Fitness-for-use of Retrospective Multicenter Electronic Health Records to Conduct Outcome Analysis for Pediatric Ulcerative Colitis

The use of electronic health records has garnered interest as an approach for conducting innovative outcome research and producing real-world evidence at a reduced cost compared to traditional clinical trials. The study aimed to evaluate the utility of deidentified EHR data from a multicenter research network to identify characteristics associated with treatment escalation (TE) in newly diagnosed pediatric ulcerative colitis patients. EHR data (01/2010-12/2021) from 13 Midwest healthcare systems (Greater Plains Collaborative) were collected for pediatric ulcerative colitis patients. We identified standard treatments, excluded missing initial therapy data, and analyzed the TE and time-to-TE outcomes. The clinical and laboratory characteristics at baseline were extracted. Logistic and Cox models were used, and the missing risk factors were imputed. Machine-learning Bayesian additive regression trees were also utilized to create partial dependence plots for assessing the associations between risk factors and clinical outcomes. A total of 502 eligible pediatric patients (aged 4-17 years) who initiated standard treatment were identified. Among them, 205 out of 502 (41%) experienced TE, with a median (P25, P75) duration of 63 (9, 237) days after the initial treatment. Additionally, 20 out of 509 (4%) patients underwent colectomy (COL) with a median (P25, P75) duration of 80 (3, 205) days. Both multivariable logistic regression and Cox proportional hazards regression demonstrated moderate discriminative power in predicting TE and time-to-TE, respectively. Common positive predictors for both TE and time-to-TE included a high monocyte proportion and elevated platelet counts. Conversely, BMI z-score, albumin, hemoglobin levels, and lymphocyte proportion were negatively associated with both TE and time-to-TE. This study demonstrates that multicenter EHR data can be used to identify a trial-comparable study sample of potentially larger size and to identify clinically meaningful endpoints for conducting outcome analysis and generating real-world evidence.