Citations

BACKGROUND: Sodium-glucose cotransporter-2 inhibitors reduce risk of hospitalization for heart failure in patients who have heart failure with preserved ejection fraction (HFpEF), but the hemodynamic mechanisms underlying these benefits remain unclear. This study sought to determine whether treatment with dapagliflozin affects pulmonary capillary wedge pressure (PCWP) at rest and during exercise in patients with HFpEF. METHODS: This was a single-center, double-blinded, randomized, placebo-controlled trial testing the effects of 10 mg of dapagliflozin once daily in patients with HFpEF. Patients with New York Heart Association class II or III heart failure, ejection fraction ≥50%, and elevated PCWP during exercise were recruited. Cardiac hemodynamics were measured at rest and during exercise using high-fidelity micromanometers at baseline and after 24 weeks of treatment. The primary end point was a change from baseline in rest and peak exercise PCWPs that incorporated both measurements, and was compared using a mixed-model likelihood ratio test. Key secondary end points included body weight and directly measured blood and plasma volumes. Expired gas analysis was performed evaluate oxygen transport in tandem with arterial lactate sampling. RESULTS: Among 38 patients completing baseline assessments (median age 68 years; 66% women; 71% obese), 37 completed the trial. Treatment with dapagliflozin resulted in reduction in the primary end point of change in PCWP at rest and during exercise at 24 weeks relative to treatment with placebo (likelihood ratio test for overall changes in PCWP; P <0.001), with lower PCWP at rest (estimated treatment difference [ETD], −3.5 mm Hg [95% CI, −6.6 to −0.4]; P =0.029) and maximal exercise (ETD, −5.7 mm Hg [95% CI, −10.8 to −0.7]; P =0.027). Body weight was reduced with dapagliflozin (ETD, −3.5 kg [95% CI, −5.9 to −1.1]; P =0.006), as was plasma volume (ETD, −285 mL [95% CI, −510 to −60]; P =0.014), but there was no significant effect on red blood cell volume. There were no differences in oxygen consumption at 20-W or peak exercise, but dapagliflozin decreased arterial lactate at 20 W (−0.70 ± 0.77 versus 0.37 ± 1.29 mM; P =0.006). CONCLUSIONS: In patients with HFpEF, treatment with dapagliflozin reduces resting and exercise PCWP, along with the favorable effects on plasma volume and body weight. These findings provide new insight into the hemodynamic mechanisms of benefit with sodium-glucose cotransporter-2 inhibitors in HFpEF. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04730947.

Background: We developed a deep learning algorithm to predict elevated coronary artery calcium (CAC) score from 12-lead electrocardiograms (CAC- ECG). We tested the hypothesis that this CAC-ECG algorithm will independently predict long-term survival. Methods: We leveraged a historical cohort of 43,210 consecutive patients who from the years 1997-2020 underwent clinically indicated ECG-gated unenhanced chest computed tomography (CT) to identify and quantify CAC and had an ECG within 1 year of the CT. Data on cardiovascular risk (CV) factors, and to calculate the Pooled Cohort Equation (PCE) for ASCVD was collected as part of preventive cardiology or general medical evaluations. We used the oldest CAC in record and excluded those taking statins at the time of the CAC. The algorithm was trained in 60% of the cohort, and the association between CAC-ECG and survival was evaluated with multivariate cox proportional hazard models in 40% of the remaining observations. Results: Of the 17,284 evaluated patients, mean ± SD age 55.9 ± 9.9, 33% female, 3,714 (21%) had elevated CAC ≥ 300. During an average of 15±5.9 years follow-up, 848 (5%) patients died. The algorithm’s area under the receiving operating characteristics curve (ROC), sensitivity, specificity, and accuracy to detect a CAC ≥ 300 were 0.83, 0.90%, 0.56%, 0.60%. Those with elevated CAC had a nearly two-fold risk of death, a value similar to those deemed positive by the ECG-CAC algorithm ( Figure A ). Risk of death increased with CAC-ECG probability quartiles ( Figure A ). The CAC-ECG algorithm enhanced predicted capabilities of the PCE across all ASCVD risk subgroups ( Figure B ), as well as in those with no CAC and elevated CAC ( Figure C ), all p for trend <0.001. Conclusions: A deep learning-enabled CAC-ECG algorithm was independently associated with long-term survival and enhanced current risk prediction paradigms. The CAC-ECG algorithm could help identify individuals at risk in primary prevention of CV disease.

Pain assessment is a complex task largely dependent on the patient’s self-report. Artificial intelligence (AI) has emerged as a promising tool for automating and objectifying pain assessment through the identification of pain-related facial expressions. However, the capabilities and potential of AI in clinical settings are still largely unknown to many medical professionals. In this literature review, we present a conceptual understanding of the application of AI to detect pain through facial expressions. We provide an overview of the current state of the art as well as the technical foundations of AI/ML techniques used in pain detection. We highlight the ethical challenges and the limitations associated with the use of AI in pain detection, such as the scarcity of databases, confounding factors, and medical conditions that affect the shape and mobility of the face. The review also highlights the potential impact of AI on pain assessment in clinical practice and lays the groundwork for further study in this area.

Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.

Unnecessary ED visits and transfers to hand clinics raise treatment costs and patient burden at trauma centers. In the present COVID-19 pandemic, needless transfers can increase patients’ risk of viral exposure. Therefore, this review analyzes different aspects of the remote diagnosis and triage of traumatic hand injuries. The most common file was photography, with the most common devices being cell phone cameras. Treatment, triage, diagnosis, cost, and time outcomes were assessed, showing concordance between teleconsultation and face-to-face patient evaluations. We conclude that photography and video consultations are feasible surrogates for ED visits in patients with traumatic hand injuries. These technologies should be leveraged to decrease treatment costs and potentially decrease the time to definitive treatment after initial evaluation.

Background: We developed a deep learning algorithm that detects coronary artery calcium (CAC) score using 12-lead electrocardiograms (CAC- ECG). We tested the hypothesis that the output from the CAC-ECG algorithm would be associated with incident atherosclerotic cardiovascular disease (ASCVD) events and that the CAC- ECG would refine the AHA/ACC Pooled Cohort Equation’s (PCE) predictive capabilities. Methods: A community-based cohort of consecutive patients seeking primary care in Olmsted County, MN, between 1998-2000 with passive follow-up via record linkage. Inclusion was identical to the PCE. The original CAC-ECG was developed in 43,210 subjects yielding an AUC of 0.83. Herein, we used the CAC-ECG output to predict a high CAC (≥ 300). Primary outcome was ASCVD defined as fatal and non-fatal myocardial infarction and ischemic stroke, secondary outcome was Major Adverse Cardiovascular Events (MACE) further including PCI, CABG, and mortality. Events were validated in duplicate. Cox proportional hazard models adjusted for variables included in the PCE and were stratified to evaluate the effect of the CAC-ECG on PCE-predicted risk. Follow-up was truncated at 10 years for PCE analyses. Results: We included 24,793 subjects, mean ± SD age 53.9 ± 12.1, 52% women, 95% white. After 16.7±3.7 yrs follow-up, 2,366 (9.5%) had ASCVD and 3,401 (13.7%) had MACE. Risk of ASCVD and MACE increased with CAC-ECG probability quintiles, independent of risk factors, p for trend <0.001 ( Fig. A-B ). The CAC-ECG enhanced the predicted capabilities of the PCE across all ASCVD risk groups ( Fig. C ). Net reclassification improved 13.7% with comparable C-statistic from 0.77 vs. 0.78 for PCE and CAC-ECG. Conclusions: CAC-ECG was associated with ASCVD and MACE and improved PCE predicted risk. The CAC-ECG algorithm could identify individuals at risk in primary prevention; Unlike the PCE, the CAC-ECG can be applied without chart review or performing a computer tomography and may be reliably used retrospectively in cohorts with digitally stored ECGs.

Importance Reduced heart rate during exercise is common and associated with impaired aerobic capacity in heart failure with preserved ejection fraction (HFpEF), but it remains unknown if restoring exertional heart rate through atrial pacing would be beneficial. Objective To determine if implanting and programming a pacemaker for rate-adaptive atrial pacing would improve exercise performance in patients with HFpEF and chronotropic incompetence. Design, Setting, and Participants Single-center, double-blind, randomized, crossover trial testing the effects of rate-adaptive atrial pacing in patients with symptomatic HFpEF and chronotropic incompetence at a tertiary referral center (Mayo Clinic) in Rochester, Minnesota. Patients were recruited between 2014 and 2022 with 16-week follow-up (last date of follow-up, May 9, 2022). Cardiac output during exercise was measured by the acetylene rebreathe technique. Interventions A total of 32 patients were recruited; of these, 29 underwent pacemaker implantation and were randomized to atrial rate responsive pacing or no pacing first for 4 weeks, followed by a 4-week washout period and then crossover for an additional 4 weeks. Main Outcomes and Measures The primary end point was oxygen consumption (V̇ o 2 ) at anaerobic threshold (V̇ o 2,AT ); secondary end points were peak V̇ o 2 , ventilatory efficiency (V̇ e /V̇ co 2 slope), patient-reported health status by the Kansas City Cardiomyopathy Questionnaire Overall Summary Score (KCCQ-OSS), and N-terminal pro-brain natriuretic peptide (NT-proBNP) levels. Results Of the 29 patients randomized, the mean age was 66 years (SD, 9.7) and 13 (45%) were women. In the absence of pacing, peak V̇ o 2 and V̇ o 2 at anaerobic threshold (V̇ o 2,AT ) were both correlated with peak exercise heart rate ( r = 0.46-0.51, P &amp;lt; .02 for both). Pacing increased heart rate during low-level and peak exercise (16/min [95% CI, 10 to 23], P &amp;lt; .001; 14/min [95% CI, 7 to 21], P &amp;lt; .001), but there was no significant change in V̇ o 2,AT (pacing off, 10.4 [SD, 2.9] mL/kg/min; pacing on, 10.7 [SD, 2.6] mL/kg/min; absolute difference, 0.3 [95% CI, −0.5 to 1.0] mL/kg/min; P = .46), peak V̇ o 2 , minute ventilation (V̇ e) /carbon dioxide production (V̇ co 2 ) slope, KCCQ-OSS, or NT-proBNP level. Despite the increase in heart rate, atrial pacing had no significant effect on cardiac output with exercise, owing to a decrease in stroke volume (−24 mL [95% CI, −43 to −5 mL]; P = .02). Adverse events judged to be related to the pacemaker device were observed in 6 of 29 participants (21%). Conclusions and Relevance In patients with HFpEF and chronotropic incompetence, implantation of a pacemaker to enhance exercise heart rate did not result in an improvement in exercise capacity and was associated with increased adverse events. Trial Registration ClinicalTrials.gov Identifier: NCT02145351

Abstract Aims The current guidelines recommend aortic valve intervention in patients with severe aortic regurgitation (AR) with the onset of symptoms, left ventricular enlargement, or systolic dysfunction. Recent studies have suggested that we might be missing the window of early intervention in a significant number of patients by following the guidelines. Methods and results The overarching goal was to determine if machine learning (ML)-based algorithms could be trained to identify patients at risk for death from AR independent of aortic valve replacement (AVR). Models were trained with five-fold cross-validation on a dataset of 1035 patients, and performance was reported on an independent dataset of 207 patients. Optimal predictive performance was observed with a conditional random survival forest model. A subset of 19/41 variables was selected for inclusion in the final model. Variable selection was performed with 10-fold cross-validation using random survival forest model. The top variables included were age, body surface area, body mass index, diastolic blood pressure, New York Heart Association class, AVR, comorbidities, ejection fraction, end-diastolic volume, and end-systolic dimension, and the relative variable importance averaged across five splits of cross-validation in each repeat were evaluated. The concordance index for predicting survival of the best-performing model was 0.84 at 1 year, 0.86 at 2 years, and 0.87 overall, respectively. Conclusion Using common echocardiographic parameters and patient characteristics, we successfully trained multiple ML models to predict survival in patients with severe AR. This technique could be applied to identify high-risk patients who would benefit from early intervention, thereby improving patient outcomes.

AbstractIntroduction/AimsIn the Diabetes Control and Complications Trial (DCCT), the minimal nerve conduction (NC) criterion for diabetic sensorimotor polyneuropathy (DSPN) was abnormality of NC in more than one peripheral nerve without specifying the attributes of NCs to be evaluated. In the present study, we assess individual and composite scores of NCs meeting the DCCT criterion and signs for improved diagnosis and assessment of DSPN severity.MethodsEvaluated were 13 attributes and 6 composite NC scores and signs and symptoms in 395 healthy subjects (HS) and 388 persons with diabetes (DM).ResultsPercent abnormality between subjects with DM and HS was remarkably different among individual attributes and the six composite NC scores. For diagnosis of DSPN using the DCCT criterion, assessment of conduction velocities (CVs) and distal latencies (DLs) provided sensitive diagnoses of DSPN. NC amplitudes provided stronger measures of severity. In studied cohorts, DSPN was staged: N0, no NC abnormality using NC score 2 (CVs and DLs), 60.0%; N1, NC abnormality only, 18.4%; N2, NC abnormality and signs of feet or legs, 16.3%; and N3, NC abnormality and signs of thighs, 5.3%.DiscussionFor sensitive and standard diagnosis of DSPN using the DCCT NC criterion, specifically defined composite scores of CVs and DLs, e.g., score 2, is recommended. A composite score of amplitudes, e.g., score 4, provides a stronger measure of neuropathy severity. Also, provided are HS reference values of evaluated attributes of NCs and estimates of staged severity of DSPN of mid North American DM cohorts.

Abstract Aims Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78–0.90] and 95% (19/20; 95% CI: 75–100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61–0.96) and 100% (2/2; 95% CI: 16–100%) sensitivity. Conclusion An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.