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Validation of a Classification Algorithm for Chronic Kidney Disease Based on Health Information Systems

Background: Chronic kidney disease (CKD) is a common condition, characterized by high burden of comorbidities, mortality and costs. There is a need for developing and validating algorithm for the diagnosis of CKD based on administrative data. Methods: We validated our previously developed algorithm...

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Autores principales: Ferraro, Pietro Manuel, Agabiti, Nera, Angelici, Laura, Cascini, Silvia, Bargagli, Anna Maria, Davoli, Marina, Gambaro, Giovanni, Marino, Claudia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9144354/
https://www.ncbi.nlm.nih.gov/pubmed/35628837
http://dx.doi.org/10.3390/jcm11102711
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author Ferraro, Pietro Manuel
Agabiti, Nera
Angelici, Laura
Cascini, Silvia
Bargagli, Anna Maria
Davoli, Marina
Gambaro, Giovanni
Marino, Claudia
author_facet Ferraro, Pietro Manuel
Agabiti, Nera
Angelici, Laura
Cascini, Silvia
Bargagli, Anna Maria
Davoli, Marina
Gambaro, Giovanni
Marino, Claudia
author_sort Ferraro, Pietro Manuel
collection PubMed
description Background: Chronic kidney disease (CKD) is a common condition, characterized by high burden of comorbidities, mortality and costs. There is a need for developing and validating algorithm for the diagnosis of CKD based on administrative data. Methods: We validated our previously developed algorithm that used administrative data of the Lazio Region (central Italy) to define the presence of CKD on the basis of serum creatinine measurements performed between 2012 and 2015 at the Policlinico Gemelli Hospital. CKD and advanced CKD were defined according to eGFR (<60 and <30 mL/min/1.73 m(2), respectively). Sensitivity, specificity, positive and negative predictive values (PPV/NPV) were computed. Results: During the time span of the study, 30,493 adult participants residing in the Lazio Region had undergone at least 2 serum creatinine measurements separated by at least 3 months. CKD and advanced CKD were present in 11.1% and 2.0% of the study population, respectively. The performance of the algorithm in the identification of CKD was high, with a sensitivity of 51.0%, specificity of 96.5%, PPV of 64.5% and NPV of 94.0%. Using advanced CKD, sensitivity was 62.9% (95% CI 59.0, 66.8), specificity 98.1%, PPV 40.4% and NPV 99.3%. Conclusion: The algorithm based on administrative data has high specificity and adequate performance for more advanced CKD; it can be used to obtain estimates of prevalence of CKD and to perform epidemiological research.
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spelling pubmed-91443542022-05-29 Validation of a Classification Algorithm for Chronic Kidney Disease Based on Health Information Systems Ferraro, Pietro Manuel Agabiti, Nera Angelici, Laura Cascini, Silvia Bargagli, Anna Maria Davoli, Marina Gambaro, Giovanni Marino, Claudia J Clin Med Article Background: Chronic kidney disease (CKD) is a common condition, characterized by high burden of comorbidities, mortality and costs. There is a need for developing and validating algorithm for the diagnosis of CKD based on administrative data. Methods: We validated our previously developed algorithm that used administrative data of the Lazio Region (central Italy) to define the presence of CKD on the basis of serum creatinine measurements performed between 2012 and 2015 at the Policlinico Gemelli Hospital. CKD and advanced CKD were defined according to eGFR (<60 and <30 mL/min/1.73 m(2), respectively). Sensitivity, specificity, positive and negative predictive values (PPV/NPV) were computed. Results: During the time span of the study, 30,493 adult participants residing in the Lazio Region had undergone at least 2 serum creatinine measurements separated by at least 3 months. CKD and advanced CKD were present in 11.1% and 2.0% of the study population, respectively. The performance of the algorithm in the identification of CKD was high, with a sensitivity of 51.0%, specificity of 96.5%, PPV of 64.5% and NPV of 94.0%. Using advanced CKD, sensitivity was 62.9% (95% CI 59.0, 66.8), specificity 98.1%, PPV 40.4% and NPV 99.3%. Conclusion: The algorithm based on administrative data has high specificity and adequate performance for more advanced CKD; it can be used to obtain estimates of prevalence of CKD and to perform epidemiological research. MDPI 2022-05-11 /pmc/articles/PMC9144354/ /pubmed/35628837 http://dx.doi.org/10.3390/jcm11102711 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ferraro, Pietro Manuel
Agabiti, Nera
Angelici, Laura
Cascini, Silvia
Bargagli, Anna Maria
Davoli, Marina
Gambaro, Giovanni
Marino, Claudia
Validation of a Classification Algorithm for Chronic Kidney Disease Based on Health Information Systems
title Validation of a Classification Algorithm for Chronic Kidney Disease Based on Health Information Systems
title_full Validation of a Classification Algorithm for Chronic Kidney Disease Based on Health Information Systems
title_fullStr Validation of a Classification Algorithm for Chronic Kidney Disease Based on Health Information Systems
title_full_unstemmed Validation of a Classification Algorithm for Chronic Kidney Disease Based on Health Information Systems
title_short Validation of a Classification Algorithm for Chronic Kidney Disease Based on Health Information Systems
title_sort validation of a classification algorithm for chronic kidney disease based on health information systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9144354/
https://www.ncbi.nlm.nih.gov/pubmed/35628837
http://dx.doi.org/10.3390/jcm11102711
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