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Differences in Longitudinal Disease Activity Between Research Cohort and Noncohort Participants with Rheumatoid Arthritis Using Electronic Health Record Data.

ABSTRACT: Objective:Research using electronic health records (EHRs) may offer advantages over observational prospective cohort studies, including lower costs and a more generalizable patient population; however, EHR data may be more biased because of the high prevalence of missing data. We took advantage of a unique clinical setting in which all patients with rheumatoid arthritis (RA) were asked to participate in a longitudinal cohort study that would examine potential biases of EHR vs. prospective cohort designs in assessment of disease outcomes, but only some chose to participate. Methods:For individuals both participating in the cohort ("cohort," n = 187) and not participating ("noncohort," n = 190), we retrieved data regarding RA disease activity and other sociodemographic and clinical factors from data recorded in the EHR between 2013 and 2017. We compared the prevalence of missing data between groups and studied differences in disease activity measures over time. Results:Disease activity measures were less likely to be missing for cohort participants compared with noncohort participants (0.2%-13% vs. 2%-22%, respectively). No significant differences were present at baseline with respect to race/ethnicity or disease activity measures between groups. However, black, non-Hispanic race/ethnicity was associated with worse longitudinal disease activity compared with white, non-Hispanic individuals in noncohort participants (β = 6.47, P =0.03) but not in cohort participants (β = -0.10, P = 0.97) (P interaction = 0.09). Conclusion:Findings suggest that data derived from the EHR were comparable to a cohort across some variables but captured racial/ethnic disparities in long-term outcomes not observed in the cohort study. Research utilizing EHR data in conjunction with cohort studies may provide new opportunities for studying health disparities.

SUBMITTER: Gianfrancesco MA

PROVIDER: S-EPMC6857989 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportanceKnowing the future condition of a patient would enable a physician to customize current therapeutic options to prevent disease worsening, but predicting that future condition requires sophisticated modeling and information. If artificial intelligence models were capable of forecasting future patient outcomes, they could be used to aid practitioners and patients in prognosticating outcomes or simulating potential outcomes under different treatment scenarios.ObjectiveTo assess the ability of an artificial intelligence system to prognosticate the state of disease activity of patients with rheumatoid arthritis (RA) at their next clinical visit.Design, setting, and participantsThis prognostic study included 820 patients with RA from rheumatology clinics at 2 distinct health care systems with different electronic health record platforms: a university hospital (UH) and a public safety-net hospital (SNH). The UH and SNH had substantially different patient populations and treatment patterns. The UH has records on approximately 1 million total patients starting in January 2012. The UH data for this study were accessed on July 1, 2017. The SNH has records on 65?000 unique individuals starting in January 2013. The SNH data for the study were collected on February 27, 2018.ExposuresStructured data were extracted from the electronic health record, including exposures (medications), patient demographics, laboratories, and prior measures of disease activity. A longitudinal deep learning model was used to predict disease activity for patients with RA at their next rheumatology clinic visit and to evaluate interhospital performance and model interoperability strategies.Main outcomes and measuresModel performance was quantified using the area under the receiver operating characteristic curve (AUROC). Disease activity in RA was measured using a composite index score.ResultsA total of 578 UH patients (mean [SD] age, 57 [15] years; 477 [82.5%] female; 296 [51.2%] white) and 242 SNH patients (mean [SD] age, 60 [15] years; 195 [80.6%] female; 30 [12.4%] white) were included in the study. Patients at the UH compared with those at the SNH were seen more frequently (median time between visits, 100 vs 180 days) and were more frequently prescribed higher-class medications (biologics) (364 [63.0%] vs 70 [28.9%]). At the UH, the model reached an AUROC of 0.91 (95% CI, 0.86-0.96) in a test cohort of 116 patients. The UH-trained model had an AUROC of 0.74 (95% CI, 0.65-0.83) in the SNH test cohort (n?=?117) despite marked differences in the patient populations. In both settings, baseline prediction using each patients' most recent disease activity score had statistically random performance.Conclusions and relevanceThe findings suggest that building accurate models to forecast complex disease outcomes using electronic health record data is possible and these models can be shared across hospitals with diverse patient populations.

Project description:ImportanceAssessing generalizability of clinical trials is important to ensure appropriate application of interventions, but most assessments provide minimal granularity on comparisons of clinical characteristics.ObjectiveTo assess the extent of underlying clinical differences between clinical trial participants and nonparticipants by using a combination of electronic health record and trial enrollment data.Design, setting, and participantsThis cross-sectional study used data obtained from a single academic medical center between September 1996 and January 2019 to identify 1645 clinical trial participants from a diverse set of 202 available trials conducted at the center. Using an aggregated resampling procedure, nonparticipants were matched to participants 1:1 based on trial conditions, number of recent visits to a health care professional, and calendar time.ExposuresClinical trial enrollment vs no enrollment.Main outcomes and measuresThe primary outcome was standardized differences in clinical characteristics between participants and nonparticipants in clinical trials stratified into the 4 most common disease domains.ResultsThis cross-sectional study included 1645 participants from 202 trials (929 [56.5%] male; mean [SD] age, 54.65 [21.38] years) and an aggregated set of 1645 nonparticipants (855 [52.0%] male; mean [SD] age, 57.24 [21.91] years). The most common disease domains for the selected trials were neoplastic disease (86 trials; 737 participants), disorders of the digestive system (31 trials; 321 participants), inflammatory disorders (28 trials; 276 participants), and disorders of the cardiovascular system (27 trials; 319 participants); trials could qualify for multiple disease domains. Among 31 conditions, the percentage of conditions for which the prevalence was lower among participants than among nonparticipants per standardized differences was 64.5% (20 conditions) for neoplastic disease trials, 61.3% (19) for digestive system trials, 58.1% (18) for inflammatory disorder trials, and 38.7% (12) for cardiovascular system trials. Among 17 medications, the percentage of medications for which use was less among participants than among nonparticipants per standardized differences was 64.7% (11) for neoplastic disease trials, 58.8% (10) for digestive system trials, 88.2% (15) for inflammatory disorder trials, and 52.9% (9) for cardiovascular system trials.Conclusions and relevanceUsing a combination of electronic health record and trial enrollment data, this study found that clinical trial participants had fewer comorbidities and less use of medication than nonparticipants across a variety of disease domains. Combining trial enrollment data with electronic health record data may be useful for better understanding of the generalizability of trial results.

Project description:BackgroundFinancial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries.ObjectiveThe aim of this study was to develop an easily implementable workflow that builds a machine learning algorithm capable of accurately identifying patients with rheumatoid arthritis from format-free text fields in electronic health records.MethodsTwo electronic health record data sets were employed: Leiden (n=3000) and Erlangen (n=4771). Using a portion of the Leiden data (n=2000), we compared 6 different machine learning methods and a naïve word-matching algorithm using 10-fold cross-validation. Performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC), and F1 score was used as the primary criterion for selecting the best method to build a classifying algorithm. We selected the optimal threshold of positive predictive value for case identification based on the output of the best method in the training data. This validation workflow was subsequently applied to a portion of the Erlangen data (n=4293). For testing, the best performing methods were applied to remaining data (Leiden n=1000; Erlangen n=478) for an unbiased evaluation.ResultsFor the Leiden data set, the word-matching algorithm demonstrated mixed performance (AUROC 0.90; AUPRC 0.33; F1 score 0.55), and 4 methods significantly outperformed word-matching, with support vector machines performing best (AUROC 0.98; AUPRC 0.88; F1 score 0.83). Applying this support vector machine classifier to the test data resulted in a similarly high performance (F1 score 0.81; positive predictive value [PPV] 0.94), and with this method, we could identify 2873 patients with rheumatoid arthritis in less than 7 seconds out of the complete collection of 23,300 patients in the Leiden electronic health record system. For the Erlangen data set, gradient boosting performed best (AUROC 0.94; AUPRC 0.85; F1 score 0.82) in the training set, and applied to the test data, resulted once again in good results (F1 score 0.67; PPV 0.97).ConclusionsWe demonstrate that machine learning methods can extract the records of patients with rheumatoid arthritis from electronic health record data with high precision, allowing research on very large populations for limited costs. Our approach is language and center independent and could be applied to any type of diagnosis. We have developed our pipeline into a universally applicable and easy-to-implement workflow to equip centers with their own high-performing algorithm. This allows the creation of observational studies of unprecedented size covering different countries for low cost from already available data in electronic health record systems.

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Differences in Longitudinal Disease Activity Between Research Cohort and Noncohort Participants with Rheumatoid Arthritis Using Electronic Health Record Data.

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