Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Developing and Validating an Algorithm to Identify Incident Chronic Dialysis Patients Using Administrative Data

Version 1 : Received: 5 May 2020 / Approved: 6 May 2020 / Online: 6 May 2020 (15:26:06 CEST)

A peer-reviewed article of this Preprint also exists.

Gibertoni, D., Voci, C., Iommi, M. et al. Developing and validating an algorithm to identify incident chronic dialysis patients using administrative data. BMC Med Inform Decis Mak 20, 185 (2020). https://doi.org/10.1186/s12911-020-01206-x Gibertoni, D., Voci, C., Iommi, M. et al. Developing and validating an algorithm to identify incident chronic dialysis patients using administrative data. BMC Med Inform Decis Mak 20, 185 (2020). https://doi.org/10.1186/s12911-020-01206-x

Journal reference: Dino Gibertoni, Kadjo Yves Cedric Adja, Davide Golinelli, Chiara Reno, Luca Regazzi, Jacopo Lenzi, Francesco Sanmarchi, Maria Pia Fantini, Patterns of COVID-19 related excess mortality in the municipalities of Northern Italy during the first wave of the p 2020, 20, 185
DOI: 10.1186/s12911-020-01206-x

Abstract

Background: Administrative healthcare databases are widespread and are often standardized with regard to their content and data coding, thus they can be used also as data sources for surveillance and epidemiological research. Chronic dialysis requires patients to frequently access hospital and clinic services, causing a heavy burden to healthcare providers. This also means that these patients are routinely tracked on administrative databases, yet very few case definitions for their identification are currently available. The aim of this study was to develop two algorithms derived from administrative data for identifying incident chronic dialysis patients and test their validity compared to the reference standard of the regional dialysis registry. Methods: The algorithms are based on data retrieved from hospital discharge records (HDR) and ambulatory specialty visits (ASV) to identify incident chronic dialysis patients in an Italian region. Subjects are included if they have at least one event in the HDR or ASV databases based on the ICD9-CM dialysis-related diagnosis or procedure codes in the study period. Exclusion criteria comprise non-residents, prevalent cases, or patients undergoing temporary dialysis, and are evaluated only on ASV data by the first algorithm, on both ASV and HDR data by the second algorithm. We validated the algorithms against the Emilia-Romagna regional dialysis registry by searching for incident patients in 2014. Results: Algorithm 1 identified 680 patients and algorithm 2 identified 676 initiating dialysis in 2014, compared to 625 patients included in the regional dialysis registry. Sensitivity for the two algorithms was respectively 90.8% and 88.4%, positive predictive value 84.0% and 82.0%, and percentage agreement was 77.4% and 74.1%. Conclusions: These results suggest that administrative data have high sensitivity and positive predictive value for the identification of incident chronic dialysis patients. Algorithm 1, which showed the higher accuracy and has a simpler case definition, can be used in place of regional dialysis registries when they are not present or sufficiently developed in a region, or to improve the accuracy and timeliness of existing registries.

Subject Areas

chronic dialysis; administrative data; hospital discharge records; ambulatory specialty visits; case definition; algorithm

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