Pragmatic strategies to address health disparities along the continuum of care in chronic liver disease.

Racial, ethnic, and socioeconomic disparities exist in the prevalence and natural history of chronic liver disease, access to care, and clinical outcomes. Solutions to improve health equity range widely, from digital health tools to policy changes. The current review outlines the disparities along the chronic liver disease health care continuum from screening and diagnosis to the management of cirrhosis and considerations of pre-liver and post-liver transplantation. Using a health equity research and implementation science framework, we offer pragmatic strategies to address barriers to implementing high-quality equitable care for patients with chronic liver disease.

Randomized Controlled Comparative Effectiveness Trial of Risk Model-Guided Clinical Decision Support for Suicide Screening.

Suicide prevention requires risk identification, appropriate intervention, and follow-up. Traditional risk identification relies on patient self-reporting, support network reporting, or face-to-face screening with validated instruments or history and physical exam. In the last decade, statistical risk models have been studied and more recently deployed to augment clinical judgment. Models have generally been found to be low precision or problematic at scale due to low incidence. Few have been tested in clinical practice, and none have been tested in clinical trials to our knowledge. Methods: We report the results of a pragmatic randomized controlled trial (RCT) in three outpatient adult Neurology clinic settings. This two-arm trial compared the effectiveness of Interruptive and Non-Interruptive Clinical Decision Support (CDS) to prompt further screening of suicidal ideation for those predicted to be high risk using a real-time, validated statistical risk model of suicide attempt risk, with the decision to screen as the primary end point. Secondary outcomes included rates of suicidal ideation and attempts in both arms. Manual chart review of every trial encounter was used to determine if suicide risk assessment was subsequently documented. Results: From August 16, 2022, through February 16, 2023, our study randomized 596 patient encounters across 561 patients for providers to receive either Interruptive or Non-Interruptive CDS in a 1:1 ratio. Adjusting for provider cluster effects, Interruptive CDS led to significantly higher numbers of decisions to screen (42%=121/289 encounters) compared to Non-Interruptive CDS (4%=12/307) (odds ratio=17.7, p-value <0.001). Secondarily, no documented episodes of suicidal ideation or attempts occurred in either arm. While the proportion of documented assessments among those noting the decision to screen was higher for providers in the Non-Interruptive arm (92%=11/12) than in the Interruptive arm (52%=63/121), the interruptive CDS was associated with more frequent documentation of suicide risk assessment (63/289 encounters compared to 11/307, p-value<0.001). Conclusions: In this pragmatic RCT of real-time predictive CDS to guide suicide risk assessment, Interruptive CDS led to higher numbers of decisions to screen and documented suicide risk assessments. Well-powered large-scale trials randomizing this type of CDS compared to standard of care are indicated to measure effectiveness in reducing suicidal self-harm. ClinicalTrials.gov Identifier: NCT05312437.

Minimizing Metabolic and Cardiac Risk Factors to Maximize Outcomes After Liver Transplantation.

Cardiovascular disease (CVD) is a leading complication after liver transplantation and has a significant impact on patients' outcomes posttransplant. The major risk factors for post-liver transplant CVD are age, preexisting CVD, nonalcoholic fatty liver disease, chronic kidney disease, and metabolic syndrome. This review explores the contemporary strategies and approaches to minimizing cardiometabolic disease burden in liver transplant recipients. We highlight areas for potential intervention to reduce the mortality of patients with metabolic syndrome and CVD after liver transplantation.

Cirrhosis-related sarcopenia may not resolve after liver transplantation.

Background & Aims: Sarcopenia has significant burden in cirrhosis and has been shown to worsen short-term post-liver transplantation (LT). This study aims to evaluate the long-term change in sarcopenia post-LT along with its associations and predictors. Methods: A retrospective study of adult patients who underwent LT at a tertiary centre between 1/1/2009 and 12/31/2018. Relevant demographic and clinical data were collected. Skeletal muscle index (SMI) was calculated using standard of care computerised tomography (CT) scans pre- and post-LT. Sarcopenia was defined using previously established cut-points. The primary outcome was SMI change post-LT and secondary outcome was post-LT mortality. Results: Out of 1165 patients, 401 met inclusion criteria (1,205 CT scans reviewed). The average age at transplant was 57 years; 63% were male. The average BMI was 28 kg/m2. Thirteen percent of females and 32% of males had sarcopenia pre-LT. Post-LT SMI declined by 4.7 cm2/m2 in the first year then by 0.39 cm2/m2 per year thereafter. Females had greater rate of decline in SMI after the first year compared with males (0.87 cm2/m2 per year vs. 0.17 cm2/m2 per year, respectively, p = 0.02). Post-LT physical rehabilitation, infection, and readmissions were not associated with SMI trajectory. At 3 years post-LT, 31% of females and 48% of males had sarcopenia. Baseline sarcopenia was the only predictor of long-term post-LT sarcopenia on multivariable analysis, but it was not associated with mortality. Conclusions: Sarcopenia does not appear to resolve post-LT and likely worsens leading to nearly doubling its prevalence in those with long-term follow-up. Immediate post-LT physical rehabilitation was not associated with SMI trajectory in our cohort. Impact and implications: The prevalence of sarcopenia is high among patients with cirrhosis; however, data are mixed on the impact of sarcopenia on post-liver transplant (LT) course and there have been no studies evaluating the long-term evolution of sarcopenia post-LT beyond 1 year. In this study, we analysed changes in muscle mass up to 3 years after transplant in 401 patients and found that sarcopenia did not resolve in most liver transplant recipients and skeletal muscle mass tended to worsen after transplant with the greatest decline in muscle mass in the first year post-LT. Interestingly, sarcopenia did not influence post-transplant outcomes. Future prospective studies are needed to further understand the natural course of sarcopenia post-LT to guide interventions aiming at reversing post-LT sarcopenia.

Clinical decision support automates care gap detection among primary care patients with nonalcoholic fatty liver disease.

BACKGROUND: Although guidelines recommend primary care-driven management of NAFLD, workflow constraints hinder feasibility. Leveraging electronic health records to risk stratify patients proposes a scalable, workflow-integrated strategy. MATERIALS AND METHODS: We prospectively evaluated an electronic health record-embedded clinical decision support system's ability to risk stratify patients with NAFLD and detect gaps in care. Patients missing annual laboratory testing to calculate Fibrosis-4 Score (FIB-4) or those missing necessary linkage to further care were considered to have a gap in care. Linkage to care was defined as either referral for elastography-based testing or for consultation in hepatology clinic depending on clinical and biochemical characteristics. RESULTS: Patients with NAFLD often lacked annual screening labs within primary care settings (1129/2154; 52%). Linkage to care was low in all categories, with <3% of patients with abnormal FIB-4 undergoing further evaluation. DISCUSSION: Significant care gaps exist within primary care for screening and risk stratification of patients with NAFLD and can be efficiently addressed using electronic health record functionality.

Predictors of renal recovery in recipients of liver transplant alone who met 2017 simultaneous liver-kidney transplant criteria.

BACKGROUND: Severe renal dysfunction is common among liver transplant (LT) candidates and often prompts simultaneous liver-kidney transplantation (SLKT) consideration. In view of 2017 United Network of Organ Sharing (UNOS) criteria for SLKT, we investigated the likelihood and predictors of renal recovery among patients who met the aforementioned criteria yet received liver transplant alone (LTA). METHODS: We retrospectively analyzed relative renal recovery (RRR; increase in eGFR to >30 ml/min) in adult LTA recipients between 1/2009 and 1/2019. RESULTS: Of 1165 LT recipients, 54 met 2017 UNOS criteria, with 37 receiving LTA. RRR occurred in 84% of LTA recipients, none of whom had pre-LT eGFR <20 ml/min. Sustained RRR (>180 days) occurred in 43% of patients. While prolonged pre-LT severe renal impairment (eGFR <30 ml/min) predicted failure to have sustained RRR (HR .19 per 90-day, CI .04-.87, p < .005), having an eGFR measurement of >30 ml/min within 90 days pre-LT (HR 5.52, CI 1.23-24.79, p .01) associated with achieving sustained RRR. Sustained RRR was protective against the composite outcome of renal replacement therapy, kidney transplant, and death (HR .21, p .01). CONCLUSION: LT candidates who meet 2017 UNOS criteria for SLKT yet undergo LTA can still have relative renal recovery post-LT, exceeding 80% on short-term follow-up and 40% on long-term follow-up. eGFR trends within 90 days pre-LT can predict sustained renal recovery, which appears protective of adverse outcomes. These recovery rates advocate for applying the more restrictive criteria for SLKT outlined in this article and increasing utilization of the safety net (SN) policy for those who do not meet the proposed criteria.

The promise of machine learning applications in solid organ transplantation.

Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor-recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.

Cirrhotic cardiomyopathy: Appraisal of the original and revised criteria in predicting posttransplant cardiac outcomes.

Cardiovascular disease (CVD) significantly contributes to morbidity and mortality after liver transplantation (LT). Cirrhotic cardiomyopathy (CCM) is a risk factor for CVD after transplant. CCM criteria were originally introduced in 2005 with a revision proposed in 2020 reflecting echocardiographic technology advancements. This study assesses the two criteria sets in predicting major adverse cardiac events (MACE) after transplant. This single-center retrospective study reviewed adult LT recipients between January 1, 2009, and December 31, 2018. Patients with insufficient pre-LT echocardiographic data, prior ischemic heart disease, portopulmonary hypertension, or longitudinal care elsewhere were excluded. The primary composite outcome was MACE (arrhythmia, heart failure, cardiac arrest, and/or cardiac death) after transplant. Of 1165 patients, 210 met the eligibility criteria. CCM was present in 162 patients (77%) per the original criteria and 64 patients (30%) per the revised criteria. There were 44 MACE and 31 deaths in the study period. Of the deaths, 38.7% occurred secondary to CVD. CCM defined by the original criteria was not associated with MACE after LT (p = 0.21), but the revised definition was significantly associated with MACE (hazard ratio [HR], 1.93; 95% confidence interval, 1.05-3.56; p = 0.04) on multivariable analysis. Echocardiographic variable analysis demonstrated low septal e' as the most predictive variable for MACE after LT (HR, 3.45; p < 0.001). CCM, only when defined by the revised criteria, was associated with increased risk for MACE after LT, validating the recently revised CCM definition. Abnormal septal e', reflecting impaired relaxation, appears to be the most predictive echocardiographic criterion for MACE after LT.

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review.

Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas within hepatology where these methods can be applied. In this review, we examine the literature pertaining to machine learning in hepatology and liver transplant medicine. We provide an overview of the strengths and limitations of ML tools and their potential applications to both clinical and molecular data in hepatology. ML has been applied to various types of data in liver disease research, including clinical, demographic, molecular, radiological, and pathological data. We anticipate that use of ML tools to generate predictive algorithms will change the face of clinical practice in hepatology and transplantation. This review will provide readers with the opportunity to learn about the ML tools available and potential applications to questions of interest in hepatology.

Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records.

Type II diabetes mellitus (T2DM) is a significant public health concern with multiple known risk factors (e.g., body mass index (BMI), body fat distribution, glucose levels). Improved prediction or prognosis would enable earlier intervention before possibly irreversible damage has occurred. Meanwhile, abdominal computed tomography (CT) is a relatively common imaging technique. Herein, we explore secondary use of the CT imaging data to refine the risk profile of future diagnosis of T2DM. In this work, we delineate quantitative information and imaging slices of patient history to predict onset T2DM retrieved from ICD-9 codes at least one year in the future. Furthermore, we investigate the role of five different types of electronic medical records (EMR), specifically 1) demographics; 2) pancreas volume; 3) visceral/subcutaneous fat volumes in L2 region of interest; 4) abdominal body fat distribution and 5) glucose lab tests in prediction. Next, we build a deep neural network to predict onset T2DM with pancreas imaging slices. Finally, motivated by multi-modal machine learning, we construct a merged framework to combine CT imaging slices with EMR information to refine the prediction. We empirically demonstrate our proposed joint analysis involving images and EMR leads to 4.25% and 6.93% AUC increase in predicting T2DM compared with only using images or EMR. In this study, we used case-control dataset of 997 subjects with CT scans and contextual EMR scores. To the best of our knowledge, this is the first work to show the ability to prognose T2DM using the patients' contextual and imaging history. We believe this study has promising potential for heterogeneous data analysis and multi-modal medical applications.

Natural Language Processing and Machine Learning to Enable Clinical Decision Support for Treatment of Pediatric Pneumonia.

Pneumonia is the most frequent cause of infectious disease-related deaths in children worldwide. Clinical decision support (CDS) applications can guide appropriate treatment, but the system must first recognize the appropriate diagnosis. To enable CDS for pediatric pneumonia, we developed an algorithm integrating natural language processing (NLP) and random forest classifiers to identify potential pediatric pneumonia from radiology reports. We deployed the algorithm in the EHR of a large children's hospital using real-time NLP. We describe the development and deployment of the algorithm, and evaluate our approach using 9-months of data gathered while the system was in use. Our model, trained on individual radiology reports, had an AUC of 0.954. The intervention, evaluated on patient encounters that could include multiple radiology reports, achieved a sensitivity, specificity, and positive predictive value of0.899, 0.949, and 0.781, respectively.

The presence of primary cilia in cancer cells does not predict responsiveness to modulation of smoothened activity.

Primary cilia are microtubule-based organelles that regulate smoothened-dependent activation of the GLI transcription factors in canonical hedgehog signaling. In many cancers, primary cilia are markedly decreased or absent. The lack of primary cilia may inhibit or alter canonical hedgehog signaling and, thereby, interfere in the cellular responsiveness to modulators of smoothened activity. Clinical trials of smoothened antagonists for cancer treatment have shown the best response in basal cell carcinomas, with limited response in other solid tumors. To determine whether the presence or absence of primary cilia in cancer cells will predict their responsiveness to modulation of smoothened activity, we compared the ability of an agonist and/or inhibitor of smoothened (SAG and SANT1, respectively) to modulate GLI-mediated transcription, as measured by GLI1 mRNA level or GLI-luciferase reporter activity, in non-cancer cells with primary cilia (ovarian surface epithelial cells and breast fibroblasts), in cancer cells that cannot assemble primary cilia (MCF7, MDA-MB-231 cell lines), and in cancer cells with primary cilia (SKOV3, PANC1 cell lines). As expected, SAG and SANT1 resulted in appropriate modulation of GLI transcriptional activity in ciliated non-cancer cells, and failed to modulate GLI transcriptional activity in cancer cells without primary cilia. However, there was also no modulation of GLI transcriptional activity in either ciliated cancer cell line. SAG treatment of SKOV3 induced localization of smoothened to primary cilia, as assessed by immunofluorescence, even though there was no increase in GLI transcriptional activity, suggesting a defect in activation of SMO in the primary cilia or in steps later in the hedgehog pathway. In contrast to SKOV3, SAG treatment of PANC1 did not cause the localization of smoothened to primary cilia. Our data demonstrate that the presence of primary cilia in the cancer epithelial cells lines tested does not indicate their responsiveness to smoothened activation or inhibition.