Primary Care Providers and Nonalcoholic Fatty Liver Disease: A Needs Assessment Survey.

BACKGROUND: Primary care providers (PCPs) face increasing numbers of patients at risk for NAFLD and are responsible for the detection of NAFLD and the decision on referral to specialists. We conducted a PCP needs assessment to ascertain the barriers and desired supports for NAFLD in primary care. METHODS: We designed a cross-sectional study of PCPs at a large diverse health system and surveyed faculty, residents, and nurse practitioners. Questions assessed NAFLD knowledge, approach to diagnosis and fibrosis testing including use of FIB-4, and attitudes toward support tools. RESULTS: The survey was sent to 115 PCPs with an 80% (n = 92) response rate. Respondents were 52% faculty and 48% residents. Over 40% were unsure of which diagnostic tests to order and which data constituted a diagnosis. PCPs were aware of the importance of fibrosis, yet few knew the components of FIB-4, few used FIB-4 in practice, and yet the most common reason for referral was to obtain fibrosis staging. The majority showed high levels of interest toward possible tools to improve NAFLD management, and only 5% perceived lack of time to be a barrier. DISCUSSION: Our survey revealed PCPs need and want strategic approaches to NAFLD. We found PCPs lack confidence in diagnosing NAFLD and are inconsistent in management strategies. PCPs had high awareness of the importance of fibrosis, but not of the FIB-4. It was encouraging that PCPs reported that time was not a major barrier and had positive attitudes toward potential practice support tools, indicating that practice guidelines designed for primary care should be created.

Underrecognition and Suboptimal Quality of Care for Nonalcoholic Fatty Liver Disease Cirrhosis in Primary Care Patients with Diabetes Mellitus.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is a leading cause of cirrhosis but is underrecognized in primary care. Cirrhosis management requires complex monitoring, and the quality of care (QoC) for NAFLD cirrhosis patients in primary care may be inadequate. METHODS: In this retrospective-prospective cohort study of primary care patients with diabetes mellitus, we identified patients with NAFLD cirrhosis by 1) evidence of cirrhosis from abdominal imaging identified by natural language processing, or 2) existence of International Classification of Diseases code for cirrhosis. A finding of either was followed by manual chart review for confirmation of both cirrhosis and NAFLD. We then determined if cirrhosis care measures were up-to-date, including hepatitis A and B vaccination, Model for End-Stage Liver Disease score components, esophagogastroduodenoscopy, and hepatocellular carcinoma screening. We created a composite score quantifying overall QoC (scale 0-8), with high QoC defined as ≥6 points. RESULTS: Among 3,028 primary care patients with diabetes mellitus, we identified 51 (1.7%) with NAFLD cirrhosis. Although 78% had ≥3 average primary care visits/year, only 24% completed hepatocellular carcinoma screening at least annually in at least 75% of years since diagnosis. The average QoC composite score was 4.9 (SD 2.4), and less than one-third had high QoC. CONCLUSIONS: NAFLD cirrhosis is prevalent but underdiagnosed in primary care, and receipt of comprehensive QoC was suboptimal. Given the rising incidence of NAFLD cirrhosis, primary care providers need improved awareness and mechanisms to ensure high QoC for this population.

Prospective study of a case-finding algorithm to detect NAFLD with advanced fibrosis in primary care patients.

BACKGROUND AND AIMS: Primary care providers need strategies to identify NAFLD patients and select for specialty referral, but proposed algorithms have only been studied in established NAFLD patients. METHODS: We implemented an algorithm for all adults with diabetes mellitus in a large primary care practice and excluded hepatitis B and C or alcohol use. Applying annual Fibrosis-4 Index and NAFLD Fibrosis Score for 5 years, we categorized patients as low-risk, indeterminate-risk, or high-risk for advanced fibrosis. We targeted all high-risk and messaged each primary care provider, recommending hepatology linkage. We collected final diagnosis and fibrosis (F0-4) outcomes. Using multivariable logistic regression, we assessed risk factors for advanced fibrosis stage (F3-4). RESULTS: Of 3028 patients, 1018 were low-risk, 577 indeterminate-risk, and 611 high-risk. There were 264 target patients; their 89 primary care providers received a message per patient suggesting hepatology referral. The majority (n=149) were referred; at triage, 118 were deemed likely NAFLD. Of these, 90 completed visits, 78/90 were diagnosed as NAFLD, and 69/78 underwent fibrosis staging, with F3 to 4 in 25/69. In multivariable analysis, hemoglobin A1c ≥8% (OR=7.02, 95% CI: 1.29-38.18) and Fibrosis-4 Index (OR=1.79, 95% CI: 1.07-2.99) were associated with increased risk of F3 to 4. CONCLUSIONS: This is the first prospective study testing a case-finding strategy in primary care and almost 1/3 of diabetes mellitus were high-risk for advanced fibrosis. When prompted, 73% of primary care providers placed referrals and 76% of patients completed visits, revealing 86% NAFLD and 36% F3 to 4. This study demonstrates the readiness for such a strategy in primary care; integrating hemoglobin A1c into this algorithm may further improve the performance of Fibrosis-4 Index in this setting.

Circadian and ultradian rhythms in normal mice and in a mouse model of Huntington's disease.

Circadian rhythms in core body temperature (CBT) have been widely studied, but fewer studies have explored higher-frequency (ultradian) rhythms in detail. We analyzed CBT recordings from young and middle-aged wild-type mice as well as from the Q175 model of Huntington's disease (HD), at sufficient temporal resolution to address the question of ultradian rhythms. We used model selection methods to show that the overall circadian pattern was better fit by a square wave than a sine wave. Then, using Fourier analysis of the CBT rhythms, we identified the spectral signature of an 8-hour oscillation that occurs in the night but not the day, an observation that can be confirmed by direct inspection of the rhythms. This diurnal amplitude modulation of the 8-hour rhythm was lost with aging as well as in the HD model. Thus, the impact of aging and disease is seen here in the loss of the ability to separate rhythms into a daytime phase and a nighttime phase. These findings raise the possibility that ultradian rhythms in CBT may be a useful biomarker for the pathology within the central nervous system.