Finding undiagnosed patients with hepatitis C virus: an application of machine learning to US ambulatory electronic medical records

Introduction

Hepatitis C virus (HCV) is one of the most common blood-borne viruses and a major cause of liver-related morbidity and mortality in the USA.1 The estimated prevalence of HCV in the USA is 1%2 with the estimated number of new (acute) infections increasing fourfold between 2010 and 2018.3 Treatment of HCV has been revolutionised in recent years by direct-acting antiviral drugs which are well tolerated and highly efficacious (>95% cure rate).4–6 These developments paved the way for the WHO to propose a global strategy to eliminate HCV as a public health threat by 2030.7 In the USA, the National Academies of Science, Engineering and Medicine developed an HCV elimination plan where improved detection of undiagnosed cases is a key element.8 This, together with the need for identifying hard-to-find patients not captured by risk-based screening, has led to increased emphasis on universal one-time HCV screening recommended as part of the American Association for the Study of Liver Diseases (AASLD) – Infectious Diseases Society of America (IDSA) guidance as well as periodic screening in high-risk individuals.6 Recent studies show that HCV screening rates remain low and recommend targeted interventions aimed at patients and physicians to boost screening rates.9 10

The advent of electronic medical records (EMR) used in combination with machine learning (ML) has presented new opportunities for screening in population health management.11 12 EMRs have been used previously to find undiagnosed HCV cases;13–15 however, these studies use simple clinical rules to prioritise patients for HCV screening. Previous work has demonstrated how ML can accurately identify undiagnosed HCV cases using US medical insurance claims and prescription data.16 Additionally, ML techniques applied to EMRs have been used for patient finding in other disease areas, such as type 1 diabetes and sepsis.17 18 Given the promise shown in applying ML to EMRs, we investigated whether undiagnosed HCV cases could be predicted by an ML algorithm using a US EMR data set. The Methods section describes how this was developed and, in the results, a benchmark of performance against universal and risk-based screening is provided. Finally, the discussion contains an appraisal of how prioritisation of patients in the US for HCV screening could be improved with the algorithm, along with the potential impact on resource utilisation and the subsequent prospective validation requirements.

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Discussion

The ML algorithm showed an increased efficiency of screening for HCV compared with universal screening and risk-based approaches, where fewer patients are required to be screened with the algorithm to identify the equivalent number of HCV cases. This supports existing research that found ML algorithms trained on EMR data can be used to predict patients’ risk of disease with high precision.16–18 Moreover, this study demonstrates the utility of an EMR-based ML algorithm in identifying HCV patients and evidences a potential benefit in deployment into clinical workflow. One way to realise this benefit is through integrating risk prediction algorithms into EMR systems, and examples of this exist; simple rule-based algorithms have been effective in increasing HCV screening rates,15 while a recent study describes the integration of a complex sepsis prediction of ML algorithm with an Epic EMR system in the USA.17 EMR integration can facilitate targeted HCV screening, which would have multiple potential clinical and operational benefits. First, effective targeting can improve the allocation of limited healthcare resources and hence the return on investment for a screening programme. Second, effective targeting would be expected to lead to improvements in rates of HCV diagnosis, treatment and transmission as well as reductions in morbidity and mortality arising from earlier diagnosis. Third, a sophisticated risk-based targeting approach can identify hard-to-find patients who may be overlooked by simple screening criteria. Finally, the algorithm outputs a continuous risk score enabling a nuanced triage process. For instance, patients with high risk scores could be proactively invited for screening, whereas patients with lower risk scores could be opportunistically screened during routine visits.

There is a need to understand biases in ML models. The ML algorithm developed here exhibits signs of representation bias (which arises through lack of generalisation to groups that are under-represented in the data). A post hoc univariate corrective approach showed promise in reducing bias across a single characteristic. This approach calculates how many patients from each characteristic’s subgroups should be screened to equalise the proportion of HCV patients identified belonging to each. However, when a single characteristic is equalised with this approach, it may worsen bias for others. Therefore, a more expansive approach to address all characteristics equitably would form part of future work.

The scope of this study is restricted to individuals who have engaged with the US healthcare system. In a future deployment setting, this would result in low chance of prioritisation for people with limited or no access to healthcare in the USA. This is particularly relevant for HCV as a high proportion of individuals infected with HCV is either uninsured or have publicly funded health insurance.29 Therefore, complementary approaches are needed, such as routine HCV screening in addiction medicine settings, correctional facilities and proactive HCV screening in sexual health settings, alongside investment in HCV treatment networks to ensure linkage to care is facilitated.30 31

The results of this study represent a proof of concept that has been developed using a US-based EMR data set. A natural next step for this algorithm is to perform further validation in an interventional prospective study that emulates the real-world deployment settings. This will help overcome some limitations of the retrospective study design. In particular, the positive cohort in this study comprises of patients who are diagnosed over a finite outcome window in the absence of the intervention of interest (ie, screening of the identified patients). Therefore, the number of false positives is overestimated for each screening intervention (universal, ML-algorithm, etc).

An important additional dimension to this study is the cost-effectiveness of the ML algorithm for screening. Previous studies have reported that risk-based HCV screening in populations such as PWID and the Baby Boomer birth cohort are cost-effective.30 31 Given the ML algorithm has further increased efficiency, it is likely that this will translate into a further increase in cost-effectiveness. A formal study of the cost-effectiveness of the ML algorithm will form an important part of future work.

This study presents strong evidence to support the use of an HCV prediction ML algorithm with large-scale EMR data. The focus of the work will now move to a pilot phase involving integration and prospective interventional validation of the algorithm in a clinical research setting. Subject to a successful pilot study, focus will shift to local deployment of the algorithm in multiple healthcare settings and geographies, which will involve collaboration with end users and on-going monitoring, with the ultimate goal of contributing to efforts towards HCV elimination by targeted increase in diagnosis rates and reducing time to diagnosis.

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