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Validation of a Classification Algorithm for Chronic Kidney Disease Based on Health Information Systems
Pietro Manuel Ferraro1,2, Nera Agabiti3, Laura Angelici3
1U.O.S. Terapia Conservativa della Malattia Renale Cronica, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy.
Insights
An algorithm using administrative data accurately identifies chronic kidney disease (CKD) and advanced CKD, aiding epidemiological research and prevalence estimation.
Area of Science:
- Nephrology
- Public Health
- Health Informatics
Background:
- Chronic kidney disease (CKD) presents a significant global health challenge with high comorbidity, mortality, and healthcare costs.
- Accurate identification of CKD using administrative data is crucial for epidemiological studies and resource allocation.
- Existing methods for CKD diagnosis often rely on clinical data, necessitating the development of administrative data-driven algorithms.
Purpose of the Study:
- To validate a previously developed algorithm for diagnosing chronic kidney disease (CKD) using administrative data.
- To assess the algorithm's performance in identifying both general CKD and advanced CKD stages.
- To determine the feasibility of using administrative data for large-scale CKD prevalence estimation and epidemiological research.
Main Methods:
- The study validated an algorithm utilizing administrative data from the Lazio Region, Italy.
- Serum creatinine measurements from 2012-2015 at Policlinico Gemelli Hospital were used for validation.
- Chronic kidney disease (CKD) and advanced CKD were defined based on estimated glomerular filtration rate (eGFR) thresholds (<60 and <30 mL/min/1.73 m2).
- Key performance metrics including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated.
Main Results:
- The study included 30,493 adult participants with at least two serum creatinine measurements.
- The prevalence of CKD and advanced CKD in the study population was 11.1% and 2.0%, respectively.
- The algorithm demonstrated high specificity (96.5%) and adequate sensitivity (51.0%) for CKD detection.
- For advanced CKD, the algorithm showed high specificity (98.1%) and a high negative predictive value (99.3%).
Conclusions:
- The algorithm based on administrative data exhibits high specificity and acceptable performance for identifying advanced CKD.
- This validated algorithm can be effectively utilized for estimating CKD prevalence in large populations.
- The findings support the use of administrative data algorithms for robust epidemiological research in nephrology.
Abstract:
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 m2, 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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