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.

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

Crisafulli, Salvatore;L'Abbate, Luca;Trifirò, Gianluca
2022

Abstract

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.
Scientific report, Acromegaly
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1074669
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