Epidemiological analysis to identify predictors of X-linked hypophosphatemia (XLH) diagnosis in an Italian pediatric population: the EPIX project

Author:

Crisafulli Salvatore,Ingrasciotta Ylenia,Vitturi Giacomo,Fontana Andrea,L’Abbate Luca,Alessi Ylenia,Ferraù Francesco,Cantarutti Luigi,Lazzerini Debora,Cannavò Salvatore,Trifirò Gianluca

Abstract

Abstract Purpose X-linked hypophosphatemia (XLH) is a rare multi-systemic disease characterized by low plasma phosphate levels. The aim of this study was to investigate the annual XLH prevalence and internally evaluate predictive algorithms’ application performance for the early diagnosis of XLH. Methods The PediaNet database, containing data on more than 400,000 children aged up to 14 years, was used to identify a cohort of XLH patients, which were matched with up to 10 controls by date of birth and gender. The annual prevalence of XLH cases per 100,000 patients registered in PediaNet database was estimated. To identify possible predictors associated with XLH diagnosis, a logistic regression model and two machine learning algorithms were applied. Predictive analyses were separately carried out including patients with at least 1 or 2 years of database history in PediaNet. Results Among 431,021 patients registered in the PediaNet database between 2007–2020, a total of 12 cases were identified with a mean annual prevalence of 1.78 cases per 100,000 patients registered in PediaNet database. Overall, 8 cases and 60 matched controls were included in the analysis. The random forest algorithm achieved the highest area under the receiver operating characteristic curve (AUC) value both in the one-year prior ID (AUC = 0.99, 95% CI = 0.99–1.00) and the two-year prior ID (AUC = 1.00, 95% CI = 1.00–1.00) analysis. Overall, the XLH predictors selected by the three predictive methods were: the number of vitamin D prescriptions, the number of recorded diagnoses of acute respiratory infections, the number of prescriptions of antihistamine for systemic use, the number of prescriptions of X-ray of the lower limbs and pelvis and the number of allergology visits. Conclusion Findings showed that data-driven machine learning models may play a prominent role for the prediction of the diagnosis of rare diseases such as XLH.

Funder

Kyowa Kyrin

Publisher

Springer Science and Business Media LLC

Reference44 articles.

1. The HYP Consortium, A gene (PEX) with homologies to endopeptidases is mutated in patients with X-linked hypophosphatemic rickets. Nat. Genet. 11, 130–136 (1995)

2. Hereditary hypophosphatemic rickets. Genetics Home Reference. (2010). http://ghr.nlm.nih.gov/condition/hereditary-hypophosphatemic-rickets (accessed 25 July 2023)

3. J.S. Steven, K.D. Marc. Hereditary Hypophosphatemic Rickets and Tumor-Induced Osteomalacia. UpToDate. (2023). https://www.uptodate.com/contents/hereditary-hypophosphatemic-rickets-and-tumor-induced-osteomalacia (accessed 25 July 2023)

4. S.S. Beck-Nielsen, B. Brock-Jacobsen, J. Gram et al. Incidence and prevalence of nutritional and hereditary rickets in southern Denmark. Eur. J. Endocrinol. 160, 491–497 (2009)

5. S. Rafaelsen, S. Johansson, H. Ræder et al. Hereditary hypophosphatemia in Norway: a retrospective population-based study of genotypes, phenotypes, and treatment complications. Eur. J. Endocrinol. 174, 125–136 (2016)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3