Prognostic Significance of Elevated Left Ventricular Filling Pressures with Exercise: Insights from a Cohort of 14,338 Patients

BACKGROUND

Exercise echocardiography can assess for cardiovascular causes of dyspnea other than coronary artery disease. However, the prevalence and prognostic significance of elevated left ventricular (LV) filling pressures with exercise is understudied.

METHODS

We evaluated 14,338 patients referred for maximal symptom-limited treadmill echocardiography. In addition to assessment of LV regional wall motion abnormalities (RWMAs), we measured patients' early diastolic mitral inflow (E), septal mitral annulus relaxation (e'), and peak tricuspid regurgitation velocity before and immediately after exercise.

RESULTS

Over a mean follow-up of 3.3 ± 3.4 years, patients with E/e' ≥15 with exercise (n = 1,323; 9.2%) had lower exercise capacity (7.3 ± 2.1 vs 9.1 ± 2.4 metabolic equivalents, P < .0001) and were more likely to have resting or inducible RWMAs (38% vs 18%, P < .0001). Approximately 6% (n = 837) had elevated LV filling pressures without RWMAs. Patients with a poststress E/e' ≥15 had a 2.71-fold increased mortality rate (2.28-3.21, P < .0001) compared with those with poststress E/e' ≤ 8. Those with an E/e' of 9 to 14, while at lower risk than the E/e' ≥15 cohort (hazard ratio [HR] = 0.58 [0.48-0.69]; P < .0001), had higher risk than if E/e' ≤8 (HR = 1.56 [1.37-1.78], P < .0001). On multivariable analysis, adjusting for age, sex, exercise capacity, LV ejection fraction, and presence of pulmonary hypertension with stress, patients with E/e' ≥15 had a 1.39-fold (95% CI, 1.18-1.65, P < .0001) increased risk of all-cause mortality compared with patients without elevated LV filling pressures. Compared with patients with E/e' ≤ 15 after exercise, patients with E/e' ≤15 at rest but elevated after exercise had a higher risk of cardiovascular death (HR = 8.99 [4.7-17.3], P < .0001).

CONCLUSION

Patients with elevated LV filling pressures are at increased risk of death, irrespective of myocardial ischemia or LV systolic dysfunction. These findings support the routine incorporation of LV filling pressure assessment, both before and immediately following stress, into the evaluation of patients referred for exercise echocardiography.

Artificial Intelligence of Arterial Doppler Waveforms to Predict Major Adverse Outcomes Among Patients Evaluated for Peripheral Artery Disease

BACKGROUND

Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all-cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether computer-assisted analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with peripheral artery disease at greatest risk for adverse outcome events.

METHODS AND RESULTS

Consecutive patients (April 1, 2015, to December 31, 2020) undergoing ankle-brachial index testing were included. Patients were randomly allocated to training, validation, and testing subsets (60%/20%/20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict major adverse cardiac events, major adverse limb events, and all-cause death at 5 years. Patients were then analyzed in groups based on the quartiles of each prediction score in the training set. Among 11 384 total patients, 10 437 patients met study inclusion criteria (mean age, 65.8±14.8 years; 40.6% women). The test subset included 2084 patients. During 5 years of follow-up, there were 447 deaths, 585 major adverse cardiac events, and 161 MALE events. After adjusting for age, sex, and Charlson comorbidity index, deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 2.44 [95% CI, 1.78-3.34]), major adverse cardiac events (HR, 1.97 [95% CI, 1.49-2.61]), and major adverse limb events (HR, 11.03 [95% CI, 5.43-22.39]) at 5 years.

CONCLUSIONS

An artificial intelligence-enabled analysis of Doppler arterial waveforms enables identification of major adverse outcomes among patients with peripheral artery disease, which may promote early adoption and adherence of risk factor modification.

Prevalence, sex differences, and implications of pulmonary hypertension in patients with apical hypertrophic cardiomyopathy

Introduction

Apical hypertrophic cardiomyopathy (ApHCM) is a subtype of hypertrophic cardiomyopathy (HCM) that affects up to 25% of Asian patients and is not as well understood in non-Asian patients. Although ApHCM has been considered a more "benign" variant, it is associated with increased risk of atrial and ventricular arrhythmias, apical thrombi, stroke, and progressive heart failure. The occurrence of pulmonary hypertension (PH) in ApHCM, due to elevated pressures on the left side of the heart, has been documented. However, the exact prevalence of PH in ApHCM and sex differences remain uncertain.

Methods

We sought to evaluate the prevalence, risk associations, and sex differences in elevated pulmonary pressures in the largest cohort of patients with ApHCM at a single tertiary center. A total of 542 patients diagnosed with ApHCM were identified using ICD codes and clinical notes searches, confirmed by cross-referencing with cardiac MRI reports extracted through Natural Language Processing and through manual evaluation of patient charts and imaging records.

Results

In 414 patients, echocardiogram measurements of pulmonary artery systolic pressure (PASP) were obtained at the time of diagnosis. The mean age was 59.4 ± 16.6 years, with 181 (44%) being females. The mean PASP was 38 ± 12 mmHg in females vs. 33 ± 9 mmHg in males (p < 0.0001). PH as defined by a PASP value of > 36 mmHg was present in 140/414 (34%) patients, with a predominance in females [79/181 (44%)] vs. males [61/233 (26%), p < 0.0001]. Female sex, atrial fibrillation, diagnosis of congestive heart failure, and elevated filling pressures on echocardiogram remained significantly associated with PH (PASP > 36 mmHg) in multivariable modeling. PH, when present, was independently associated with mortality [hazard ratio 1.63, 95% CI (1.05-2.53), p = 0.028] and symptoms [odds ratio 2.28 (1.40, 3.71), p < 0.001].

Conclusion

PH was present in 34% of patients with ApHCM at diagnosis, with female sex predominance. PH in ApHCM was associated with symptoms and increased mortality.

Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study

BACKGROUND

Management of abdominal aortic aneurysms (AAAs) requires serial imaging surveillance to evaluate the aneurysm dimension. Natural language processing (NLP) has been previously developed to retrospectively identify patients with AAA from electronic health records (EHRs). However, there are no reported studies that use NLP to identify patients with AAA in near real-time from radiology reports.

OBJECTIVE

This study aims to develop and validate a rule-based NLP algorithm for near real-time automatic extraction of AAA diagnosis from radiology reports for case identification.

METHODS

The AAA-NLP algorithm was developed and deployed to an EHR big data infrastructure for near real-time processing of radiology reports from May 1, 2019, to September 2020. NLP extracted named entities for AAA case identification and classified subjects as cases and controls. The reference standard to assess algorithm performance was a manual review of processed radiology reports by trained physicians following standardized criteria. Reviewers were blinded to the diagnosis of each subject. The AAA-NLP algorithm was refined in 3 successive iterations. For each iteration, the AAA-NLP algorithm was modified based on performance compared to the reference standard.

RESULTS

A total of 360 reports were reviewed, of which 120 radiology reports were randomly selected for each iteration. At each iteration, the AAA-NLP algorithm performance improved. The algorithm identified AAA cases in near real-time with high positive predictive value (0.98), sensitivity (0.95), specificity (0.98), F1 score (0.97), and accuracy (0.97).

CONCLUSIONS

Implementation of NLP for accurate identification of AAA cases from radiology reports with high performance in near real time is feasible. This NLP technique will support automated input for patient care and clinical decision support tools for the management of patients with AAA.  .

Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports

Background

Cardiac magnetic resonance (CMR) imaging is important for diagnosis and risk stratification of hypertrophic cardiomyopathy (HCM) patients. However, collection of information from large numbers of CMR reports by manual review is time-consuming, error-prone and costly. Natural language processing (NLP) is an artificial intelligence method for automated extraction of information from narrative text including text in CMR reports in electronic health records (EHR). Our objective was to assess whether NLP can accurately extract diagnosis of HCM from CMR reports.

Methods

An NLP system with two tiers was developed for information extraction from narrative text in CMR reports; the first tier extracted information regarding HCM diagnosis while the second extracted categorical and numeric concepts for HCM classification. We randomly allocated 200 HCM patients with CMR reports from 2004 to 2018 into training (100 patients with 185 CMR reports) and testing sets (100 patients with 206 reports).

Results

NLP algorithms demonstrated very high performance compared to manual annotation. The algorithm to extract HCM diagnosis had accuracy of 0.99. The accuracy for categorical concepts included HCM morphologic subtype 0.99, systolic anterior motion of the mitral valve 0.96, mitral regurgitation 0.93, left ventricular (LV) obstruction 0.94, location of obstruction 0.92, apical pouch 0.98, LV delayed enhancement 0.93, left atrial enlargement 0.99 and right atrial enlargement 0.98. Accuracy for numeric concepts included maximal LV wall thickness 0.96, LV mass 0.99, LV mass index 0.98, LV ejection fraction 0.98 and right ventricular ejection fraction 0.99.

Conclusions

NLP identified and classified HCM from CMR narrative text reports with very high performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-02017-y.

Sociotechnical Intervention for Improved Delivery of Preventive Cardiovascular Care to Rural Communities: Participatory Design Approach

Background

Clinical practice guidelines recommend antiplatelet and statin therapies as well as blood pressure control and tobacco cessation for secondary prevention in patients with established atherosclerotic cardiovascular diseases (ASCVDs). However, these strategies for risk modification are underused, especially in rural communities. Moreover, resources to support the delivery of preventive care to rural patients are fewer than those for their urban counterparts. Transformative interventions for the delivery of tailored preventive cardiovascular care to rural patients are needed.

Objective

A multidisciplinary team developed a rural-specific, team-based model of care intervention assisted by clinical decision support (CDS) technology using participatory design in a sociotechnical conceptual framework. The model of care intervention included redesigned workflows and a novel CDS technology for the coordination and delivery of guideline recommendations by primary care teams in a rural clinic.

Methods

The design of the model of care intervention comprised 3 phases: problem identification, experimentation, and testing. Input from team members (n=35) required 150 hours, including observations of clinical encounters, provider workshops, and interviews with patients and health care professionals. The intervention was prototyped, iteratively refined, and tested with user feedback. In a 3-month pilot trial, 369 patients with ASCVDs were randomized into the control or intervention arm.

Results

New workflows and a novel CDS tool were created to identify patients with ASCVDs who had gaps in preventive care and assign the right care team member for delivery of tailored recommendations. During the pilot, the intervention prototype was iteratively refined and tested. The pilot demonstrated feasibility for successful implementation of the sociotechnical intervention as the proportion of patients who had encounters with advanced practice providers (nurse practitioners and physician assistants), pharmacists, or tobacco cessation coaches for the delivery of guideline recommendations in the intervention arm was greater than that in the control arm.

Conclusions

Participatory design and a sociotechnical conceptual framework enabled the development of a rural-specific, team-based model of care intervention assisted by CDS technology for the transformation of preventive health care delivery for ASCVDs.

Natural language processing of implantable cardioverter-defibrillator reports in hypertrophic cardiomyopathy: A paradigm for longitudinal device follow-up

Background

The follow-up of implantable cardioverter-defibrillators (ICDs) generates large amounts of valuable structured and unstructured data embedded in device interrogation reports.

Objective

We aimed to build a natural language processing (NLP) model for automated capture of ICD-recorded events from device interrogation reports using a single-center cohort of patients with hypertrophic cardiomyopathy (HCM).

Methods

A total of 687 ICD interrogation reports from 247 HCM patients were included. Using a derivation set of 480 reports, we developed a rule-based NLP algorithm based on unstructured (free-text) data from the interpretation field of the ICD reports to identify sustained atrial and ventricular arrhythmias, and ICD therapies. A separate model based on structured numerical tabulated data was also developed. Both models were tested in a separate set of the 207 remaining ICD reports. Diagnostic performance was determined in reference to arrhythmia and ICD therapy annotations generated by expert manual review of the same reports.

Results

The NLP system achieved sensitivity 0.98 and 0.99, and F1-scores 0.98 and 0.92 for arrhythmia and ICD therapy events, respectively. In contrast, the performance of the structured data model was significantly lower with sensitivity 0.33 and 0.76, and F1-scores 0.45 and 0.78, for arrhythmia and ICD therapy events, respectively.

Conclusion

An automated NLP system can capture arrhythmia events and ICD therapies from unstructured device interrogation reports with high accuracy in HCM. These findings demonstrate the feasibility of an NLP paradigm for the extraction of data for clinical care and research from ICD reports embedded in the electronic health record.

Detection of hypertrophic cardiomyopathy by an artificial intelligence electrocardiogram in children and adolescents

BACKGROUND

There is no established screening approach for hypertrophic cardiomyopathy (HCM). We recently developed an artificial intelligence (AI) model for the detection of HCM based on the 12‑lead electrocardiogram (AI-ECG) in adults. Here, we aimed to validate this approach of ECG-based HCM detection in pediatric patients (age ≤ 18 years).

METHODS

We identified a cohort of 300 children and adolescents with HCM (mean age 12.5 ± 4.6 years, male 68%) who had an ECG and echocardiogram at our institution. Patients were age- and sex-matched to 18,439 non-HCM controls. Diagnostic performance of the AI-ECG model for the detection of HCM was estimated using the previously identified optimal diagnostic threshold of 11% (the probability output derived by the model above which an ECG is considered to belong to an HCM patient).

RESULTS

Mean AI-ECG probabilities of HCM were 92% and 5% in the case and control groups, respectively. The area under the receiver operating characteristic curve (AUC) of the AI-ECG model for HCM detection was 0.98 (95% CI 0.98-0.99) with corresponding sensitivity 92% and specificity 95%. The positive and negative predictive values were 22% and 99%, respectively. The model performed similarly in males and females and in genotype-positive and genotype-negative HCM patients. Performance tended to be superior with increasing age. In the age subgroup <5 years, the test's AUC was 0.93. In comparison, the AUC was 0.99 in the age subgroup 15-18 years.

CONCLUSIONS

A deep-learning, AI model can detect pediatric HCM with high accuracy from the standard 12‑lead ECG.

Hemodynamic Heterogeneity of Reduced Cardiac Reserve Unmasked by Volumetric Exercise Echocardiography

Background: Two-dimensional volumetric exercise stress echocardiography (ESE) provides an integrated view of left ventricular (LV) preload reserve through end-diastolic volume (EDV) and LV contractile reserve (LVCR) through end-systolic volume (ESV) changes. Purpose: To assess the dependence of cardiac reserve upon LVCR, EDV, and heart rate (HR) during ESE. Methods: We prospectively performed semi-supine bicycle or treadmill ESE in 1344 patients (age 59.8 ± 11.4 years; ejection fraction = 63 ± 8%) referred for known or suspected coronary artery disease. All patients had negative ESE by wall motion criteria. EDV and ESV were measured by biplane Simpson rule with 2-dimensional echocardiography. Cardiac index reserve was identified by peak-rest value. LVCR was the stress-rest ratio of force (systolic blood pressure by cuff sphygmomanometer/ESV, abnormal values ≤2.0). Preload reserve was defined by an increase in EDV. Cardiac index was calculated as stroke volume index * HR (by EKG). HR reserve (stress/rest ratio) <1.85 identified chronotropic incompetence. Results: Of the 1344 patients, 448 were in the lowest tertile of cardiac index reserve with stress. Of them, 303 (67.6%) achieved HR reserve <1.85; 252 (56.3%) had an abnormal LVCR and 341 (76.1%) a reduction of preload reserve, with 446 patients (99.6%) showing ≥1 abnormality. At binary logistic regression analysis, reduced preload reserve (odds ratio [OR]: 5.610; 95% confidence intervals [CI]: 4.025 to 7.821), chronotropic incompetence (OR: 3.923, 95% CI: 2.915 to 5.279), and abnormal LVCR (OR: 1.579; 95% CI: 1.105 to 2.259) were independently associated with lowest tertile of cardiac index reserve at peak stress. Conclusions: Heart rate assessment and volumetric echocardiography during ESE identify the heterogeneity of hemodynamic phenotypes of impaired chronotropic, preload or LVCR underlying a reduced cardiac reserve.

Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies

Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate ("A-by-G" grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.

NATURAL LANGUAGE PROCESSING BASED MACHINE LEARNING MODEL USING CARDIAC MRI REPORTS TO IDENTIFY HYPERTROPHIC CARDIOMYOPATHY PATIENTS

Hypertrophic Cardiomyopathy (HCM) is the most common genetic heart disease in the US and is known to cause sudden death (SCD) in young adults. While significant advancements have been made in HCM diagnosis and management, there is a need to identify HCM cases from electronic health record (EHR) data to develop automated tools based on natural language processing guided machine learning (ML) models for accurate HCM case identification to improve management and reduce adverse outcomes of HCM patients. Cardiac Magnetic Resonance (CMR) Imaging, plays a significant role in HCM diagnosis and risk stratification. CMR reports, generated by clinician annotation, offer rich data in the form of cardiac measurements as well as narratives describing interpretation and phenotypic description. The purpose of this study is to develop an NLP-based interpretable model utilizing impressions extracted from CMR reports to automatically identify HCM patients. CMR reports of patients with suspected HCM diagnosis between the years 1995 to 2019 were used in this study. Patients were classified into three categories of yes HCM, no HCM and, possible HCM. A random forest (RF) model was developed to predict the performance of both CMR measurements and impression features to identify HCM patients. The RF model yielded an accuracy of 86% (608 features) and 85% (30 features). These results offer promise for accurate identification of HCM patients using CMR reports from EHR for efficient clinical management transforming health care delivery for these patients.

Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review

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.

Usability of a Digital Registry to Promote Secondary Prevention for Peripheral Artery Disease Patients

Objective

To evaluate usability of a quality improvement tool that promotes guideline-based care for patients with peripheral arterial disease (PAD).

Patients and Methods

The study was conducted from July 19, 2018, to August 21, 2019. We compared the usability of a PAD cohort knowledge solution (CKS) with standard management supported by an electronic health record (EHR). Two scenarios were developed for usability evaluation; the first for the PAD-CKS while the second evaluated standard EHR workflow. Providers were asked to provide opinions about the PAD-CKS tool and to generate a System Usability Scale (SUS) score. Metrics analyzed included time required, number of mouse clicks, and number of keystrokes.

Results

Usability evaluations were completed by 11 providers. SUS for the PAD-CKS was excellent at 89.6. Time required to complete 21 tasks in the CKS was 4 minutes compared with 12 minutes for standard EHR workflow (median, P = .002). Completion of CKS tasks required 34 clicks compared with 148 clicks for the EHR (median, P = .002). Keystrokes for CKS task completion was 8 compared with 72 for EHR (median, P = .004). Providers indicated that overall they found the tool easy to use and the PAD mortality risk score useful.

Conclusions

Usability evaluation of the PAD-CKS tool demonstrated time savings, a high SUS score, and a reduction of mouse clicks and keystrokes for task completion compared to standard workflow using the EHR. Provider feedback regarding the strengths and weaknesses also created opportunities for iterative improvement of the PAD-CKS tool.

Explanatory Analysis of a Machine Learning Model to Identify Hypertrophic Cardiomyopathy Patients from EHR Using Diagnostic Codes

Hypertrophic cardiomyopathy (HCM) is a genetic heart disease that is the leading cause of sudden cardiac death (SCD) in young adults. Despite the well-known risk factors and existing clinical practice guidelines, HCM patients are underdiagnosed and sub-optimally managed. Developing machine learning models on electronic health record (EHR) data can help in better diagnosis of HCM and thus improve hundreds of patient lives. Automated phenotyping using HCM billing codes has received limited attention in the literature with a small number of prior publications. In this paper, we propose a novel predictive model that helps physicians in making diagnostic decisions, by means of information learned from historical data of similar patients. We assembled a cohort of 11,562 patients with known or suspected HCM who have visited Mayo Clinic between the years 1995 to 2019. All existing billing codes of these patients were extracted from the EHR data warehouse. Target ground truth labeling for training the machine learning model was provided by confirmed HCM diagnosis using the gold standard imaging tests for HCM diagnosis echocardiography (echo), or cardiac magnetic resonance (CMR) imaging. As the result, patients were labeled into three categories of "yes definite HCM", "no HCM phenotype", and "possible HCM" after a manual review of medical records and imaging tests. In this study, a random forest was adopted to investigate the predictive performance of billing codes for the identification of HCM patients due to its practical application and expected accuracy in a wide range of use cases. Our model performed well in finding patients with "yes definite", "possible" and "no" HCM with an accuracy of 71%, weighted recall of 70%, the precision of 75%, and weighted F1 score of 72%. Furthermore, we provided visualizations based on multidimensional scaling and the principal component analysis to provide insights for clinicians' interpretation. This model can be used for the identification of HCM patients using their EHR data, and help clinicians in their diagnosis decision making.

Conversion of left atrial volume to diameter for automated estimation of sudden cardiac death risk in hypertrophic cardiomyopathy

Background

A 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.

Methods

The 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.

Results

The 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.

Conclusions

Substitution of measured LA diameter by estimated LA diameter in the HCM‐SCD calculator did not reclassify risk.

The polygenic architecture of left ventricular mass mirrors the clinical epidemiology

Left ventricular (LV) mass is a prognostic biomarker for incident heart disease and all-cause mortality. Large-scale genome-wide association studies have identified few SNPs associated with LV mass. We hypothesized that a polygenic discovery approach using LV mass measurements made in a clinical population would identify risk factors and diseases associated with adverse LV remodeling. We developed a polygenic single nucleotide polymorphism-based predictor of LV mass in 7,601 individuals with LV mass measurements made during routine clinical care. We tested for associations between this predictor and 894 clinical diagnoses measured in 58,838 unrelated genotyped individuals. There were 29 clinical phenotypes associated with the LV mass genetic predictor at FDR q < 0.05. Genetically predicted higher LV mass was associated with modifiable cardiac risk factors, diagnoses related to organ dysfunction and conditions associated with abnormal cardiac structure including heart failure and atrial fibrillation. Secondary analyses using polygenic predictors confirmed a significant association between higher LV mass and body mass index and, in men, associations with coronary atherosclerosis and systolic blood pressure. In summary, these analyses show that LV mass-associated genetic variability associates with diagnoses of cardiac diseases and with modifiable risk factors which contribute to these diseases.

Artificial Intelligence in Cardiology: Present and Future

Artificial intelligence (AI) is a nontechnical, popular term that refers to machine learning of various types but most often to deep neural networks. Cardiology is at the forefront of AI in medicine. For this review, we searched PubMed and MEDLINE databases with no date restriction using search terms related to AI and cardiology. Articles were selected for inclusion on the basis of relevance. We highlight the major achievements in recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take center stage in the field. Artificial intelligence requires a close collaboration among computer scientists, clinical investigators, clinicians, and other users in order to identify the most relevant problems to be solved. Best practices in the generation and implementation of AI include the selection of ideal data sources, taking into account common challenges during the interpretation, validation, and generalizability of findings, and addressing safety and ethical concerns before final implementation. The future of AI in cardiology and in medicine in general is bright as the collaboration between investigators and clinicians continues to excel.

Automated extraction of sudden cardiac death risk factors in hypertrophic cardiomyopathy patients by natural language processing

BACKGROUND

The management of hypertrophic cardiomyopathy (HCM) patients requires the knowledge of risk factors associated with sudden cardiac death (SCD). SCD risk factors such as syncope and family history of SCD (FH-SCD) as well as family history of HCM (FH-HCM) are documented in electronic health records (EHRs) as clinical narratives. Automated extraction of risk factors from clinical narratives by natural language processing (NLP) may expedite management workflow of HCM patients. The aim of this study was to develop and deploy NLP algorithms for automated extraction of syncope, FH-SCD, and FH-HCM from clinical narratives.

METHODS AND RESULTS

We randomly selected 200 patients from the Mayo HCM registry for development (n = 100) and testing (n = 100) of NLP algorithms for extraction of syncope, FH-SCD as well as FH-HCM from clinical narratives of EHRs. The clinical reference standard was manually abstracted by 2 independent annotators. Performance of NLP algorithms was compared to aggregation and summarization of data entries in the HCM registry for syncope, FH-SCD, and FH-HCM. We also compared the NLP algorithms with billing codes for syncope as well as responses to patient survey questions for FH-SCD and FH-HCM. These analyses demonstrated NLP had superior sensitivity (0.96 vs 0.39, p < 0.001) and comparable specificity (0.90 vs 0.92, p = 0.74) and PPV (0.90 vs 0.83, p = 0.37) compared to billing codes for syncope. For FH-SCD, NLP outperformed survey responses for all parameters (sensitivity: 0.91 vs 0.59, p = 0.002; specificity: 0.98 vs 0.50, p < 0.001; PPV: 0.97 vs 0.38, p < 0.001). NLP also achieved superior sensitivity (0.95 vs 0.24, p < 0.001) with comparable specificity (0.95 vs 1.0, p-value not calculable) and positive predictive value (PPV) (0.92 vs 1.0, p = 0.09) compared to survey responses for FH-HCM.

CONCLUSIONS

Automated extraction of syncope, FH-SCD and FH-HCM using NLP is feasible and has promise to increase efficiency of workflow for providers managing HCM patients.

Provider Survey on Automated Clinical Decision Support System for Cardiovascular Risk Assessment

Despite progress made in establishing primary and secondary preventive strategies for cardiovascular diseases, there are significant gaps between guideline recommended strategies and implementation of recommendations in practice. A clinical decision support (CDS) system entitled CV Risk Profile was developed at Mayo Clinic Rochester as a targeted solution for this gap in preventive cardiovascular care. The system remained in use for 10 years until it became non-functional in 2018 during transition to a new electronic health record (EHR). This study investigated provider opinions regarding the cardiovascular disease CDS system while it was still in operation, to determine if there exists a provider reported need for a similar system to be developed for use within the new EHR.

Provider Survey on Automated Clinical Decision Support for Cardiovascular Risk Assessment

OBJECTIVE

To investigate provider opinions regarding a clinical decision support (CDS) system for cardiovascular risk assessment and for the creation of a replacement system.

METHODS

From March to April 2018, an invitation letter with a link to a self-administered web-based survey was sent via e-mail to 279 providers with primary appointment in the Department of Cardiovascular Medicine, Mayo Clinic, Rochester. The e-mail was sent to providers on March 8, 2018 and the survey closed on April 16, 2018.

RESULTS

One hundred providers responded to the survey yielding an overall response rate of 35.8%. Of these, 52 (52%) indicated they had used the cardiovascular (CV) risk profile CDS system and were classified as users and prompted to continue the survey. Among users, 42 (80.8%) indicated use of the CDS was either important (25; 48.1%) or very important (17; 32.7%) in their clinical practice; 45 (86.5%) responded that the system was very easy (17; 32.7%) or easy (28; 53.8%) to use. In addition, 48 (96.0%) users indicated that the CV risk profile supported their thought process at the point-of-care; 47 (97.9%) users indicated similar functionalities should be implemented into the new electronic health record system and 41 (85.4%) users reported new functionalities should also be incorporated.

CONCLUSIONS

For most users, the CDS system was easy to use and supported clinical thought process at the point-of-care. Users also felt their practice was supported and should continue to be supported by CDS systems providing individualized patient information at the point-of-care.

Association of Ankle-Brachial Indices With Limb Revascularization or Amputation in Patients With Peripheral Artery Disease

IMPORTANCE

The prevalence and morbidity of peripheral artery disease (PAD) are high, with limb outcomes including revascularization and amputation. In community-dwelling patients with PAD, the role of noninvasive evaluation for risk assessment and rates of limb outcomes have not been established to date.

OBJECTIVE

To evaluate whether ankle-brachial indices are associated with limb outcomes in community-dwelling patients with PAD.

DESIGN, SETTING, AND PARTICIPANTS

A population-based, observational, test-based cohort study of patients was performed from January 1, 1998, to December 31, 2014. Data analysis was conducted from July 15 to December 15, 2017. Participants included a community-based cohort of 1413 patients with PAD from Olmsted County, Minnesota, identified by validated algorithms deployed to electronic health records. Automated algorithms identified limb outcomes used to build Cox proportional hazards regression models. Ankle-brachial indices and presence of poorly compressible arteries were electronically identified from digital data sets. Guideline-recommended management strategies within 6 months of diagnosis were also electronically retrieved, including therapy with statins, antiplatelet agents, angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers, and smoking abstention.

MAIN OUTCOMES AND MEASURES

Ankle-brachial index (index ≤0.9 indicates PAD; <.05, severe PAD; and ≥1.40, poorly compressible arteries) and limb revascularization or amputation.

RESULTS

Of 1413 patients, 633 (44.8%) were women; mean (SD) age was 70.8 (13.3) years. A total of 283 patients (20.0%) had severe PAD (ankle-brachial indices <0.5) and 350 (24.8%) had poorly compressible arteries (ankle-brachial indices ≥1.4); 780 (55.2%) individuals with less than severe disease formed the reference group. Only 32 of 283 patients (11.3%) with severe disease and 68 of 350 patients (19.4%) with poorly compressible arteries were receiving 4 guideline-recommended management strategies. In the severe disease subgroup, the 1-year event rate for revascularization was 32.4% (90 events); in individuals with poorly compressible arteries, the 1-year amputation rate was 13.9% (47 events). In models adjusted for age, sex, and critical limb ischemia, poorly compressible arteries were associated with amputation (hazard ratio [HR], 3.12; 95% CI, 2.16-4.50; P < .001) but not revascularization (HR, 0.91; 95% CI, 0.69-1.20; P = .49). In contrast, severe disease was associated with revascularization (HR, 2.69; 95% CI, 2.15-3.37; P < .001) but not amputation (HR, 1.30; 95% CI, 0.82-2.07; P = .27).

CONCLUSIONS AND RELEVANCE

Community-dwelling patients with severe PAD or poorly compressible arteries have high rates of revascularization or limb loss, respectively. Guideline-recommended management strategies for secondary risk prevention are underused in the community.

Innovative Informatics Approaches for Peripheral Artery Disease: Current State and Provider Survey of Strategies for Improving Guideline-Based Care

OBJECTIVE

To quantify compliance with guideline recommendations for secondary prevention in peripheral artery disease (PAD) using natural language processing (NLP) tools deployed to an electronic health record (EHR) and investigate provider opinions regarding clinical decision support (CDS) to promote improved implementation of these strategies.

PATIENTS AND METHODS

Natural language processing was used for automated identification of moderate to severe PAD cases from narrative clinical notes of an EHR of patients seen in consultation from May 13, 2015, to July 27, 2015. Guideline-recommended strategies assessed within 6 months of PAD diagnosis included therapy with statins, antiplatelet agents, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and smoking abstention. Subsequently, a provider survey was used to assess provider knowledge regarding PAD clinical practice guidelines, comfort in recommending secondary prevention strategies, and potential role for CDS.

RESULTS

Among 73 moderate to severe PAD cases identified by NLP, only 12 (16%) were on 4 guideline-recommended strategies. A total of 207 of 760 (27%) providers responded to the survey; of these 141 (68%) were generalists and 66 (32%) were specialists. Although 183 providers (88%) managed patients with PAD, 51 (25%) indicated they were uncomfortable doing so; 138 providers (67%) favored the development of a CDS system tailored for their practice and 146 (71%) agreed that an automated EHR-derived mortality risk score calculator for patients with PAD would be helpful.

CONCLUSION

Natural language processing tools can identify cases from EHRs to support quality metric studies. Findings of this pilot study demonstrate gaps in application of guideline-recommended strategies for secondary risk prevention for patients with moderate to severe PAD. Providers strongly support the development of CDS systems tailored to assist them in providing evidence-based care to patients with PAD at the point of care.

Burden of hospitalization in clinically diagnosed peripheral artery disease: A community-based study

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.

Surveillance of Peripheral Arterial Disease Cases Using Natural Language Processing of Clinical Notes

Peripheral arterial disease (PAD) is a chronic disease that affects millions of people worldwide and yet remains underdiagnosed and undertreated. Early detection is important, because PAD is strongly associated with an increased risk of mortality and morbidity. In this study, we built a PAD surveillance system using natural language processing (NLP) for early detection of PAD from narrative clinical notes. Our NLP algorithm had excellent positive predictive value (0.93) and identified 41% of PAD cases before the initial ankle-brachial index (ABI) test date while in 12% of cases the NLP algorithm detected PAD on the same date as the ABI (the gold standard for comparison). Hence, our system ascertains PAD patients in a timely and accurate manner. In conclusion, our PAD surveillance NLP algorithm has the potential for translation to clinical practice for use in reminding clinicians to order ABI tests in patients with suspected PAD and to reinforce the implementation of guideline recommended risk modification strategies in patients diagnosed with PAD.

Frequency, Predictors, and Implications of Abnormal Blood Pressure Responses During Dobutamine Stress Echocardiography

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.

Mining peripheral arterial disease cases from narrative clinical notes using natural language processing

OBJECTIVE

Lower extremity peripheral arterial disease (PAD) is highly prevalent and affects millions of individuals worldwide. We developed a natural language processing (NLP) system for automated ascertainment of PAD cases from clinical narrative notes and compared the performance of the NLP algorithm with billing code algorithms, using ankle-brachial index test results as the gold standard.

METHODS

We compared the performance of the NLP algorithm to (1) results of gold standard ankle-brachial index; (2) previously validated algorithms based on relevant International Classification of Diseases, Ninth Revision diagnostic codes (simple model); and (3) a combination of International Classification of Diseases, Ninth Revision codes with procedural codes (full model). A dataset of 1569 patients with PAD and controls was randomly divided into training (n = 935) and testing (n = 634) subsets.

RESULTS

We iteratively refined the NLP algorithm in the training set including narrative note sections, note types, and service types, to maximize its accuracy. In the testing dataset, when compared with both simple and full models, the NLP algorithm had better accuracy (NLP, 91.8%; full model, 81.8%; simple model, 83%; P < .001), positive predictive value (NLP, 92.9%; full model, 74.3%; simple model, 79.9%; P < .001), and specificity (NLP, 92.5%; full model, 64.2%; simple model, 75.9%; P < .001).

CONCLUSIONS

A knowledge-driven NLP algorithm for automatic ascertainment of PAD cases from clinical notes had greater accuracy than billing code algorithms. Our findings highlight the potential of NLP tools for rapid and efficient ascertainment of PAD cases from electronic health records to facilitate clinical investigation and eventually improve care by clinical decision support.

Impact of Stress Testing for Coronary Artery Disease Screening in Asymptomatic Patients With Diabetes Mellitus: A Community-Based Study in Olmsted County, Minnesota

OBJECTIVE

To evaluate the impact of screening stress testing for coronary artery disease in asymptomatic patients with diabetes in a community-based population.

PATIENTS AND METHODS

This observational study included 3146 patients from Olmsted County, Minnesota, with no history of coronary artery disease or cardiac symptoms in whom diabetes was newly diagnosed from January 1, 1992, through December 31, 2008. With combined all-cause mortality and myocardial infarction as the primary outcome, weighted Cox proportional hazards regression was performed with screening stress testing within 2 years of diabetes diagnosis as the time-dependent covariate. For descriptive analysis, participants were classified by their clinical experience during the first 2 years postdiagnosis as screened (asymptomatic, underwent stress test), unscreened (asymptomatic, no stress test), or symptomatic (experienced symptoms or event).

RESULTS

Among the screened and unscreened participants, 54% (1358 of 2538) were men; the mean (SD) age at diabetes diagnosis was 55 years (13.8 years), and 97% (2442 of 2520) had type 2 diabetes. In event-free survival analysis, 292 patients comprised the screened cohort and 2246 patients comprised the unscreened cohort. Death or myocardial infarction occurred in 454 patients (32 patients in the screened cohort and 422 in the unscreened cohort [5-year rate, 1.9% and 5.3%, respectively]) during median (interquartile range) follow-up of 9.1 years (5.3-12.5 years). Screening stress testing was associated with improved event-free survival (hazard ratio, 0.61; P=.004), independent of cardiac risk factors. However, while stress test results were abnormal in 47 of the 292 screened patients (16%), only 6 (2%) underwent coronary revascularization.

CONCLUSION

Although screening cardiac stress testing in asymptomatic patients with diabetes in this community-based population was associated with improvement in long-term event-free survival, this result does not appear to occur by coronary revascularization alone.

Identifying Peripheral Arterial Disease Cases Using Natural Language Processing of Clinical Notes

Peripheral arterial disease (PAD) is a chronic disease that affects millions of people worldwide. Ascertaining PAD status from clinical notes by manual chart review is labor intensive and time consuming. In this paper, we describe a natural language processing (NLP) algorithm for automated ascertainment of PAD status from clinical notes using predetermined criteria. We developed and evaluated our system against a gold standard that was created by medical experts based on manual chart review. Our system ascertained PAD status from clinical notes with high sensitivity (0.96), positive predictive value (0.92), negative predictive value (0.99) and specificity (0.98). NLP approaches can be used for rapid, efficient and automated ascertainment of PAD cases with implications for patient care and epidemiologic research.

Stress echocardiography: what is new and how does it compare with myocardial perfusion imaging and other modalities?

Cardiovascular disease is a leading cause of morbidity and mortality, and noninvasive strategies to diagnose and risk stratify patients remain paramount in the evaluative process. Stress echocardiography is a well-established, versatile, real-time imaging modality with advantages including lack of radiation exposure, portability, and affordability. Innovative techniques in stress echocardiography include myocardial contrast echocardiography, deformation imaging, three-dimensional (3D) echocardiography, and assessment of coronary flow reserve. Myocardial perfusion imaging with single-photon emission computed tomography (SPECT) or positron emission tomography (PET) are imaging alternatives, and stress cardiac magnetic resonance imaging and coronary computed tomography (CT) angiography, including CT perfusion imaging, are emerging as newer approaches. This review will discuss recent and upcoming developments in the field of stress testing, with an emphasis on stress echocardiography while highlighting comparisons with other modalities.

Typical blood pressure response during dobutamine stress echocardiography of patients without known cardiovascular disease who have normal stress echocardiograms

AIMS

Blood pressure (BP) responses during dobutamine stress echocardiography (DSE) have not been systematically studied. Consequently, it is not known what constitutes a normal or an abnormal BP response to dobutamine stress. We sought to define the typical BP response during DSE of patients not known to have cardiovascular disease.

METHODS AND RESULTS

Of 24 134 patients who underwent DSE from November 2003 to December 2012 at Mayo Clinic, Rochester, MN, 2968 were selected for inclusion in this retrospective study. Excluded were patients with a history of hypertension, diabetes, or coronary artery disease, and those taking vasoactive medications. Patients who had baseline and/or stress-induced wall motion abnormalities were also excluded. The distribution of the study population's BP responses during DSE was Gaussian; we defined cut-point values for normative BP responses at 2 SD for each decade of age and for the whole study population. During DSE, systolic BP (SBP) increased from baseline to peak stress (Δ +2.9 ± 24 mmHg, P < 0.0001) and diastolic BP (DBP) decreased (Δ -7.4 ± 14 mmHg). BP changes were age and sex dependent; men and younger patients had greater ΔSBP and lesser ΔDBP, compared with women and older patients. Patients who received atropine had higher peak BP values than patients who did not receive atropine, due to greater ΔSBP (+7.4 ± 26 vs. -0.5 ± 22 mmHg, P < 0.0001) and lesser ΔDBP (-4 ± 14 vs. -9.7 ± 12 mmHg, P < 0.0001). This atropine effect was present in men and women, and was more pronounced in younger patients. The normative peak SBP values ranged from 82 to 182 mmHg.

CONCLUSION

BP responses during DSE vary and depend on patients' age, gender, and the use of atropine. We describe the typical BP responses seen during DSE and report normative reference values, which can be used for defining normal and abnormal BP responses to dobutamine stress.

A perspective on the New American College of Cardiology/American Heart Association guidelines for cardiovascular risk assessment

The recently published American College of Cardiology (ACC)/American Heart Association (AHA) guidelines for cardiovascular risk assessment provide equations to estimate the 10-year and lifetime atherosclerotic cardiovascular disease (ASCVD) risk in African Americans and non-Hispanic whites, include stroke as an adverse cardiovascular outcome, and emphasize shared decision making. The guidelines provide a valuable framework that can be adapted on the basis of clinical judgment and individual/institutional expertise. In this review, we provide a perspective on the new guidelines, highlighting what is new, what is controversial, and potential adaptations. We recommend obtaining family history of ASCVD at the time of estimating ASCVD risk and consideration of imaging to assess subclinical disease burden in patients at intermediate risk. In addition to the adjuncts for ASCVD risk estimation recommended in the guidelines, measures that may be useful in refining risk estimates include carotid ultrasonography, aortic pulse wave velocity, and serum lipoprotein(a) levels. Finally, we stress the need for research efforts to improve assessment of ASCVD risk given the suboptimal performance of available risk algorithms and suggest potential future directions in this regard.

Yield of noncardiac biopsy for the diagnosis of transthyretin cardiac amyloidosis

Transthyretin (ATTR) cardiac amyloidosis may be because of mutant transthyretin causing familial amyloid cardiomyopathy (FAC) or wild-type transthyretin causing systemic senile amyloidosis (SSA). Histologic confirmation is often challenging and may require endomyocardial biopsy (EMB). The purpose of this study was to determine the frequency of amyloid protein deposition in positive noncardiac organ biopsy or fat aspiration in patients with ATTR cardiac amyloidosis. The medical records of 286 patients (mean age 66 ± 11, 85% men) with a diagnosis of ATTR cardiac amyloidosis at our institution who underwent noncardiac biopsy or subcutaneous fat aspiration were reviewed, including 186 patients (65%) with FAC and 100 patients (35%) with SSA. One hundred and thirty-one patients (46%) had EMB, all of which were positive. There were 210 patients (73%) with positive noncardiac tissue sampling, including 175 patients (94%) with FAC and 35 patients (35%) with SSA (p <0.001). There were 141 patients (76%) with FAC and 84 patients (84%) with SSA who underwent fat aspiration, and 67% and 14% were positive, respectively, whereas 100 (54%) and 64 (64%) underwent bone marrow biopsy, and 41% and 30% were positive, respectively. Rectal and sural nerve biopsies were performed in 52 (28%) and 54 (29%) patients with FAC and were positive in 81% and 83%, respectively. Biopsy of other noncardiac sites was performed with relatively lower frequency. In conclusion, although EMB is more commonly required to establish the diagnosis of SSA than FAC, noncardiac biopsy or fat aspiration could be considered as initial testing in patients evaluated for ATTR cardiac amyloidosis with characteristic echocardiography findings.

Genotype, echocardiography, and survival in familial transthyretin amyloidosis

BACKGROUND

More than 100 transthyretin (TTR) variants have been identified which cause familial systemic amyloidosis. It has been increasingly recognized that TTR variants of familial systemic amyloidosis contribute to clinical characteristics, including age at diagnosis, cardiac phenotype and survival.

METHODS

Two hundred and eighty-two patients who underwent genotyping for TTR variants were identified. This study focused on 116 patients representing the three most common TTR variants; T60A (n = 58), V30M (n = 37) and V122I (n = 21). The remaining subjects (n = 61) were distributed amongst 33 different genotypes and excluded from analysis.

RESULTS

Age at diagnosis was similar by genotype. Septal, posterior wall thickness, right ventricular systolic pressure and left ventricular mass index were greater and LVEF lower in the V122I subgroup. At mean follow up of 3.0 ± 2.6 years there were 62 deaths. V30M patients had the best survival. Survival was similar between V122I and T60A patients. The association of genotype with mortality persisted after adjustments for clinical variables.

CONCLUSIONS

For familial TTR amyloidosis cardiac involvement is frequent and mortality high for T60A, V122I and V30M genotypes. Specific genotype predicted severity of phenotypic expression as measured by echocardiography and survival.

Billing code algorithms to identify cases of peripheral artery disease from administrative data

Objective

To construct and validate billing code algorithms for identifying patients with peripheral arterial disease (PAD).

Methods

We extracted all encounters and line item details including PAD-related billing codes at Mayo Clinic Rochester, Minnesota, between July 1, 1997 and June 30, 2008; 22 712 patients evaluated in the vascular laboratory were divided into training and validation sets. Multiple logistic regression analysis was used to create an integer code score from the training dataset, and this was tested in the validation set. We applied a model-based code algorithm to patients evaluated in the vascular laboratory and compared this with a simpler algorithm (presence of at least one of the ICD-9 PAD codes 440.20–440.29). We also applied both algorithms to a community-based sample (n=4420), followed by a manual review.

Results

The logistic regression model performed well in both training and validation datasets (c statistic=0.91). In patients evaluated in the vascular laboratory, the model-based code algorithm provided better negative predictive value. The simpler algorithm was reasonably accurate for identification of PAD status, with lesser sensitivity and greater specificity. In the community-based sample, the sensitivity (38.7% vs 68.0%) of the simpler algorithm was much lower, whereas the specificity (92.0% vs 87.6%) was higher than the model-based algorithm.

Conclusions

A model-based billing code algorithm had reasonable accuracy in identifying PAD cases from the community, and in patients referred to the non-invasive vascular laboratory. The simpler algorithm had reasonable accuracy for identification of PAD in patients referred to the vascular laboratory but was significantly less sensitive in a community-based sample.

Association of TNFSF8 polymorphisms with peripheral neutrophil count

OBJECTIVE

To investigate the association between 347 single-nucleotide polymorphisms within candidate genes of the tumor necrosis factor, interleukin 1 and interleukin 6 families with neutrophil count.

PATIENTS AND METHODS

Four hundred cases with heart failure after myocardial infarction (MI) were matched by age, sex, and date of incident MI to 694 controls (MI without post-MI heart failure). Both genotypes and neutrophil count at admission for incident MI were available in 314 cases and 515 controls.

RESULTS

We found significant associations between the TNFSF8 poly morphisms rs927374 (P=5.1 x 10(-5)) and rs2295800 (P=1.3 x 10(-4)) and neutrophil count; these single-nucleotide polymorphisms are in high linkage disequilibrium (r(2)=0.97). Associations persisted after controlling for clinical characteristics and were unchanged after adjusting for case-control status. For rs927374, the neutrophil count of GG homozygotes (7.6±5.1) was 16% lower than that of CC homozygotes (9.0±5.2).

CONCLUSION

The TNFSF8 polymorphisms rs927374 and rs2295800 were associated with neutrophil count. This finding suggests that post-MI inflammatory response is genetically modulated.

Troponin T levels and infarct size by SPECT myocardial perfusion imaging

OBJECTIVES

To evaluate the relationship between serial cardiac troponin T (cTnT) levels with infarct size and left ventricular ejection fraction by gated single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) in patients with acute myocardial infarction (AMI).

BACKGROUND

Current guidelines recommend the use of cTnT as the biomarker of choice for the diagnosis of AMI. Data relating cTnT to SPECT-MPI in patients with AMI are limited.

METHODS

A subset of patients with their first AMI participating in a community-based cohort of AMI in Olmsted County, Minnesota, were prospectively studied. Serial cTnT levels were evaluated at presentation, <12 h and 1, 2, and 3 days after onset of pain. Peak cTnT was defined as the maximum cTnT value.

RESULTS

A total of 121 patients (age, 61 ± 13 years; 31% women) with AMI underwent gated SPECT-MPI at a median (25th percentile, 75th percentile) of 10 (5, 15) days post-AMI. The type of infarct was non-ST-segment elevation myocardial infarction in 61%, and 13% were anterior in location. The median infarct size was 1% (0%, 11%) and the median gated left ventricular ejection fraction was 54% (47%, 60%). Fifty-nine patients (49% of the population) had no measurable infarction by SPECT-MPI. Independent predictors of measurable SPECT-MPI infarct size included cTnT at days 1, 2, and 3 and peak cTnT, but not at presentation or <12 h. In receiver-operator characteristic analysis, the area under the curve was highest at day 3. Receiver-operator characteristic analysis demonstrated a cutoff of 1.5 ng/ml for peak cTnT for the detection of measurable infarct size.

CONCLUSIONS

In a community-based cohort of patients with their first AMI, independent predictors of measurable SPECT-MPI infarct size included cTnT at days 1, 2, and 3 and peak cTnT. In contrast, cTnT level at presentation and <12 h was not an independent predictor of myocardial infarction size as assessed by SPECT-MPI. Receiver-operator characteristic analysis demonstrated a cutoff value peak cTnT of 1.5 ng/ml for the detection of measurable infarct.

Left ventricular function and C-reactive protein levels in acute myocardial infarction

To examine left ventricular (LV) function in patients after acute myocardial infarction (AMI) and assess its relation to C-reactive protein (CRP) as a measure of the early inflammatory response. We measured the CRP levels early after AMI and correlated them with the early structural and functional cardiac alterations. From November 2002 to December 2007, we prospectively enrolled community subjects who had experienced an AMI, as defined by standardized criteria, measured the CRP level, and obtained an echocardiogram. The study consisted of 514 patients (mean age 67 +/- 15 years, 59% men). CRP was measured early after symptom onset (median 6.1 hours; twenty-fifth to seventy-fifth percentile 2.2 to 11.1). The median CRP level was 4.8 mg/L (twenty-fifth to seventy-fifth percentile 1.8 to 24). The echocardiograms were obtained at a median of 1 day after AMI. The wall motion score index, LV ejection fraction, and LV diameter were similar across the CRP tertiles (all p >0.05). Greater CRP levels were associated with the presence of moderate or severe diastolic dysfunction (p = 0.002) and moderate or severe mitral regurgitation (p <0.001). The association with moderate or severe mitral regurgitation was independent of the clinical characteristics and ST-segment elevation status. In conclusion, at the initial phase of AMI, CRP elevation was associated with the presence and severity of mitral regurgitation and diastolic dysfunction. This suggests that inflammation is related to the ventricular remodeling processes, independently of LV systolic function.

Neutrophilia predicts death and heart failure after myocardial infarction: a community-based study

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 x10(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.

Effect of second-generation sulfonylureas on survival in patients with diabetes mellitus after myocardial infarction

OBJECTIVE

To examine possible adverse effects of sulfonylureas on survival among patients with diabetes mellitus (DM) who experience a myocardial infarction (MI).

PATIENTS AND METHODS

Residents of Olmsted County, Minnesota, with an MI that met standardized criteria from January 1, 1985, through December 31, 2002, were followed up for mortality.

RESULTS

Among 2189 patients with MI (mean+/-SD age, 68+/-14 years; 1237 men [57%]), 409 (19%) had DM. The 23 patients treated with first-generation sulfonylureas, biguanides, or thiazolidinediones were excluded from analyses. Among the remaining 386 patients with DM, 120 (31%) were taking second-generation sulfonylureas, 180 (47%) were taking insulin, and 86 (22%) were receiving nonpharmacological treatment. Patients with DM treated with second-generation sulfonylureas were more likely to be men and have higher creatinine clearance than those treated with insulin. After adjusting for age, sex, Killip class, duration of DM, creatinine clearance, and reperfusion therapy or revascularization, patients treated with second-generation sulfonylureas had a lower risk of death than did diabetic patients receiving insulin (hazard ratio, 0.41; 95% confidence interval, 0.21-0.80; P=.009).

CONCLUSION

These population-based data do not support the concern about an adverse effect of second-generation sulfonylureas on survival after MI and underscore the importance of population-based studies of surveillance of drug safety.

Effect of second-generation sulfonylureas on survival in patients with diabetes mellitus after myocardial infarction

OBJECTIVE

To examine possible adverse effects of sulfonylureas on survival among patients with diabetes mellitus (DM) who experience a myocardial infarction (MI).

PATIENTS AND METHODS

Residents of Olmsted County, Minnesota, with an MI that met standardized criteria from January 1, 1985, through December 31, 2002, were followed up for mortality.

RESULTS

Among 2189 patients with MI (mean+/-SD age, 68+/-14 years; 1237 men [57%]), 409 (19%) had DM. The 23 patients treated with first-generation sulfonylureas, biguanides, or thiazolidinediones were excluded from analyses. Among the remaining 386 patients with DM, 120 (31%) were taking second-generation sulfonylureas, 180 (47%) were taking insulin, and 86 (22%) were receiving nonpharmacological treatment. Patients with DM treated with second-generation sulfonylureas were more likely to be men and have higher creatinine clearance than those treated with insulin. After adjusting for age, sex, Killip class, duration of DM, creatinine clearance, and reperfusion therapy or revascularization, patients treated with second-generation sulfonylureas had a lower risk of death than did diabetic patients receiving insulin (hazard ratio, 0.41; 95% confidence interval, 0.21-0.80; P=.009).

CONCLUSION

These population-based data do not support the concern about an adverse effect of second-generation sulfonylureas on survival after MI and underscore the importance of population-based studies of surveillance of drug safety.

Left ventricular function and heart failure in myocardial infarction: impact of the new definition in the community

OBJECTIVES

The aim of this study is to evaluate ventricular function and the occurrence of heart failure (HF) among persons with myocardial infarction (MI) meeting only troponin criteria compared to persons meeting creatine kinase and its MB fraction (CK-MB) criteria.

BACKGROUND

The 2000 American College of Cardiology/European Society of Cardiology MI definition enabled identification of MIs meeting only troponin-based criteria. Data on ventricular function and HF among these are lacking.

METHODS

Between November 2002 and May 2006, we prospectively identified 835 persons with MI in the community using standardized criteria including cardiac pain, electrocardiogram, and biomarkers. Troponin and CK-MB were prospectively measured in all; each patient was classified according to the criteria met.

RESULTS

We performed echocardiograms (median of 1 day post-MI) in 482 patients (age 68+/-15 years; 45% women); 363 patients met CK-MB criteria, whereas 119 met only troponin criteria. The latter had lower wall motion score index (1.3+/-0.4 vs 1.5+/-0.5 for CK-MB; P<.01). Diastolic dysfunction was similar in both groups. After 1 year of follow up, 142 patients developed post-MI HF. Patients meeting only troponin criteria had a lower risk of HF after adjustment for age, sex, comorbidity (hazard ratio 0.56, 95% confidence interval 0.37-0.85, P<.01), which persisted after further adjustments for systolic or diastolic function.

CONCLUSIONS

In the community, the prospective application of the new MI definition identifies patients meeting only troponin criteria with better systolic function than cases meeting CK-MB criteria. Such MIs have a lower risk of subsequent HF. These findings are important for risk stratification in clinical practice.

Exercise oscillatory ventilation: instability of breathing control associated with advanced heart failure

BACKGROUND

Instability of breathing control due to heart failure (HF) manifests as exercise oscillatory ventilation (EOV). Prior descriptions of patients with EOV have not been controlled and have been limited to subjects with left ventricular ejection fraction (LVEF) of <or= 0.40. The aim of this study was to compare clinical characteristics including ventilatory responses of subjects with EOV to those of control subjects with HF matched for LVEF.

METHODS

Subjects (n = 47) were retrospectively identified from 1,340 consecutive patients referred for cardiopulmonary exercise testing. Study inclusion required EOV without consideration of LVEF while control subjects (n = 47) were composed of HF patients with no EOV matched for LVEF. Characteristics for each group were summarized and compared.

RESULTS

For EOV subjects, the mean LVEF was 0.37 (range, 0.11 to 0.70), and 19 subjects (41%) had an LVEF of >or= 0.40. Compared to control subjects, EOV subjects had increased left atrial dimension, mitral E-wave velocity, and right heart pressures as well as decreased exercise tidal volume response, functional capacity, rest and exercise end-tidal carbon dioxide, and increased ventilatory equivalent for carbon dioxide and dead space ventilation (all p < 0.05). Multivariate analysis demonstrated atrial fibrillation (odds ratio, 6.7; p = 0.006), digitalis therapy (odds ratio, 0.27; p = 0.02), New York Heart Association class (odds ratio, 3.5; p = 0.0006), rest end-tidal carbon dioxide (odds ratio, 0.87; p = 0.005), and peak heart rate (odds ratio, 0.98; p = 0.02) were independently associated with EOV.

CONCLUSIONS

Patients with EOV have clinical characteristics and exercise ventilatory responses consistent with more advanced HF than patients with comparable LV systolic function; EOV may occur in HF patients with an LVEF of >or= 0.40.

Participation bias and its impact on the assembly of a genetic specimen repository for a myocardial infarction cohort

OBJECTIVE

To assess participation bias in the assembly of a specimen repository for genetic studies and to examine the association of participation with outcome within the Olmsted County myocardial infarction (MI) cohort.

PARTICIPANTS AND METHODS

From January 1, 1979, to May 31, 2006, 3081 persons had MI in Olmsted County, MN. Face-to-face contact was used to recruit patients who were hospitalized for an acute event. Persons who had had an MI before establishment of this repository were contacted by mail. At initial contact, we sought consent to use blood samples for genetic studies. Persons who refused were contacted by mail and were asked to consent to the use of stored tissue samples. For deceased subjects, stored tissue was collected when available.

RESULTS

Of the 3081 persons in the Olmsted County MI cohort, 1994 participated in the study; 1007 (50.5%) blood and 987 (49.5%) tissue specimens were provided. Participants were more likely to be younger men with hypertension, comorbidities, and non-ST-segment elevation MI (all, P<.05). Participants who provided blood specimens were more likely to have non-ST-segment elevation MI and lower Killip class than those who provided tissue. After adjustment for age, sex, hypertension, ST-segment elevation, Killip class, and comorbidities, participation was not associated with outcome. Participants who provided blood specimens were less likely to have heart failure (hazard ratio, 0.49; 95% confidence interval, 0.40-0.59; P<.01) or to die (hazard ratio, 0.16; 95% confidence interval, 0.12-0.21; P<.01) than those who provided tissue.

CONCLUSION

A variety of sources can be used to assemble community specimen repositories. Baseline characteristics differed between participants and nonparticipants and, among participants, by specimen source. Participants who provided blood specimens had better outcomes than those who provided tissue specimens. No survival advantage was observed for participants after combining blood and tissue specimens.