Development and evaluation of an osteoarthritis risk model for integration into primary care health information technology

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients' lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71-0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-β signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis.

Implementing artificial intelligence in Canadian primary care: Barriers and strategies identified through a national deliberative dialogue

BACKGROUND

With large volumes of longitudinal data in electronic medical records from diverse patients, primary care is primed for disruption by artificial intelligence (AI) technology. With AI applications in primary care still at an early stage in Canada and most countries, there is a unique opportunity to engage key stakeholders in exploring how AI would be used and what implementation would look like.

OBJECTIVE

To identify the barriers that patients, providers, and health leaders perceive in relation to implementing AI in primary care and strategies to overcome them.

DESIGN

12 virtual deliberative dialogues. Dialogue data were thematically analyzed using a combination of rapid ethnographic assessment and interpretive description techniques.

SETTING

Virtual sessions.

PARTICIPANTS

Participants from eight provinces in Canada, including 22 primary care service users, 21 interprofessional providers, and 5 health system leaders.

RESULTS

The barriers that emerged from the deliberative dialogue sessions were grouped into four themes: (1) system and data readiness, (2) the potential for bias and inequity, (3) the regulation of AI and big data, and (4) the importance of people as technology enablers. Strategies to overcome the barriers in each of these themes were highlighted, where participatory co-design and iterative implementation were voiced most strongly by participants.

LIMITATIONS

Only five health system leaders were included in the study and no self-identifying Indigenous people. This is a limitation as both groups may have provided unique perspectives to the study objective.

CONCLUSIONS

These findings provide insight into the barriers and facilitators associated with implementing AI in primary care settings from different perspectives. This will be vital as decisions regarding the future of AI in this space is shaped.

Prediction Models to Estimate the Future Risk of Osteoarthritis in the General Population: A Systematic Review

OBJECTIVE

To evaluate the performance and applicability of multivariable prediction models for osteoarthritis (OA).

METHODS

This was a systematic review and narrative synthesis using 3 databases (EMBASE, PubMed, and Web of Science) from inception to December 2021. We included general population longitudinal studies reporting derivation, comparison, or validation of multivariable models to predict individual risk of OA incidence, defined by recognized clinical or imaging criteria. We excluded studies reporting prevalent OA and joint arthroplasty outcome. Paired reviewers independently performed article selection, data extraction, and risk-of-bias assessment. Model performance, calibration, and retained predictors were summarized.

RESULTS

A total of 26 studies were included, reporting 31 final multivariable prediction models for incident knee (23), hip (4), hand (3) and any-site OA (1), with a median of 121.5 (range 27-12,803) outcome events, a median prediction horizon of 8 years (range 2-41), and a median of 6 predictors (range 3-24). Age, body mass index, previous injury, and occupational exposures were among the most commonly included predictors. Model discrimination after validation was generally acceptable to excellent (area under the curve = 0.70-0.85). Either internal or external validation processes were used in most models, although the risk of bias was often judged to be high with limited applicability to mass application in diverse populations.

CONCLUSION

Despite growing interest in multivariable prediction models for incident OA, focus remains predominantly on the knee, with reliance on data from a small pool of appropriate cohort data sets, and concerns over general population applicability.

Osteoarthritis year in review 2021: epidemiology & therapy

This "Year in review" presents a selection of research themes and individual studies from the clinical osteoarthritis (OA) field (epidemiology and therapy) and includes noteworthy descriptive, analytical-observational, and intervention studies. The electronic database search for the review was conducted in Medline, Embase and medRxiv (15th April 2020 to 1st April 2021). Following study screening, the following OA-related themes emerged: COVID-19; disease burden; occupational risk; prediction models; cartilage loss and pain; stem cell treatments; novel pharmacotherapy trials; therapy for less well researched OA phenotypes; benefits and challenges of Individual Participant Data (IPD) meta-analyses; patient choice-balancing benefits and harms; OA and comorbidity; and inequalities in OA. Headline study findings included: a longitudinal cohort study demonstrating no evidence for a harmful effect of non-steroidal anti-inflammatory drugs (NSAIDs) in terms of COVID-19 related deaths; a Global Burden of Disease study reporting a 102% increase in crude incidence rate of OA in 2017 compared to 1990; a longitudinal study reporting cartilage thickness loss was associated with only a very small degree of worsening in pain over 2 years; an exploratory analysis of a non-OA randomised controlled trial (RCT) finding reduced risk of total joint replacement with an Interleukin -1β inhibitor (canakinumab); a significant relationship between cumulative disadvantage and clinical outcomes of pain and depression mediated by perceived discrimination in a secondary analysis from a RCT; worsening socioeconomic circumstances were associated with future arthritis diagnosis in an innovative natural experiment (with implications for unique research possibilities arising from the COVID-19 pandemic context).

Identification of most important features based on a fuzzy ensemble technique: Evaluation on joint space narrowing progression in knee osteoarthritis patients

OBJECTIVE

Feature selection (FS) is a crucial and at the same time challenging processing step that aims to reduce the dimensionality of complex classification or regression problems. Various techniques have been proposed in the literature to address this challenge with emphasis to medical applications. However, each one of the existing FS algorithms come with its own advantages and disadvantages introducing a certain level of bias.

MATERIALS AND METHODS

To avoid bias and alleviate the defectiveness of single feature selection results, an ensemble FS methodology is proposed in this paper that aggregates the results of several FS algorithms (filter, wrapper and embedded ones). Fuzzy logic is employed to combine multiple feature importance scores thus leading to a more robust selection of informative features. The proposed fuzzy ensemble FS methodology was applied on the problem of knee osteoarthritis (KOA) prediction with special emphasis on the progression of joint space narrowing (JSN). The proposed FS methodology was integrated into an end-to-end machine learning pipeline and a thorough experimental evaluation was conducted using data from the Osteoarthritis Initiative (OAI) database. Several classifiers were investigated for their suitability in the task of JSN prediction and the best performing model was then post-hoc analyzed by using the SHAP method.

RESULTS

The results showed that the proposed method presented a better and more stable performance in contrast to other competitive feature selection methods, leading to an average accuracy of 78.14% using XG Boost at 31 selected features. The post-hoc explainability highlighted the important features that contribute to the classification of patients with JSN progression.

CONCLUSIONS

The proposed fuzzy feature selection approach improves the performance of the predictive models by selecting a small optimal subset of features compared to popular feature selection methods.