Whitepaper Title

Background

Although 3,000,000 Americans are afflicted with Inflammatory Bowel Disease (IBD) encompassing Crohn’s Disease (CD) and Ulcerative Colitis (UC)1, timely diagnosis is a challenge due to non-specific and overlapping symptoms.2 More than 20% of patients may be initially misdiagnosed3causing delayed diagnosis and treatment, potentially leading to increased risk of complications2and irreversible mucosal damage.4 Artificial intelligence (AI) models can alert physicians to patients who would otherwise be misdiagnosed, potentially improving patient outcomes and reducing costs. We aimed to develop PredictAI, a proprietary AI Gradient Boosted Decision Tree based machine learning algorithm and test its accuracy in identifying undiagnosed CD and UC in the primary care setting.

Methods

This was a retrospective study of 2,471,267 patients’ electronic medical records (EMR) from Maccabi Healthcare Services in Israel. Sufficient data was available between the years 2010- 2020, of which 2 consecutive years (2015-2016) were pre-assigned to the test set. Inclusion criteria were: (i) CD or UC ICD code as defined by Maccabi’s Registry,5(ii) no other autoimmune disease diagnosis, (iii) at least 4 years of data antedating first suspicion by primary care physician (PCP) of IBD were available. First suspicion was defined as any diagnostic test, procedure, or referral to a specialist, indicating suspicion of IBD. Here we included adult data only.

Results

Of 2,471,267 patients, 1,214 had a first-time diagnosis of IBD and available antedating data in the years 2015-2016. Of these, PredictAI identified 126, 120, 104, 93 patients 1, 2, 3 and 4 years prior to initial PCP suspicion, respectively. For CD, it predicted 83/229 (36%) of patients 1 year prior to PCP initial suspicion of disease, 70/211 (33%) patients 2 years prior,
60/179 (33%) patients 3 years prior and 56/148 (38%) patients 4 years prior. Discriminatory accuracy area under the curve (AUC) was 76%, 75%, 75% and 78%, 1, 2, 3 and 4 years before initial PCP suspicion, respectively (figure 1). Corresponding early-identification ratio and discriminatory accuracy for UC was 42/238 (17%, AUC=71%), 50/225 (22%, AUC=70%), 44/187 (23%, AUC=72%) 37/161 (23%, AUC=74%), for 1,2,3 and 4 years before initial PCP suspicion of UC, respectively (figure 2). Specificity for CD and UC each was above 90%.

125/467
120/436 27.5%
104/366 28.4%
93/309 30.1%

Conclusions

PredictAI accurately identified CD and UC diagnosis in 17-38% of patients presenting to primary care up to 4 years prior to PCP’s initial suspicion, potentially reducing time to diagnosis.

 

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