A machine learning-based prediction model for gout in hyperuricemics: a nationwide cohort study

Author:

Brikman Shay12ORCID,Serfaty Liel3,Abuhasira Ran456,Schlesinger Naomi7,Bieber Amir1ORCID,Rappoport Nadav3ORCID

Affiliation:

1. Rheumatic Diseases Unit, Emek Medical Center , Afula, Israel

2. Rappaport Faculty of Medicine, Technion , Haifa, Israel

3. Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev , Be'er Sheva, Israel

4. Clinical Research Center, Soroka University Medical Center , Be'er Sheva, Israel

5. Faculty of Health Sciences, Ben-Gurion University of the Negev , Be'er Sheva, Israel

6. Department of Internal Medicine B, Rabin Medical Center, Beilinson Campus , Petah Tikva, Israel

7. Division of Rheumatology, Department of Medicine, Spencer Fox Eccles School of Medicine, University of Utah , Salt Lake City, UT, USA

Abstract

Abstract Objective To develop a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout. Methods A retrospective nationwide Israeli cohort study used the Clalit Health Insurance database of 473 124 individuals to identify adults 18 years or older with at least two serum urate measurements exceeding 6.8 mg/dl between January 2007 and December 2022. Patients with a prior gout diagnosis or on gout medications were excluded. Patients’ demographic characteristics, community and hospital diagnoses, routine medication prescriptions and laboratory results were used to train a risk prediction model. A machine learning model, XGBoost, was developed to predict the risk of gout. Feature selection methods were used to identify relevant variables. The model's performance was evaluated using the receiver operating characteristic area under the curve (ROC AUC) and precision-recall AUC. The primary outcome was the diagnosis of gout among hyperuricemic patients. Results Among the 301 385 participants with hyperuricemia included in the analysis, 15 055 (5%) were diagnosed with gout. The XGBoost model had a ROC-AUC of 0.781 (95% CI 0.78–0.784) and precision-recall AUC of 0.208 (95% CI 0.195–0.22). The most significant variables associated with gout diagnosis were serum uric acid levels, age, hyperlipidemia, non-steroidal anti-inflammatory drugs and diuretic purchases. A compact model using only these five variables yielded a ROC-AUC of 0.714 (95% CI 0.706–0.723) and a negative predictive value (NPV) of 95%. Conclusions The findings of this cohort study suggest that a machine learning-based prediction model had relatively good performance and high NPV for identifying hyperuricemic participants at risk of developing gout.

Publisher

Oxford University Press (OUP)

Reference34 articles.

1. Gout;Dalbeth;Lancet,2016

2. Gout;Mikuls;New Engl J Med,2022

3. Treating to target: a strategy to cure gout;Perez-Ruiz;Rheumatology (Oxford),2009

4. The crystallization of monosodium urate;Martillo;Curr Rheumatol Rep,2014

5. Studies of urate crystallisation in relation to gout;Fiddis;Ann Rheum Dis,1983

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