Role of Artificial Intelligence in Improving Syncope Management.

Syncope is common in the general population and a common presenting symptom in acute care settings. Substantial costs are attributed to the care of patients with syncope. Current challenges include differentiating syncope from its mimickers, identifying serious underlying conditions that caused the syncope, and wide variations in current management. Although validated risk tools exist, especially for short-term prognosis, there is inconsistent application, and the current approach does not meet patient needs and expectations. Artificial intelligence (AI) techniques, such as machine learning methods including natural language processing, can potentially address the current challenges in syncope management. Preliminary evidence from published studies indicates that it is possible to accurately differentiate syncope from its mimickers and predict short-term prognosis and hospitalisation. More recently, AI analysis of electrocardiograms has shown promise in detection of serious structural and functional cardiac abnormalities, which has the potential to improve syncope care. Future AI studies have the potential to address current issues in syncope management. AI can automatically prognosticate risk in real time by accessing traditional and nontraditional data. However, steps to mitigate known problems such as generalisability, patient privacy, data protection, and liability will be needed. In the past AI has had limited impact due to underdeveloped analytical methods, lack of computing power, poor access to powerful computing systems, and availability of reliable high-quality data. All impediments except data have been solved. AI will live up to its promise to transform syncope care if the health care system can satisfy AI requirement of large scale, robust, accurate, and reliable data.

Deep Learning for Echo Analysis, Tracking, and Evaluation of Mitral Regurgitation (DELINEATE-MR).

BACKGROUND: Artificial intelligence, particularly deep learning (DL), has immense potential to improve the interpretation of transthoracic echocardiography (TTE). Mitral regurgitation (MR) is the most common valvular heart disease and presents unique challenges for DL, including the integration of multiple video-level assessments into a final study-level classification. METHODS: A novel DL system was developed to intake complete TTEs, identify color MR Doppler videos, and determine MR severity on a 4-step ordinal scale (none/trace, mild, moderate, and severe) using the reading cardiologist as a reference standard. This DL system was tested in internal and external test sets with performance assessed by agreement with the reading cardiologist, weighted κ, and area under the receiver-operating characteristic curve for binary classification of both moderate or greater and severe MR. In addition to the primary 4-step model, a 6-step MR assessment model was studied with the addition of the intermediate MR classes of mild-moderate and moderate-severe with performance assessed by both exact agreement and ±1 step agreement with the clinical MR interpretation. RESULTS: A total of 61 689 TTEs were split into train (n=43 811), validation (n=8891), and internal test (n=8987) sets with an additional external test set of 8208 TTEs. The model had high performance in MR classification in internal (exact accuracy, 82%; κ=0.84; area under the receiver-operating characteristic curve, 0.98 for moderate/severe MR) and external test sets (exact accuracy, 79%; κ=0.80; area under the receiver-operating characteristic curve, 0.98 for moderate or greater MR). Most (63% internal and 66% external) misclassification disagreements were between none/trace and mild MR. MR classification accuracy was slightly higher using multiple TTE views (accuracy, 82%) than with only apical 4-chamber views (accuracy, 80%). In subset analyses, the model was accurate in the classification of both primary and secondary MR with slightly lower performance in cases of eccentric MR. In the analysis of the 6-step classification system, the exact accuracy was 80% and 76% with a ±1 step agreement of 99% and 98% in the internal and external test set, respectively. CONCLUSIONS: This end-to-end DL system can intake entire echocardiogram studies to accurately classify MR severity and may be useful in helping clinicians refine MR assessments.

Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. More than 600 U.S. Food and Drug Administration-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular applications, highlighting the growing opportunities for AI to augment care. This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI. Further discussions in this review involve an approach to understanding the larger context in which AI-augmented care may exist, and include a discussion of the need for rigorous evaluation, appropriate infrastructure for deployment, ethics and equity assessments, regulatory oversight, and viable business cases for deployment. Embracing this rapidly evolving technology while setting an appropriately high evaluation benchmark with careful and patient-centered implementation will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Impact of cardiac size on diagnostic performance of single-photon emission computed tomography myocardial perfusion imaging: insights from the REgistry of Fast Myocardial Perfusion Imaging with NExt generation single-photon emission computed tomography.

AIMS: Variation in diagnostic performance of single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been observed, yet the impact of cardiac size has not been well characterized. We assessed whether low left ventricular volume influences SPECT MPI's ability to detect obstructive coronary artery disease (CAD) and its interaction with age and sex. METHODS AND RESULTS: A total of 2066 patients without known CAD (67% male, 64.7 ± 11.2 years) across nine institutions underwent SPECT MPI with solid-state scanners followed by coronary angiography as part of the REgistry of Fast Myocardial Perfusion Imaging with NExt Generation SPECT. Area under receiver-operating characteristic curve (AUC) analyses evaluated the performance of quantitative and visual assessments according to cardiac size [end-diastolic volume (EDV); <20th vs. ≥20th population or sex-specific percentiles], age (<75 vs. ≥75 years), and sex. Significantly decreased performance was observed in patients with low EDV compared with those without (AUC: population 0.72 vs. 0.78, P = 0.03; sex-specific 0.72 vs. 0.79, P = 0.01) and elderly patients compared with younger patients (AUC 0.72 vs. 0.78, P = 0.03), whereas males and females demonstrated similar AUC (0.77 vs. 0.76, P = 0.67). The reduction in accuracy attributed to lower volumes was primarily observed in males (sex-specific threshold: EDV 0.69 vs. 0.79, P = 0.01). Accordingly, a significant decrease in AUC, sensitivity, specificity, and negative predictive value for quantitative and visual assessments was noted in patients with at least two characteristics of low EDV, elderly age, or male sex. CONCLUSION: Detection of CAD with SPECT MPI is negatively impacted by small cardiac size, most notably in elderly and male patients.

Deep learning to detect left ventricular structural abnormalities in chest X-rays.

BACKGROUND AND AIMS: Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportunity to build scalable screening tools for structural abnormalities indicative of Stage B or worse heart failure with deep learning methods. In this study, a model was developed to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs. METHODS: A total of 71 589 unique CXRs from 24 689 different patients completed within 1 year of echocardiograms were identified. Labels for SLVH, DLV, and a composite label indicating the presence of either were extracted from echocardiograms. A deep learning model was developed and evaluated using area under the receiver operating characteristic curve (AUROC). Performance was additionally validated on 8003 CXRs from an external site and compared against visual assessment by 15 board-certified radiologists. RESULTS: The model yielded an AUROC of 0.79 (0.76-0.81) for SLVH, 0.80 (0.77-0.84) for DLV, and 0.80 (0.78-0.83) for the composite label, with similar performance on an external data set. The model outperformed all 15 individual radiologists for predicting the composite label and achieved a sensitivity of 71% vs. 66% against the consensus vote across all radiologists at a fixed specificity of 73%. CONCLUSIONS: Deep learning analysis of CXRs can accurately detect the presence of certain structural abnormalities and may be useful in early identification of patients with LV hypertrophy and dilation. As a resource to promote further innovation, 71 589 CXRs with adjoining echocardiographic labels have been made publicly available.

Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease.

BACKGROUND: Valvular heart disease is an important contributor to cardiovascular morbidity and mortality and remains underdiagnosed. Deep learning analysis of electrocardiography (ECG) may be useful in detecting aortic stenosis (AS), aortic regurgitation (AR), and mitral regurgitation (MR). OBJECTIVES: This study aimed to develop ECG deep learning algorithms to identify moderate or severe AS, AR, and MR alone and in combination. METHODS: A total of 77,163 patients undergoing ECG within 1 year before echocardiography from 2005-2021 were identified and split into train (n = 43,165), validation (n = 12,950), and test sets (n = 21,048; 7.8% with any of AS, AR, or MR). Model performance was assessed using area under the receiver-operating characteristic (AU-ROC) and precision-recall curves. Outside validation was conducted on an independent data set. Test accuracy was modeled using different disease prevalence levels to simulate screening efficacy using the deep learning model. RESULTS: The deep learning algorithm model accuracy was as follows: AS (AU-ROC: 0.88), AR (AU-ROC: 0.77), MR (AU-ROC: 0.83), and any of AS, AR, or MR (AU-ROC: 0.84; sensitivity 78%, specificity 73%) with similar accuracy in external validation. In screening program modeling, test characteristics were dependent on underlying prevalence and selected sensitivity levels. At a prevalence of 7.8%, the positive and negative predictive values were 20% and 97.6%, respectively. CONCLUSIONS: Deep learning analysis of the ECG can accurately detect AS, AR, and MR in this multicenter cohort and may serve as the basis for the development of a valvular heart disease screening program.

Relation of Body Mass Index to Transthyretin Cardiac Amyloidosis Particularly in Black and Hispanic Patients (from the SCAN-MP Study).

Heart failure with preserved ejection fraction is a heterogeneous clinical syndrome that includes distinct subtypes with different pathophysiologies, genetics, and treatment. Distinguishing heart failure with preserved ejection fraction caused by transthyretin cardiac amyloidosis (ATTR-CA) is critical given its specific treatment. We analyzed a single-center retrospective cohort to determine the association of body mass index (BMI) with a composite of either ATTR-CA or the valine-to-isoleucine substitution (Val122Ile) variant genotype (ATTR-CA+Val122Ile). These BMI differences were prospectively evaluated in the multicenter Screening for Cardiac Amyloidosis using nuclear imaging for Minority Populations (SCAN-MP) study of Black and Hispanic patients with heart failure. The association of BMI with ATTR-CA+Val122Ile was compared by Wilcoxon rank sum analysis and combined with age, gender, and maximum left ventricle wall thickness in multivariable logistic regression. In the retrospective analysis (n = 469), ATTR-CA+Val122Ile was identified in n = 198 (40%), who had a lower median BMI (25.8 kg/m2, interquartile range [IQR] 23.4 to 28.9) than other patients (27.1 kg/m2, IQR 23.9 to 32.0) (p <0.001). In multivariable logistic regression, BMI <30 kg/m2 (odds ratio 2.6, 95% confidence interval 1.5 to 4.5) remained independently associated with ATTR-CA+Val122Ile with a greater association in Black and Hispanic patients (odds ratio 5.8, 95% confidence interval 1.7 to 19.6). In SCAN-MP (n = 201), 17 (8%) had either ATTR-CA (n = 10) or were Val122Ile carriers (n = 7) with negative pyrophosphate scans. BMI was lower (25.4 kg/m2 [IQR 24.3 to 28.2]) in ATTR-CA+Val122Ile patients than in non-amyloid patients (32.7 kg/m2 [28.3 to 38.6]) (p <0.001), a finding that persisted in multivariable analysis (p = 0.002). In conclusion, lower BMI is associated with ATTR-CA+Val122Ile in heart failure with increased left ventricle wall thickness, particularly in Black and Hispanic patients, and may aid in the identification of those benefiting from ATTR-CA evaluation.

Meta-Analysis of Point-of-Care Lung Ultrasonography Versus Chest Radiography in Adults With Symptoms of Acute Decompensated Heart Failure.

Acute decompensated heart failure (ADHF) is a primary cause of older adults presenting to the emergency department with acute dyspnea. Point-of-care lung ultrasound (LUS) has shown comparable or superior diagnostic accuracy in comparison with a chest x-ray (CXR) in patients presenting with symptoms of ADHF. The systematic review and meta-analysis aimed to elucidate the sensitivity and specificity of LUS in comparison with CXR for diagnosing ADHF and summarize the rapidly growing body of evidence in this domain. A total of 5 databases were searched through February 18, 2021, to identify observational studies that reported on the use of LUS compared with CXR in diagnosing ADHF in patients presenting with shortness of breath. Meta-analysis was conducted on the sensitivities and specificities of each diagnostic method. A total of 8 studies reporting on 2,787 patients were included in this meta-analysis. For patients presenting with signs and symptoms of ADHF, LUS was found to be more sensitive than CXR (91.8% vs 76.5%) and more specific than CXR (92.3% vs 87.0%) for the detection of cardiogenic pulmonary edema. In conclusion, LUS is more sensitive and specific than CXR in detecting pulmonary edema. This highlights the importance of sonographic B-lines, along with the accurate interpretation of clinical data, in the diagnosis of ADHF. In addition to its convenience, reduced costs, and reduced radiation exposure, LUS should be considered an effective alternative to CXR for evaluating patients with dyspnea in the setting of ADHF.

False positive technetium-99m pyrophosphate scintigraphy in a patient with cardiac amyloidosis light chain: Case report.

INTRODUCTION: Patients with cardiac amyloidosis light chain (AL) present with negative Tc-99m pyrophosphate (PYP) scintigraphy (absent or mild heart uptake). On the contrary, patients with cardiac amyloidosis transthyretin (ATTR) present with positive Tc-99m PYP scanning (intensive heart uptake). We present a false positive Tc-99m PYP scintigraphy (grade 2, the heart-to-contralateral ratio is 1.65) in a patient with AL. PATIENT CONCERNS: A 42-year-old Chinese man complained of effort intolerance, chest discomfort, and short of breath progressively over 1 year. New York Heart Association Class III. Physical examination showed legs swelling. Laboratory revealed elevated brain natriuretic peptide of 23,031 ng/mL (0-88) and Troponin-T of 273.4 ng/mL (0-14). DIAGNOSIS: Cardiac amyloidosis light chain. Evidences: free light chains (FLCs): decreased serum free kappa/lambda ratio of 0.043 (0.31-1.56). Immunofixation electrophoresis: a positive lambda light chain monoclonal protein. Cardiac biopsy: HE: Ambiguity Congo red strain. Myocardial immunofluorescence: positive lambda light chain. Myocardial immunohistochemistry: positive lambda light chain, negative kappa light chain, and TTR. INTERVENTIONS: Furosemide 40 mg qd, torasemide 20 mg qd, spirolactone 20 mg qd, potassium chloride 10 mL per 500 mL urine, atorvastatin calcium tablet 20 mg qd, aspirin enteric-coated tablets 100 mg qd during the 2-weeks in-hospital. OUTCOMES: The patient died 2 months later after discharge. CONCLUSION: False positive Tc-99m PYP scintigraphy may rarely presented in patients with cardiac amyloidosis light chain. So, the clonal plasma cell process based on the FLCs and immunofixation is a base to rule out AL cardiac amyloidosis when we interpret a positive Tc-99m PYP scintigraphy.

Cardiac Corrected QT Interval Changes Among Patients Treated for COVID-19 Infection During the Early Phase of the Pandemic.

Importance: Critical illness, a marked inflammatory response, and viruses such as SARS-CoV-2 may prolong corrected QT interval (QTc). Objective: To evaluate baseline QTc interval on 12-lead electrocardiograms (ECGs) and ensuing changes among patients with and without COVID-19. Design, Setting, and Participants: This cohort study included 3050 patients aged 18 years and older who underwent SARS-CoV-2 testing and had ECGs at Columbia University Irving Medical Center from March 1 through May 1, 2020. Patients were analyzed by treatment group over 5 days, as follows: hydroxychloroquine with azithromycin, hydroxychloroquine alone, azithromycin alone, and neither hydroxychloroquine nor azithromycin. ECGs were manually analyzed by electrophysiologists masked to COVID-19 status. Multivariable modeling evaluated clinical associations with QTc prolongation from baseline. Exposures: COVID-19, hydroxychloroquine, azithromycin. Main Outcomes and Measures: Mean QTc prolongation, percentage of patients with QTc of 500 milliseconds or greater. Results: A total of 965 patients had more than 2 ECGs and were included in the study, with 561 (58.1%) men, 198 (26.2%) Black patients, and 191 (19.8%) aged 80 years and older. There were 733 patients (76.0%) with COVID-19 and 232 patients (24.0%) without COVID-19. COVID-19 infection was associated with significant mean QTc prolongation from baseline by both 5-day and 2-day multivariable models (5-day, patients with COVID-19: 20.81 [95% CI, 15.29 to 26.33] milliseconds; P < .001; patients without COVID-19: -2.01 [95% CI, -17.31 to 21.32] milliseconds; P = .93; 2-day, patients with COVID-19: 17.40 [95% CI, 12.65 to 22.16] milliseconds; P < .001; patients without COVID-19: 0.11 [95% CI, -12.60 to 12.81] milliseconds; P = .99). COVID-19 infection was independently associated with a modeled mean 27.32 (95% CI, 4.63-43.21) millisecond increase in QTc at 5 days compared with COVID-19-negative status (mean QTc, with COVID-19: 450.45 [95% CI, 441.6 to 459.3] milliseconds; without COVID-19: 423.13 [95% CI, 403.25 to 443.01] milliseconds; P = .01). More patients with COVID-19 not receiving hydroxychloroquine and azithromycin had QTc of 500 milliseconds or greater compared with patients without COVID-19 (34 of 136 [25.0%] vs 17 of 158 [10.8%], P = .002). Multivariable analysis revealed that age 80 years and older compared with those younger than 50 years (mean difference in QTc, 11.91 [SE, 4.69; 95% CI, 2.73 to 21.09]; P = .01), severe chronic kidney disease compared with no chronic kidney disease (mean difference in QTc, 12.20 [SE, 5.26; 95% CI, 1.89 to 22.51; P = .02]), elevated high-sensitivity troponin levels (mean difference in QTc, 5.05 [SE, 1.19; 95% CI, 2.72 to 7.38]; P < .001), and elevated lactate dehydrogenase levels (mean difference in QTc, 5.31 [SE, 2.68; 95% CI, 0.06 to 10.57]; P = .04) were associated with QTc prolongation. Torsades de pointes occurred in 1 patient (0.1%) with COVID-19. Conclusions and Relevance: In this cohort study, COVID-19 infection was independently associated with significant mean QTc prolongation at days 5 and 2 of hospitalization compared with day 0. More patients with COVID-19 had QTc of 500 milliseconds or greater compared with patients without COVID-19.

Association between antecedent statin use and decreased mortality in hospitalized patients with COVID-19.

The coronavirus disease 2019 (COVID-19) can result in a hyperinflammatory state, leading to acute respiratory distress syndrome (ARDS), myocardial injury, and thrombotic complications, among other sequelae. Statins, which are known to have anti-inflammatory and antithrombotic properties, have been studied in the setting of other viral infections, but their benefit has not been assessed in COVID-19. This is a retrospective analysis of patients admitted with COVID-19 from February 1st through May 12th, 2020 with study period ending on June 11th, 2020. Antecedent statin use was assessed using medication information available in the electronic medical record. We constructed a multivariable logistic regression model to predict the propensity of receiving statins, adjusting for baseline sociodemographic and clinical characteristics, and outpatient medications. The primary endpoint includes in-hospital mortality within 30 days. A total of 2626 patients were admitted during the study period, of whom 951 (36.2%) were antecedent statin users. Among 1296 patients (648 statin users, 648 non-statin users) identified with 1:1 propensity-score matching, statin use is significantly associated with lower odds of the primary endpoint in the propensity-matched cohort (OR 0.47, 95% CI 0.36-0.62, p < 0.001). We conclude that antecedent statin use in patients hospitalized with COVID-19 is associated with lower inpatient mortality.

Frequency of Atrial Arrhythmia in Hospitalized Patients With COVID-19.

There is growing evidence that COVID-19 can cause cardiovascular complications. However, there are limited data on the characteristics and importance of atrial arrhythmia (AA) in patients hospitalized with COVID-19. Data from 1,029 patients diagnosed with of COVID-19 and admitted to Columbia University Medical Center between March 1, 2020 and April 15, 2020 were analyzed. The diagnosis of AA was confirmed by 12 lead electrocardiographic recordings, 24-hour telemetry recordings and implantable device interrogations. Patients' history, biomarkers and hospital course were reviewed. Outcomes that were assessed were intubation, discharge and mortality. Of 1,029 patients reviewed, 82 (8%) were diagnosed with AA in whom 46 (56%) were new-onset AA 16 (20%) recurrent paroxysmal and 20 (24%) were chronic persistent AA. Sixty-five percent of the patients diagnosed with AA (n=53) died. Patients diagnosed with AA had significantly higher mortality compared with those without AA (65% vs 21%; p < 0.001). Predictors of mortality were older age (Odds Ratio (OR)=1.12, [95% Confidence Interval (CI), 1.04 to 1.22]); male gender (OR=6.4 [95% CI, 1.3 to 32]); azithromycin use (OR=13.4 [95% CI, 2.14 to 84]); and higher D-dimer levels (OR=2.8 [95% CI, 1.1 to 7.3]). In conclusion, patients diagnosed with AA had 3.1 times significant increase in mortality rate versus patients without diagnosis of AA in COVID-19 patients. Older age, male gender, azithromycin use and higher baseline D-dimer levels were predictors of mortality.

Admission Cardiac Diagnostic Testing with Electrocardiography and Troponin Measurement Prognosticates Increased 30-Day Mortality in COVID-19.

Background Cardiovascular involvement in coronavirus disease 2019 (COVID-19) is common and leads to worsened mortality. Diagnostic cardiovascular studies may be helpful for resource appropriation and identifying patients at increased risk for death. Methods and Results We analyzed 887 patients (aged 64±17 years) admitted with COVID-19 from March 1 to April 3, 2020 in New York City with 12 lead electrocardiography within 2 days of diagnosis. Demographics, comorbidities, and laboratory testing, including high sensitivity cardiac troponin T (hs-cTnT), were abstracted. At 30 days follow-up, 556 patients (63%) were living without requiring mechanical ventilation, 123 (14%) were living and required mechanical ventilation, and 203 (23%) had expired. Electrocardiography findings included atrial fibrillation or atrial flutter (AF/AFL) in 46 (5%) and ST-T wave changes in 306 (38%). 27 (59%) patients with AF/AFL expired as compared to 181 (21%) of 841 with other non-life-threatening rhythms (P<0.001). Multivariable analysis incorporating age, comorbidities, AF/AFL, QRS abnormalities, and ST-T wave changes, and initial hs-cTnT ≥20 ng/L showed that increased age (HR 1.04/year), elevated hs-cTnT (HR 4.57), AF/AFL (HR 2.07), and a history of coronary artery disease (HR 1.56) and active cancer (HR 1.87) were associated with increased mortality. Conclusions Myocardial injury with hs-cTnT ≥20 ng/L, in addition to cardiac conduction perturbations, especially AF/AFL, upon hospital admission for COVID-19 infection is associated with markedly increased risk for mortality than either diagnostic abnormality alone.