Dataset Information

Machine Learning Applied to Patient-Reported Outcomes to Classify Physician-Derived Measures of Rheumatoid Arthritis Disease Activity.

ABSTRACT:

Objective

Patient-reported outcome (PRO) data have assumed increasing importance in the care of patients with rheumatoid arthritis (RA), yet physician-derived disease activity measures, such as Clinical Disease Activity Index (CDAI), remain the most accepted metrics to assess disease activity. The possibility that newer longitudinal PRO data might be used as a proxy for the CDAI has not been evaluated.

Methods

Using data from a large pragmatic trial, we evaluated patients with RA initiating golimumab intravenous or infliximab. The classification target was low disease activity (LDA) (CDAI ≤10) at the first visit between months 3 and 12. Data were randomly partitioned into training (80%) and test (20%) data sets. Multiple machine learning (ML) methods (eg, random forests, gradient boosting, support vector machines) were used to classify CDAI disease activity category, conduct feature selection, and assess feature importance. Model performance evaluated cross-validated error, comparing different ML approaches using both training and test data.

Results

A total of 494 patients were analyzed, and 36.4% achieved LDA. The most important classification features included several Patient-Reported Outcomes Measurement Information System measures (social participation, pain interference, pain intensity, and physical function), patient global, and baseline CDAI. Among all ML methods, random forests performed best. Overall model accuracy and positive predictive values for all ML methods were approximately 80%.

Conclusion

ML methods coupled with longitudinal PRO data appear useful and can achieve reasonable accuracy in classifying LDA among patients starting a new biologic. This approach has promise for real-world evidence generation in the common circumstance when physician-derived disease activity data are not available yet PRO measures are.

SUBMITTER: Curtis JR

PROVIDER: S-EPMC9746663 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Machine Learning Applied to Patient-Reported Outcomes to Classify Physician-Derived Measures of Rheumatoid Arthritis Disease Activity.

Curtis Jeffrey R JR Su Yujie Y Black Shawn S Xu Stephen S Langholff Wayne W Bingham Clifton O CO Kafka Shelly S Xie Fenglong F

ACR open rheumatology 20221011 12

<h4>Objective</h4>Patient-reported outcome (PRO) data have assumed increasing importance in the care of patients with rheumatoid arthritis (RA), yet physician-derived disease activity measures, such as Clinical Disease Activity Index (CDAI), remain the most accepted metrics to assess disease activity. The possibility that newer longitudinal PRO data might be used as a proxy for the CDAI has not been evaluated.<h4>Methods</h4>Using data from a large pragmatic trial, we evaluated patients with RA ...[more]

PMID: 36220128

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Project description:BACKGROUND:There is significant promise in analyzing physician patient-sharing networks to indirectly measure care coordination, yet it is unknown whether these measures reflect patients' perceptions of care coordination. OBJECTIVE:To evaluate the associations between network-based measures of care coordination and patient-reported experience measures. DESIGN:We analyzed patient-sharing physician networks within group practices using data made available by the Centers for Medicare and Medicaid Services. SUBJECTS:Medicare beneficiaries who provided responses to the Consumer Assessment of Healthcare Providers and Systems (CAHPS) Survey in 2016 (data aggregated by physician group practice made available through the Physician Compare 2016 Group Public Reporting). MAIN MEASURES:The outcomes of interest were patient-reported experience measures reflecting aspects of care coordination (CAHPS). The predictor variables of interests were physician group practice density (the number of physician pairs who share patients adjusting for the total number of physician pairs) and clustering (the extent to which sets of three physicians share patients). KEY RESULTS:Four hundred seventy-six groups had patient-reported measures available. Patients' perception of "Clinicians working together for your care" was significantly positively associated with both physician group practice density (Est (95 % CI) = 5.07(0.83, 9.33), p = 0.02) and clustering (Est (95 % CI) = 3.73(1.01, 6.44), p = 0.007). Physician group practice clustering was also significantly positively associated with "Getting timely care, appointments, and information" (Est (95 % CI) = 4.63(0.21, 9.06), p = 0.04). CONCLUSIONS:This work suggests that network-based measures of care coordination are associated with some patient-reported experience measures. Evaluating and intervening on patient-sharing networks may provide novel strategies for initiatives aimed at improving quality of care and the patient experience.

Dataset Information

Machine Learning Applied to Patient-Reported Outcomes to Classify Physician-Derived Measures of Rheumatoid Arthritis Disease Activity.

Objective

Methods

Results

Conclusion

Publications

Machine Learning Applied to Patient-Reported Outcomes to Classify Physician-Derived Measures of Rheumatoid Arthritis Disease Activity.

Similar Datasets

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