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

Anand, Vidhu / Covington, Megan K / Saraswati, Ushasi / Scott, Christopher G / Lee, Alexander T / Frantz, Robert P / Anavekar, Nandan S / Geske, Jeffrey B / Arruda-Olson, Adelaide M / Klarich, Kyle W

Frontiers in cardiovascular medicine

2024 Volume 10, Page(s) 1288747

Abstract: 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" ... ...

Abstract	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 ( 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.
Language	English
Publishing date	2024-01-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2781496-8
ISSN	2297-055X
ISSN	2297-055X
DOI	10.3389/fcvm.2023.1288747
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Article ; Online: Incidence of newly recognized atrial fibrillation in patients with obstructive hypertrophic cardiomyopathy treated with Mavacamten.

Castrichini, Matteo / Alsidawi, Said / Geske, Jeffrey B / Newman, Darrell B / Arruda-Olson, Adelaide M / Bos, J Martijn / Ommen, Steve R / Siontis, Konstantinos C / Ackerman, Michael J / Giudicessi, John R

Heart rhythm

2024

Language	English
Publishing date	2024-04-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	2229357-7
ISSN	1556-3871 ; 1547-5271
ISSN (online)	1556-3871
ISSN	1547-5271
DOI	10.1016/j.hrthm.2024.04.055
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Article ; Online: Genotype Influences Mavacamten Responsiveness in Obstructive Hypertrophic Cardiomyopathy.

Giudicessi, John R / Alsidawi, Said / Geske, Jeffrey B / Newman, Darrell B / Arruda-Olson, Adelaide M / Bos, J Martijn / Ommen, Steve R / Ackerman, Michael J

Mayo Clinic proceedings

2024 Volume 99, Issue 2, Page(s) 341–343

MeSH term(s)	Humans ; Cardiomyopathy, Hypertrophic/drug therapy ; Cardiomyopathy, Hypertrophic/genetics ; Genotype ; Benzylamines ; Uracil
Chemical Substances	MYK-461 ; Benzylamines ; Uracil (56HH86ZVCT)
Language	English
Publishing date	2024-01-11
Publishing country	England
Document type	Journal Article
ZDB-ID	124027-4
ISSN	1942-5546 ; 0025-6196
ISSN (online)	1942-5546
ISSN	0025-6196
DOI	10.1016/j.mayocp.2023.11.019
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Article: Radiolucent Mechanical Valve: Chest Radiography Conundrum.

Alabdaljabar, Mohamad S / Turgul, Genya / Arruda-Olson, Adelaide M / Geske, Jeffrey B

Journal of the Saudi Heart Association

2021 Volume 33, Issue 4, Page(s) 294–295

Language	English
Publishing date	2021-10-15
Publishing country	Saudi Arabia
Document type	Journal Article
ZDB-ID	2515647-0
ISSN	1016-7315
ISSN	1016-7315
DOI	10.37616/2212-5043.1270
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Article ; Online: Artificial Intelligence of Arterial Doppler Waveforms to Predict Major Adverse Outcomes Among Patients Evaluated for Peripheral Artery Disease.

McBane, Robert D / Murphree, Dennis H / Liedl, David / Lopez-Jimenez, Francisco / Attia, Itzhak Zachi / Arruda-Olson, Adelaide M / Scott, Christopher G / Prodduturi, Naresh / Nowakowski, Steve E / Rooke, Thom W / Casanegra, Ana I / Wysokinski, Waldemar E / Houghton, Damon E / Bjarnason, Haraldur / Wennberg, Paul W

Journal of the American Heart Association

2024 Volume 13, Issue 3, Page(s) e031880

Abstract: 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 ...

Abstract	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.
MeSH term(s)	Humans ; Female ; Middle Aged ; Aged ; Aged, 80 and over ; Male ; Artificial Intelligence ; Peripheral Arterial Disease/diagnostic imaging ; Risk Factors
Language	English
Publishing date	2024-01-19
Publishing country	England
Document type	Randomized Controlled Trial ; Journal Article
ZDB-ID	2653953-6
ISSN	2047-9980 ; 2047-9980
ISSN (online)	2047-9980
ISSN	2047-9980
DOI	10.1161/JAHA.123.031880
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Article: Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study.

Gaviria-Valencia, Simon / Murphy, Sean P / Kaggal, Vinod C / McBane Ii, Robert D / Rooke, Thom W / Chaudhry, Rajeev / Alzate-Aguirre, Mateo / Arruda-Olson, Adelaide M

JMIR medical informatics

2023 Volume 11, Page(s) e40964

Abstract: 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 ... ...

Abstract	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. .
Language	English
Publishing date	2023-02-24
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2798261-0
ISSN	2291-9694
ISSN	2291-9694
DOI	10.2196/40964
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Article ; Online: Prognostic Significance of Elevated Left Ventricular Filling Pressures with Exercise: Insights from a Cohort of 14,338 Patients.

Luong, Christina L / Anand, Vidhu / Padang, Ratnasari / Oh, Jae K / Arruda-Olson, Adelaide M / Bird, Jared G / Pislaru, Cristina / Thaden, Jeremy J / Pislaru, Sorin V / Pellikka, Patricia A / McCully, Robert B / Kane, Garvan C

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography

2023 Volume 37, Issue 4, Page(s) 382–393.e1

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Humans ; Prognosis ; Ventricular Function, Left ; Ventricular Dysfunction, Left/diagnostic imaging ; Exercise Test ; Stroke Volume ; Coronary Artery Disease ; Diastole
Language	English
Publishing date	2023-11-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	1035622-8
ISSN	1097-6795 ; 0894-7317
ISSN (online)	1097-6795
ISSN	0894-7317
DOI	10.1016/j.echo.2023.11.012
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Article: Explanatory Analysis of a Machine Learning Model to Identify Hypertrophic Cardiomyopathy Patients from EHR Using Diagnostic Codes.

Farahani, Nasibeh Zanjirani / Arunachalam, Shivaram Poigai / Sundaram, Divaakar Siva Baala / Pasupathy, Kalyan / Enayati, Moein / Arruda-Olson, Adelaide M

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine

2021 Volume 2020, Page(s) 1932–1937

Abstract: 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- ... ...

Abstract	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.
Language	English
Publishing date	2021-01-13
Publishing country	United States
Document type	Journal Article
ISSN	2156-1125
ISSN	2156-1125
DOI	10.1109/bibm49941.2020.9313231
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Article: NATURAL LANGUAGE PROCESSING BASED MACHINE LEARNING MODEL USING CARDIAC MRI REPORTS TO IDENTIFY HYPERTROPHIC CARDIOMYOPATHY PATIENTS.

Sundaram, Divaakar Siva Baala / Arunachalam, Shivaram P / Damani, Devanshi N / Farahani, Nasibeh Zanjirani / Enayati, Moein / Pasupathy, Kalyan S / Arruda-Olson, Adelaide M

Proceedings of the ... Design of Medical Devices Conference. Design of Medical Devices Conference

2021 Volume 2021

Abstract: 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 ... ...

Abstract	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.
Language	English
Publishing date	2021-05-11
Publishing country	United States
Document type	Journal Article
DOI	10.1115/dmd2021-1076
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Article ; Online: Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports.

Dewaswala, Nakeya / Chen, David / Bhopalwala, Huzefa / Kaggal, Vinod C / Murphy, Sean P / Bos, J Martijn / Geske, Jeffrey B / Gersh, Bernard J / Ommen, Steve R / Araoz, Philip A / Ackerman, Michael J / Arruda-Olson, Adelaide M

BMC medical informatics and decision making

2022 Volume 22, Issue 1, Page(s) 272

Abstract: 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- ... ...

Abstract	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.
MeSH term(s)	Humans ; Stroke Volume ; Natural Language Processing ; Artificial Intelligence ; Ventricular Function, Right ; Cardiomyopathy, Hypertrophic/diagnostic imaging ; Cardiomyopathy, Hypertrophic/pathology ; Magnetic Resonance Imaging ; Magnetic Resonance Spectroscopy
Language	English
Publishing date	2022-10-18
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2046490-3
ISSN	1472-6947 ; 1472-6947
ISSN (online)	1472-6947
ISSN	1472-6947
DOI	10.1186/s12911-022-02017-y
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