Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Hepatol. Aug 27, 2022; 14(8): 1598-1607
Published online Aug 27, 2022. doi: 10.4254/wjh.v14.i8.1598
Simple diagnostic algorithm identifying at-risk nonalcoholic fatty liver disease patients needing specialty referral within the United States
Naim Alkhouri, Pankaj Aggarwal, Phuc Le, Julia Payne, Celine Sakkal, Prido Polanco, Stephen Harrison, Mazen Noureddin
Naim Alkhouri, Pankaj Aggarwal, Celine Sakkal, Prido Polanco, Department of Hepatology, Arizona Liver Health, Chandler, AZ 85712, United States
Phuc Le, Julia Payne, Center for Value-Based Care Research, Cleveland Clinic, Cleveland, OH 44195, United States
Stephen Harrison, Department of Research, Pinnacle Research, San Antonio, TX 78229, United States
Mazen Noureddin, Department of Hepatology, Cedars Sinai Medical Center, Los Angeles, CA 90048, United States
Author contributions: Alkhouri N, Sakkal C, and Polanco P contributed to the data collection; Le P and Payne J contributed to the data collection; Alkhouri N and Aggarwal P contributed to the drafting and revision of the manuscript; Harrison S and Noureddin M contributed to the revision of the manuscript.
Supported by AHRQ grant, No. R01HS026937 (Le P and Payne J).
Institutional review board statement: This study has been reviewed and approved by the Arizona Liver Health IRB (protocol 2020-ALH-NAFLD-01).
Informed consent statement: As this was a retrospective review from a deidentified patient database, individual informed consent of subject participants was not applicable as per the institutional review board.
Conflict-of-interest statement: There are no potential conflicts (financial, professional, or personal) to disclose by all the authors with respect to data availability, animal research, consent to participate, consent to publish, plant reproducibility, clinical trials registration, author contribution, or conflicts of interest.
Data sharing statement: No additional data available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Naim Alkhouri, MD, Department of Hepatology, Arizona Liver Health, 1601 N Swan Rd, Tucson, AZ 85712, United States. naim.alkhouri@gmail.com
Received: March 7, 2022
Peer-review started: March 7, 2022
First decision: June 8, 2022
Revised: June 30, 2022
Accepted: August 10, 2022
Article in press: August 10, 2022
Published online: August 27, 2022
ARTICLE HIGHLIGHTS
Research background

Nonalcoholic fatty liver disease (NAFLD) presents a significant public health crisis to primary care physicians and endocrinologists. This growing need necessitates a simple and efficient algorithm that can streamline the process of subspecialty referral to hepatology.

Research motivation

More than half of all patients with NAFLD are at low risk for advanced fibrosis. Though there are no Food and Drug Administration -approved agents for nonalcoholic steatohepatitis (NASH) presently, the efficient identification of patients with NASH with advanced fibrosis will be paramount in the care of these patients.

Research objectives

This study aims to create and enact a diagnostic algorithm for all patients with suspected NAFLD to identify the patients at high risk for advanced fibrosis.

Research methods

Patients with suspected NAFLD were identified in the NHANES database who had historical FibroScan data. FIB4 and FAST scores were calculated for these patients. Those with FIB4 > 1.3 and/or FAST score > 0.67 were deemed high risk for advanced fibrosis.

Research results

Of the 3669 patients meeting the inclusion and exclusion criteria, only 75 patients had both an elevated FIB4 and an elevated FAST score which represents roughly 2.0% of the overall population. Among the 737 patients with type 2 diabetes mellitus, 42 patients (5.1%) were found to have both elevated FIB4 and FAST scores.

Research conclusions

Given an overwhelming number of patients are referred to hepatology who are most likely at low risk for advanced fibrosis, the utilization of this algorithm by referring providers would help to streamline the process for referrals and eventually more seamlessly identify patients at risk for advanced fibrosis who may need therapy for NASH.

Research perspectives

As novel therapeutic agents are currently being studied in patients with NASH with advanced fibrosis, the creation and implementation of a diagnostic algorithm to efficiently identify patients needing therapy becomes increasingly important. Given the wide range of noninvasive tests, this algorithmic approach using two popular tests helps to capture patients at risk for advanced fibrosis while reassuring low-risk patients.