B0393 - Inflammatory Bowel Disease Population Analyzer Tool for Health Systems (IBD PATH): A Case Study Risk Stratifying IBD Patients for Clinical Outcomes

Monday, October 24, 2022

10:00 AM – 12:00 PM ET

Location: Crown Ballroom

Has Audio

Presenting Author(s)

AP

Aarti A. Patel, PharmD, MBA

Janssen Scientific Affairs, LLC
Titusville, NJ

Edmond K. Kabagambe, PhD, MS, MBA¹, Kathleen L. Deering, PharmD², Aarti A. Patel, PharmD, MBA³, Joseph A. Capelouto, MHA¹, Ardem Elmayan, MPH¹, Sheena Babin, PharmD¹, Shamita Shah, MD¹
¹Ochsner Health, New Orleans, LA; ²EPI-Q, Inc, Oak Brook, IL; ³Janssen Scientific Affairs, LLC, Titusville, NJ

Introduction: Inflammatory bowel disease (IBD) is associated with substantial economic burden and it’s increasing prevalence will only exacerbate the costs. Population health management (PHM) strategies like risk stratification are needed to increase quality of care and improve health outcomes while reducing costs. Risk stratiﬁcation is also integral for IBD patient management according to care pathways/guidelines. Previous research suggests risk stratification using American Gastroenterological Association (AGA) care pathways was infrequently documented. IBD PATH was developed to identify patient risk and potential gaps in care using electronic medical record (EMR) data. We used IBD PATH to conduct a real-world case study to identify data standardization gaps and facilitate PHM efforts within Ochsner Health (Ochsner).

Methods: Ochsner data included patients with IBD visits between Jan. 2020 and Dec. 2021 and data variables; medication name and ordered date, visit dates and associated diagnosis and procedure codes. Using a standardized template included in IBD PATH, a subset of patients had additional unstructured clinical data from EMR converted into structured data. These variables are based on AGA care pathway risk factors for Crohn’s disease (CD) and ulcerative colitis (UC) complications. The EMR dataset, formatted per the tool specifications, was uploaded into the tool. Descriptive analyses were performed through IBD PATH.

Results: AGA risk was not documented in the EMR. Unstructured risk factors were collected for a total of 164 patients (124 CD, 71 UC), Table 1. Majority of cases (82% CD, 77% UC) were classified as moderate(mod)/high risk and the primary risk factors perianal disease for CD and previous steroid-requiring disease for UC. While 30% of mod/high risk patients had a biologic medication record, nearly 50% did not have a IBD treatment record. Evidence of steroid and narcotic analgesic use was high in those with mod/high risk. Mod/high risk patients were also more likely to have had IBD-related hospitalizations, emergency department visits, and procedures in the follow-up period.

Discussion: Risk stratiﬁcation of patients with IBD is not explicitly documented in the EMR. Ochsner population is nearly 36% Black, who have been found to have more perianal disease. Tools such as IBD PATH can inform PHM of patients diagnosed with IBD, facilitating the identification of potential population level gaps in care for further assessment. Results are dependent on the completeness of data uploaded.

Figure: Figure 1. Patient-Level Report

	Crohn’s Disease			Ulcerative Colitis
	Total N=124	Moderate/ High Risk N=102	Low Risk N=22	Total N=71	Moderate/ High Risk N=55	Low Risk N=16
AGA Risk Level Characteristics, n (%)
Crohn’s Disease Risk Factors
Age < 30 years at diagnosis	22 (18)	22 (22)	---
Extensive anatomic involvement	7 (6)	7 (7)	---
Perianal disease	83 (67)	83 (81)	---
Severe rectal disease	32 (26)	32 (31)	---
Deep ulcers	9 (7)	9 (9)	---
Previous surgical resection	0 (0)	0 (0)	---
Stricturing behavior	17 (14)	17 (17)	---
Penetrating behavior	24 (19)	24 (24)	---
Ulcerative Colitis Disease Risk Factors
Age < 40 years				16 (23)	16 (29)	---
Extensive colitis				33 (46)	33 (60)	---
Steroid-requiring disease				43 (61)	43 (78)	---
Deep ulcers				2 (3)	2 (4)	---
History of hospitalization				4 (6)	4 (7)	---
High CRP and ESR				0 (0)	0 (0)	---
Clostridium difficile infection				4 (6)	4 (7)	---
Cytomegalovirus infection				1 (1)	1 (2)	---
Medication Utilization, n (%)
Biologics	38 (31)	35 (92)	3 (8)	16 (23)	16 (100)	7 (13)
Adalimumab	12 (32)	10 (83)	2 (17)	5 (31)	5 (100)	0 (0)
Immunomodulators	1 (8)	0 (0)	1 (100)	1 (20)	1 (100)	0 (0)
Steroids	2 (17)	2 (100)	0 (0)	0 (0)	0 (0)	0 (0)
Certolizumab pegol	0 (0)	0 (0)	0 (0)	1 (6)	1 (100)	0 (0)
Immunomodulators	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Steroids	0 (0)	0 (0)	0 (0)	1 (100)	1 (100)	0 (0)
Golimumab				1 (6)	1 (100)	0 (0)
Immunomodulators				0 (0)	0 (0)	0 (0)
Steroids				0 (0)	0 (0)	0 (0)
Infliximab	14 (56)	14 (100)	0 (0)	2 (13)	2 (100)	0 (0)
Immunomodulators	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Steroids	2 (14)	2 (100)	0 (0)	0 (0)	0 (0)	0 (0)
Vedolizumab	10 (26)	9 (90)	1 (10)	5 (31)	5 (100)	0 (0)
Immunomodulators	1 (10)	0 (0)	1 (100)	1 (20)	1 (100)	0 (0)
Steroids	1 (10)	1 (100)	0 (0)	1 (20)	1 (100)	0 (0)
Ustekinumab	3 (8)	3 (100)	0 (0)	3 (19)	3 (100)	0 (0)
Immunomodulators	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Steroids	1 (33)	1 (100)	0 (0)	1 (33)	1 (100)	0 (0)
Immunomodulators	5 (4)	3 (60)	2 (40)	4 (6)	3 (75)	1 (25)
Azathioprine	5 (100)	3 (60)	2 (40)	4 (100)	3 (75)	1 (25)
5-Aminosalicylic acids				12 (17)	9 (75)	3 (25)
Mesalamine				10 (83)	7 (70)	3 (30)
Sulfasalazine				2 (17)	2 (100)	0 (0)
Steroids	16 (13)	15 (94)	1 (6)	9 (13)	9 (100)	0 (0)
Quality Indicators, n (%)
IBD-related Hospitalizations	25 (20)	22 (88)	3 (12)	8 (11)	7 (88)	1 (13)
IBD-related Emergency Department visits	55 (44)	47 (85)	8 (15)	21 (30)	17 (81)	4 (19)
IBD-related Surgeries	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
IBD-related Procedures	50 (40)	43 (86)	7 (14)	23 (32)	20 (87)	3 (13)
No evidence of steroid therapy	108 (87)	87 (81)	21 (19)	62 (87)	46 (74)	16 (26)
Evidence of steroid therapy	16 (13)	15 (94)	1 (6)	9 (13)	9 (100)	0 (0)
Evidence of psychosocial screening	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Evidence of narcotic analgesic use	107 (86)	94 (88)	13 (12)	48 (68)	35 (77)	11 (23)

Table: Table 1: IBD Risk Level Characteristics, Medication Use, and Quality Indicators

Disclosures:

Edmond Kabagambe: Janssen Scientific Affairs, LLC – Grant/Research Support. Pfizer – Grant/Research Support.

Kathleen Deering: Janssen Scientific Affairs, LLC – Grant/Research Support.

Aarti Patel: Janssen Scientific Affairs, LLC – Employee, Stock-publicly held company(excluding mutual/index funds).

Joseph Capelouto: Janssen Scientific Affairs, LLC – Grant/Research Support.

Ardem Elmayan: Janssen Scientific Affairs, LLC – Grant/Research Support.

Sheena Babin: Janssen Scientific Affairs, LLC – Grant/Research Support, Speakers Bureau. Pharmacyclics – Speakers Bureau.

Shamita Shah: Janssen Scientific Affairs, LLC – Grant/Research Support.

Edmond K. Kabagambe, PhD, MS, MBA¹, Kathleen L. Deering, PharmD², Aarti A. Patel, PharmD, MBA³, Joseph A. Capelouto, MHA¹, Ardem Elmayan, MPH¹, Sheena Babin, PharmD¹, Shamita Shah, MD¹. B0393 - Inflammatory Bowel Disease Population Analyzer Tool for Health Systems (IBD PATH): A Case Study Risk Stratifying IBD Patients for Clinical Outcomes, ACG 2022 Annual Scientific Meeting Abstracts. Charlotte, NC: American College of Gastroenterology.