Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis

Atul Deodhar,Tara Grabowsky,Oodaye Shukla,Cody Garges,Theresa Arndt,Yujin Park,Martin Rozycki

doi:10.1007/s10067-019-04553-x

Abstract

ObjectiveTo develop a predictive mathematical model for the early identification of ankylosing spondylitis (AS) based on the medical and pharmacy claims history of patients with and without AS.MethodsThis retrospective study used claims data from Truven databases from January 2006 to September 2015 (Segment 1) and October 2015 to February 2018 (Segment 2). Machine learning identified features differentiating patients with AS from matched controls; selected features were used as inputs in developing Model A/B to identify patients likely to have AS. Model A/B was trained and developed in Segment 1, and patients predicted to have AS in Segment 1 were followed up in Segment 2 to evaluate the predictive capability of Model A/B.ResultsOf 228,471 patients in Segment 1 without any history of AS, Model A/B predicted 1923 patients to have AS. Ultimately, 1242 patients received an AS diagnosis in Segment 2; 120 of these were correctly predicted by Model A/B, yielding a positive predictive value (PPV) of 6.24%. The diagnostic accuracy of Model A/B compared favorably with that of a clinical model (PPV, 1.29%) that predicted AS based on spondyloarthritis features described in the Assessment of SpondyloArthritis international Society classification criteria. A simplified linear regression model created to test the operability of Model A/B yielded a lower PPV (2.55%).ConclusionsModel A/B performed better than a clinically based model in predicting a diagnosis of AS among patients in a large claims database; its use may contribute to early recognition of AS and a timely diagnosis.

Highlights

Ankylosing spondylitis (AS) is a chronic inflammatory disease manifested by progressive spinal stiffness and fusion; this disease primarily involves the sacroiliac joints in the initial stages [1,2,3]
This study was conducted in accordance with the Guidelines for Good Pharmacoepidemiology Practices of the International Society for Pharmacoepidemiology, the Strengthening the Reporting of Observational Studies in Epidemiology guidelines, and the ethical principles in the Declaration of Helsinki
An overview of the diagnosis, procedure, and prescription codes used in Models A and B from 0 to 12 months before diagnosis is shown in Fig. S1a and b, respectively

Summary

Introduction

Ankylosing spondylitis (AS) is a chronic inflammatory disease manifested by progressive spinal stiffness and fusion; this disease primarily involves the sacroiliac joints in the initial stages [1,2,3]. In the USA, it is estimated that the prevalence of AS ranges from 0.2 to 0.5% [4]. AS is thought to affect approximately 350,000 people in the USA, it is an underdiagnosed condition and can take approximately 14 years before it is diagnosed correctly [5]. In the USA, the majority of patients experiencing the onset of low back pain visit general practitioners, orthopedists, or chiropractors. The ability of these providers to accurately diagnose AS is unknown, and there are no clear guidelines to refer

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Conclusion