Citations

Abstract Aims The increasing need for diagnostic echocardiography tests presents challenges in preserving the quality and promptness of reports. While Large Language Models (LLMs) have proven effective in summarizing clinical texts, their application in echo remains underexplored. Methods and results Adult echocardiography studies, conducted at the Mayo Clinic from 1 January 2017 to 31 December 2017, were categorized into two groups: development (all Mayo locations except Arizona) and Arizona validation sets. We adapted open-source LLMs (Llama-2, MedAlpaca, Zephyr, and Flan-T5) using In-Context Learning and Quantized Low-Rank Adaptation fine-tuning (FT) for echo report summarization from ‘Findings’ to ‘Impressions.’ Against cardiologist-generated Impressions, the models’ performance was assessed both quantitatively with automatic metrics and qualitatively by cardiologists. The development dataset included 97 506 reports from 71 717 unique patients, predominantly male (55.4%), with an average age of 64.3 ± 15.8 years. EchoGPT, a fine-tuned Llama-2 model, outperformed other models with win rates ranging from 87% to 99% in various automatic metrics, and produced reports comparable to cardiologists in qualitative review (significantly preferred in conciseness (P < 0.001), with no significant preference in completeness, correctness, and clinical utility). Correlations between automatic and human metrics were fair to modest, with the best being RadGraph F1 scores vs. clinical utility (r = 0.42) and automatic metrics showed insensitivity (0–5% drop) to changes in measurement numbers. Conclusion EchoGPT can generate draft reports for human review and approval, helping to streamline the workflow. However, scalable evaluation approaches dedicated to echo reports remains necessary.

AbstractBackgroundAccurately estimating glomerular filtration rate (GFR) is crucial for dosing medications in hospitalized patients. Due to limitations of serum creatinine for GFR estimation, serum cystatin C (CysC) has been explored as an alternative functional kidney biomarker. This study assessed discordance between eGFRCr and eGFRCysC in a large sample of hospitalized patients and examined the frequency of renally eliminated medications affected by eGFR discordance.MethodsThis multisite historical study included adults hospitalized between 2011 and 2023 with CysC and serum creatinine reported within 24 h of each other. The first concurrent biomarker pair for each patient was analyzed. eGFR discordance and use of renally eliminated medications were described.Results17,718 hospitalized patients with concurrent creatinine and CysC assessments were included. The median eGFRCr was 65 mL/min, and the eGFRCysC was 46 mL/min. The median absolute difference of eGFRCr−eGFRCysC was 15 mL/min, and 7972 patients (45%) had a > 30% absolute difference. There was a significantly greater percentage of patients with an eGFR <30 mL/min based on eGFRCysC (26%) compared to eGFRCr (15%) (p < 0.001). Patients were prescribed an average of 20 medications in the 24 h surrounding the concurrent biomarker assessment. Renally eliminated medications accounted for 39% ± 13% of medication orders, and 80% of patients with eGFR discordance were prescribed five or more renally eliminated medications.ConclusionSubstantial eGFR discordance between eGFRCysC and eGFRCr was observed in hospitalized patients, which directly affects the dosing of renally eliminated medications. Further research is needed to optimize the pharmacotherapy of renally eliminated medications with discordant GFR assessments to improve medication safety and effectiveness.

Chronic kidney disease (CKD) affects more than 10% of the world’s population. Hemodialysis, along with peritoneal dialysis and renal transplant, is one of the renal replacement therapies offered to patients with CKD/end-stage renal disease (ESRD). To proceed with hemodialysis, vascular access is required. The two means of long-term access are arteriovenous fistula (AVF) and arteriovenous graft (AVG). Multiple therapies have been created to help the long-term patency of AVFs. These therapies are needed as 40% of AVFs fail within the first year and additional intervention is required. Much of the existing research has focused on biomarkers, immune cells, hypoxia, and cell-based therapies. Regeneration therapy using mesenchymal stem cells seeks to investigate other ways that we can treat AVF failure. Mesenchymal stem cells are harvested as two main types, fetal and adult. Fetal cells are harvested at different times in fetal gestation and from multiple sources, placental blood, Whartons jelly, and amniotic stem cell fluid. Taken together, this review summarizes the different preclinical/clinical studies conducted using different types of MSCs towards vascular regenerative medicine and further highlights its potential to be a suitable alternative approach to enhance AVF patency.

INTRODUCTION: We aimed to compare pregnancy outcomes of women with inflammatory bowel disease using biosimilar vs originator infliximab (IFX). METHODS: In a prospective cohort of pregnant women with inflammatory bowel disease, we collected characteristics, medications, pregnancy outcomes, and developmental milestones. We compared outcomes by IFX biosimilar or originator use via bivariate statistics. RESULTS: A total of 100 pregnant women on originator IFX and 20 on biosimilar IFX were included. There were no differences in pregnancy complications between groups (48% vs 35%, P = 0.29). Infant developmental milestones were comparable at 12 months. DISCUSSION: Biosimilar IFX is not associated with adverse pregnancy or infant outcomes.

Background: Ventricular-arterial coupling (VAC) refers to the ratio of afterload (effective arterial elastance) to contractility (left ventricular (LV) end-systolic elastance) as an integrated marker of cardiac performance. Echocardiography might be used to evaluate VAC through a simple time-efficient bedside evaluation. Hypothesis: VAC is useful for mortality risk stratification in an unselected cardiac intensive care unit (CICU) cohort. Aims: We sought to determine whether the VAC ratio, defined using the ratio of LV end-systolic volume (LVESV) to stroke volume (SV), predicted mortality in the CICU. Methods: Consecutive patients admitted to the Mayo Clinic CICU between 2007 and 2018 were included. The simplified VAC ratio was calculated as the LVESV calculated from 2D measurements using the Teichholz formula divided by the SV calculated from the LV outflow tract pulsed wave Doppler measurement, with values >1 reflecting ventricular-arterial uncoupling. The main outcome was all-cause in-hospital mortality. Results: We included 4685 patients with a median age of 69 (58-79) years; 37% were females. The median VAC ratio was 1.0 (0.7 to 1.8) and 21% of patients had a VAC ratio >2. Most clinical and echocardiographic variables differed in patients with higher VAC ratio, reflecting greater severity of illness with worse cardiac systolic and diastolic function. A total of 329 (7.0%) patients died during hospitalization. An incremental association was observed between a higher VAC and in-hospital mortality (unadjusted OR 1.22, 95% CI 1.16-1.29, p<0.001). Patients with a VAC ratio >1 (unadjusted OR 2.85, 95% CI 2.22-3.69, p <0.001) and especially those with VAC ratio >2 (unadjusted OR 4.28, 95% CI 3.24-5.69, p<0.001) had higher in-hospital mortality overall and in each admission diagnosis subgroup of interest ( Figure ). After multivariable adjustment, patients with a VAC ratio >2 remained at higher risk of in-hospital mortality (adjusted OR 1.63, 95% CI 1.17-2.28, p = 0.01). Conclusions: Ventricular-arterial uncoupling was associated with worse outcomes in an unselected CICU cohort. The echocardiographic VAC ratio is a simple non-invasive bedside measure that can be used for risk prediction in the CICU.

Background: AI-ECG is a cost-effective tool for left ventricular dysfunction (LVD) screening. However, its cost-effectiveness for other forms of structural heart disease (SHD) is unknown. While AI-ECG is inexpensive, a drawback is low positive predictive value (PPV), which leads to high costs from unnecessary follow-up tests. Therefore, strategies to improve the yield of AI-ECG-based screening are needed. Aim: To evaluate the cost savings of a stepwise approach to SHD screening with AI-ECG followed by POCUS compared to AI-ECG alone. Methods: 286 adult outpatients undergoing AI-ECG were selected at random. Participants received same-day POCUS and had a recent TTE (our gold standard for SHD). We evaluated four SHDs: aortic stenosis (AS), cardiac amyloidosis (CA), HCM, and LVD. The costs of AI-ECG ($75) and TTE ($1,305) were obtained from Healthcare Bluebook. The cost of POCUS ($100) was estimated independently. Cost savings were analyzed for simultaneous screening for all forms of SHD and screening for individual SHDs. Results: AI-ECG identified potential SHD in 125 patients, but only 39 were true positives by TTE (31% PPV). In AI-ECG positive patients, POCUS demonstrated findings of SHD in 52/125. Compared to TTE, this stepwise approach yielded 32 true positives and 20 false positives (62% PPV). The cost per patient diagnosed with SHD was $4,733 with AI-ECG alone but decreased to $3,182 with stepwise screening (33% cost savings). Screening for individual SHDs resulted in cost reduction from $18,724 to $6,315 (66% savings) for AS, $21,023 to $12,230 (42% savings) for CA, $9,883 to $6,175 (38% savings) for HCM, and $4,019 to $3,582 (11% savings) for LVD. Conclusions: Stepwise screening for SHD with AI-ECG followed by POCUS significantly reduces costs compared to AI-ECG alone. We also suggest a model for parallel screening for multiple SHDs, which is likely more cost-effective than screening for individual SHDs.

Background: Aortic regurgitation (AR) is a prevalent valve disease with a long latent period to symptoms. Recent data has suggested the role of novel markers of myocardial overload in assessing onset of decompensation. Method: We sought to evaluate the role of unsupervised cluster analyses in identifying different clinical clusters, including clinical status, and a large number of echocardiographic variables including left ventricular (LV) volumes, and their association with mortality. Patients with ≥moderate-severe chronic AR identified using echocardiography at Mayo Clinic, Rochester were retrospectively analyzed. Primary outcome was all-cause mortality censored at aortic valve surgery/last follow-up. Uniform Manifold Approximation and Projection (UMAP) with K -means algorithm was used to cluster patients using clinical and, echocardiographic variables at the time of presentation. Missing data were imputed with the Multiple Imputation by Chained Equations (MICE) method. A supervised approach trained on the training set was used to find cluster membership in a hold-out validation set. Log-rank tests were used to assess differences in mortality rates between the clusters, both in the training and validation sets. Results: Three distinct clusters were identified among 1100 patients (log-rank P for survival <0.001). Cluster 1 (n=337), which included younger males with severe AR but fewer symptoms, showed the best survival, 75.6% (69.5, 82.3). Cluster 2 (n=235), older and more females with elevated filling pressures, showed intermediate survival of 64.2 % (56.8, 72.5). Cluster 3 (n=253), characterized by severe symptomatic AR, demonstrated the lowest survival of 45.3 % (34.4, 59.8) at 5 years. Similar clusters were formed in the internal validation cohort. Conclusion: Distinct clusters with variable echocardiographic features and mortality differences exist within patients with chronic ≥moderate-severe AR. Recognizing these clusters can refine individual risk stratification and clinical decision-making after verification in future prospective studies.

Background: Left ventricular diastolic dysfunction (LVDD) predicts mortality in cardiac intensive care unit (CICU) patients. A novel artificial intelligence enhanced electrocardiogram (AIECG) algorithm can predict LVDD and mortality in general populations but has not been examined in the cardiac intensive care unit (CICU). We aim to assess if LVDD by AI-ECG is associated with in-hospital and one-year mortality in CICU patients. Methods: In this retrospective cohort study, we included consecutive unique adults admitted to the Mayo Clinic CICU from 2007 to 2018 with an admission AIECG, which AI assigned LVDD grade (0 to 3). Medial mitral E/e’ ratio >15 on transthoracic echocardiogram (TTE) defined elevated filling pressures. AIECG and TTE assessment of LVDD were used to assign patients to four groups based on TTE as gold standard: true and false negative, and true and false positive. In-hospital mortality was evaluated using multivariable logistic regression. One-year survival was evaluated using Kaplan-Meier curves and multivariable Cox proportional-hazards. Results: We included 11,868 patients (median age 69.5 years, 37.7% females); 48% had heart failure and 44% had acute coronary syndromes. AIECG LVDD grades were: grade 0 (normal), 33%; grade 1, 7%; grade 2, 39%; grade 3, 21%. In-hospital (adjusted OR) and one-year (adjusted HR) mortality increased in each higher AIECG LVDD grade (Figure A/B), before and after adjustment including TTE measurements. Patients with grade 2-3 LVDD by AIECG and medial mitral E/e’ ratio >15 by TTE (true positive) had the highest in-hospital (adjusted OR 2.5 [1.7-3.9]) and one-year (adjusted HR 1.9 [1.5-2.5]) mortality (Figure C/D), and mortality was elevated similarly in patients with either grade 2-3 LVDD by AIECG (false positive) or medial mitral E/e’ ratio >15 by TTE (false negative). Conclusions: The AIECG LVDD grade was strongly associated with in-hospital and one-year mortality in CICU patients, even after adjusting for clinical variables and TTE measurements. Patients with concordant AIECG and TTE for elevated filling pressures (true positive) were at highest risk.

Abstract Background and Aims Individuals with inflammatory bowel disease (IBD) experience a high symptom burden, including abdominal pain, fatigue, anxiety, depression, and sleep disturbances; yet, little is known regarding the relationship between sex and gender on symptoms. We sought to report symptom severity for cisgender men, cisgender women, and transgender and gender-diverse (TGD) individuals. In addition, we used network analysis to identify core symptoms and explore if symptoms and their relationships differ between cisgender men and cisgender women. Methods This was a cross-sectional study. We recruited adults with IBD online through ResearchMatch. Individuals responded to Patient-Reported Outcomes Measurement Information symptom questionnaires, as well as demographic and clinical questionnaires. Network analysis was used to identify the core symptoms driving the symptom structure. Results One-hundred and fifty-seven (63.3%) participants identified as cisgender women, 84 (33.9%) as cisgender men, and 7 (2.8%) as TGD. Cisgender men (M = 61.8) and TGD (M = 61.3) groups reported higher abdominal pain levels compared with cisgender women (M = 57.8; P = .02). Transgender and gender-diverse individuals reported higher depression levels (M = 64.8) compared with cisgender men (M = 56.7) and cisgender women (M = 54.4; P = .01). Using a network analysis approach, anxiety and fatigue emerged as core symptoms for the entire sample (clinically active and inactive disease), and among only those with active clinical disease. Fatigue was a top core symptom for cisgender women; anxiety emerged as a top core symptom for cisgender men. Conclusions This study highlights that fatigue and anxiety are core symptoms among individuals with IBD and demonstrates a potential sex and/or gender difference in core symptoms. Replication of this study is needed with further consideration of inclusion of TGD patients.