P2827 - Computable Phenotypes for NAFLD and NASH Identify Patients with Significant Comorbidities yet Most Remain Undiagnosed

Tuesday, October 26, 2021

10:30 AM – 4:00 PM PT

Location: Shoreline Exhibit Hall

Has Audio

Presenting Author(s)

SC

Sunny Chung, MD

Duke University Medical Center
Durham, NC, United States

Sunny Chung, MD¹, Matthew Phelan, MS², Benjamin Goldstein, PhD³, Kara Wegermann, MD¹, Joseph Wawrzynski, MD¹, Tzu-Hao Lee, MD¹, Jacqueline B. Henson, MD³, Melanie N. Cabezas, MD³, Phillip Ambery, MD⁴, Neha Pagidipati, MD³, Cynthia Moylan, MD, MS¹
¹Duke University Medical Center, Durham, NC; ²Duke Clinical Research Institute, Durham, NC; ³Duke University, Durham, NC; ⁴AstraZeneca Pharmaceuticals, LP, Cambridge, England, United Kingdom

Introduction: Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease, yet remains underdiagnosed in real-world practice. Several clinical tools exist to identify possible NAFLD and nonalcoholic steatohepatitis (NASH). Incorporating these into computable phenotypes may help identify patients using electronic health records (EHR), improve clinical care and long-term outcomes. Our objective was to create and evaluate computable phenotypes for NAFLD and NASH using previously validated scores.

Methods: We conducted a retrospective study of adults using the Duke University Health System (DUHS) between 2014 and 2019. Patients (pts) with a body mass index < 25kg/m² and liver disease diagnosis other than NAFLD or NASH were excluded. The first cohort (NAFLD1) included pts with alanine aminotransferase (ALT) > 40 for men and > 31 for women on 2 occasions six months apart within 2 years. The second NAFLD cohort (NAFLD2) included patients with an ICD9/10 code for NAFLD (K76.0). The first cohort of NASH (NASH1) pts included those that met NAFLD1 criteria plus a FIBROSIS-4 score > 2.67. The second NASH (NASH2) cohort included pts with a NASH diagnosis (K75.81). Chi squared and post hoc Z tests were used to compare cohorts.

Results: Out of 359,643 pts who met inclusion criteria, 33,971 pts met criteria for NAFLD1, and 12,544 had a NAFLD diagnosis (NAFLD2). 7,888 met criteria for NASH1 and 2,648 had a NASH diagnosis (NASH2). For both NAFLD and NASH, clinical comorbidities (Table) were significantly more prevalent in NAFLD1 and NASH1 cohorts. White pts were more likely to be diagnosed with NASH than those identified by FIBROSIS-4 score alone (82% vs 69%, p < 0.005). Fewer black pts were diagnosed with NASH (7.8% vs. 24.8%, p < 0.005). Fewer NAFLD2 or NASH2 pts were treated with statins. All cause hospitalizations were high regardless of diagnosis (Figure).

Discussion: Very few pts were diagnosed with NAFLD (3.5%) or NASH (0.7%) in this 5 years period. The number of pts meeting criteria by computable phenotype was higher suggesting significant underdiagnosis, particularly among black pts. Our results suggest that pts with NAFLD and NASH phenotypes but no diagnosis are more likely to have downstream comorbidities. Metabolic syndrome features like hypertension and hyperlipidemia should trigger suspicion for NAFLD and NASH in overweight pts. Further studies are needed to understand the reasons behind the underdiagnosis and the increased downstream comorbidities.

Figure: Rates of Hospitalizations and Liver Biopsy for Different NAFLD and NASH Phenotypes.
CV Hosp = Rate of hospitalizations due to cardiovascular causes other than heart failure. HF Hosp = Rate of hospitalizations due to heart failure.

	NAFLD1	NAFLD2	p value	NASH1	NASH2	p value
White race	23457 (69.1%)	8728 (69.6%)	0.136	5437 (68.9%)	2169 (81.9%)	< 0.05
Black race	7215 (21.2%)	2238 (17.8%)	< 0.05	1956 (24.8%)	206 (7.8%)	< 0.05
Hypertension	13908 (40.9%)	5346 (33.8%)	< 0.05	3892 (49.3%)	926 (35.0%)	< 0.05
Hyperlipidemia	11917 (35.1%)	4758 (30.0%)	< 0.05	2696 (34.2%)	836 (31.6%)	0.07
T2DM	9429 (27.8%)	3596 (28.7%)	0.098	2736 (34.7%)	1056 (39.9%)	< 0.05
CAD	2247 (6.6%)	711 (5.7%)	< 0.05	1100 (13.9%)	170 (6.4%)	< 0.05
Stroke/TIA	2077 (6.1%)	668 (5.3%)	< 0.05	670 (8.5%)	170 (6.4%)	< 0.05
PAD	2247 (6.6%)	711 (5.7%)	< 0.05	1100 (13.9%)	170 (6.4%)	< 0.05
HFrEF	203 (0.6%)	22 (0.2%)	< 0.05	115 (1.5%)	12 (0.5%)	< 0.05
HFpEF	599 (1.8%)	161 (1.3%)	< 0.05	291 (3.7%)	54 (2.0%)	< 0.05
Statin prescription	13878 (40.9%)	4453 (35.5%)	< 0.05	4109 (52.1%)	1003 (37.9%)	< 0.05
ACEi/ARB prescription	14070 (41.4%)	4890 (39.0%)	< 0.05	3856 (48.9%)	1039 (39.2%)	< 0.05
BB prescription	11441 (33.7%)	3983 (31.8%)	< 0.05	4022 (51.0%)	950 (35.9%)	< 0.05

Table: Comparison of Demographics, Comorbidities, and Medications of Different Phenotypes of NAFLD and NASH.
T2DM = Type 2 Diabetes Mellitus. CAD = Coronary artery disease. PAD = peripheral artery disease. HFrEF = Heart failure with reduced ejection fraciton. HFpEF = Heart failure with preserved ejection fraction. ACEi = ACE inhibitor. ARB = angiotensin receptor blocker. BB = beta blocker

Disclosures:
Sunny Chung indicated no relevant financial relationships.
Matthew Phelan indicated no relevant financial relationships.
Benjamin Goldstein indicated no relevant financial relationships.
Kara Wegermann indicated no relevant financial relationships.
Joseph Wawrzynski indicated no relevant financial relationships.
Tzu-Hao Lee indicated no relevant financial relationships.
Jacqueline Henson indicated no relevant financial relationships.
Melanie Cabezas indicated no relevant financial relationships.
Phillip Ambery: AstraZeneca – Employee, Stockholder/Ownership Interest (excluding diversified mutual funds).
Neha Pagidipati: Amgen – Consultant, Grant/Research Support. AstraZeneca – Consultant, Grant/Research Support. Boehringer Ingelheim – Advisory Committee/Board Member, Consultant, Grant/Research Support. Eli Lilly – Consultant, Grant/Research Support. Novartis – Grant/Research Support. Novo Nordisk – Advisory Committee/Board Member, Consultant, Grant/Research Support. Regeneron – Grant/Research Support. Sanofi – Grant/Research Support. Verify Life Sciences – Grant/Research Support.
Cynthia Moylan indicated no relevant financial relationships.

Sunny Chung, MD¹, Matthew Phelan, MS², Benjamin Goldstein, PhD³, Kara Wegermann, MD¹, Joseph Wawrzynski, MD¹, Tzu-Hao Lee, MD¹, Jacqueline B. Henson, MD³, Melanie N. Cabezas, MD³, Phillip Ambery, MD⁴, Neha Pagidipati, MD³, Cynthia Moylan, MD, MS¹. P2827 - Computable Phenotypes for NAFLD and NASH Identify Patients with Significant Comorbidities yet Most Remain Undiagnosed, ACG 2021 Annual Scientific Meeting Abstracts. Las Vegas, Nevada: American College of Gastroenterology.