Arruda‐Olson, Adelaide M.

Echocardiography (Echo), a widely available, noninvasive, and portable bedside imaging tool, is the most frequently used imaging modality in assessing cardiac anatomy and function in clinical practice. On the other hand, its operator dependability introduces variability in image acquisition, measurements, and interpretation. To reduce these variabilities, there is an increasing demand for an operator- and interpreter-independent Echo system empowered with artificial intelligence (AI), which has been incorporated into diverse areas of clinical medicine. Recent advances in AI applications in computer vision have enabled us to identify conceptual and complex imaging features with the self-learning ability of AI models and efficient parallel computing power. This has resulted in vast opportunities such as providing AI models that are robust to variations with generalizability for instantaneous image quality control, aiding in the acquisition of optimal images and diagnosis of complex diseases, and improving the clinical workflow of cardiac ultrasound. In this review, we provide a state-of-the art overview of AI-empowered Echo applications in cardiology and future trends for AI-powered Echo technology that standardize measurements, aid physicians in diagnosing cardiac diseases, optimize Echo workflow in clinics, and ultimately, reduce healthcare costs.

AbstractBackgroundA subset of patients with hypertrophic cardiomyopathy (HCM) is at high risk of sudden cardiac death (SCD). Practice guidelines endorse use of a risk calculator, which requires entry of left atrial (LA) diameter. However, American Society of Echocardiography (ASE) guidelines recommend the use of LA volume index (LAVI) for routine quantification of LA size. The aims of this study were to (a) develop a model to estimate LA diameter from LAVI and (b) evaluate whether substitution of measured LA diameter by estimated LA diameter derived from LAVI reclassifies HCM‐SCD risk.MethodsThe study cohort was comprised of 500 randomly selected HCM patients who underwent transthoracic echocardiography (TTE). LA diameter and LAVI were measured offline using digital clips from TTE. Linear regression models were developed to estimate LA diameter from LAVI. A European Society of Cardiology endorsed equation estimated SCD risk, which was measured using LA diameter and estimated LA diameter derived from LAVI.ResultsThe mean LAVI was 48.5 ± 18.8 mL/m2. The derived LA diameter was 45.1 mm (SD: 5.5 mm), similar to the measured LA diameter (45.1 mm, SD: 7.1 mm). Median SCD risk at 5 years estimated by measured LA diameter was 2.22% (interquartile range (IQR): 1.39, 3.56), while median risk calculated by estimated LA diameter was 2.18% (IQR: 1.44, 3.52). 476/500 (95%) patients maintained the same risk classification regardless of whether the measured or estimated LA diameter was used.ConclusionsSubstitution of measured LA diameter by estimated LA diameter in the HCM‐SCD calculator did not reclassify risk.

Abstract Objective Determine if information regarding hypertrophic cardiomyopathy (HCM) can be accurately retrieved from cardiac magnetic resonance (CMR) reports using natural language processing (NLP). Background CMR imaging is used for diagnosis and risk stratification of HCM. Manual annotation of information from CMR is time-consuming. NLP is an artificial intelligence method for automating extraction of information from narrative text. Methods We identified 200 HCM patients who had CMR reports from 1998 to 2018. These patients were randomly allocated into training (100 patients with 185 CMR reports) and testing sets (100 patients with 206 reports). An NLP system with 2 tiers was developed; the first extracted information regarding HCM diagnosis while second extracted categorical or numeric concepts for HCM classification. NLP performance was compared with gold-standard manual annotation. Results NLP algorithms achieved very high performance across all concepts with mean positive predictive value (PPV) = 0.96. An outlier was the performance for abstracting the presence of an apical pouch from CMR reports, which had noticeably lower PPV= 0.78 which be attributed to the low number of cases with this finding. Conclusions The algorithms developed can be translated to clinical decision support systems to increase efficiency and contribute to improved quality of care. Funding Acknowledgement Type of funding source: Other. Main funding source(s): Study supported by the National Heart, Lung and Blood Institute of National Institutes of Health (K01HL124045), the Mayo Clinic Center for Clinical and Translational Science (CCaTS), and the Mayo Clinic K2R award. Content is solely the responsibility of authors and does not necessarily represent official views of the National Institutes of Health.

Background Automated individualized risk prediction tools linked to electronic health records ( EHR s) are not available for management of patients with peripheral arterial disease. The goal of this study was to create a prognostic tool for patients with peripheral arterial disease using data elements automatically extracted from an EHR to enable real‐time and individualized risk prediction at the point of care. Methods and Results A previously validated phenotyping algorithm was deployed to an EHR linked to the Rochester Epidemiology Project to identify peripheral arterial disease cases from Olmsted County, MN, for the years 1998 to 2011. The study cohort was composed of 1676 patients: 593 patients died over 5‐year follow‐up. The c‐statistic for survival in the overall data set was 0.76 (95% confidence interval [CI], 0.74–0.78), and the c‐statistic across 10 cross‐validation data sets was 0.75 (95% CI, 0.73–0.77). Stratification of cases demonstrated increasing mortality risk by subgroup (low: hazard ratio, 0.35 [95% CI, 0.21–0.58]; intermediate‐high: hazard ratio, 2.98 [95% CI, 2.37–3.74]; high: hazard ratio, 8.44 [95% CI, 6.66–10.70], all P <0.0001 versus the reference subgroup). An equation for risk calculation was derived from Cox model parameters and β estimates. Big data infrastructure enabled deployment of the real‐time risk calculator to the point of care via the EHR . Conclusions This study demonstrates that electronic tools can be deployed to EHR s to create automated real‐time risk calculators to predict survival of patients with peripheral arterial disease. Moreover, the prognostic model developed may be translated to patient care as an automated and individualized real‐time risk calculator deployed at the point of care.

The burden and predictors of hospitalization over time in community-based patients with peripheral artery disease (PAD) have not been established. This study evaluates the frequency, reasons and predictors of hospitalization over time in community-based patients with PAD. We assembled an inception cohort of 1798 PAD cases from Olmsted County, MN, USA (mean age 71.2 years, 44% female) from 1 January 1998 through 31 December 2011 who were followed until 2014. Two age- and sex-matched controls ( n = 3596) were identified for each case. ICD-9 codes were used to ascertain the primary reasons for hospitalization. Patients were censored at death or last follow-up. The most frequent reasons for hospitalization were non-cardiovascular: 68% of 8706 hospitalizations in cases and 78% of 8005 hospitalizations in controls. A total of 1533 (85%) cases and 2286 (64%) controls ( p < 0.001) were hospitalized at least once; 1262 (70%) cases and 1588 (44%) controls ( p < 0.001) ≥ two times. In adjusted models, age, prior hospitalization and comorbid conditions were independently associated with increased risk of recurrent hospitalizations in both groups. In cases, severe PAD (ankle–brachial index < 0.5) (HR: 1.25; 95% CI: 1.15, 1.36) and poorly compressible arteries (HR: 1.26; 95% CI: 1.16, 1.38) were each associated with increased risk for recurrent hospitalization. We demonstrate an increased rate of hospitalization in community-based patients with PAD and identify predictors of recurrent hospitalizations. These observations may inform strategies to reduce the burden of hospitalization of PAD patients.

Background— It is not known whether abnormal blood pressure (BP) responses during dobutamine stress echocardiography (DSE) are associated with abnormal test results, nor if such results indicate obstructive coronary artery disease (CAD). We sought to define the frequency of abnormal BP responses during DSE and their impact on accuracy of test results. Methods and Results— We studied 21 949 patients who underwent DSE at Mayo Clinic, Rochester, MN, grouped by peak systolic BP achieved during the test. We also analyzed a subgroup who underwent coronary angiography within 30 days after positive DSE. The positive predictive value of DSE was calculated for each BP group. Patients with hypertensive response (n=1905; 9%) were more likely to have positive DSE than those with normal (n=19 770; 90%) or hypotensive (n=274; 1%) BP responses (32% versus 21% versus 23%, respectively; P <0.0001). Angiography, performed in 1126 patients, showed obstructive CAD (≥50% stenosis) in 814 patients and severe CAD (≥70% stenosis) in 708 patients. Positive predictive value of DSE was similar for patients who had hypertensive and normal BP responses (69% versus 73%; P =0.3), considering 50% stenosis cut point. The proportion of severe CAD (≥70% stenosis) was lower in patients who had hypertensive response compared with those who had normal BP response (54% versus 65%; P =0.005). Conclusions— Patients with hypertensive response during DSE are more likely to have stress-induced myocardial ischemia compared with those with normal or hypotensive BP responses but are not more likely to have false-positive DSE results. They are, however, less likely to have higher grade or multivessel CAD.

Background— The relationship between neutrophils and outcomes post–myocardial infarction (MI) is not completely characterized. We examined the associations of neutrophil count with mortality and post-MI heart failure (HF) and their incremental value for risk discrimination in the community. Methods and Results— MI was diagnosed with cardiac pain, biomarkers, and Minnesota coding of the ECG. Neutrophil count at presentation, reported as counts ×10 9 /L, was categorized by tertiles (lower tertile, <5.7; middle tertile, 5.7 to 8.5; upper tertile, >8.5). From 1979 to 2002, 2047 incident MIs occurred in Olmsted County, Minn (mean age, 68±14 years; 44% women). Median (25th to 75th percentile) neutrophil count was 7.0 (5.1 to 9.5). Within 3 years post-MI, 577 patients died, and 770 developed HF. Overall survival and survival free of HF decreased with increased neutrophil tertile ( P <0.001). Compared with the lower tertile, the age and sex adjusted hazard ratio for death was 1.44 (95% CI, 1.14 to 1.81) for the middle tertile and 2.60 (95% CI, 2.10 to 3.22) for the upper tertile ( P <0.001). Similarly, for HF, the hazard ratio was 1.32 (95% CI, 1.09 to 1.59) for the middle and 2.12 (95% CI, 1.77 to 2.53) for the upper tertile ( P <0.001). These associations persisted after adjustment for risk factors, comorbidities, Killip class, revascularization, and ejection fraction. Neutrophil count improved risk discrimination as indicated by increases in the area under the receiver operating characteristic curves (all P <0.05) and by the integrated discrimination improvement analysis (all P <0.001). Conclusions— In the community, the neutrophil count was strongly and independently associated with death and HF post-MI and improved risk discrimination over traditional predictors.