Cardiovascular Diagnosis Research Articles

Sexual Satisfaction and Quality of Life in Cardiovascular Patients: The Mediating Role of Anxiety.

Cardiovascular diseases represent one of the most important problems for public health. Research indicates that elderly patients consider sexual satisfaction as a fundamental aspect of their quality of life and a better sexual function is related to higher general wellbeing. The main objective of this study was to investigate the mediating role of anxiety and depression in the relationship between quality of life and sexual satisfaction in cardiovascular (CVD) patients. The sample comprised 128 adult patients, males and females, hospitalized in a Cardiology Rehabilitation clinical center. To collect data, the following were used: a demographic information sheet, the left ventricular ejection fraction (EF) to evaluate cardiac function, cardiovascular diagnosis type, the HADS scale to evaluate anxiety and depression states, a test for sexual satisfaction evaluation (SAS) and the SF-36 survey to measure quality of life. The results indicated that only SF-36 physical health is indirectly related to SAS through its relationship with anxiety. A mediating model was proposed to explore the underlying association between sexual satisfaction and quality of life. We recommend investigating perceived general health and sexuality as clinical indicators for therapeutic decisions and risk evaluation for the management of cardiovascular diseases.

Applied Sciences—Special Issue on Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy

Ongoing developments in computing and data acquisition, along with continuous advances in medical imaging technology, computational modelling, robotics and visualization have revolutionized many medical specialties and, in particular, diagnostic and interventional cardiology [...]

Medical Data Transformations in Healthcare Systems with the Use of Natural Language Processing Algorithms

Machine learning has only recently begun to see its application in medicine and is still facing quite a few challenges that prevent it from being more widely used. Problems such as high data dimensionality and the lack of a common data schema still remain relevant. It is worth examining the usage of machine learning in the context of healthcare and deploying selected machine learning algorithms on the problem of cardiovascular disease diagnosis. Cardiovascular diseases are currently the most common cause of death in the world. Many of them develop for a long time in an asymptomatic way, and when the first symptoms become visible, it is often too late to implement effective treatment. For this reason, it is important to carry out regular diagnostic tests that will allow you to detect a given disease at an early stage. It is then possible to implement appropriate treatment that will prevent the occurrence of an advanced form of the disease. While doing so, it attempts to analyse data from different sources and utilizing natural language processing to combat data heterogeneity. The paper assesses the efficiency of various approaches of machine learning (i.e., TR-SVM (Terminated Ramp–Support Vector Machine), TWNFI (Transductive Neuro-Fuzzy Inference), Naive Bayes) when applied in the healthcare field and proposes the solutions to the problem of plain text data transformation and data heterogeneity with the help of natural language processing. The algorithms used for diagnosis were implemented, tested and their performance compared, with their parameters also investigated, making it easier to choose an algorithm better suited for a specific case. Whereas TRSVM is better suited for smaller datasets with a high amount of dimensions, TWNFI performs better on larger ones and does not have the performance problems.

Prevalence of chromosomal disorders in cases with congenital heart defect: registry-based study from Denmarkbetween 2008 and 2018.

To estimate the prevalence of chromosomal conditions in all fetuses and children with major congenital heart defect (CHD) in Denmark between 2008 and 2018. This was a national registry-based study including all singleton pregnancies with a prenatally or postnatally diagnosed major CHD usually requiring surgery within the first year after birth and a due date between July 2008 and December 2018 in Denmark. Data were retrieved from the Danish Fetal Medicine Database (DFMD) and the Danish Cytogenetic Central Register(DCCR) in October 2020. The DCCR contains information on all prenatal and postnatal genetic analyses, including karyotyping, chromosomal microarray, polymerase chain reaction, multiplex ligation-dependent probe amplification and fluorescence in-situ hybridization. All cases were reviewed by a clinical geneticist, and genetic changes were classified as pathogenic, likely pathogenic, variant of uncertain significance, likely benign or benign. Pathogenic and likely pathogenic variants were considered to be abnormal. Cases with CHD without any registered chromosomal analysis reported were considered genetically normal. Isolated CHD was defined as a case with major CHD without any other structural malformations detected prenatally or postnatally. Results are given as n (%). Comparisons between isolated and non-isolated cases were performed using logistic regression analysis, and data are presented as odds ratios (ORs) with 95% CIs. A total of 8482 cases with any cardiovascular diagnosis were retrieved from the DFMD. Twins (n = 112) and minor CHD cases (n = 6921) were excluded, resulting in 1449 cases with major CHD. Of the included cases, 918 (63.4%) underwent chromosomal analysis. An abnormal test result was found in 187 cases, giving a prevalence of a chromosomal condition of 12.9% (95% CI, 11.2-14.7%) among all cases with major CHD. The highest prevalence of a chromosomal condition was found in cases with pulmonary atresia with intact ventricular septum and those with truncus arteriosus (both 28.6%), while the lowest prevalence was found in cases with transposition of the great arteries(2.2%) and congenitally corrected transposition of the great arteries (0%). In isolated cases of transposition of the great arteries, the prevalence of a chromosomal condition was 0.6%. The overall OR for a chromosomal condition in non-isolated cases compared with isolated cases was 2.72 (95% CI, 1.90-3.88). We found an overall prevalence of a chromosomal condition of 12.9% among cases with major CHD in a national cohort with a high participation rate in first- and second-trimester screening, without employing whole genome and whole exome sequencing. The prevalence of a chromosomal condition varied considerably according to CHD diagnosis and presence of associated extracardiac malformations. These findings are important for prenatal counseling. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Perception and Counseling for Cardiac Health in Breast Cancer Survivors.

Context: Breast cancer survivors have increased cardiovascular disease (CVD) risk compared to those without cancer history. CVD is the leading cause of death for breast cancer survivors. Objective: To assess current CVD risk counseling practices and risk perception in breast cancer survivors. Study design and analysis: Interviews conducted with breast cancer survivors. Analysis of categorical data by frequency and quantitative variables by mean and standard deviation. Inductive qualitative analysis performed using NVIVO. Setting: Academic Family Medicine Outpatient Practices Population studied: Breast cancer survivors with an identified primary care provider. Intervention/instrument: Interviews on CVD risk behaviors, risk perception, challenges with risk reduction, and previous history of risk counseling. Outcome measures: Self-reported history of CVD, risk perception, and risk behaviors. Results: The average age of participants (n=19) was 57 with 57% being white and 32% African American. Of interviewed women, 89.5% reported a personal history and 89.5% reported a family history of CVD. Only 52.6% had previously reported receipt of CVD counseling. Primary care providers most commonly provided the counseling (72.7%), however it was additionally provided by oncology (27.3%). Among breast cancer survivors, 31.6% perceived they were at increased CVD risk and 47.5% were unsure of their relative CVD risk compared to women their age. Factors affecting perceived CVD risk included family history, cancer treatments, cardiovascular diagnoses, and lifestyle factors. Video (78.9%) and text messaging (68.4%) were the most highly reported mechanisms through which breast cancer survivors requested to receive additional information and counseling on CVD risk and risk reduction. Commonly reported barriers to adopting risk reduction strategies (such as increasing physical activity) included time, resources, physical limitations, and competing responsibilities. Barriers specific to survivorship status include concerns for immune status during COVID, physical limitations associated with cancer treatment, and psychosocial aspects of cancer survivorship. Conclusions: These data suggest improving the frequency and content of CVD risk reduction counseling is needed. Strategies should identify the best methods for providing CVD counseling, and should address general barriers as well as unique challenges faced by cancer survivors.

A Review on Artificial Intelligence and Quantum Machine Learning for Heart Disease Diagnosis: Current Techniques, Challenges and Issues, Recent Developments, and Future Directions

This study presents a comprehensive analysis of the existing techniques and applications of artificial intelligence (AI) to cardiovascular disease diagnosis. The application of AI to the diagnosis of cardiac diseases can enhance diagnostic precision, diagnostic throughput, and patient outcomes. This literature survey analyzes state-of-the-art AI-based methods, rates their efficiency, examines potential future research and development avenues, and finds challenges and limitations, providing a foundational overview of main developments in AI, machine learning, deep learning, and quantum computing in relation to heart disease prevention. This study seeks to guide the use of AI-based techniques for heart disease detection, having an ultimate objective of enhancing patient outcomes through research and development. This review mainly emphasizes the significance of further studying and advancing AI for its ability to revolutionize the diagnosis and management of heart diseases.

IMPLEMENTATION OF CLAHE CONTRAST ENHANCEMENT & OTSU THRESHOLDING IN RETINAL IMAGE PROCESSING

In both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic or computer-aided diagnosis systems. Unlike typical foreground and background segmentation in normal image processing, there are three problems making the retinal vessel segmentation task even harder. First, the retinal color image tends to be red everywhere, thus having a lower contrast than normal image segmentation. Second, most retinal color images suffer from unbalanced illumination and make it harder to recognize background. Third, the symptom for retinopathy has unexpected color and shape, thus making it more difficult to separate vessel from noises. Under such circumstance, the research of the retinal blood vessel segmentation has brought much attention and been developed. In this work, the input is a retinal color image and the output will be a binary image of the vessel and non-vessel pixels. Preprocessing with CLAHE contrast enhancement, Vessel Extraction with Otsu thresholding is implemented along with performance parameters Accuracy, Sensitivity and Specificity. In the proposed work 20 images are used from the database called Digital Retinal Images for Vessel Extraction. The performance parameters Specificity, Sensitivity, Accuracy are evaluated.

Recent development of piezoelectric biosensors for physiological signal detection and machine learning assisted cardiovascular disease diagnosis.

As cardiovascular disease stands as a global primary cause of mortality, there has been an urgent need for continuous and real-time heart monitoring to effectively identify irregular heart rhythms and to offer timely patient alerts. However, conventional cardiac monitoring systems encounter challenges due to inflexible interfaces and discomfort during prolonged monitoring. In this review article, we address these issues by emphasizing the recent development of the flexible, wearable, and comfortable piezoelectric passive sensor assisted by machine learning technology for diagnosis. This innovative device not only harmonizes with the dynamic mechanical properties of human skin but also facilitates continuous and real-time collection of physiological signals. Addressing identified challenges and constraints, this review provides insights into recent advances in piezoelectric cardiac sensors, from devices to circuit systems. Furthermore, this review delves into the integration of machine learning technologies, showcasing their pivotal role in facilitating continuous and real-time assessment of cardiac status. The synergistic combination of flexible piezoelectric sensor design and machine learning holds substantial potential in automating the detection of cardiac irregularities with minimal human intervention. This transformative approach has the power to revolutionize patient care paradigms.

Application of nanotechnology in thrombus therapy.

The diseases caused by two pathological processes, thrombosis, and thromboembolism, are clinically known as thrombotic diseases, which seriously threaten human life and health, and their incidence rate is the highest among various diseases. Research on thrombotic diseases is one of the focuses and hotspots of contemporary medical research. Nanomedicine is a new branch of nanotechnology used in the medical field, and nanomaterials are widely used in medical imaging and drug delivery to help diagnose and treat major diseases such as cancer. With the gradual maturity of nanotechnology, new nanomaterials have recently been used in antithrombotic drugs and released accurately at lesions, which has improved antithrombotic therapy safety. Nanosystems can be employed for cardiovascular diagnosis in future as they can aid in diagnosing pathological diseases and treat them with targeted delivery systems. Unlike other reviews, we herein aim to illustrate the progress of nanosystems in thrombosis therapy. This paper mainly describes how a drug-loaded nanosystem can control drug release under various conditions and accurately treat thrombus, summarizing the research progress of nanotechnology in antithrombotic therapy so that clinicians can better understand nanotechnology and its applications and provide new ideas for treating thrombosis.

Post‒COVID-19 Condition Treatment and Management Rapid Scoping Review


 This rapid living scoping review provides an up-to-date map of the latest published evidence, and identifies research gaps regarding the treatment and management for post–COVID-19 condition.
 Most of the included studies were from a few countries, particularly the US, the UK, and Canada. Also, most of the studies were case reports or series, meaning they included few participants.
 Most of the identified published research on treatments for post‒COVID-19 condition focused on:
 
 vaccines administered 3 months after initial infection
 pharmacological interventions for cardiovascular, neurological, and pulmonary symptoms and diagnoses
 non-pharmacological interventions for pulmonary symptoms.
 
 
 There were fewer studies related to other types of symptoms and organ systems.
 There were notable evidence gaps across all treatments for post–COVID-19 condition, as noted by most of the evidence being from case reports, in which physicians may more often report patients with uncommon symptoms or diagnoses.

Normothermic Versus Hypothermic Norwood Procedure.

Either deep hypothermia with circulatory arrest or hypothermic perfusion with antegrade selective cerebral perfusion is used during the Norwood procedure for hypoplastic left heart syndrome. Normothermic perfusion has been described for pediatric patients. The aim of this study was to compare the early outcomes of patients undergoing the Norwood procedure with antegrade selective cerebral perfusion under hypothermia with the procedure under normothermia. From 2005 to 2020, 117 consecutive patients with hypoplastic left heart syndrome underwent the Norwood procedure: 68 (58.2%) under hypothermia and 49 (41.8%) under normothermia. Antegrade selective cerebral perfusion flow was adjusted to maintain right radial arterial pressure above 50 mm Hg, and a flow rate of 40 to 50 mL kg-1 min-1. Baseline characteristics, operative data, and postoperative outcomes including lactate recovery time were compared. The baseline characteristics and cardiovascular diagnosis were similar in both groups. The normothermic group had a significantly shorter bypass time (in minutes) of 90.31 (±31.60) versus 123.63 (±25.33), a cross-clamp time of 45.24 (±16.35) versus 81.93 (±16.34), and an antegrade selective cerebral perfusion time of 25.61 (±13.84) versus 47.30 (±14.35) (P < .001). There were no statistically significant differences in the immediate postoperative course, or in terms of in-hospital mortality, which totaled 9 (18.4%) in the normothermic group, and 10 (14.9%) in the hypothermic group (P = .81). The normothermic Norwood procedure with selective cerebral perfusion is feasible and safe in terms of in-hospital mortality and short-term outcomes. It is comparable to the standard hypothermic Norwood with selective cerebral perfusion.

845 THE PREDICTIVE ROLE OF HIGH-SENSITIVITY CARDIAC TROPONIN T IN THE EMERGENCY ROOM: COMBINING BIOMARKERS WITH CLINICAL DATA LEADS TO THE DIAGNOSIS OF CARDIOVASCULAR DISEASE

Abstract Background Small amounts of high-sensitivity cardiac troponin (hs-cTn) I and T can be detected in healthy individuals or can occur in the absence of any clinical features of myocardial ischemia (myocardial injury). However, it is not possible to clinically distinguish whether and which increase in hs-cTn are due to which mechanisms. Moreover, predictive data regarding hs-cTnT in an emergency room (ER) setting are scanty. Consequently, with this monocentric retrospective study, we aimed at expanding the knowledge of this biomarker in this setting. Methods Consecutive adult patients presenting at the ER and hospitalized with a first abnormal hs-cTnT value (≥14 ng/L) were enrolled for 6 months. The baseline hs-cTnT value and the ensuing variations were correlated with the clinical presentation and diagnosis. Subsequently, multivariable models were built to assess which clinical and laboratory variables most influenced hospital admissions. Results At a first screening, 4660 patients were considered, and 4149 patients were enrolled (Figure 1). Of these, 1555 (37.5%) had a first hs-cTnT ≥14 ng/L, and 1007 (65%) were hospitalized according to the following diagnoses: acute coronary syndrome (ACS, 182; 18%), non-ACS cardiovascular disease (337; 34%) and non-cardiovascular disease (487; 48%). Higher hs-cTnT values as well as significant hs-cTnT variations were found in the ACS group (p &amp;lt; 0.01). Furthermore, in the latter, the mean percentage of variation of hs-cTnT was higher, while it was intermediate in the non-ACS group, and low in those with non-cardiovascular disease (407.5%, 270.6% and 12.4%, respectively). Only syncope and CRP (OR: 0.08, 95% CI: 0.02–0.39, p &amp;lt;0.01 and OR: 0.9988, 95% CI: 0.9979–0.9998, p = 0.02, respectively) or CRP (OR: 0.9948, 95% CI: 0.9908–0.9989, p = 0.01) and NT-proBNP (OR: 1.0002, 95% CI: 1.0000–1.0004, p = 0.02) were independent predictors of a cardiovascular disease diagnosis. On the other hand, only chest pain (OR: 22.91, 95% CI: 3.97–132.32, p &amp;lt; 0.01) and eGFR (OR: 1.04, 95% CI: 1.004–1.083, p = 0.03) were significantly associated with the ACS diagnosis. Conclusion In our study, differently from the investigated hs-cTnT, only clinical variables predicted hospitalizations in different patients’ subgroups.

Predictors of ICU Mortality among Mechanically Ventilated Patients: An Inception Cohort Study from a Tertiary Care Center in Addis Ababa, Ethiopia.

Mechanical ventilation is a life-saving intervention for patients with critical illnesses, yet it is associated with higher mortality in resource-constrained settings. This study intended to determine factors associated with the mortality of mechanically ventilated adult intensive care unit (ICU) patients. A one-year retrospective inception cohort study was conducted using manual chart review in ICU patients (age >13) admitted to Tikur Anbessa Specialized Hospital (Addis Ababa, Ethiopia) from September 2019 to September 2020; mechanically ventilated patients were followed to hospital discharge. Demographic, clinical, and outcome data were collected; logistic regression was used to determine mortality predictors in the ICU. A total of 160 patients were included; 85/160 (53.1%) were females and the mean (SD) age was 38.9 (16.2) years. The commonest indication for ICU admission was a respiratory problem (n = 97/160, 60.7%). ICU and hospital mortality were 60.7% (n = 97/160) and 63.1% (n = 101/160), respectively. Coma (Glasgow Coma Score <8 or 7 with an endotracheal tube (7T)) (adjusted odds ratio [AOR] 6.3, 95% confidence interval 1.19-33.00), cardiovascular diagnosis (AOR 5.05 [1.80-14.15]), and a very low serum albumin level (<2 g/dl) (AOR 4.9 [1.73-13.93]) were independent predictors of mortality (P < 0.05). The most commonly observed complication was ICU acquired infection (n = 48, 30%). ICU mortality in ventilated patients is high. Coma, a very low serum albumin level (<2 g/dl), and cardiovascular diagnosis were independent predictors of mortality. A multifaceted approach focused on developing and implementing context appropriate guidelines and improving skilled healthcare worker availability may prove effective in reducing mortality.

Cardiovascular Diseases, Medications, and ALS: A Population-Based Case-Control Study

Introduction: We investigated the associations between antecedent all-cause CVD diagnoses, cause-specific CVD diagnosis, and CVD medication prescriptions with the risk of developing amyotrophic lateral sclerosis (ALS). Materials and Methods: We conducted a population-based case-control study of U.S. Medicare enrollees from 2006 to 2013. The final sample included 3,714 incident ALS cases and 18,570 controls (matched on age, sex, enrollment length, and county). Information was collected from Medicare Parts A, B, and D administrative claims data on hypertension, ischemic heart disease, heart failure, acute myocardial infarction, atrial fibrillation, prescriptions of angiotensin-converting enzyme inhibitors, angiotensin II receptors blockers, calcium channel blockers, beta blockers, and antiarrhythmics. Associations were evaluated using conditional logistic regression adjusting for age, sex, race/ethnicity, geographical location, alcohol and tobacco use, and socioeconomic status. Results: The odds ratio (OR) for having one or more ICD-9 codes for any cardiovascular disease diagnosis at least 24 months prior to the date of ALS diagnosis was 0.85 (95% confidence interval [CI]: 0.78–0.92). Cardiovascular conditions that were inversely associated with ALS included heart failure (OR = 0.79; 95% CI 0.70–0.89), atrial fibrillation (OR = 0.81; 95% CI 0.77–0.92), and hypertension (OR = 0.91; 95% CI 0.84–0.98). Exposures to several classes of cardiovascular medications were inversely associated with ALS risk even after adjusting for confounding by indication, including ACE inhibitors (OR = 0.84, 95% CI 0.77–0.91), calcium channel blockers (OR = 0.64, 95% CI 0.59–0.70), and beta blockers (OR = 0.76, 95% CI 0.71–0.83). Discussion/Conclusion: These findings merit additional research, including animal studies and pilot clinical trials, to further evaluate and evidence the effects of ACEIs, CCBs, and BBs on the risk of developing and clinical expression of ALS.

Wearables as an Aid to Cardiovascular Diagnosis in a Young Runner.

1Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN 2Division of Cardiology, University of Texas Southwestern Medical Center, Dallas, TX Address for correspondence: William O. Roberts, MD, MS, FACSM, MHealth Fairview Phalen Village Clinic, 1414 Maryland Avenue East, St. Paul, MN 55106; E-mail: [email protected].

Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events.

BACKGROUND. CT-based body composition (BC) measurements have historically been too resource intensive to analyze for widespread use and have lacked robust comparison with traditional weight metrics for predicting cardiovascular risk. OBJECTIVE. The aim of this study was to determine whether BC measurements obtained from routine CT scans by use of a fully automated deep learning algorithm could predict subsequent cardiovascular events independently from weight, BMI, and additional cardiovascular risk factors. METHODS. This retrospective study included 9752 outpatients (5519 women and 4233 men; mean age, 53.2 years; 890 patients self-reported their race as Black and 8862 self-reported their race as White) who underwent routine abdominal CT at a single health system from January 2012 through December 2012 and who were given no major cardiovascular or oncologic diagnosis within 3 months of undergoing CT. Using publicly available code, fully automated deep learning BC analysis was performed at the L3 vertebral body level to determine three BC areas (skeletal muscle area [SMA], visceral fat area [VFA], and subcutaneous fat area [SFA]). Age-, sex-, and race-normalized reference curves were used to generate z scores for the three BC areas. Subsequent myocardial infarction (MI) or stroke was determined from the electronic medical record. Multivariable-adjusted Cox proportional hazards models were used to determine hazard ratios (HRs) for MI or stroke within 5 years after CT for the three BC area z scores, with adjustment for normalized weight, normalized BMI, and additional cardiovascular risk factors (smoking status, diabetes diagnosis, and systolic blood pressure). RESULTS. In multivariable models, age-, race-, and sex-normalized VFA was associated with subsequent MI risk (HR of highest quartile compared with lowest quartile, 1.31 [95% CI, 1.03-1.67], p = .04 for overall effect) and stroke risk (HR of highest compared with lowest quartile, 1.46 [95% CI, 1.07-2.00], p = .04 for overall effect). In multivariable models, normalized SMA, SFA, weight, and BMI were not associated with subsequent MI or stroke risk. CONCLUSION. VFA derived from fully automated and normalized analysis of abdominal CT examinations predicts subsequent MI or stroke in Black and White patients, independent of traditional weight metrics, and should be considered an adjunct to BMI in risk models. CLINICAL IMPACT. Fully automated and normalized BC analysis of abdominal CT has promise to augment traditional cardiovascular risk prediction models.

New Real-Time Impulse Noise Removal Method Applied to Chest X-ray Images.

In this paper, we propose a new Modified Laplacian Vector Median Filter (MLVMF) for real-time denoising complex images corrupted by "salt and pepper" impulsive noise. The method consists of two rounds with three steps each: the first round starts with the identification of pixels that may be contaminated by noise using a Modified Laplacian Filter. Then, corrupted pixels pass a neighborhood-based validation test. Finally, the Vector Median Filter is used to replace noisy pixels. The MLVMF uses a 5 × 5 window to observe the intensity variations around each pixel of the image with a rotation step of π/8 while the classic Laplacian filters often use rotation steps of π/2 or π/4. We see better identification of noise-corrupted pixels thanks to this rotation step refinement. Despite this advantage, a high percentage of the impulsive noise may cause two or more corrupted pixels (with the same intensity) to collide, preventing the identification of noise-corrupted pixels. A second round is then necessary using a second set of filters, still based on the Laplacian operator, but allowing focusing only on the collision phenomenon. To validate our method, MLVMF is firstly tested on standard images, with a noise percentage varying from 3% to 30%. Obtained performances in terms of processing time, as well as image restoration quality through the PSNR (Peak Signal to Noise Ratio) and the NCD (Normalized Color Difference) metrics, are compared to the performances of VMF (Vector Median Filter), VMRHF (Vector Median-Rational Hybrid Filter), and MSMF (Modified Switching Median Filter). A second test is performed on several noisy chest x-ray images used in cardiovascular disease diagnosis as well as COVID-19 diagnosis. The proposed method shows a very good quality of restoration on this type of image, particularly when the percentage of noise is high. The MLVMF provides a high PSNR value of 5.5% and a low NCD value of 18.2%. Finally, an optimized Field-Programmable Gate Array (FPGA) design is proposed to implement the proposed method for real-time processing. The proposed hardware implementation allows an execution time equal to 9 ms per 256 × 256 color image.

Abstract 11467: Novel Identification Strategy to Increase Diversity in Heart Failure Clinical Trials

Introduction: Women and racial/ethnic minorities remain underrepresented in cardiovascular (CV) clinical trials, thus limiting the generalizability of study findings and the use of novel CV therapeutic testing among those who often have the greatest burden of CV disease. One potential solution is to shift the identification of trial participants from traditional academic centers to large registries. To this end, we tested whether an Emergency Medical Services (EMS) platform could identify patients in the pre-hospital setting who were potential candidates for heart failure (HF) clinical trials. Second, we sought to determine the demographic underpinnings of this population. Methods: The Biospatial platform used EMS electronic patient care reports (ePCR) as the foundational data source. Records of pre-hospital patients with an EMS provider impression of HF were identified through Biospatial’s artificial intelligence algorithm. In a subset of patients, ePCR records in Florida were linked to hospital diagnosis codes utilizing the Florida Health Information Exchange. Results: Between February 1, 2021 and April 30, 2022, we identified 18,198,085 records in the US with a 911 response and patient disposition requiring EMS transport to a local emergency room. There were 63,595 records in which an EMS provider recorded a primary or secondary impression of HF. Of those, 49% were female, 60.3% were White, 22.7% Black, 3.2% Hispanic/Latino, and 13.8% other or unknown. Next, as a test case, the Florida Health Information Exchange was utilized to determine whether an EMS impression of HF was confirmed by a hospital ICD-10 HF code (I50). There were 5363 records in Florida that met the criteria for a 911 call. Of those, 2355 had linked hospital data and 1560 had a populated diagnosis code. Sixty-four percent (64%) of individuals identified by EMS as having HF in the pre-hospital setting received a hospital diagnosis of HF. Conclusion: The utilization of an EMS registry is a novel strategy to identify a diverse population of HF patients for inclusion into clinical trials. Further work is needed to optimize the EMS heart failure algorithm for clinical trial participant identification and to test this strategy for other CV diagnoses such as myocardial infarction and stroke.

Abstract 13737: An Electronic Health Record-Based Algorithm to Optimize Secondary Prevention Using Lipoprotein(a), Apolipoprotein B, Cholesterol, and Myocardial Infarction Diagnosis: ABCDs Prevention Program

Background : According to the 2022 American Heart Association Heart Disease and Stroke Statistics, coronary heart disease remains the leading cause of death attributable to cardiovascular disease (CVD). In 2016 only ~1 in 4 Medicare beneficiaries who were eligible for cardiac rehab (CR) participated. We explored the use of cardiovascular biomarkers and electronic health records (EHRs) to facilitate the early identification of patients at high risk for CVD. Methods : The EHR algorithm uses a combination of cardiovascular biomarkers and ICD diagnosis codes to automatically identify and refer patients to select secondary prevention programs: (1) traditional CR, (2) lipid clinic, (3) virtual CR (Corrie Health Program). In order to determine program eligibility our model uses criteria of lipoprotein(a) &gt;70 nmol/L, apolipoprotein B &gt;90 mg/dL, low-density lipoprotein cholesterol &gt;150 mg/dL, triglycerides &gt;200 mg/dL , and/or a diagnosis of ST-elevation myocardial infarction (MI) or non-ST-elevation MI as depicted in Figure 1. Lipid biomarker cutoffs were defined to maximize sensitivity for familial hyperlipidemia. Results : In a test environment 90% of patients deemed eligible by the algorithm received appropriate referrals to traditional, virtual (Corrie) CR and lipid clinics. The algorithm is currently being used in both inpatient units and outpatient clinics at Johns Hopkins Hospital and Johns Hopkins Bayview. Sixty-six percent of the orders placed increased referrals to CR and lipid clinic and 33% of the orders placed increased referrals to virtual cardiac rehab. Conclusions : Building an EHR-based algorithm to individualize CVD prevention using cardiovascular biomarkers and diagnoses may increase early identification and intervention on high-risk patients.

Abstract 11337: Impact of Covid-19 Pandemic and Telemedicine on Cardiac Rehabilitation Compliance

Introduction: The COVID-19 pandemic changed healthcare access and delivery. This study aims to evaluate the effect of the COVID-19 pandemic on cardiac rehabilitation compliance. Methods: A total of 1888 patients enrolled in Kaiser Permanente Mid-Atlantic States Cardiac Rehabilitation (CR) Program from 2014 to 2022 were included. Demographic data including sex, age, smoking status, weight, HbA1c, and entry diagnosis were evaluated retrospectively from patient charts. Compliance was defined as &gt;/=75% attendance of a total of 12 virtual sessions. Baseline characteristics and compliance were compared between patients who enrolled prior to (N=1496) and after (N=392) the start of the COVID-19 pandemic in March 2020. Data was analyzed using Chi-squared and t-test models. Significance was defined as p&lt;0.05. Results: A significant increase in CR compliance was observed after the start of the pandemic (74% vs 56%, p&lt;0.001). The compliant patients in the pandemic group were younger, had better glycemic control, and less tobacco use (all p &lt; 0.05). They were also more likely to have multiple cardiovascular diagnoses on enrollment of the program. Sex and starting BMI did not significantly defer between the groups. Conclusion: While the pandemic has limited access to care in some areas, we report an increase in virtual CR compliance since the start of the pandemic, particularly among younger patients with better risk factor control. Increased virtual visit options may partly account for this increase, and future work should focus on how to increase accessibility to virtual care for patients of all ages across the spectrum of cardiovascular risk.