High concordance between nonalcoholic fatty liver disease and metabolic dysfunction associated steatotic liver disease in the TARGET-NASH real world cohort.

INTRODUCTION: This study investigates the applicability of the new MASLD nomenclature to the real-world TARGET-NASH US adult cohort. METHODS: The new MASLD/MASH nomenclature was applied to patients enrolled with pragmatic diagnoses of NAFL, NASH and NASH cirrhosis and concordance was determined between the definitions. RESULTS: 99% of TARGET-NASH participants met the new MASLD diagnostic criteria. 1484/1541 (96.3%, kappa 0.974) NAFL patients (MASL), 2195/2201 (99.7%, kappa 0.998) NASH patients (MASH), and 1999/2003 (99.8%, kappa 0.999) NASH cirrhosis patients met the new criteria. CONCLUSION: The new MASLD nomenclature is highly concordant with the prior TARGET-NASH pragmatic definitions.

Impact of Pruritus on Quality of Life and Current Treatment Patterns in Patients with Primary Biliary Cholangitis.

BACKGROUND AND AIMS: Patients with primary biliary cholangitis (PBC) often suffer with pruritus. We describe the impact of pruritus on quality of life and how it is managed in a real-world cohort. METHODS: TARGET-PBC is a longitudinal observational cohort of patients with PBC across the USA. Data include information from medical records for three years prior to the date of consent up to 5 years of follow-up. Enrolled patients were asked to complete patient-reported outcome surveys: PBC-40, 5-D itch, and the PROMIS fatigue survey. Kruskal-Wallis tests were used to compare differences in symptoms between groups. RESULTS: A total of 211 patients with completed PRO surveys were included in the current study. PRO respondents were compared with non-respondents in the TARGET-PBC population and were broadly similar. Pruritus was reported in 170 patients (81%), with those reporting clinically significant pruritus (30%) scoring worse across each domain of the PBC-40 and 5-D itch, more frequently having cirrhosis, and having significantly greater levels of fatigue. Patients reporting clinically significant pruritus were more likely to receive treatment, but 33% had never received treatment (no itch = 43.9%, mild itch = 38.3%). CONCLUSIONS: The prevalence of pruritus was high in this population, and those reporting clinically significant pruritus had a higher likelihood of having advanced disease and worse quality of life. However, this study found that pruritus in PBC is under-treated. This may be due in part to ineffectiveness of current treatments, poor tolerance, or the lack of FDA-approved medications for pruritus.

Noninvasive Diagnosis of Nonalcoholic Steatohepatitis and Advanced Liver Fibrosis Using Machine Learning Methods: Comparative Study With Existing Quantitative Risk Scores.

BACKGROUND: Nonalcoholic steatohepatitis (NASH), advanced fibrosis, and subsequent cirrhosis and hepatocellular carcinoma are becoming the most common etiology for liver failure and liver transplantation; however, they can only be diagnosed at these potentially reversible stages with a liver biopsy, which is associated with various complications and high expenses. Knowing the difference between the more benign isolated steatosis and the more severe NASH and cirrhosis informs the physician regarding the need for more aggressive management. OBJECTIVE: We intend to explore the feasibility of using machine learning methods for noninvasive diagnosis of NASH and advanced liver fibrosis and compare machine learning methods with existing quantitative risk scores. METHODS: We conducted a retrospective analysis of clinical data from a cohort of 492 patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD), NASH, or advanced fibrosis. We systematically compared 5 widely used machine learning algorithms for the prediction of NAFLD, NASH, and fibrosis using 2 variable encoding strategies. Then, we compared the machine learning methods with 3 existing quantitative scores and identified the important features for prediction using the SHapley Additive exPlanations method. RESULTS: The best machine learning method, gradient boosting (GB), achieved the best area under the curve scores of 0.9043, 0.8166, and 0.8360 for NAFLD, NASH, and advanced fibrosis, respectively. GB also outperformed 3 existing risk scores for fibrosis. Among the variables, alanine aminotransferase (ALT), triglyceride (TG), and BMI were the important risk factors for the prediction of NAFLD, whereas aspartate transaminase (AST), ALT, and TG were the important variables for the prediction of NASH, and AST, hyperglycemia (A1c), and high-density lipoprotein were the important variables for predicting advanced fibrosis. CONCLUSIONS: It is feasible to use machine learning methods for predicting NAFLD, NASH, and advanced fibrosis using routine clinical data, which potentially can be used to better identify patients who still need liver biopsy. Additionally, understanding the relative importance and differences in predictors could lead to improved understanding of the disease process as well as support for identifying novel treatment options.

Variation in Alanine Aminotransferase in Children with Non-Alcoholic Fatty Liver Disease.

Background: Pediatric non-alcoholic fatty liver disease (NAFLD) is a major public health concern. Aminotransferase (ALT) is frequently used for screening and monitoring, but few studies have reported typical patterns of ALT elevation in children. Methods: TARGET-NASH is a real-world longitudinal observational cohort of patients with NAFLD receiving care across the United States. Analyses included children enrolled between 1 August 2016, and 12 October 2020, with at least one ALT measurement after enrollment. Peak ALT was based on the first and last available record and categorized into clinical cut points: <70 IU/L, >70−<250 IU/L, and >250 IU/L. A chi-squared test was used to compare differences in proportions, and a Kruskal−Wallis test was used to compare the medians and distributions of continuous responses. Results: Analyses included 660 children with a median age of 13 years. Of the 660, a total of 187 had undergone a biopsy and were more likely to be Hispanic or Latino (67% vs. 57%, p = 0.02) and to have cirrhosis (10% vs. 1%, p < 0.001). The highest ALT scores ranged from 28 U/L to 929 U/L; however, these scores varied across time. The prevalence of cirrhosis or any liver fibrosis stage was most common among children with a peak ALT > 70 U/L. Conclusions: Large variability was seen in ALT among children, including many values > 250 U/L. Higher levels of ALT were associated with increased prevalence of comorbidities and more advanced stages of NAFLD. These findings support an increased need for therapeutics and disease severity assessment in children with peak ALT > 70 U/L.

Factors Associated With Readmission in the United States Following Hospitalization With Coronavirus Disease 2019.

BACKGROUND: Patients hospitalized for coronavirus disease 2019 (COVID-19) may experience complications following hospitalization and require readmission. In this analysis, we estimated the rate and risk factors associated with COVID-19-related readmission and inpatient mortality. METHODS: In this retrospective cohort study, we used deidentified chargemaster data from 297 hospitals across 40 US states on patients hospitalized with COVID-19 from 15 February 2020 through 9 June 2020. Demographics, comorbidities, acute conditions, and clinical characteristics of first hospitalization are summarized. Multivariable logistic regression was used to measure risk factor associations with 30-day readmission and in-hospital mortality. RESULTS: Among 29 659 patients, 1070 (3.6%) were readmitted. Readmitted patients were more likely to have diabetes, hypertension, cardiovascular disease (CVD), or chronic kidney disease (CKD) vs those not readmitted (P < .0001) and to present on first admission with acute kidney injury (15.6% vs 9.2%), congestive heart failure (6.4% vs 2.4%), or cardiomyopathy (2.1% vs 0.8%) (P < .0001). Higher odds of readmission were observed in patients aged >60 vs 18-40 years (odds ratio [OR], 1.92; 95% confidence interval [CI], 1.48-2.50) and those admitted in the Northeast vs West (OR, 1.43; 95% CI, 1.14-1.79) or South (OR, 1.28; 95% CI, 1.11-1.49). Comorbidities including diabetes (OR, 1.34; 95% CI, 1.12-1.60), CVD (OR, 1.46; 95% CI, 1.23-1.72), CKD stage 1-5 (OR, 1.51; 95% CI, 1.25-1.81), and CKD stage 5 (OR, 2.27; 95% CI, 1.81-2.86) were associated with higher odds of readmission; 12.3% of readmitted patients died during second hospitalization. CONCLUSIONS: Among this large US population of patients hospitalized with COVID-19, readmission was associated with certain comorbidities and acute conditions during first hospitalization. These findings may inform strategies to mitigate risks of readmission due to COVID-19 complications.

Real-world evidence in hepatocellular carcinoma.

Real-world evidence includes all health-related information, such as electronic health records, insurance claims, pharmacy records and wearables that are obtained outside of clinical trials. These data can provide critical insights into the natural history of disease and evaluate the safety and effectiveness of treatment regimens used in clinical practice. Real-world data have been applied to varying degrees by global regulatory agencies to inform and expedite many phases of drug development and help refine the use of therapeutic regimens after marketing, especially in populations that are under-represented in registration trials. For the management of hepatocellular carcinoma, early detection provides the best chance for curative therapies, whose success has been evaluated in numerous cohorts. The availability of novel systemic therapies, including kinase inhibitors and immunotherapies, has provided new treatment options and improved survival in patients with advanced stage hepatocellular carcinoma. Real-world longitudinal observational studies can help understand the long-term safety and effectiveness of these agents.

Cost-Effectiveness of the Wellness Incentives and Navigation (WIN) Program.

OBJECTIVES: Promoting patient involvement in managing co-occurring physical and mental health conditions is increasingly recognized as critical to improving outcomes and controlling costs in this growing chronically ill population. The main objective of this study was to conduct an economic evaluation of the Wellness Incentives and Navigation (WIN) intervention as part of a longitudinal randomized pragmatic clinical trial for chronically ill Texas Medicaid enrollees with co-occurring physical and mental health conditions. METHODS: The WIN intervention used a personal navigator, motivational interviewing, and a flexible wellness expense account to increase patient activation, that is, the patient's knowledge, skills, and confidence in managing their self-care and co-occurring physical and mental health conditions. Regression models were fit to both participant-level quality-adjusted life years (QALYs) and total costs of care (including the intervention) controlling for demographics, health status, poverty, Medicaid managed care plan, intervention group, and baseline health utility and costs. Incremental costs and QALYs were calculated based on the difference in predicted costs and QALYs under intervention versus usual care and were used to calculate the incremental cost-effectiveness ratios (ICERs). Confidence intervals were calculated using Fieller's method, and sensitivity analyses were performed. RESULTS: The mean ICER for the intervention compared with usual care was $12 511 (95% CI $8971-$16 842), with a sizable majority of participants (70%) having ICERs below $40 000. The WIN intervention also produced higher QALY increases for participants who were sicker at baseline compared to those who were healthier at baseline. CONCLUSION: The WIN intervention shows considerable promise as a cost-effective intervention in this challenging chronically ill population.

Diabetes medication regimens and patient clinical characteristics in the national patient-centered clinical research network, PCORnet.

We used electronic medical record (EMR) data in the National Patient-Centered Clinical Research Network (PCORnet) to characterize "real-world" prescription patterns of Type 2 diabetes (T2D) medications. We identified a retrospective cohort of 613,203 adult patients with T2D from 33 datamarts (median patient number: 12,711) from 2012 through 2017 using a validated computable phenotype. We characterized outpatient T2D prescriptions for each patient in the 90 days before and after cohort entry, as well as demographics, comorbidities, non-T2D prescriptions, and clinical and laboratory variables in the 730 days prior to cohort entry. Approximately half of the individuals in the cohort were females and 20% Black. Hypertension (60.3%) and hyperlipidemia (50.5%) were highly prevalent. Most patients were prescribed either a single T2D drug class (42.2%) or had no evidence of a T2D prescription in the EMR (42.4%). A smaller percentage was prescribed multiple T2D drug types (15.4%). Among patients prescribed a single T2D drug type, metformin was the most common (42.6%), followed by insulin (18.2%) and sulfonylureas (13.9%). Newer classes represented approximately 13% of single T2D drug type prescriptions (dipeptidyl peptidase-4 inhibitors [6.6%], glucagon-like peptide-1 receptor agonists [2.5%], thiazolidinediones [2.0%], and sodium-glucose cotransporter-2 inhibitors [1.6%]). Among patients prescribed multiple T2D drug types, the most common combination was metformin and sulfonylureas (63.5%). Metformin-based regimens were highly prevalent in PCORnet's T2D population, whereas newer agents were prescribed less frequently. PCORnet is a novel source for the potential conduct of observational studies among patients with T2D.

Addressing health disparities in type 1 diabetes through peer mentorship.

Pronounced health disparities exist in type 1 diabetes (T1D) based on socioeconomic status (SES) yet there are a lack of programs designed to promote health equity for vulnerable communities. The All for ONE (Outreach, Networks, and Education) mentoring program was piloted pairing college students and publicly insured teenagers with T1D to assess feasibility as a possible intervention. There were 22 mentors recruited (mean age 20 ± 2 years; 17 [77%] females; mean HbA1c 8.4 ± 1.5%) and matched with mentees based on gender. There were 42 teens randomized to treatment and control groups including 22 teens in the treatment group (age 14 ± 2 years; 17 [77%] females; HbA1c 9.8 ± 2.3%) and 20 teens in the control group (age 14 ± 2 years; 15 [75%] females; HbA1c 8.9 ± 2.0%) followed over 9 months. Outcome measures included HbA1c and the Children's Hope Scale. The intervention included automated text reminders for blood glucose monitoring, text exchanges, social events with education, and clinic visits with mentors/mentees. Mean change in HbA1c for teens was +0.09% in the intervention group, compared with +0.28% in the control group (P = .61); college students had a reduction in HbA1c of -0.22% (P = .38). Treatment group teens had marked improvement in their hope for the future compared to control group teens (P = .04) and were more likely to attend clinic visits (P = .02). This program established feasibility for a model that could be replicated and modified for other types of settings. Additional research is warranted to study the potential long-term benefits of participating in the All for ONE mentoring program.

Performance of a computable phenotype for identification of patients with diabetes within PCORnet: The Patient-Centered Clinical Research Network.

PURPOSE: PCORnet, the National Patient-Centered Clinical Research Network, represents an innovative system for the conduct of observational and pragmatic studies. We describe the identification and validation of a retrospective cohort of patients with type 2 diabetes (T2DM) from four PCORnet sites. METHODS: We adapted existing computable phenotypes (CP) for the identification of patients with T2DM and evaluated their performance across four PCORnet sites (2012-2016). Patients entered the cohort on the earliest date they met one of three CP categories: (CP1) coded T2DM diagnosis (ICD-9/ICD-10) and an antidiabetic prescription, (CP2) diagnosis and glycosylated hemoglobin (HbA1c) ≥6.5%, or (CP3) an antidiabetic prescription and HbA1c ≥6.5%. We required evidence of health care utilization in each of the 2 prior years for each patient, as we also developed an incident T2DM CP to identify the subset of patients without documentation of T2DM in the 365 days before t0 . Among a systematic sample of patients, we calculated the positive predictive value (PPV) for the T2DM CP and incident-T2DM CP using electronic health record (EHR) review as reference. RESULTS: The CP identified 50 657 patients with T2DM. The PPV of patients randomly selected for validation was 96.2% (n = 1572; CI:95.1-97.0) and was consistently high across sites. The PPV for the incident-T2DM CP was 5.8% (CI:4.5-7.5). CONCLUSIONS: The T2DM CP accurately and efficiently identified patients with T2DM across multiple sites that participate in PCORnet, although the incident T2DM CP requires further study. PCORnet is a valuable data source for future epidemiological and comparative effectiveness research among patients with T2DM.

Development and validation of an instrument to measure collaborative goal setting in the care of patients with diabetes.

OBJECTIVE: Despite known benefits of patient-perceived collaborative goal setting, we have a limited ability to monitor this process in practice. We developed the Patient Measure of Collaborative Goal Setting (PM-CGS) to evaluate the use of collaborative goal setting from the patient's perspective. RESEARCH DESIGN AND METHODS: A random sample of 400 patients aged 40 years or older, receiving diabetes care from the Virginia Commonwealth University Health System between 8/2012 and 8/2013, were mailed a survey containing potential PM-CGS items (n=44) as well as measures of patient demographics, perceived self-management competence, trust in their physician, and self-management behaviors. Confirmatory factor analysis was used to evaluate construct validity. External validity was evaluated via a structural equation model (SEM) that tested the association of the PM-CGS with self-management behaviors. The direct and two mediated (via trust and self-efficacy) pathways were tested. RESULTS: A total of 259 patients responded to the survey (64% response rate), of which 192 were eligible for inclusion. Results from the factor analysis supported a 37-item measure of patient-perceived CGS spanning five domains: listen and learn; share ideas; caring relationship; measurable objective; and goal achievement support (χ=4366.13, p<0.001; RMSEA=0.08). Results from the SEM supported the external validity of the PM-CGS. The relationship between CGS and self-management was partially mediated by perceived competence (p<0.05). The direct effect between the PM-CGS and self-management was significant (p<0.001). CONCLUSIONS: CGS can be validly measured by the 37-item PM-CGS. Use of the PM-CGS can help illustrate actionable deficits in goal-setting discussions.

Adding the patient's voice to our understanding of collaborative goal setting: How do patients with diabetes define collaborative goal setting?

BACKGROUND: Patient reports of collaborative goal setting have repeatedly been associated with improved health outcomes, and the American Diabetes Association specifically encourages collaborative goal setting as a component of high quality care. Current limitations in our understanding of what needs to transpire for patients to denote goal setting as "collaborative", remain a barrier to fostering collaborative goal setting in practice. METHODS: Four focus groups were conducted among 19 patients with diabetes. A semi-structured focus group guide was used to explore patient perceptions of collaborative goal setting and what needed to happen for goals to be considered collaboratively set. Focus group transcripts were coded using thematic analysis. RESULTS: Collaborative goal setting was described by patients as occurring within the context of a caring relationship where patients and health care providers: (1) listen and learn from each other; (2) share ideas; (3) agree on a measurable objective; and (4) support goal achievement. Patients also articulated clear responsibilities for themselves and clinicians and described collaborative goal setting as a process that occurs over time. CONCLUSIONS: Patients perceived collaborative goal setting as a multidimensional process that occurs over time within the context of a caring relationship and encompasses distinct patient and clinician responsibilities.

Patient-reported use of collaborative goal setting and glycemic control among patients with diabetes.

OBJECTIVE: Little is known about how patient-clinician communication leads to better outcomes. Among patients with diabetes, we describe patient-reported use of collaborative goal setting and evaluate whether perceived competency and physician trust mediate the association between collaborative goal setting and glycemic control. METHODS: Data from a patient survey administered in 2008 to a cohort of insured patients aged 18+ years with diabetes who initiated oral mono-therapy between 2000 and 2005 were joined with pharmaceutical claims data for the prior 12 months and laboratory data for the prior and subsequent 12 months (N=1065). A structural equation model (SEM) was used to test mediation models controlling for baseline HbA1c. RESULTS: The hypothesized mediation model was supported. Patient-reported use of more collaborative goal setting was associated with greater perceived self-management competency and increased level of trust in the physician (p<0.05). In turn, both greater perceived competence and increased trust were associated with increased control (p<0.05). CONCLUSIONS: Findings indicate that engaging patients in collaborative goal setting during clinical encounters has potential to foster a trusting patient-clinician relationship as well as enhance patient perceived competence, thereby improving clinical control. PRACTICE IMPLICATIONS: Fostering collaborative goal setting may yield payoffs in improved clinical outcomes among patients with diabetes.