Identification of High-Risk Patients With Nonalcoholic Fatty Liver Disease Using Noninvasive Tests From Primary Care and Endocrinology Real-World Practices.
2021
INTRODUCTION We aimed to identify high-risk nonalcoholic fatty liver disease (NAFLD) patients seen at the primary care and endocrinology practices and link them to gastrohepatology care. METHODS Using the electronic health record, patients who either had the diagnosis of type 2 diabetes or had 2 of 3 other metabolic risk factors met criteria for inclusion in the study. Using noninvasive fibrosis tests (NITs) to identify high risk of fibrosis, patients who met the NIT prespecified criteria were referred to gastrohepatology for clinical assessment and transient elastography. RESULTS From 7,555 patients initially screened, 1707 (22.6%) met the inclusion criteria, 716 (42%) agreed to enroll, and 184 (25.7%) met the prespecified NIT criteria and eligibility for linkage to GE-HEP where 103 patients (68 ± 9 years of age, 50% men, 56% white) agreed to undergo linkage assessments. Their NIT scores were APRI of 0.38 ± 0.24, FIB-4 of 1.98 ± 0.87, and NAFLD Fibrosis Score of 0.36 ± 1.03; 68 (66%) linked patients had controlled attenuation parameter >248 dB/m, 62 (60%) had liver stiffness 12 kPa. Liver stiffness for the overall group was 6.7 ± 4.2 kPa, controlled attenuation parameter 282 ± 64 dB/m, and FAST score 0.22 ± 0.22. Linked patients with presumed advanced fibrosis had significantly higher body mass index (36.4 ± 6.6 vs 31.2 ± 6.4 kg/m2, P = 0.025) and higher NIT scores (APRI 0.89 ± 0.52 vs 0.33 ± 0.14, FIB-4 3.21 ± 2.06 vs 1.88 ± 0.60, and NAFLD Fibrosis Score 1.58 ± 1.33 vs 0.25 ± 0.94). DISCUSSION By applying a simple prespecified multistep algorithm using electronic health record with clinical risk factors and NITs followed by transient elastography, patients with nonalcoholic fatty liver disease seen in PCP and ENDO practices can be easily identified.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
32
References
3
Citations
NaN
KQI