Development of a Novel Algorithm to Identify People with High Likelihood of Adult Growth Hormone Deficiency in a US Healthcare Claims Database

Objective. Adult growth hormone deficiency (AGHD) is an underdiagnosed disease associated with increased morbidity and mortality. Identifying people who may benefit from growth hormone (GH) therapy can be challenging, as many AGHD symptoms resemble those of aging. We developed an algorithm to potent...

Full description

Saved in:
Bibliographic Details
Main Authors: Kevin C. J. Yuen, Anna Camilla Birkegard, Lewis S. Blevins, David R. Clemmons, Andrew R. Hoffman, Nicky Kelepouris, Janice M. Kerr, Jens M. Tarp, Maria Fleseriu
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:International Journal of Endocrinology
Online Access:http://dx.doi.org/10.1155/2022/7853786
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832562176202637312
author Kevin C. J. Yuen
Anna Camilla Birkegard
Lewis S. Blevins
David R. Clemmons
Andrew R. Hoffman
Nicky Kelepouris
Janice M. Kerr
Jens M. Tarp
Maria Fleseriu
author_facet Kevin C. J. Yuen
Anna Camilla Birkegard
Lewis S. Blevins
David R. Clemmons
Andrew R. Hoffman
Nicky Kelepouris
Janice M. Kerr
Jens M. Tarp
Maria Fleseriu
author_sort Kevin C. J. Yuen
collection DOAJ
description Objective. Adult growth hormone deficiency (AGHD) is an underdiagnosed disease associated with increased morbidity and mortality. Identifying people who may benefit from growth hormone (GH) therapy can be challenging, as many AGHD symptoms resemble those of aging. We developed an algorithm to potentially help providers stratify people by their likelihood of having AGHD. Design. The algorithm was developed with, and applied to, data in the anonymized Truven Health MarketScan® claims database. Patients. A total of 135 million adults in the US aged ≥18 years with ≥6 months of data in the Truven database. Measurements. Proportion of people with high, moderate, or low likelihood of having AGHD, and differences in demographic and clinical characteristics among these groups. Results. Overall, 0.5%, 6.0%, and 93.6% of people were categorized into groups with high, moderate, or low likelihood of having AGHD, respectively. The proportions of females were 59.3%, 71.6%, and 50.4%, respectively. People in the high- and moderate-likelihood groups tended to be older than those in the low-likelihood group, with 58.3%, 49.0%, and 37.6% aged >50 years, respectively. Only 2.2% of people in the high-likelihood group received GH therapy as adults. The high-likelihood group had a higher incidence of comorbidities than the low-likelihood group, notably malignant neoplastic disease (standardized difference −0.42), malignant breast tumor (−0.27), hyperlipidemia (−0.26), hypertensive disorder (−0.25), osteoarthritis (−0.23), and heart disease (−0.22). Conclusions. This algorithm may represent a cost-effective approach to improve AGHD detection rates by identifying appropriate patients for further diagnostic testing and potential GH replacement treatment.
format Article
id doaj-art-393e27e69bfd4e0c9f0663801f845c60
institution Kabale University
issn 1687-8345
language English
publishDate 2022-01-01
publisher Wiley
record_format Article
series International Journal of Endocrinology
spelling doaj-art-393e27e69bfd4e0c9f0663801f845c602025-02-03T01:23:10ZengWileyInternational Journal of Endocrinology1687-83452022-01-01202210.1155/2022/7853786Development of a Novel Algorithm to Identify People with High Likelihood of Adult Growth Hormone Deficiency in a US Healthcare Claims DatabaseKevin C. J. Yuen0Anna Camilla Birkegard1Lewis S. Blevins2David R. Clemmons3Andrew R. Hoffman4Nicky Kelepouris5Janice M. Kerr6Jens M. Tarp7Maria Fleseriu8Barrow Pituitary CenterNovo Nordisk A/SDepartment of NeurosurgeryDepartment of MedicineDepartment of MedicineNovo Nordisk Inc.Department of EndocrinologyNovo Nordisk A/SPituitary CenterObjective. Adult growth hormone deficiency (AGHD) is an underdiagnosed disease associated with increased morbidity and mortality. Identifying people who may benefit from growth hormone (GH) therapy can be challenging, as many AGHD symptoms resemble those of aging. We developed an algorithm to potentially help providers stratify people by their likelihood of having AGHD. Design. The algorithm was developed with, and applied to, data in the anonymized Truven Health MarketScan® claims database. Patients. A total of 135 million adults in the US aged ≥18 years with ≥6 months of data in the Truven database. Measurements. Proportion of people with high, moderate, or low likelihood of having AGHD, and differences in demographic and clinical characteristics among these groups. Results. Overall, 0.5%, 6.0%, and 93.6% of people were categorized into groups with high, moderate, or low likelihood of having AGHD, respectively. The proportions of females were 59.3%, 71.6%, and 50.4%, respectively. People in the high- and moderate-likelihood groups tended to be older than those in the low-likelihood group, with 58.3%, 49.0%, and 37.6% aged >50 years, respectively. Only 2.2% of people in the high-likelihood group received GH therapy as adults. The high-likelihood group had a higher incidence of comorbidities than the low-likelihood group, notably malignant neoplastic disease (standardized difference −0.42), malignant breast tumor (−0.27), hyperlipidemia (−0.26), hypertensive disorder (−0.25), osteoarthritis (−0.23), and heart disease (−0.22). Conclusions. This algorithm may represent a cost-effective approach to improve AGHD detection rates by identifying appropriate patients for further diagnostic testing and potential GH replacement treatment.http://dx.doi.org/10.1155/2022/7853786
spellingShingle Kevin C. J. Yuen
Anna Camilla Birkegard
Lewis S. Blevins
David R. Clemmons
Andrew R. Hoffman
Nicky Kelepouris
Janice M. Kerr
Jens M. Tarp
Maria Fleseriu
Development of a Novel Algorithm to Identify People with High Likelihood of Adult Growth Hormone Deficiency in a US Healthcare Claims Database
International Journal of Endocrinology
title Development of a Novel Algorithm to Identify People with High Likelihood of Adult Growth Hormone Deficiency in a US Healthcare Claims Database
title_full Development of a Novel Algorithm to Identify People with High Likelihood of Adult Growth Hormone Deficiency in a US Healthcare Claims Database
title_fullStr Development of a Novel Algorithm to Identify People with High Likelihood of Adult Growth Hormone Deficiency in a US Healthcare Claims Database
title_full_unstemmed Development of a Novel Algorithm to Identify People with High Likelihood of Adult Growth Hormone Deficiency in a US Healthcare Claims Database
title_short Development of a Novel Algorithm to Identify People with High Likelihood of Adult Growth Hormone Deficiency in a US Healthcare Claims Database
title_sort development of a novel algorithm to identify people with high likelihood of adult growth hormone deficiency in a us healthcare claims database
url http://dx.doi.org/10.1155/2022/7853786
work_keys_str_mv AT kevincjyuen developmentofanovelalgorithmtoidentifypeoplewithhighlikelihoodofadultgrowthhormonedeficiencyinaushealthcareclaimsdatabase
AT annacamillabirkegard developmentofanovelalgorithmtoidentifypeoplewithhighlikelihoodofadultgrowthhormonedeficiencyinaushealthcareclaimsdatabase
AT lewissblevins developmentofanovelalgorithmtoidentifypeoplewithhighlikelihoodofadultgrowthhormonedeficiencyinaushealthcareclaimsdatabase
AT davidrclemmons developmentofanovelalgorithmtoidentifypeoplewithhighlikelihoodofadultgrowthhormonedeficiencyinaushealthcareclaimsdatabase
AT andrewrhoffman developmentofanovelalgorithmtoidentifypeoplewithhighlikelihoodofadultgrowthhormonedeficiencyinaushealthcareclaimsdatabase
AT nickykelepouris developmentofanovelalgorithmtoidentifypeoplewithhighlikelihoodofadultgrowthhormonedeficiencyinaushealthcareclaimsdatabase
AT janicemkerr developmentofanovelalgorithmtoidentifypeoplewithhighlikelihoodofadultgrowthhormonedeficiencyinaushealthcareclaimsdatabase
AT jensmtarp developmentofanovelalgorithmtoidentifypeoplewithhighlikelihoodofadultgrowthhormonedeficiencyinaushealthcareclaimsdatabase
AT mariafleseriu developmentofanovelalgorithmtoidentifypeoplewithhighlikelihoodofadultgrowthhormonedeficiencyinaushealthcareclaimsdatabase