Self-Efficacy for Exercise in Adults with Lifetime Depression and Low Physical Activity.

People may be more likely to exercise if they have self-efficacy for exercise (SEE). We conducted an exploratory analysis of SEE using data from a clinical trial designed to increase physical activity (N = 340). We evaluated correlates of baseline SEE and the relationship between baseline SEE and physical activity. Low SEE at baseline was correlated with lower well-being, physical activity, and higher depression at baseline. Participants with high (vs. low) baseline SEE had higher physical activity (but no differential change in activity) over time. These data highlight the potential role of SEE in psychological health and physical activity.

Decreasing Case Fatality Rates for Patients With Cirrhosis Infected With SARS-CoV-2: A National COVID Cohort Collaborative Study.

BACKGROUND & AIMS: The virulence and severity of SARS-CoV-2 infections have decreased over time in the general population due to vaccinations and improved antiviral treatments. Whether a similar trend has occurred in patients with cirrhosis is unclear. We used the National COVID Cohort Collaborative (N3C) to describe the outcomes over time. METHODS: We utilized the N3C level 3 data set with uncensored dates to identify all patients with chronic liver disease (CLD) with and without cirrhosis who had SARS-CoV-2 infection as of November 2023. We described the observed 30-day case fatality rate (CFR) by month of infection. We used adjusted survival analyses to calculate relative hazard of death by month of infection compared with infection at the onset of the COVID-19 pandemic. RESULTS: We identified 117,811 total patients with CLD infected with SARS-CoV-2 between March 2020 and November 2023: 27,428 (23%) with cirrhosis and 90,383 (77%) without cirrhosis. The observed 30-day CFRs during the entire study period were 1.1% (1016) for patients with CLD without cirrhosis and 6.3% (1732) with cirrhosis. Observed 30-day CFRs by month of infection varied throughout the pandemic and showed a sustained downward trend since 2022. Compared with infection in Quarter 2 of 2020 (at the beginning of the pandemic), the adjusted hazards of death at 30 days for infection in Quarter 3 of 2023 were 0.20 (95% confidence interval [CI], 0.08-0.50) for patients with CLD without cirrhosis and 0.35 (95% CI, 0.18-0.69) for patients with CLD with cirrhosis. CONCLUSIONS: In this N3C study, we found that the observed 30-day CFR decreased progressively for patients with CLD both with and without cirrhosis, consistent with broader trends seen in the general population.

Adoption of self-measured blood pressure monitoring in underserved communities: Program evaluation in primary care.

RATIONALE: Self-measured blood pressure (SMBP) monitoring is crucial for hypertension management, yet its adoption, particularly among disadvantaged populations, remains low. 'Love Your Heart' is a quality improvement program providing free standard SMBP devices to hypertensive patients, aiming to enhance adoption and assess its impact on blood pressure control. AIMS AND OBJECTIVES: This study evaluates the 'Love Your Heart' program's implementation success through assessing adoption rates and exploring changes in systolic blood pressure (SBP) among participants. We aim to understand factors influencing adoption and potential benefits of SMBP monitoring in a diverse, socially disadvantaged patient population. METHODS: We retrospectively evaluated the 'Love Your Heart' program using electronic health records (EHR) at a primary care site in Chicago. Adult patients with hypertension were enroled in the 6-month program, which included education sessions and free SMBP devices. Adoption was measured by participation in program components, and changes in SBP were analysed based on adoption status. Statistical analyses were conducted using SAS software, adhering to STROBE reporting guidelines. RESULTS: Of 621 eligible patients, 104 participated, with 83 included in the evaluation. Despite all participants receiving free SMBP devices, adoption rates were modest, with only 7% sharing readings with the care team. However, patients who received device instructions demonstrated greater decreases in SBP compared to those who did not. Although not statistically significant, clinically meaningful decreases in SBP were observed among adopters. CONCLUSION: The 'Love Your Heart' program highlights the challenges of promoting SMBP monitoring among disadvantaged patient populations. While providing free devices addresses access barriers, low reporting to the care team suggests the need for further support mechanisms. Future research should explore strategies to enhance SMBP adoption and integration into clinical care, particularly in settings lacking automated data transmission systems.

Associations of cannabis use and body mass index-The Coronary Artery Risk Development in Young Adults (CARDIA) study.

BACKGROUND: With increasing use of cannabis, we need to know if cannabis use and Body Mass Index (BMI) are associated. METHODS: The Coronary Artery Risk Development in Young Adults Study followed Black and White adults over 30 years with assessments every 2 to 5 years in four centers in the USA. We assessed self-reported current and computed cumulative cannabis exposure at every visit, and studied associations with BMI, adjusted for relevant covariables in mixed longitudinal models. We also applied marginal structural models (MSM) accounting for the probability of having stopped cannabis over the last 5 years. RESULTS: At the Year 30 visit, 1,912 (58 %) identified as women and 1,600 (48 %) as Black, mean age was 56 (SD 2) years. While 2,849 (85 %) had ever used cannabis, 479 (14 %) currently used cannabis. Overall, participants contributed to 35,882 individual visits over 30 years. In multivariable adjusted models, mean BMI was significantly lower in daily cannabis users (26.6 kg/m2, 95 %CI 26.3 to 27.0) than in participants without current use (27.7 kg/m2, 95 %CI 27.5 to 27.9, p < 0.001). Cumulative cannabis use was not associated with BMI. The MSM showed no change in BMI when stopping cannabis use over a 5-year period (ß=0.2 kg/m2 total, 95 %CI -0.2 to 0.6). CONCLUSIONS: Current cannabis use was associated with lower BMI, but cumulative cannabis use and cessation were not. This suggests that recreational cannabis use may not lead to clinically relevant changes in BMI and that the association between current cannabis use and lower BMI is likely due to residual confounding.

Disparities in Patient Portal Engagement Among Patients With Hypertension Treated in Primary Care.

Importance: Patient portals are increasingly used for patient-clinician communication and to introduce interventions aimed at improving blood pressure control. Objective: To characterize patient portal use among patients with hypertension managed in primary care. Design, Settings, and Participants: This retrospective cohort study used electronic health records linked with patient portal log file data from a large, diverse Midwestern health care system. Patients with hypertension who had a primary care visit from January 1, 2021, to December 31, 2021, were included. The first visit in 2021 was considered the baseline visit; patient portal engagement was evaluated during the following year. Multivariate logistic regressions, presented as odds ratios (ORs) and 95% CIs, were used to evaluate associations between patient characteristics and patient portal engagement, adjusting for potential confounders. Exposures: Primary exposures included 4 sociodemographic factors routinely collected in the electronic health record: race and ethnicity, insurance, preferred language, and smoking status. Main Outcomes and Measures: Indicators of patient engagement with the patient portal included accessing the patient portal at least once, accessing the portal within 7 days of at least 50.0% of primary care physician (PCP) visits, frequent logins (<28 vs ≥28), messaging (<2 vs ≥2), and sharing home blood pressure readings. Results: Among 366 871 patients (mean [SD], 63.5 [12.6] years), 52.8% were female, 3.4% were Asian, 7.8% were Hispanic, 19.7% were non-Hispanic Black, 66.9% were non-Hispanic White, and 2.3% were of other race or ethnicity. During the 1-year study period starting in 2021, 70.5% accessed the patient portal at least once, 60.2% accessed around the time of their PCP visits, 35.7% accessed the portal frequently, 28.9% engaged in messaging, and 8.7% shared home blood pressure readings. Compared with White patients, non-Hispanic Black and Hispanic patients had lower odds of any access (Black: OR, 0.53; 95% CI, 0.52-0.54; Hispanic: OR, 0.66; 95% CI, 0.64-0.68), access around PCP visit time (Black: OR, 0.49; 95% CI, 0.48-0.50; Hispanic: OR, 0.62; 95% CI, 0.60-0.64), frequent access (Black: OR, 0.56; 95% CI, 0.55-0.57; Hispanic: OR, 0.71; 95% CI, 0.69-0.73), and messaging (Black: OR, 0.63; 95% CI, 0.61-0.64); Hispanic: OR, 0.71; 95% CI, 0.69-0.73). Conclusions and Relevance: This cohort study of patients with hypertension found clear sociodemographic disparities in patient portal engagement among those treated in primary care. Without special efforts to engage patients with portals, interventions that use patient portals to target hypertension may exacerbate disparities.

Development of a liver disease-specific large language model chat interface using retrieval-augmented generation.

BACKGROUND AND AIMS: Large language models (LLMs) have significant capabilities in clinical information processing tasks. Commercially available LLMs, however, are not optimized for clinical uses and are prone to generating hallucinatory information. Retrieval-augmented generation (RAG) is an enterprise architecture that allows the embedding of customized data into LLMs. This approach "specializes" the LLMs and is thought to reduce hallucinations. APPROACH AND RESULTS: We developed "LiVersa," a liver disease-specific LLM, by using our institution's protected health information-complaint text embedding and LLM platform, "Versa." We conducted RAG on 30 publicly available American Association for the Study of Liver Diseases guidance documents to be incorporated into LiVersa. We evaluated LiVersa's performance by conducting 2 rounds of testing. First, we compared LiVersa's outputs versus those of trainees from a previously published knowledge assessment. LiVersa answered all 10 questions correctly. Second, we asked 15 hepatologists to evaluate the outputs of 10 hepatology topic questions generated by LiVersa, OpenAI's ChatGPT 4, and Meta's Large Language Model Meta AI 2. LiVersa's outputs were more accurate but were rated less comprehensive and safe compared to those of ChatGPT 4. RESULTS: We evaluated LiVersa's performance by conducting 2 rounds of testing. First, we compared LiVersa's outputs versus those of trainees from a previously published knowledge assessment. LiVersa answered all 10 questions correctly. Second, we asked 15 hepatologists to evaluate the outputs of 10 hepatology topic questions generated by LiVersa, OpenAI's ChatGPT 4, and Meta's Large Language Model Meta AI 2. LiVersa's outputs were more accurate but were rated less comprehensive and safe compared to those of ChatGPT 4. CONCLUSIONS: In this demonstration, we built disease-specific and protected health information-compliant LLMs using RAG. While LiVersa demonstrated higher accuracy in answering questions related to hepatology, there were some deficiencies due to limitations set by the number of documents used for RAG. LiVersa will likely require further refinement before potential live deployment. The LiVersa prototype, however, is a proof of concept for utilizing RAG to customize LLMs for clinical use cases.

Factors associated with anxiety during the first two years of the COVID-19 pandemic in the United States: An analysis of the COVID-19 Citizen Science study.

COVID-19 increased the prevalence of clinically significant anxiety in the United States. To investigate contributing factors we analyzed anxiety, reported online via monthly Generalized Anxiety Disorders-7 (GAD-7) surveys between April 2020 and May 2022, in association with self-reported worry about the health effects of COVID-19, economic difficulty, personal COVID-19 experience, and subjective social status. 333,292 anxiety surveys from 50,172 participants (82% non-Hispanic white; 73% female; median age 55, IQR 42-66) showed high levels of anxiety, especially early in the pandemic. Anxiety scores showed strong independent associations with worry about the health effects of COVID-19 for oneself or family members (GAD-7 score +3.28 for highest vs. lowest category; 95% confidence interval: 3.24, 3.33; p<0.0001 for trend) and with difficulty paying for basic living expenses (+2.06; 1.97, 2.15, p<0.0001) in multivariable regression models after adjusting for demographic characteristics, COVID-19 case rates and death rates, and personal COVID-19 experience. High levels of COVID-19 health worry and economic stress were each more common among participants reporting lower subjective social status, and median anxiety scores for those experiencing both were in the range considered indicative of moderate to severe clinical anxiety disorders. In summary, health worry and economic difficulty both contributed to high rates of anxiety during the first two years of the COVID-19 pandemic in the US, especially in disadvantaged socioeconomic groups. Programs to address both health concerns and economic insecurity in vulnerable populations could help mitigate pandemic impacts on anxiety and mental health.

Applying human-centered design to the construction of a cirrhosis management clinical decision support system.

BACKGROUND: Electronic health record (EHR)-based clinical decision support is a scalable way to help standardize clinical care. Clinical decision support systems have not been extensively investigated in cirrhosis management. Human-centered design (HCD) is an approach that engages with potential users in intervention development. In this study, we applied HCD to design the features and interface for a clinical decision support system for cirrhosis management, called CirrhosisRx. METHODS: We conducted technical feasibility assessments to construct a visual blueprint that outlines the basic features of the interface. We then convened collaborative-design workshops with generalist and specialist clinicians. We elicited current workflows for cirrhosis management, assessed gaps in existing EHR systems, evaluated potential features, and refined the design prototype for CirrhosisRx. At the conclusion of each workshop, we analyzed recordings and transcripts. RESULTS: Workshop feedback showed that the aggregation of relevant clinical data into 6 cirrhosis decompensation domains (defined as common inpatient clinical scenarios) was the most important feature. Automatic inference of clinical events from EHR data, such as gastrointestinal bleeding from hemoglobin changes, was not accepted due to accuracy concerns. Visualizations for risk stratification scores were deemed not necessary. Lastly, the HCD co-design workshops allowed us to identify the target user population (generalists). CONCLUSIONS: This is one of the first applications of HCD to design the features and interface for an electronic intervention for cirrhosis management. The HCD process altered features, modified the design interface, and likely improved CirrhosisRx's overall usability. The finalized design for CirrhosisRx proceeded to development and production and will be tested for effectiveness in a pragmatic randomized controlled trial. This work provides a model for the creation of other EHR-based interventions in hepatology care.

Association of nirmatrelvir for acute SARS-CoV-2 infection with subsequent Long COVID symptoms in an observational cohort study.

Oral nirmatrelvir/ritonavir is approved as treatment for acute COVID-19, but the effect of treatment during acute infection on risk of Long COVID is unknown. We hypothesized that nirmatrelvir treatment during acute SARS-CoV-2 infection reduces risk of developing Long COVID and rebound after treatment is associated with Long COVID. We conducted an observational cohort study within the Covid Citizen Science (CCS) study, an online cohort study with over 100 000 participants. We included vaccinated, nonhospitalized, nonpregnant individuals who reported their first SARS-CoV-2 positive test March-August 2022. Oral nirmatrelvir/ritonavir treatment was ascertained during acute SARS-CoV-2 infection. Patient-reported Long COVID symptoms, symptom rebound and test-positivity rebound were asked on subsequent surveys at least 3 months after SARS-CoV-2 infection. A total of 4684 individuals met the eligibility criteria, of whom 988 (21.1%) were treated and 3696 (78.9%) were untreated; 353/988 (35.7%) treated and 1258/3696 (34.0%) untreated responded to the Long COVID survey (n = 1611). Among 1611 participants, median age was 55 years and 66% were female. At 5.4 ± 1.3 months after infection, nirmatrelvir treatment was not associated with subsequent Long COVID symptoms (odds ratio [OR]: 1.15; 95% confidence interval [CI]: 0.80-1.64; p = 0.45). Among 666 treated who answered rebound questions, rebound symptoms or test positivity were not associated with Long COVID symptoms (OR: 1.34; 95% CI: 0.74-2.41; p = 0.33). Within this cohort of vaccinated, nonhospitalized individuals, oral nirmatrelvir treatment during acute SARS-CoV-2 infection and rebound after nirmatrelvir treatment were not associated with Long COVID symptoms more than 90 days after infection.

Preceding Night Sleep Quality and Atrial Fibrillation Episodes in the I-STOP-AFIB Randomized Trial.

BACKGROUND: Chronic sleep disruption is associated with incident atrial fibrillation (AF), but it is unclear whether poor sleep quality acutely triggers AF. OBJECTIVES: The aim of this study was to characterize the relationship between a given night's sleep quality and the risk of a discrete AF episode. METHODS: Patients with symptomatic paroxysmal AF in the I-STOP-AFIB (Individualized Studies of Triggers of Paroxysmal Atrial Fibrillation) trial reported sleep quality on a daily basis. Participants were also queried daily regarding AF episodes and were provided smartphone-based mobile electrocardiograms (ECGs) (KardiaMobile, AliveCor). RESULTS: Using 15,755 days of data from 419 patients, worse sleep quality on any given night was associated with a 15% greater odds of a self-reported AF episode the next day (OR: 1.15; 95% CI: 1.10-1.20; P < 0.0001) after adjustment for the day of the week. No statistically significant associations between worsening sleep quality and mobile ECG-confirmed AF events were observed (OR: 1.04; 95% CI: 0.95-1.13; P = 0.43), although substantially fewer of these mobile ECG-confirmed events may have limited statistical power. Poor sleep was also associated with longer self-reported AF episodes, with each progressive category of worsening sleep associated with 16 (95% CI: 12-21; P < 0.001) more minutes of AF the next day. CONCLUSIONS: Poor sleep was associated with an immediately heightened risk for self-reported AF episodes, and a dose-response relationship existed such that progressively worse sleep was associated with longer episodes of AF the next day. These data suggest that sleep quality may be a potentially modifiable trigger relevant to the near-term risk of a discrete AF episode.

Email-Based Recruitment Into the Health eHeart Study: Cohort Analysis of Invited Eligible Patients.

BACKGROUND: Web- or app-based digital health studies allow for more efficient collection of health data for research. However, remote recruitment into digital health studies can enroll nonrepresentative study samples, hindering the robustness and generalizability of findings. Through the comprehensive evaluation of an email-based campaign on recruitment into the Health eHeart Study, we aim to uncover key sociodemographic and clinical factors that contribute to enrollment. OBJECTIVE: This study sought to understand the factors related to participation, specifically regarding enrollment, in the Health eHeart Study as a result of a large-scale remote email recruitment campaign. METHODS: We conducted a cohort analysis on all invited University of California, San Francisco (UCSF) patients to identify sociodemographic and clinical predictors of enrollment into the Health eHeart Study. The primary outcome was enrollment, defined by account registration and consent into the Health eHeart Study. The email recruitment campaign was carried out from August 2015 to February 2016, with electronic health record data extracted between September 2019 and December 2019. RESULTS: The email recruitment campaign delivered at least 1 email invitation to 93.5% (193,606/206,983) of all invited patients and yielded a 3.6% (7012/193,606) registration rate among contacted patients and an 84.1% (5899/7012) consent rate among registered patients. Adjusted multivariate logistic regression models analyzed independent sociodemographic and clinical predictors of (1) registration among contacted participants and (2) consent among registered participants. Odds of registration were higher among patients who are older, women, non-Hispanic White, active patients with commercial insurance or Medicare, with a higher comorbidity burden, with congestive heart failure, and randomized to receive up to 2 recruitment emails. The odds of registration were lower among those with medical conditions such as dementia, chronic pulmonary disease, moderate or severe liver disease, paraplegia or hemiplegia, renal disease, or cancer. Odds of subsequent consent after initial registration were different, with an inverse trend of being lower among patients who are older and women. The odds of consent were also lower among those with peripheral vascular disease. However, the odds of consent remained higher among patients who were non-Hispanic White and those with commercial insurance. CONCLUSIONS: This study provides important insights into the potential returns on participant enrollment when digital health study teams invest resources in using email for recruitment. The findings show that participant enrollment was driven more strongly by sociodemographic factors than clinical factors. Overall, email is an extremely efficient means of recruiting participants from a large list into the Health eHeart Study. Despite some improvements in representation, the formulation of truly diverse studies will require additional resources and strategies to overcome persistent participation barriers.

Electronic health record intervention to increase use of NSAIDs as analgesia for hospitalised patients: a cluster randomised controlled study.

BACKGROUND: Prescribing non-opioid pain medications, such as non-steroidal anti-inflammatory (NSAIDs) medications, has been shown to reduce pain and decrease opioid use, but it is unclear how to effectively encourage multimodal pain medication prescribing for hospitalised patients. Therefore, the aim of this study is to evaluate the effect of prechecking non-opioid pain medication orders on clinician prescribing of NSAIDs among hospitalised adults. METHODS: This was a cluster randomised controlled trial of adult (≥18 years) hospitalised patients admitted to three hospital sites under one quaternary hospital system in the USA from 2 March 2022 to 3 March 2023. A multimodal pain order panel was embedded in the admission order set, with NSAIDs prechecked in the intervention group. The intervention group could uncheck the NSAID order. The control group had access to the same NSAID order. The primary outcome was an increase in NSAID ordering. Secondary outcomes include NSAID administration, inpatient pain scores and opioid use and prescribing and relevant clinical harms including acute kidney injury, new gastrointestinal bleed and in-hospital death. RESULTS: Overall, 1049 clinicians were randomised. The study included 6239 patients for a total of 9595 encounters. Both NSAID ordering (36 vs 43%, p<0.001) and administering (30 vs 34%, p=0.001) by the end of the first full hospital day were higher in the intervention (prechecked) group. There was no statistically significant difference in opioid outcomes during the hospitalisation and at discharge. There was a statistically but perhaps not clinically significant difference in pain scores during both the first and last full hospital day. CONCLUSIONS: This cluster randomised controlled trial showed that prechecking an order for NSAIDs to promote multimodal pain management in the admission order set increased NSAID ordering and administration, although there were no changes to pain scores or opioid use. While prechecking orders is an important way to increase adoption, safety checks should be in place.

Institutionalizing Community-engaged Translational Science in an Academic Institution: A Community Stakeholder-Driven Process.

BACKGROUND: Although studies have described the power imbalance in academic-community partnerships, little has been published describing how community-based participatory research-informed practitioners can change academic institutions to promote more effective community-engaged research. OBJECTIVES: This paper describes a university-funded community-based participatory project in which academic researchers and their community partners worked together to articulate, develop and advocate for institutionalizing best practices for equitable partnerships throughout the university. METHODS: Findings derive from a collaborative ethnographic process evaluation. RESULTS: The study describes the integral steps proposed to promote equitable community-university research collaboration, the process by which these principles and best practice recommendations were developed, and the institutional change outcomes of this process. CONCLUSIONS: When universities make even small investments toward promoting and nurturing community-engaged research, the quality of the science can be enhanced to advance health equity and community-university relationships can improve, particularly if based on trust, mutual respect, and openness to accomplish a shared vision.

Development of a Liver Disease-Specific Large Language Model Chat Interface using Retrieval Augmented Generation.

Background: Large language models (LLMs) have significant capabilities in clinical information processing tasks. Commercially available LLMs, however, are not optimized for clinical uses and are prone to generating incorrect or hallucinatory information. Retrieval-augmented generation (RAG) is an enterprise architecture that allows embedding of customized data into LLMs. This approach "specializes" the LLMs and is thought to reduce hallucinations. Methods: We developed "LiVersa," a liver disease-specific LLM, by using our institution's protected health information (PHI)-complaint text embedding and LLM platform, "Versa." We conducted RAG on 30 publicly available American Association for the Study of Liver Diseases (AASLD) guidelines and guidance documents to be incorporated into LiVersa. We evaluated LiVersa's performance by comparing its responses versus those of trainees from a previously published knowledge assessment study regarding hepatitis B (HBV) treatment and hepatocellular carcinoma (HCC) surveillance. Results: LiVersa answered all 10 questions correctly when forced to provide a "yes" or "no" answer. Full detailed responses with justifications and rationales, however, were not completely correct for three of the questions. Discussions: In this study, we demonstrated the ability to build disease-specific and PHI-compliant LLMs using RAG. While our LLM, LiVersa, demonstrated more specificity in answering questions related to clinical hepatology - there were some knowledge deficiencies due to limitations set by the number and types of documents used for RAG. The LiVersa prototype, however, is a proof of concept for utilizing RAG to customize LLMs for clinical uses and a potential strategy to realize personalized medicine in the future.

Associations between tobacco and cannabis use and anxiety and depression among adults in the United States: Findings from the COVID-19 citizen science study.

BACKGROUND: Little is known about whether people who use both tobacco and cannabis (co-use) are more or less likely to have mental health disorders than single substance users or non-users. We aimed to examine associations between use of tobacco and/or cannabis with anxiety and depression. METHODS: We analyzed data from the COVID-19 Citizen Science Study, a digital cohort study, collected via online surveys during 2020-2022 from a convenience sample of 53,843 US adults (≥ 18 years old) nationwide. Past 30-day use of tobacco and cannabis was self-reported at baseline and categorized into four exclusive patterns: tobacco-only use, cannabis-only use, co-use of both substances, and non-use. Anxiety and depression were repeatedly measured in monthly surveys. To account for multiple assessments of mental health outcomes within a participant, we used Generalized Estimating Equations to examine associations between the patterns of tobacco and cannabis use with each outcome. RESULTS: In the total sample (mean age 51.0 years old, 67.9% female), 4.9% reported tobacco-only use, 6.9% cannabis-only use, 1.6% co-use, and 86.6% non-use. Proportions of reporting anxiety and depression were highest for the co-use group (26.5% and 28.3%, respectively) and lowest for the non-use group (10.6% and 11.2%, respectively). Compared to non-use, the adjusted odds of mental health disorders were highest for co-use (Anxiety: OR = 1.89, 95%CI = 1.64-2.18; Depression: OR = 1.77, 95%CI = 1.46-2.16), followed by cannabis-only use, and tobacco-only use. Compared to tobacco-only use, co-use (OR = 1.35, 95%CI = 1.08-1.69) and cannabis-only use (OR = 1.17, 95%CI = 1.00-1.37) were associated with higher adjusted odds for anxiety, but not for depression. Daily use (vs. non-daily use) of cigarettes, e-cigarettes, and cannabis were associated with higher adjusted odds for anxiety and depression. CONCLUSIONS: Use of tobacco and/or cannabis, particularly co-use of both substances, were associated with poor mental health. Integrating mental health support with tobacco and cannabis cessation may address this co-morbidity.

Predicting post-liver transplant outcomes in patients with acute-on-chronic liver failure using Expert-Augmented Machine Learning.

Liver transplantation (LT) is a treatment for acute-on-chronic liver failure (ACLF), but high post-LT mortality has been reported. Existing post-LT models in ACLF have been limited. We developed an Expert-Augmented Machine Learning (EAML) model to predict post-LT outcomes. We identified ACLF patients who underwent LT in the University of California Health Data Warehouse. We applied the RuleFit machine learning (ML) algorithm to extract rules from decision trees and create intermediate models. We asked human experts to rate the rules generated by RuleFit and incorporated these ratings to generate final EAML models. We identified 1384 ACLF patients. For death at 1 year, areas under the receiver-operating characteristic curve were 0.707 (confidence interval [CI] 0.625-0.793) for EAML and 0.719 (CI 0.640-0.800) for RuleFit. For death at 90 days, areas under the receiver-operating characteristic curve were 0.678 (CI 0.581-0.776) for EAML and 0.707 (CI 0.615-0.800) for RuleFit. In pairwise comparisons, both EAML and RuleFit models outperformed cross-sectional models. Significant discrepancies between experts and ML occurred in rankings of biomarkers used in clinical practice. EAML may serve as a method for ML-guided hypothesis generation in further ACLF research.