cu/index.php/mil/article/view/17622
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