Citations

Lactulose-based hepatic encephalopathy treatment requires bowel movements/day titration, which is improved with Bristol stool scale (BSS) incorporation. Dieta app evaluates artificial intelligence (AI)-based BSS (AI-BSS) with stool images. Initially, controls (N = 13) and cirrhosis patients on lactulose/not on lactulose (n = 33) were trained on the app. They entered self-reported BSS (self-BSS) with AI-BSS communicated. Lactulose dose changes were tracked. A subset (n = 12) was retested with AI communication blocked. Most subjects were comfortable with the app. Self/AI-BSS and lactulose dose/AI-BSS correlation increased with app use. AI-BSS communications improved insight into self-BSS over time. Dieta app to gauge stool AI characteristics was acceptable and increased insight into lactulose dose and BSS in cirrhosis.

AbstractArthrofibrosis, which causes joint motion restrictions, is a common complication following total knee arthroplasty (TKA). Key features associated with arthrofibrosis include myofibroblast activation, knee stiffness, and excessive scar tissue formation. We previously demonstrated that adiponectin levels are suppressed within the knee tissues of patients affected by arthrofibrosis and showed that AdipoRon, an adiponectin receptor agonist, exhibited anti‐fibrotic properties in human mesenchymal stem cells. In this study, the therapeutic potential of AdipoRon was evaluated on TGFβ1‐mediated myofibroblast differentiation of primary human knee fibroblasts and in a mouse model of knee stiffness. Picrosirius red staining revealed that AdipoRon reduced TGFβ1‐induced collagen deposition in primary knee fibroblasts derived from patients undergoing primary TKA and revision TKA for arthrofibrosis. AdipoRon also reduced mRNA and protein levels of ACTA2, a key myofibroblast marker. RNA‐seq analysis corroborated the anti‐myofibrogenic effects of AdipoRon. In our knee stiffness mouse model, 6 weeks of knee immobilization, to induce a knee contracture, in conjunction with daily vehicle (DMSO) or AdipoRon (1, 5, and 25 mg/kg) via intraperitoneal injections were well tolerated based on animal behavior and weight measurements. Biomechanical testing demonstrated that passive extension angles (PEAs) of experimental knees were similar between vehicle and AdipoRon treatment groups in mice evaluated immediately following immobilization. Interestingly, relative to vehicle‐treated mice, 5 mg/kg AdipoRon therapy improved the PEA of the experimental knees in mice that underwent 4 weeks of knee remobilization following the immobilization and therapy. Together, these studies revealed that AdipoRon may be an effective therapeutic modality for arthrofibrosis.

ABSTRACT Purpose: The aim of the study is to evaluate whether serial assessment of shock severity can improve prognostication in intensive care unit (ICU) patients. Materials and Methods: This is a retrospective cohort of 21,461 ICU patient admissions from 2014 to 2018. We assigned the Society for Cardiovascular Angiography and Interventions (SCAI) Shock Stage in each 4-h block during the first 24 h of ICU admission; shock was defined as SCAI Shock stage C, D, or E. In-hospital mortality was evaluated using logistic regression. Results: The admission SCAI Shock stages were as follows: A, 39.0%; B, 27.0%; C, 28.9%; D, 2.6%; and E, 2.5%. The SCAI Shock stage subsequently increased in 30.6%, and late-onset shock developed in 30.4%. In-hospital mortality was higher in patients who had shock on admission (11.9%) or late-onset shock (7.3%) versus no shock (4.3%). Persistence of shock predicted higher mortality (adjusted OR = 1.09; 95% CI = 1.06–1.13, for each ICU block with shock). The mean SCAI Shock stage had higher discrimination for in-hospital mortality than the admission or maximum SCAI Shock stage. Dynamic modeling of the SCAI Shock classification improved discrimination for in-hospital mortality (C-statistic = 0.64–0.71). Conclusions: Serial application of the SCAI Shock classification provides improved mortality risk stratification compared with a single assessment on admission, facilitating dynamic prognostication.

Abstract Research has established that dementia care partners may encounter socio-emotional and physical health concerns as a consequence of providing dementia care. Similarly, care partners for people living with Traumatic Brain Injury (TBI) often experience a range of stressors and negative mental health outcomes due to their care demands. This study aims to support the unique subset of family care partners facing the challenge of caring for relatives who have both diseases, Alzheimer’s Disease/Alzheimer’s Disease related dementia (AD/ADRD) and a history of TBI. Phase I of this study focused on the design and subsequent refinement of the TBI-AD/ADRD Caregiver Support Intervention (TACSI). TACSI is a six-session telehealth intervention providing tailored psychosocial and psychoeducational coaching. Session topics include education on TBI and dementia, stress management, lifestyle planning, and communication skills. In partnership with the Minneapolis VA and the Mayo Clinic-Rochester, we enrolled 15 care partners for Phase I. Mixed methods data from 3-month follow-up surveys and qualitative data from post-intervention interviews established the feasibility, utility, and acceptance of TACSI. Care partners found the program valuable and would recommend it to others. They reported that TACSI helped with their stress management. Some participants shared that TACSI would provide greater benefit to newer caregivers needing more education on the diseases and/or caregiving. Phase I feedback will be applied to improve TACSI content and delivery for the Phase II randomized controlled trial evaluation. TACSI Phase I utility, acceptance, and feasibility outcomes will be highlighted as well as next steps for evaluation.

Abstract Background: Many patients use artificial intelligence (AI) chatbots as a rapid source of health information. This raises important questions about the reliability and effectiveness of AI chatbots in delivering accurate and understandable information. Purpose: To evaluate and compare the accuracy, conciseness, and readability of responses from OpenAI ChatGPT-4 and Google Bard to patient inquiries concerning the novel 177Lu-PSMA-617 therapy for prostate cancer. Materials and Methods: Two experts listed the 12 most commonly asked questions by patients on 177Lu-PSMA-617 therapy. These twelve questions were prompted to OpenAI ChatGPT-4 and Google Bard. AI-generated responses were distributed using an online survey platform (Qualtrics) and blindly rated by eight experts. The performances of the AI chatbots were evaluated and compared across three domains: accuracy, conciseness, and readability. Additionally, potential safety concerns associated with AI-generated answers were also examined. The Mann-Whitney U and chi-square tests were utilized to compare the performances of AI chatbots. Results: Eight experts participated in the survey, evaluating 12 AI-generated responses across the three domains of accuracy, conciseness, and readability, resulting in 96 assessments (12 responses x 8 experts) for each domain per chatbot. ChatGPT-4 provided more accurate answers than Bard (2.95 ± 0.671 vs 2.73 ± 0.732, p=0.027). Bard's responses had better readability than ChatGPT-4 (2.79 ± 0.408 vs 2.94 ± 0.243, p=0.003). Both ChatGPT-4 and Bard achieved comparable conciseness scores (3.14 ± 0.659 vs 3.11 ± 0.679, p=0.798). Experts categorized the AI-generated responses as incorrect or partially correct at a rate of 16.6% for ChatGPT-4 and 29.1% for Bard. Bard's answers contained significantly more misleading information than those of ChatGPT-4 (p = 0.039). Conclusion: AI chatbots have gained significant attention, and their performance is continuously improving. Nonetheless, these technologies still need further improvements to be considered reliable and credible sources for patients seeking medical information on 177Lu-PSMA-617 therapy.

Background: Patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS) often require vasoactive medications to maintain organ perfusion. However, there are limited data on the real-world use of vasoactive medications in this population. Methods: We used the Vizient® Clinical Data Base to identify patients aged ≥18 years who were admitted between 2015-2019 with AMI-CS and required mechanical ventilation. We report the proportion of patients receiving vasoactive medications by day as well as site-level variability for vasoactive medication strategies using the median odds ratio (MOR), which quantifies the odds that a patient receives a strategy at two randomly selected sites after adjusting for covariates. Results: We identified 10,758 patients from 160 centers admitted with AMI-CS. The median age was 65.8 years, 29.8% were female, 83.6% received vasoactive medications in the first 5 days, and 56.9% received temporary mechanical support. The most used vasoactive medication on day 1 was norepinephrine (48.2%), followed by epinephrine (31.5%), and dopamine (16.9%) ( Figure ). On day 1, 65.9% and 36.2% of patients receiving vasoactive medications were prescribed ≥2 or ≥3 different medications, respectively, including 111 different combinations. Among patients receiving inotropes (N=4,485), there was a nearly 3-fold difference in milrinone use across sites after accounting for differences in patient characteristics (MOR 2.89; 95% confidence interval [CI]: 2.39-3.38). For patients receiving either norepinephrine, dopamine, or epinephrine on day 1 (N=3,224), there was a nearly 2-fold difference in the use of norepinephrine as a first-line vasopressor across sites (MOR 1.84; 95% CI: 1.59-2.06). Conclusion: Norepinephrine is the most commonly used vasoactive medication in patients with AMI-CS. However, there is substantial site-level variability in vasoactive medication strategies, which require further study.

Background: Heart failure (HF) is a complex trait influenced by environmental and genetic factors. Genome-wide association studies (GWAS) have found common genetic variants associated with HF risk, but the extent that common and rare variants explain risk and implicate similar genes and pathways in HF remains less understood. Methods: For common variants, we meta-analyze existing GWAS of all-cause HF with data from the Million Veteran Program, including 207,346 individuals with HF and 2,151,210 without in diverse populations. We examine rare coding variants in the Penn Medicine BioBank (PMBB) using gene-collapsing association studies and burden heritability regression. We test for an interaction between a polygenic risk score (PRS) based on our GWAS meta-analysis and truncating variants in TTN (TTNtv). Results: GWAS meta-analysis identified 176 risk loci (146 previously unreported), implicating known risk factors for HF and genes involved in cardiac development. New signals at GIPR/ GLP1R highlight potential contributions of dysregulated glucose homeostasis and metabolism in HF. Gene collapsing studies identified associations between HF and rare loss-of-function variants in TTN (OR 2.94, 95% CI 2.43-3.56) and MYBPC3 (OR 7.71, 95% CI 3.97-14.97) at exome-wide significance. HF heritability mediated through common variants (4.5%) was significantly higher than for rare variants (1.8%), but rare variant heritability was further enriched in cardiomyopathy genes compared to common variants (27-fold vs. 3.4-fold). We found a significant interaction between a new multi-ancestry HF-associated PRS (AUC 0.72, 95% CI 0.72-0.73) and TTNtv carrier status (interaction OR 1.32, 95% CI 1.06-1.64). Conclusions: Common and rare variants contribute to HF heritability. Common variant HF heritability is highly polygenic, while rare variant burden heritability appears more concentrated in core cardiomyopathy genes. HF risk conferred by TTNtv carrier status is further increased by an individual’s polygenic contribution. Our results support the continued inclusion of cardiomyopathy genes in rare-variant genetic testing panels for HF but suggest a meaningful polygenic component to HF exists that is not currently captured by clinical genetic testing.

Introduction: Hyperkalemia has been associated with increased mortality in cardiac intensive care unit (CICU) patients. An artificial intelligence (AI) augmented electrocardiogram (ECG) can predict hyperkalemia, and other AI-ECG algorithms have predicted mortality in CICU patients. Hypothesis: We hypothesized that the AI-ECG hyperkalemia algorithm could stratify mortality risk beyond the serum potassium measurement. Methods: We included 11 234 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who had both a 12 lead ECG and blood potassium (K) level obtained at admission with K>=5 mEq/L defining hyperkalemia. ECGs underwent AI evaluation for probability of hyperkalemia (probability >=0.5 defined as positive). Hospital mortality was analyzed using logistic regression and survival to one year was estimated using Kaplan-Meier and Cox analysis. Results: The mean age was 70 years, 37.8% were females, and 92.4% were white. Chronic kidney disease was present in 20%. The mean K value was 4.2 mEq/L, and 13% (n=1460) had hyperkalemia. AI-ECG predicted hyperkalemia in 34% (n=3820). In-hospital mortality was higher in patients with laboratory and/or AI-ECG hyperkalemia, and these patients had lower survival out to 1 year. Mortality was lowest in patients with normal K and negative AI-ECG and highest in patients with both laboratory-confirmed hyperkalemia and AI-ECG predicted hyperkalemia. Patients with isolated laboratory hyperkalemia (false-negative AI-ECG) had higher mortality than those with isolated AI-ECG hyperkalemia (false-positive AI-ECG). Conclusions: AI-ECG probability of hyperkalemia, even with a normal laboratory K, was associated with higher short-term and long-term mortality in CICU patients. The laboratory K value and AI-ECG probability of hyperkalemia are complementary risk factors. The AI algorithm can detect prognostically important ECG abnormalities which may reflect the effects of K levels on the myocardium.

Introduction: The right ventricle [RV] adapts to pulmonary hypertension [PH] by increasing contractility to maintain RV-PA coupling. With increased afterload, RV contractility cannot compensate, resulting in RV failure. TAPSE/RVSP ratio is a non-invasive measure of RV-PA coupling. We aim to assess TAPSE/RVSP ratio for predicting PH severity and survival across a spectrum of PH in the PVDOMICS cohort. Methods: PVDOMICS subjects from the 5 World Symposium PH groups and comparators were evaluated. TAPSE/RVSP ratio, in mm/mmHg, was derived for each group. PH subjects were stratified by TAPSE/RVSP ratio into low < 0.19, intermediate ≥ 0.19 to < 0.32, and high ratio > 0.32. The relationship between the ratio and RHC parameters (mean PA pressure [mPAP] and pulmonary vascular resistance [PVR]), and between-group differences were evaluated by one-way ANOVA. Kaplan-Meier curves and log-rank tests were used to characterize and compare survival between groups. Results: Data were available for 514 PH subjects (58 ± 15 yrs, 64% female) and 179 comparators (61 ± 13 yrs, 59% female). Mean TAPSE/RVSP ratio was 0.35 ± 0.18 in PH vs 0.63 ± 0.26 in comparators (p < 0.001). Mean TAPSE/RVSP across the PH groups 1-5 were similar, 0.33 ± 0.19, 0.33 ± 0.16, 0.37 ± 0.15, 0.41 ± 0.21 and 0.43 ± 0.29 (p=NS). Within PH subjects, 99 (19.2%) had low, 173 (33.7%) had intermediate and 242 (47.1%) had high ratio. Those with low ratio had higher mPAP (53.6 ± 11.8 mmHg), compared to intermediate (44.6 ± 11.7 mmHg) and high ratio (34.1 ± 10.2 mmHg) [p < 0.001]. Those with low ratio had higher PVR (10.1 ± 5.1 WU), compared to intermediate (6.8 ± 4.1 WU) and high ratio (4.1 ± 2.1 WU) [p < 0.001]. Survival was significantly higher for those with high TAPSE/RVSP ratio compared to those with low ratio (p < 0.001) [Figure]. Discussion: TAPSE/RVSP ratio correlates well with invasive markers to predict PH severity. This useful measure of RV-PA coupling also enables stratification for outcome, with ratio > 0.32 having the best survival rates.

Introduction: A single assessment of the SCAI shock classification robustly predicts mortality, and a repeat assessment may improve prognostication. Hypothesis: We hypothesized that frequent serial assignment of the SCAI shock stage could further improve risk stratification. Methods: Unique AHA Level 1 cardiac intensive care unit (CICU) admissions at a single center from 2015 to 2018 were reviewed retrospectively. Time-stamped electronic health record data were used to assign the SCAI shock stage in each 4-hour block during the first 24 hours of CICU admission. Shock was defined as SCAI shock stage C, D, or E. In-hospital mortality was evaluated using logistic regression. Results: Among 2,918 CICU patients, 1,537 (52.7%) met criteria for shock during >=1 block and 266 (9.1%) died in hospital. The SCAI shock stage on admission was: A, 37.6%; B, 31.5%; C, 25.9%; D, 1.8%; E, 3.3%. Patients with worsening SCAI shock stage after admission (first 4 hours) were at higher risk of mortality ( Figure A ), as were patients who met SCAI criteria for shock (particularly those with shock on admission, Figure B ). The pattern was consistent in patients with acute coronary syndromes, heart failure, or cardiac arrest (who had very high mortality). Each higher admission (aOR 1.36, 95% CI 1.18-1.56, AUC 0.70), maximum (aOR 1.59, 95% CI 1.37-1.85, AUC 0.73) and 24 hour mean (aOR 2.42, 95% CI 1.99-2.95, AUC 0.78) SCAI shock stage were incrementally associated with increasing in-hospital mortality. Discrimination was highest for the mean SCAI shock stage (p <0.05). Each additional 4 hour block meeting SCAI criteria for shock was associated with higher mortality (aOR 1.15, 95% CI 1.07-1.24). Conclusions: Dynamic assessment of shock using serial SCAI shock classification assignment can improve mortality risk stratification in CICU patients. Cumulative time in shock was a strong mortality risk indicator, highlighting the area under the curve of shock severity as an important determinant of outcomes.