Identification of Axial Spondyloarthritis Patients in a Large Dataset: The Development and Validation of Novel Methods

The association of TNF inhibitor use with incident cardiovascular events in radiographic axial spondyloarthritis

BACKGROUND

Whether tumor necrosis factor inhibitor (TNFi) use is cardioprotective among individuals with radiographic axial spondyloarthritis (r-axSpA), who have heightened cardiovascular (CV) risk, is unclear. We tested the association of TNFi use with incident CV outcomes in r-axSpA.

METHODS

We identified a r-axSpA cohort within a Veterans Affairs database between 2002 and 2019 using novel phenotyping methods and secondarily using ICD codes. TNFi use was assessed as a time-varying exposure using pharmacy dispense records. The primary outcome was incident CV disease identified using ICD codes for coronary artery disease, myocardial infarction or stroke. We fit Cox models with inverse probability weights to estimate the risk of each outcome with TNFi use versus non-use. Analyses were performed in the overall cohort, and separately in two periods (2002-2010, 2011-2019) to account for secular trends.

RESULTS

Using phenotyping we identified 26,928 individuals with an r-axSpA diagnosis (mean age 63.4 years, 94 % male); at baseline 3633 were TNFi users and 23,295 were non-users. During follow-up of a mean 3.3 ± 4.2 years, 674 (18.6 %) TNFi users had incident CVD versus 11,838 (50.8 %) non-users. In adjusted analyses, TNFi use versus non-use was associated with lower risk of incident CVD (HR 0.34, 95 % CI 0.29-0.40) in the cohort overall, and in the two time periods separately.

CONCLUSION

In this r-axSpA cohort identified using phenotyping methods, TNFi use versus non-use had a lower risk of incident CVD. These findings provide reassurance regarding the CV safety of TNFi agents for r-axSpA treatment. Replication of these results in other cohorts is needed.

Machine learning models with time-series clinical features to predict radiographic progression in patients with ankylosing spondylitis

Objective

Ankylosing spondylitis (AS) is chronic inflammatory arthritis causing structural damage and radiographic progression to the spine due to repeated and continuous inflammation over a long period. This study establishes the application of machine learning models to predict radiographic progression in AS patients using time-series data from electronic medical records (EMRs).

Methods

EMR data, including baseline characteristics, laboratory findings, drug administration, and modified Stoke AS Spine Score (mSASSS), were collected from 1,123 AS patients between January 2001 and December 2018 at a single center at the time of first (T1), second (T2), and third (T3) visits. The radiographic progression of the (n+1)th visit (Pn+1=(mSASSSn+1-mSASSSn)/(Tn+1-Tn)≥1 unit per year) was predicted using follow-up visit datasets from T1 to Tn. We used three machine learning methods (logistic regression with the least absolute shrinkage and selection operation, random forest, and extreme gradient boosting algorithms) with three-fold cross-validation.

Results

The random forest model using the T1 EMR dataset best predicted the radiographic progression P2 among the machine learning models tested with a mean accuracy and area under the curves of 73.73% and 0.79, respectively. Among the T1 variables, the most important variables for predicting radiographic progression were in the order of total mSASSS, age, and alkaline phosphatase.

Conclusion

Prognosis predictive models using time-series data showed reasonable performance with clinical features of the first visit dataset when predicting radiographic progression.

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature

The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.

Natural language processing in low back pain and spine diseases: A systematic review

Natural Language Processing (NLP) is a discipline at the intersection between Computer Science (CS), Artificial Intelligence (AI), and Linguistics that leverages unstructured human-interpretable (natural) language text. In recent years, it gained momentum also in health-related applications and research. Although preliminary, studies concerning Low Back Pain (LBP) and other related spine disorders with relevant applications of NLP methodologies have been reported in the literature over the last few years. It motivated us to systematically review the literature comprised of two major public databases, PubMed and Scopus. To do so, we first formulated our research question following the PICO guidelines. Then, we followed a PRISMA-like protocol by performing a search query including terminologies of both technical (e.g., natural language and computational linguistics) and clinical (e.g., lumbar and spine surgery) domains. We collected 221 non-duplicated studies, 16 of which were eligible for our analysis. In this work, we present these studies divided into sub-categories, from both tasks and exploited models’ points of view. Furthermore, we report a detailed description of techniques used to extract and process textual features and the several evaluation metrics used to assess the performance of the NLP models. However, what is clear from our analysis is that additional studies on larger datasets are needed to better define the role of NLP in the care of patients with spinal disorders.

Identifying Patients With Axial Spondyloarthritis in Large Datasets: Expanding Possibilities for Observational Research

OBJECTIVE

Observational research of axial spondyloarthritis (axSpA) is limited by a lack of methods for identifying diverse axSpA phenotypes in large datasets. Algorithms were previously designed to identify a broad spectrum of patients with axSpA, including patients not identifiable with diagnosis codes. The study objective was to estimate the performance of axSpA identification methods in the general Veterans Affairs (VA) population.

METHODS

A patient sample with known axSpA status (n = 300) was established with chart review. For feasibility, this sample was enriched with veterans with axSpA risk factors. Algorithm performance outcomes included sensitivities, positive predictive values (PPV), and F1 scores (an overall performance metric combining sensitivity and PPV). Performance was estimated with unweighted outcomes for the axSpA-enriched sample and inverse probability weighted (IPW) outcomes for the general VA population. These outcomes were also assessed for traditional identification methods using diagnosis codes for the ankylosing spondylitis (AS) subtype of axSpA.

RESULTS

The mean age was 54.7 and 92% were male. Unweighted F1 scores (0.59-0.74) were higher than IPW F1 scores (0.48-0.65). The full algorithm had the best overall performance (F1_IPW 0.65). The Early Algorithm was the most inclusive (sensitivity_IPW 0.90, PPV_IPW 0.38). The traditional method using ≥ 2 AS diagnosis codes from rheumatology had the highest PPV (PPV_IPW 0.84, sensitivity_IPW 0.34).

CONCLUSION

The axSpA identification methods demonstrated a range of performance attributes in the general VA population that may be appropriate for various types of studies. The novel identification algorithms may expand the scope of research by enabling identification of more diverse axSpA populations.