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This research aims to develop an electronic medical record-based digital phenotype for identifying immune-related colitis in cancer patients receiving immune checkpoint inhibitors.

June 18, 2026

Development of a Digital Phenotype for Immune-Related Colitis

Key Points

This research aims to develop an electronic medical record-based digital phenotype for identifying immune-related colitis in cancer patients receiving immune checkpoint inhibitors.
Adult patients on immune checkpoint inhibitors from August 1, 2020, to June 30, 2023, were studied.
Deidentified EMR data were extracted to develop a rule-based digital phenotype.
Manual chart review was used as the gold standard for validation.
Among 1,464 adults treated with immune checkpoint inhibitors, 147 had confirmed immune-related colitis.
The digital phenotype showed 80% sensitivity and 97% specificity.
The overall accuracy of the digital phenotype was 89% with a positive predictive value of 74%.

Abstract

PURPOSE Immune-related colitis (IR-colitis) is a significant side effect of immune checkpoint inhibitor (ICI) therapy for an expanding group of cancers. Timely identification is required to optimize outcomes, but existing approaches to retrospective case detection are resource intensive. We sought to develop an electronic medical record (EMR)-based digital phenotype to identify patients with IR-colitis. METHODS Adult patients commenced on ICIs from August 1, 2020, to June 30, 2023, at the Peter MacCallum Cancer Centre were studied. ICIs were prescribed as monotherapy or in combination with other treatment modalities for a variety of solid cancers. Deidentified EMR data were extracted. A rule-based digital phenotype was developed using expert-selected features, with manual chart review serving as the gold standard comparator. RESULTS The ICI-treated cohort comprised 1,464 adults, with a male predominance (62%). The most frequent cancer diagnoses were melanoma (28.5%) and non–small cell lung cancer (20.5%). Administered ICIs included nivolumab (41.4%) and ipilimumab (23.2%). On manual chart review, 147 (10.0%), 1,287 (87.9%), and 30 (2%) patients were defined as IR-colitis cases, controls, and equivocal cases, respectively. For the best-performing rule-based digital phenotype, sensitivity was 0.80 (95% CI, 0.73 to 0.86), specificity was 0.97 (95% CI, 0.96 to 0.98), positive predictive value was 0.74 (95% CI, 0.66 to 0.80), negative predictive value was 0.98 (95% CI, 0.97 to 0.98), accuracy was 0.89 (95% CI, 0.85 to 0.92), positive likelihood ratio (LR) was 24.6 (95% CI, 18.08 to 33.47), and negative LR was 0.2 (95% CI, 0.15 to 0.28). The F1 score was 0.77. CONCLUSION We successfully developed an accurate digital phenotype for IR-colitis using real-world EMR data. It allows rapid and accurate cohort identification, which may be adapted to support retrospective immune-related adverse event surveillance and translational biomarker research in the real world.

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