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Development and testing of diagnostic algorithms to identify patients with acromegaly in Southern Italian claims databases

Acromegaly is a rare disease characterized by an excessive production of growth-hormone and insulin-like growth factor 1, typically resulting from a GH-secreting pituitary adenoma. This study was aimed at comparing and measuring accuracy of newly and previously developed coding algorithms for the id...

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Autores principales: Crisafulli, Salvatore, Fontana, Andrea, L’Abbate, Luca, Ientile, Valentina, Gianfrilli, Daniele, Cozzolino, Alessia, De Martino, Maria Cristina, Ragonese, Marta, Sultana, Janet, Barone-Adesi, Francesco, Trifirò, Gianluca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9508179/
https://www.ncbi.nlm.nih.gov/pubmed/36151305
http://dx.doi.org/10.1038/s41598-022-20295-4
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author Crisafulli, Salvatore
Fontana, Andrea
L’Abbate, Luca
Ientile, Valentina
Gianfrilli, Daniele
Cozzolino, Alessia
De Martino, Maria Cristina
Ragonese, Marta
Sultana, Janet
Barone-Adesi, Francesco
Trifirò, Gianluca
author_facet Crisafulli, Salvatore
Fontana, Andrea
L’Abbate, Luca
Ientile, Valentina
Gianfrilli, Daniele
Cozzolino, Alessia
De Martino, Maria Cristina
Ragonese, Marta
Sultana, Janet
Barone-Adesi, Francesco
Trifirò, Gianluca
author_sort Crisafulli, Salvatore
collection PubMed
description Acromegaly is a rare disease characterized by an excessive production of growth-hormone and insulin-like growth factor 1, typically resulting from a GH-secreting pituitary adenoma. This study was aimed at comparing and measuring accuracy of newly and previously developed coding algorithms for the identification of acromegaly using Italian claims databases. This study was conducted between January 2015 and December 2018, using data from the claims databases of Caserta Local Health Unit (LHU) and Sicily Region in Southern Italy. To detect acromegaly cases from the general target population, four algorithms were developed using combinations of diagnostic, surgical procedure and co-payment exemption codes, pharmacy claims and specialist’s visits. Algorithm accuracy was assessed by measuring the Youden Index, sensitivity, specificity, positive and negative predictive values. The percentage of positive cases for each algorithm ranged from 7.9 (95% CI 6.4–9.8) to 13.8 (95% CI 11.7–16.2) per 100,000 inhabitants in Caserta LHU and from 7.8 (95% CI 7.1–8.6) to 16.4 (95% CI 15.3–17.5) in Sicily Region. Sensitivity of the different algorithms ranged from 71.1% (95% CI 54.1–84.6%) to 84.2% (95% CI 68.8–94.0%), while specificity was always higher than 99.9%. The algorithm based on the presence of claims suggestive of acromegaly in ≥ 2 different databases (i.e., hospital discharge records, copayment exemptions registry, pharmacy claims and specialist visits registry) achieved the highest Youden Index (84.2) and the highest positive predictive value (34.8; 95% CI 28.6–41.6). We tested four algorithms to identify acromegaly cases using claims databases with high sensitivity and Youden Index. Despite identifying rare diseases using real-world data is challenging, this study showed that robust validity testing may yield the identification of accurate coding algorithms.
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spelling pubmed-95081792022-09-25 Development and testing of diagnostic algorithms to identify patients with acromegaly in Southern Italian claims databases Crisafulli, Salvatore Fontana, Andrea L’Abbate, Luca Ientile, Valentina Gianfrilli, Daniele Cozzolino, Alessia De Martino, Maria Cristina Ragonese, Marta Sultana, Janet Barone-Adesi, Francesco Trifirò, Gianluca Sci Rep Article Acromegaly is a rare disease characterized by an excessive production of growth-hormone and insulin-like growth factor 1, typically resulting from a GH-secreting pituitary adenoma. This study was aimed at comparing and measuring accuracy of newly and previously developed coding algorithms for the identification of acromegaly using Italian claims databases. This study was conducted between January 2015 and December 2018, using data from the claims databases of Caserta Local Health Unit (LHU) and Sicily Region in Southern Italy. To detect acromegaly cases from the general target population, four algorithms were developed using combinations of diagnostic, surgical procedure and co-payment exemption codes, pharmacy claims and specialist’s visits. Algorithm accuracy was assessed by measuring the Youden Index, sensitivity, specificity, positive and negative predictive values. The percentage of positive cases for each algorithm ranged from 7.9 (95% CI 6.4–9.8) to 13.8 (95% CI 11.7–16.2) per 100,000 inhabitants in Caserta LHU and from 7.8 (95% CI 7.1–8.6) to 16.4 (95% CI 15.3–17.5) in Sicily Region. Sensitivity of the different algorithms ranged from 71.1% (95% CI 54.1–84.6%) to 84.2% (95% CI 68.8–94.0%), while specificity was always higher than 99.9%. The algorithm based on the presence of claims suggestive of acromegaly in ≥ 2 different databases (i.e., hospital discharge records, copayment exemptions registry, pharmacy claims and specialist visits registry) achieved the highest Youden Index (84.2) and the highest positive predictive value (34.8; 95% CI 28.6–41.6). We tested four algorithms to identify acromegaly cases using claims databases with high sensitivity and Youden Index. Despite identifying rare diseases using real-world data is challenging, this study showed that robust validity testing may yield the identification of accurate coding algorithms. Nature Publishing Group UK 2022-09-23 /pmc/articles/PMC9508179/ /pubmed/36151305 http://dx.doi.org/10.1038/s41598-022-20295-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Crisafulli, Salvatore
Fontana, Andrea
L’Abbate, Luca
Ientile, Valentina
Gianfrilli, Daniele
Cozzolino, Alessia
De Martino, Maria Cristina
Ragonese, Marta
Sultana, Janet
Barone-Adesi, Francesco
Trifirò, Gianluca
Development and testing of diagnostic algorithms to identify patients with acromegaly in Southern Italian claims databases
title Development and testing of diagnostic algorithms to identify patients with acromegaly in Southern Italian claims databases
title_full Development and testing of diagnostic algorithms to identify patients with acromegaly in Southern Italian claims databases
title_fullStr Development and testing of diagnostic algorithms to identify patients with acromegaly in Southern Italian claims databases
title_full_unstemmed Development and testing of diagnostic algorithms to identify patients with acromegaly in Southern Italian claims databases
title_short Development and testing of diagnostic algorithms to identify patients with acromegaly in Southern Italian claims databases
title_sort development and testing of diagnostic algorithms to identify patients with acromegaly in southern italian claims databases
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9508179/
https://www.ncbi.nlm.nih.gov/pubmed/36151305
http://dx.doi.org/10.1038/s41598-022-20295-4
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