bluuebunny/crossref_metadata_2025 huggingface dataset:
Sex-related outcomes in patients receiving shortened or longer dual antiplatelet treatment after percutaneous coronary intervention: a systematic review and meta-analysis
Authors: Apostolos A , Nastouli K M , Bozika M , Papafaklis M , Karanasos A , Kramvis A , Trigka A , Toutouzas K , Davlouros P , Tsigkas G
Published: October 2024

Background

There are known gender disparities in the pathophysiology, clinical presentation, and prognosis of coronary artery disease between male and female patients. Although antiplatelet therapy cessation is more frequent among women, female gender is an independent factor for major bleeding after percutaneous coronary interventions. Considering that the ischemic and bleeding risk of women undergoing PCI differ from men, it follows that the safety and efficacy of shortened, one or 3 months, DAPT (S-DAPT) compared to longer DAPT (L-DAPT) after PCI might differ between the sexes.

Purpose

Our systematic review and meta-analysis aims to explore the safety and efficacy of S-DAPT versus L-DAPT in men and women having undergone PCI.

Methods

Three major databases (MEDLINE, Cochrane Central Register of Controlled Trials, and Scopus) were screened for eligible randomized – controlled trials, or subgroup or post – hoc analyses of them. The studies should compare S-DAPT with L-DAPT in male and female patients undergoing PCI, providing separate data. The endpoints were Net Adverse Clinical Events (NACE), Major Adverse Cardiac Events (MACE), all-cause mortality, myocardial infarction and major bleeding. All endpoints were used as per trial defined and should be evaluated in 12-months. The effect of different DAPT regimens was assessed by calculating the risk ratio (RR) with 95% confidence intervals (CIs), using a random-effects model (Mantel-Haenzel).

Results

A total of eight studies and 46,476 patients were included; women were underrepresented being only the 26% (N=12,063) of the total participants. MACE did not differ significantly in the two arms in the total sample (RR: 0.94, 95%CI: 0.86-1.04); however, they were significantly reduced in women treated with S-DAPT (RR: 0.82, 95%CI: 0.70-0.97) (p-interaction: 0.049). (Figure 1) S-DAPT was associated with a significant reduction in both NACE (RR: 0.91, 95% CI: 0.85-0.99) (Figure 2A) and major bleeding (RR: 0.73, 95% CI: 0.57-0.95) (Figure 2C) in the total sample, without significant differences between male and female patients (p-interaction: 0.14 and 0.053, respectively). All-cause mortality (RR: 0.86, 95%CI: 0.73-1.01) (Figure 2B) and myocardial infarction rates (RR: 1.03, 95%CI: 0.86-1.24) (Figure 2D) were similar in both S-DAPT and L-DAPT in the total sample, without any significant subgroup differences (p-interaction: 0.13 and 0.051, respectively).

Conclusions

Shortened duration of DAPT significantly decreased MACE in women, in contrast with male patients, showing that women may benefit from abbreviated duration of DAPT. Further studies specifically investigating the optimal duration of DAPT in women are warranted and a sex-based approach could be considered in the management of antiplatelet treatment after PCI.A. NACE B. Mortality C. Bleedings D. MI
Shortened versus standard duration of dual antiplatelet treatment after percutaneous coronary intervention in patients with and without chronic kidney disease: a systematic review and meta-analysis
Authors: Apostolos A , Bozika M , Nastouli K M , Papafaklis M , Karanasos A , Koros R , Theofilatos A , Toutouzas K , Davlouros P , Tsigkas G
Published: October 2024

Background

Impaired renal function is an established risk factor for recurrent cardiac events. The severity of chronic kidney disease (CKD) is related with an increased risk for both ischemic and bleeding complications. As a result, the clinical implications of antithrombotic therapies may be different in patients with CKD as compared to those without. Recently, shortened duration of DAPT, namely one to three months, after percutaneous coronary intervention (PCI) has been proven as a safe and feasible strategy in various different clinical scenarios, like in high-bleeding risk patients, in acute coronary syndromes and complex PCI. However, it remains debatable whether DAPT should be shortened in patients with high ischemic and bleeding risk, such as those with CKD.

Purpose

The aim of our systematic review and meta-analysis is to explore the safety and efficacy of shortened therapy (1-3 months; S–DAPT) in patients without and without CKD undergoing PCI.

Methods

Three major databases (MEDLINE, Cochrane Central Register of Controlled Trials, and Scopus) were screened for eligible randomized – controlled trials, or subgroup or post – hoc analyses of them. The studies should compare shortened (S–DAPT) with longer (>3 months) DAPT (L–DAPT) in patients undergoing PCI, and include data on CKD presence. The endpoints were Net Adverse Clinical Events (NACE) and Major Adverse Cardiac Events (MACE) and should be evaluated at 12 months. NACE and MACE were used as per trial defined. The effect of different DAPT regimens was assessed by calculating the risk ratio (RR) with 95% confidence interval (CI) using random-effects model (Mantel-Haenzel).

Results

A total of eight studies and 56,660 patients were included in our analysis; a notable proportion of patients (9.7%, N=5,466) suffered from CKD. Our analysis showed an overall benefit of S-DAPT in NACE (RR: 0.88, 95%CI: 0.81 – 0.95), which was consistent in patients with and without CKD (p-interaction=0.58). (Figure 1) No significant difference regarding MACE was observed between S-DAPT and L-DAPT (RR: 0.98, 95%CI: 0.90 – 1.06) in both patients with and without CKD (p-interaction=0.19). (Figure 2)

Conclusions

Our analysis showed an overall benefit of S-DAPT in NACE without any difference in MACE, with similar findings among patients with and without CKD. Thus, DAPT shortening could be a feasible and safe approach when it is required, even in patients with impaired renal function. Further randomized controlled trials focusing on patients with CKD are required for validating the previous results and exploring further benefits of abbreviated duration of DAPT.Net adverse clinical eventsMajor adverse cardiac events
Prediction of QT interval in ECGs through baseline wander removal and deep learning technique
Authors: Kim Y R , Lee S , Park J , Yang H J
Published: October 2024

Background

In electrocardiograms (ECGs), the QT interval, calculated from the onset of the Q-wave to the end of the T-wave, reflects the repolarization status of the heart. Currently, the QT interval automatically measured in 12-lead electrocardiograms is inaccurately derived as the median value, rather than utilizing the more precise mean value of QT intervals from all leads. This approach fails to provide information on heterogeneous QT prolongation crucial for predicting arrhythmias, as it does not accurately represent the values of QT intervals in each lead.

Purpose

Traditional analysis of ECGs based on QT intervals necessitates direct interpretation by cardiologists, leading to potential errors in diagnosis. In response to this challenge, we present a model that integrates low-pass filtering and continuous wavelet transformation to eliminate baseline wander noise occurring during ECG measurements. Moreover, to account for temporal features, we incorporate Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) to estimate QT intervals.

Methods

The dataset used in this study was the QT Database, comprising ECG data for 105 patients recorded at a sampling rate of 250Hz over a duration of 15 minutes. Each ECG was associated with annotations for two leads and information regarding the P, QRS, and T waves. We structured the dataset into a ratio of approximately 8:1:1, resulting in 51 patients for training (15,490 samples), 6 patients for validation (1,564 samples), and 7 patients for testing (1,682 samples).

Results

For the evaluation of deep learning, we selected Accuracy and F1-Score as metrics. We experimented with two configurations: the proposed model with two Bi-LSTM layers and an alternative with a single Bi-LSTM layer of 100 units. We also tested a similar recurrent neural network, GRU, with the same unit configuration. The results indicated that the method with two Bi-LSTM layers outperformed others in both Accuracy and F1-Score.

Conclusion

By incorporating CNN and Bi-LSTM, we conducted QT interval estimation, leveraging temporal features. This methodology facilitated the elimination of diverse noise types during ECG measurements, resulting in enhanced accuracy for QT interval estimation through the consideration of temporal ECG characteristics. In our future research endeavors, we plan to deploy this model for predicting arrhythmias, specifically targeting conditions like long QT syndrome and drug-induced QT prolongation.Methods and result
Health literacy in adult cardiac surgery patients; a multi-site Australian study
Authors: Wynne R , Mctier L , Rowe C , Abbs M , Kerr D
Published: October 2024

Background

Almost 60% of Australians report low levels of health literacy (HL). International evidence indicates limited comprehension of the risks and benefits of cardiac surgery, in addition to understanding the basic elements of informed consent.

Purpose

The aim of this study was to explore HL in adult cardiac surgical candidates.

Methods

We recruited consecutive English speaking adult patients from a major public (Site 1), major private (Site 2) and regional hospital (Site 3) in Victoria, Australia. Prior to discharge, a Registered Nurse conducted a semi-structured interview using the Health Literacy Questionnaire (HLQ). The HLQ is a multidimensional validated instrument comprised of 9 independent scales. Data were analysed using descriptive statistics, t-tests and ANOVA.

Results

We recruited 164 (29.5%) eligible participants from; Site 1 (63, 32.1%), Site 2 (49, 19.3%) and Site 3 (52, 49.5%) respectively. Average age was 64.6 (SD 11.8), most participants were male (n = 134, 81.7%), few lived alone (n = 28, 17.1%), 122 (74.4%) identified as being Australian 5 (4.1%) of whom were indigenous and 9 (5.5%) spoke a language other than English at home. The majority of participants highest level of education was aligned with a trade certificate (n = 48, 29.3%) or not completing high school (44, 26.8%) and most were retired (n = 68, 41.5%), working full time (n = 51, 31.1%) or part time (n = 24, 14.6%). Less than half had private insurance (n = 72, 43.9%), more than half had presented to emergency (n = 87, 53.0%) in the previous 12 months and a history of cardiac complaints (n = 137, 83.5%) was as expected. In this study participants felt understood and supported by healthcare providers (Scale 1 M 3.32, SD 0.26), had sufficient information to manage their health (Scale 2 M 3.13, SD 0.35), proactively managed their health (Scale 3 M 2.98, SD 0.39), had strong social supports (Scale 4 M 3.31, SD 0.36), could appraise health information (Scale 5 M 2.88, SD 0.49) and were proactively engaged (Scale 6 M 4.00, SD 0.50). More challenging was their ability to advocate on their own behalf (Scale 7 M 3.80, SD 0.60), to explore information (Scale 8 M 3.70, SD 0.60) and to understand all written information (Scale 9 M 3.90, SD 0.60). Site 2 participants were highly educated, less likely to live alone, more likely to have private insurance, had fewer emergency presentations and higher scores on scales 6 to 9 (p < 0.05) that measure health care interactions, than those from Site 1 or 3.

Conclusions

There was a high level of HL in this cohort and findings indicate patient strengths in relation to HL differed according to site. In measuring individual skills and experience of participants interacting with services, systems and the health care environment this study provides valuable insight into HL levels in cardiac surgery candidates.
Risk factors for delirium in patients aged 60 years and over with recent myocardial infarction
Authors: Niemiec M , Burchacka A , Baranski K , Bednarek A , Banka P , Mecka K , Mizia-Stec K
Published: October 2024

Introduction

Delirium is a form of acute brain dysfunction and geriatric patients are particularly vulnerable to this health problem. Modifiable and unmodifiable risk factors may influence delirium appearance.

Purpose

The study aimed to assess the prevalence and characteristic of delirium risk factors in patients ≥ 60 years of age hospitalized due to acute myocardial infarction (AMI).

Methods

The study included 405 consecutive patients (mean age: 73.1[8.5], males: 248/61.2 %) hospitalized due to AMI divided and characterized according to the in-hospital delirium presence. The following sets of data were analysed: demographic data, length of hospitalization, type of AMI, coronary intervention, multimorbidity, accompanying symptoms, psychiatric conditions, and pharmacotherapy used.

Results

Of 405 patients, 57 (14%, mean age: 80.9[7.3] years, males: 33/58%) experienced delirium. Patients with delirium were older (80.9[7.3] vs 71.82[8.1] years), all of them presented multimorbidity, they more frequently used polypharmacy (96.5 vs 30.2%) and their hospitalization was longer (8.0[1.4] vs 4.6[1.0] days) as compared to the patients without delirium. Patients with delirium more frequently experience in-hospital reversible problems: periprocedural complications (limited number of radial access, prolonged compression and pain after the procedure, pseudoaneurysm of femoral artery, as well as the following accompanying symptoms: fever (40.4% vs 0.9%), infections (78.9% vs 3.7%), pulmonary oedema (73.7% vs 0.6%), hypoxemia (91.1% vs 98.3%), urinary catheter (96.5% vs 17.2%), dehydration (89.5% vs 6.6%), and insomnia (71.9% vs 0.3%) compared to patients without delirium (Table 1). Delirium was also associated with mild (49.1% vs 0.3%, p<0.0001) and moderate (47.4% vs 9.2%) dementia and symptoms of mild depression (59.6% vs 2.3%) (P<0.0001 for all).

Valvular heart disease (OR=4.78; 95%CI: 1.10-2.70; p<0.0001, pulmonary oedema (OR=66.79; 95%CI: 12.04-370.34, p<0.001), and dehydration (OR=37.26; 95%CI: 10.50-132.27, p<0.001) were risk factors for delirium occurrence. Analysis of all factors related to delirium revealed that the coexistence of ≥ 11 concerned 44% of the delirium population (Table 1).

Conclusions

The in-hospital course of AMI is complicated by delirium occurrence in 14% of patients ≥ 60 years old. Multimorbidity and polypharmacy regard most of the subjects with delirium. The potent reversible complications of the in-hospital course of AMI associated with delirium should be recognized and limited to prevent this problem.Table1.Number of delirium risk factors.
Does perceived usability of a multi-component mobile device intervention improve health-related quality of life?
Authors: Candelaria D , Serafica R , Reyes A T , Cacciata M , Sta Maria A , Lee J A , Stromberg A , Evangelista L S
Published: October 2024

Background

Good health-related quality of life (HRQL) is essential to healthy aging. Prior studies have demonstrated that mobile device technologies can promote HRQL in older adults. However, evidence of technology acceptance in older adults is often inconsistent and poorly understood.

Purpose

This study aims to 1) describe technology acceptance using efficiency, learnability, user experience (i.e., acceptability), and perceived control from the USABILITY survey of a multi-component mobile device intervention consisting of a wearable activity tracker (Fitbit Versa), access to Lose It app, and text messages (for experimental group only) in older adults at risk for cardiovascular disease, and 2) examine the correlation between technology acceptance and changes in HRQL before and after using each component of the digital intervention.

Methods

Data were collected using a purpose-built technology usability survey, and HRQL was assessed using the 12-Item Short Form Survey (SF-12), which measured perceived physical and mental health. Correlations were used to identify associations between technology acceptance and HRQL.

Results

The mean age of participants (n=54) was 65.4 ± 6.0 years; a majority were females (60%), married (53%), Hispanic (66%), and had a high school education or higher (76%). Table 1 shows that the experimental group had considerably higher HRQL at three months, but no differences in technology acceptance were noted. Fitbit technology adoption was found to be correlated with physical HRQL (r=304, p=0.027). The Lose It app showed no correlation with HRQL. Despite receiving higher usability ratings, SMS messages were not correlated with physical or mental HRQL.

Conclusion

Our findings demonstrate that older persons at risk for cardiovascular disease find mobile device intervention components acceptable. However, each component affected physical and mental health differently. These findings inform more complete technology acceptance models for older individuals' technology design, education, and deployment. Older persons prefer personalized communication, such as customized text messages, as the most favored aspect of the multi-component mobile device intervention. It warrants a larger clinical trial to determine its efficacy on HRQL.
Nurse led assessment of congestion in outpatient heart failure patients: remote dielectric sensing versus lung ultrasound comparison
Authors: Abbo A , Elad B , Moshe E I , Sabra O , Abboud A , Gepstein L , Caspi O
Published: October 2024

Introduction

Congestion stands as a primary driver behind exacerbating heart failure (HF), leading to hospitalizations and increased mortality rates. Despite the evident significance of congestion as a prognostic indicator, there exists a pressing need for a straightforward, trustworthy assessment method that caters to various healthcare providers' needs.

Aims

To assess the feasibility and compare the accuracy of nurse-led congestion assessment using radar based dielectric sensing (ReDS) to a "gold-standard" point of care assessment using lung ultrasound (LUS).

Methods

A retrospective analysis of consecutive patient visits at the ambulatory advanced HF clinic was used in which both exams (ReDS and LUS) were conducted. Study population included heart failure patients (Stage C, NYHA II-IV). ReDS, an objective non-invasive, transcutaneous measurement of lung fluid status, was assessed by HF nurse and LUS, evaluated using 6 measurement zones, was performed by HF cardiologist.

Results

During 17-month period and 174 clinic visits (103 patients), ReDS and LUS were recorded simultaneously. Mean ReDS reading was 33%±6% vs. mean LUS of 2.3±3.2 B-lines. Pearson correlation coefficient between the readings was moderate r= 0.41 (p<0.05). For LUS cutoff of 3 B-lines, ReDS sensitivity was 55.5% and specificity was 70% (p<0.01). ReDS negative predictive value (NPV) was 88% with positive predictive value (PPV) of 27%. ReDS overall accuracy was 68%.

Conclusion

Despite its low sensitivity, nurse-led ReDS assessment is feasible and possess a relatively high NPV allowing to rule out congestion at point-of-care. The simplicity of use empowers more health providers to assess congestion.
Role of the cardiology pharmacist in improving quality of care for patients with acute myocardial infarction based on ESC-ACCA quality indicators
Authors: Al Farhan H , Yaseen I F
Published: October 2024

Background

The European Society of Cardiology-Acute Cardiovascular Care Association (ESC-ACCA) suggested the use of quality indicators (QIs) to improve quality of care for patients with acute myocardial infarction (AMI). Among these QIs are prescribing of angiotensin-converting enzyme inhibitor (ACEi) and beta blocker (BB) if left ventricular ejection fraction (LVEF) was ≤40%, and prescribing high intensity statin and dual antiplatelet therapy (DAPT) before discharging patients from the coronary care unit (CCU).

Purpose

To identify the role of cardiology clinical pharmacy specialist in improving quality of care for patients with AMI based on ESC-ACCA QIs.

Methods

A cross-sectional study was conducted at a single CCU in Iraq between January 2021 and August 2023. Patients with AMI with or without LVEF ≤40% were included in the study. Patients' profiles were reviewed to collect data about suggestions by the cardiology clinical pharmacy specialist to initiate high intensity statin, addition of a second antiplatelet therapy to keep patients on DAPT, or addition of ACEi and/or BB if LVEF ≤40%.

Results

Total number of patients included was 326 of whom 119 (37%) patients had LVEF ≤40%. Drug interventions by the cardiology clinical pharmacy specialist with the initiation of statin and addition of a second antiplatelet therapy to optimize DAPT for patients with AMI and initiation of ACEi and/or BB for patients with AMI and LVEF ≤40% are shown in table 1 and figure 1. Drug interventions were based on the updated ESC guidelines and agreed by the interventional cardiologists.

Conclusions

Role of the cardiology clinical pharmacy specialist was important in bridging gaps in the medical therapy and improving quality of care for patients with AMI and/or LVEF ≤40% by optimizing patients medications particularly for the initiation of ACEi. ACEi was initiated in about one-quarter of the patients with AMI and LVEF ≤40% based on the suggestion of the cardiology clinical pharmacy specialist and approved by the interventional cardiologists.
Association of 5-times sit-to-stand test with muscle weakness and prognosis in older patients with cardiovascular disease
Authors: Tsubaki Y , Kamiya K , Hamazaki N , Nozaki K , Ichikawa T , Uchida S , Noda T , Ueno K , Miki T , Ogura K , Hotta K , Maekawa E , Yamaoka-Tojo M , Matsunaga A , Ako J
Published: October 2024

Background/Introduction

In the elderly with cardiovascular disease (CVD), maintaining muscle strength and physical function is essential for preventing adverse outcomes like falls and hospitalizations. The decline in lower limb strength signals increased morbidity and mortality risks. The 5-times sit-to-stand test (5STS) is a practical method for assessing lower limb strength, yet its predictive value for muscle weakness and prognosis in older CVD patients is underexplored.

Purpose

This study aims to elucidate the 5STS's role in identifying muscle weakness and its impact on the prognosis of older patients with CVD, offering insights for targeted interventions.

Methods

We included 3585 patients aged ≥ 65 years with CVD (mean age, 75.8 ± 6.6 years, 64.5% male) who underwent assessment of the 5STS before discharge. Maximal quadriceps isometric strength (QIS) was evaluated as a marker of lower limb strength and expressed relative to body weight (% body weight: BW). The primary outcome was all-cause mortality. We used the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for each sex to calculate the cut-off values of the 5STS for low QIS (male < 45%BW, female < 35%BW), which have been reported to be associated with prognosis. In addition, we performed Kaplan-Meier curves and Cox regression analyses in five groups defined by 5STS time quartile and 5STS inability to assess the prognostic significance of 5STS. Cox regression analysis was performed as a multivariate model including the following variables; age, sex, body mass index, acute coronary syndrome, heart failure, hypertension, diabetes, dyslipidemia, current smoker, prior myocardial infarction, prior heart failure, prior chronic obstructive pulmonary disease, prior dementia, albumin, hemoglobin, estimated glomerular filtration rate, C-reactive protein.

Result

Over a median follow-up of 2.23 years (interquartile range 0.87-4.52 years), 657 (18.3%) patients died. Kaplan-Meier analysis indicated that both slow 5STS performance and inability to execute the 5STS were significantly linked with poorer prognosis (log-rank p < 0.001, Figure 1). After comprehensive adjustment, the slowest quartile and inability to perform the 5STS were associated with significantly higher mortality rates compared to the fastest quartile (HR 1.34; 95% CI: 1.05-1.72; p = 0.021; HR: 2.28; 95% CI: 1.60-3.24; p < 0.001, Figure 2). The ROC AUC for 5STS in identifying low QIS was 0.75 (95% CI: 0.73-0.77) for males and 0.75 (95% CI: 0.72-0.77) for females, with cut-off times established at 9.71s for males and 9.48s for females.

Conclusion

The 5STS, assessable in diverse settings without specialized equipment and reflecting key clinical outcomes, could serve as a standard tool for evaluating lower limb function.
ECGWiz: a unified and generalizable AI model for comprehensive ECG diagnosis
Authors: Vatsaraj I , Unde I , Loeffler S , Mohsen Y , Aronis K , Carrick R , Wu K , Chrispin J , Trayanova N
Published: October 2024

Background

Deep learning has demonstrated diagnostic performance in electrocardiogram (ECG) analysis comparable to that of clinicians. However, existing models primarily excel in diagnosing common conditions such as arrhythmias and heart blocks, which are easily identifiable using standard predictor variables. Conversely, models aiming to uncover novel insights, such as detecting structural changes or the presence of myocardial scar, often lack generalizability due to insufficient data, model architecture limitations, and other factors. Moreover, these models frequently encounter challenges, such as a decline in performance when applied to external datasets. To address these issues comprehensively, we propose the ECGWiz — a unified model capable of tackling diverse ECG-related problems with consistently high performance.

Objective

To develop a generalizable Foundation AI model for ECG diagnosis capable of detecting diverse cardiac abnormalities and diseases using few training samples.

Methods

We utilized publicly available datasets like PTB-XL, CPSC, etc. To build our model, totaling approximately 80,000 ECGs. These 12 lead ECGs are scaled and filtered before using them for training. The AI model is pretrained using Self Supervised Learning and Contrastive Learning strategies. Further, the model undergoes supervised training using a hierarchy of labels that increase in granularity, encompassing arrhythmias, heart blocks, other cardiac abnormalities, heart failure, sudden cardiac death and more. Lastly, the model is prepared for Few-Shot Learning — where the model learns from a minimal set of samples (5 to 10 ECGs) of the target disease. For testing the model, we are using unique datasets like PROSE which consists of non-ischemic cardiomyopathy patients with and without myocardial scaring detected using cardiac MRI imaging at the Johns Hopkins University.

Results

Our model, trained on a dataset of only 5 ECG recordings, demonstrated a 74% accuracy in identifying myocardial scarring as detected by LGE MRI, utilizing a standard 12-lead ECG. This performance is comparable to that of conventional deep learning methods, which necessitate datasets containing upwards of 120 ECG recordings.

Conclusion

Our model represents a promising stride in creating a unified and adaptable foundation AI model for comprehensive ECG-based diagnosis. Its capacity to learn from a minimal set of examples signifies a robust framework that can evolve and adapt to new challenges in ECG diagnosis. Ongoing improvements, particularly through dataset expansion, underscore its potential to enhance cardiovascular diagnostics, offering precise and efficient patient care.
Artificial intelligence applied to electrocardiographic images for scalable screening of cardiac amyloidosis
Authors: Sangha V , Oikonomou E K , Krumholz H M , Miller E J , Khera R
Published: October 2024

Background

Transthyretin amyloid cardiomyopathy (ATTR-CM) remains largely under-recognized, under-diagnosed, and under-treated. We hypothesized that the myocardial remodeling seen in ATTR-CM may be detectable through artificial intelligence (AI) applied to 12-lead electrocardiographic (ECG) images.

Purpose

To develop an AI-ECG algorithm that can detect ATTR-CM from images of 12-lead electrocardiograms.

Methods

Across 5 hospitals of a large U.S.-based hospital system, we identified patients with ATTR-CM, defined by the presence of a positive bone scintigraphy scan or pharmacotherapy with an approved transthyretin stabilizer between 2015 and the first half of 2023. The development cohort consisted of 1,011 ECGs from 234 patients (age 79 [IQR:70-85], n=176 (17.4%) women), who were age- and sex-matched in a 10:1 ratio to 10,110 ECGs from 10,110 controls (age 79 [IQR:70-85] years, n=1,800 [17.7%] female). A convolutional neural network (CNN) pre-trained using a bio-contrastive pretext on ECGs before 2015 was fine-tuned for ATTR-CM using 5-fold cross-validation and subsequently tested in an independent set of cases (139 ECGs in 47 patients; age 80 [75-86] years, n=44 (31.7% women)) and matched controls (1390 ECGs and patients) from the second half of 2023.

Results

The AUROC (area under the receiver operating characteristic curve) of the AI-ECG model for discriminating ATTR-CM in the cross-validation set was 0.90 [95%CI: 0.89-0.91], and in the leave-out, temporally distinct dataset was 0.915 [95%CI: 0.89-0.94] (A), with a sensitivity of 0.85 [95%CI: 0.79-0.91] and specificity 0.80 [95%CI 0.78-0.82]. Representative saliency maps are shown in panel B. In simulations, the positive predictive value ranged from 0.12 [95%CI: 0.10-0.13] at a 3% prevalence level to 0.59 [95%CI: 0.56-0.62] at 25% prevalence.

Conclusions

We demonstrate that AI applied directly to ECG images represents a promising approach for the screening of ATTR-CM.
Smart watch for the detection of exercise-induced transient ST segment changes
Authors: Spaccarotella C , Polimeni Alberto P A , Ferraro Daniel F D , Franzone Anna A F , Piccolo Raffaele P R , Curcio Antonio C A , Indolfi Ciro C I , Giovanni Esposito E G
Published: October 2024

Background

Smartwatches are largely used and allowed to perform electrocardiogram one lead, usually a modified DI, used to diagnose atrial fibrillation. It is unknown whether or not smartwatches could be used to identify electrocardiographic transient ST changes during exercise-induced myocardial ischemia. The present study assessed the feasibility and reliability of using an Apple watch to record V5 in subjects scheduled for standard ECG stress tests.

Methods

One hundred eighty-nine subjects were included in this study. The mean age was 61+9 years, and 21.6 % were women (41 subjects). A single thoracic lead V5 was obtained with an Apple watch, positioned at the fifth intercostal space anterior axillary line, by creating a circuit between the back of the watch and the right index resting on the crown for 30 seconds. Four subjects were excluded because of the poor quality of the Apple watch signal. Forty-nine patients (25,92%) had a positive stress test with standard 12 lead ECG, 136 (71,96 %) negative stress test, and 4 (2,12%) the ECGs were non-diagnostic. The mean difference or bias between the Apple Watch and standard exercise-induced ST changes was quantified as 0.054, suggesting a generally minimal discrepancy in their readings. This consistency is critically affirmed by Cohen's Kappa (k) coefficient of 0.665. 9-1.0)

Results

A total of 189 subjects were included in the study; the mean age was 61 years (SD 9), and 21.6 % were women (41 subjects). Insightful outcomes were gleaned from the Bland-Altman analysis, which probed the concordance between the smartwatch ECG and the conventional standard ECG in measuring ST-segment deviations (Bias 0,054; SD 0.31, from -0.56 to 0.67, 95% limits of agreement), as vividly depicted in Figure. The dashed blue lines in Figure demarcate the 95% limits of agreement, which span from -0.56 to 0.67, offering a tangible visualization of the range within which most individual differences resided. A notable harmony was unveiled in the intricate exploration of the concordance between the two diagnostic methodologies, substantiated by an agreement of 87.6%. This demonstrates that, in most instances, the methodologies provided analogous results, exhibiting a commendable consistency between them in a substantial proportion of cases.

Assuming the results of standard ECGs as the reference values, Apple Watch ST deviation showed a sensitivity of 69% and specificity of 94 %. The positive predictive value was 0.80952381, and the negative predictive value was 0,8951049.

Conclusions

In conclusion, this study demonstrated that the Apple Watch could detect transient ST changes with a sensitivity of 69% and a specificity of 94 %. Further prospective studies should be performed to assess the potential role of detecting transient myocardial ischemia with a smartwatch in a general population at risk.
Deep learning for quantification of myocardial scar and microvascular obstruction in late gadolinium enhancement cardiovascular magnetic resonance images
Authors: Leng S , Yang X L , Tan R S , Zong D X , Cheng J , Sia C H , Low A S , Chan M , Hausenloy D J , Zhong L
Published: October 2024

Objectives

Detecting and measuring myocardial scar and microvascular obstruction (MVO) accurately following reperfusion therapy is clinically vital for patients with acute myocardial infarction (AMI). Current clinical practice relies on manual delineation of endocardial borders as well as hyper- and hypo-enhanced regions on late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) images for scar and MVO quantification, respectively. However, this approach is subjective, labor-intensive, and time-consuming. The objective of this study is to develop and assess a convolutional neural network (CNN)-based method for the automated quantification of LGE scar and MVO in AMI patients.

Methods

A total of 1836 LGE images from 252 patients with AMI were analyzed, with the cohort comprising 232 males and an average age of 56±9 years. Our approach, utilizing a combined architecture of cascaded YOLOv8 (You Only Look Once version 8) and nnU-Net, was implemented for the simultaneous segmentation of the left ventricular (LV) myocardium, scar, and MVO in LGE images. Independent datasets for training, validation, and testing were maintained in an 8:1:1 ratio. Performance evaluation was conducted by comparing the results to manual scar and MVO quantification, utilizing metrics such as the Dice similarity coefficient (DSC) and Bland-Altman analysis.

Results

The automated segmentation process took 0.05 seconds per image. The segmentation accuracy, as assessed by the DSC, yielded values of 0.96±0.04 (per-patient, n=25) and 0.96±0.05 (per-slice, n=184) for scar segmentation, and 0.86±0.17 (per-patient) and 0.89±0.15 (per-slice) for MVO segmentation. In per-patient analysis, the volumes of automatically segmented scar and MVO demonstrated strong agreement with manually derived results, with biases [limits of agreement] of -0.3 [-3.0, 2.4] cm³ and 0.1 [-0.2, 0.3] cm³, respectively. Furthermore, there was strong concordance between automatically and manually derived scar size as a percentage of LV mass (-0.2 [-3.1, 2.7] %) and MVO size as a percentage of scar (1.2 [-5.6, 8.0] %).

Conclusions

The findings demonstrate excellent agreement between our proposed automated deep-learning solution and clinical expert annotation, suggesting its potential as a predictive tool for prognosis and clinical outcomes in patients with AMI.
Machine learning model using heart rate variability for the prediction of vasovagal syncope
Authors: Cho J H
Published: October 2024

Background and Aims:

Vasovagal syncope (VVS) is a reversible condition that can cause serious events, such as cerebral hemorrhage due to head trauma. Its occurrence is difficult to predict; therefore, this study aimed to develop machine learning (ML) models using heart rate variability (HRV) to predict the occurrence of VVS.

Methods and Results

We investigated 80 patients with syncope who underwent Holter monitoring during the head-up tilt test. Twenty-six ML models for the prediction of VVS using HRV parameters were examined. We randomly split the subjects into training and testing sets at a 7:3 ratio (training set: 46 positive and 9 negative subjects; testing set: 21 positive and 4 negative subjects). The XGB classifier achieved the highest performance compared with other algorithms (accuracy, 0.72; area under the curve, 0.801; sensitivity, 0.857; specificity, 0.621). When examining the time taken to predict syncope occurrence using the developed prediction model, it was found that the model could forecast approximately 3 min (equivalent to 193.2 beats) prior to the onset of symptoms.

Conclusion

The ML model utilizing HRV parameters was proficient in preemptively predicting VVS incidents. In practical applications, this approach can effectively enhance VVS prediction by leveraging the HRV, thereby aiding in the prevention of unforeseen accidents.
Identifying heart failure dynamics using multi-point electrocardiograms and deep learning
Authors: Nishihara Y , Nishimori M , Shibata S , Shinohara M , Hirata K , Tanaka H
Published: October 2024

Background

Heart failure (HF) hospitalizations are related to poor survival outcomes, emphasizing the need for early detection and intervention. Recent studies have reported the potential of deep learning algorithms in identifying heart failure through electrocardiogram (ECG) with notable accuracy. However, evidence supporting their utility in the ongoing monitoring and follow-up of HF patients remains uncertain. Advanced diagnostic tools that can effectively track heart failure progression or improvement over time are needed.

Purpose

To address this challenge, we developed a deep learning model analyzing 12-lead ECG waveforms at two time points, aiming to detect HF deterioration or improvement, potentially revolutionizing HF management.

Methods

This study analyzed 32,045 ECGs from 6,892 unique adult patients (aged 18 years and older) visiting the Department of Cardiology at Kobe University Hospital. The participants were randomly assigned to training (4,284 patients), validation (1,034 patients), and test (1,034 patients) datasets. ElecTransformer was developed to classify HF status into deteriorated, improved, and no change classes based on ECG waveform signals at two different time points and to estimate brain natriuretic peptide (BNP) levels. Performance metrics, such as the area under the receiver operating characteristic curve (AUROC) and accuracy, were calculated, and attention mapping via gradient-weighted class activation mapping was utilized to interpret the model's decision-making ability.

Results

The patients had an average age of 65 years (±15.4 years) and BNP levels averaged 71.5 pg/mL (25.8 to 185.2 pg/mL). For HF status classification, ElecTransformer achieved an AUROC of 0.845 and an accuracy of 0.821, improving to an AUROC of 0.885 and an accuracy of 0.833 with baseline BNP integration. The ElecTransformer accurately estimated BNP levels from ECG data, evidenced by a correlation coefficient of 0.798. Averaged attention mapping showed that the QRS complex was the most important feature for determining model outputs with lesser effects seen for P waves and T waves variance

Conclusions

Harnessing a Transformer-based deep learning model to analyze multiple ECGs markedly improves the accuracy in detecting changes in heart failure status. This method offers a valuable non-invasive option for monitoring heart failure progression, potentially leading to better patient management and earlier intervention in heart failure treatment.Architecture and Performance of ModelsAveraged attention mapping
Personalized prediction model specific for sudden cardiac death in the population: a machine learning study based on EHR
Authors: Chocron R , Laurenceau T , Youssif Y , Bougouin W , Beganton F , Empanan J P , Chopin N , Jouven X
Published: October 2024

Background

Predicting sudden cardiac death (SCD) in the general population remains a significant challenge, particularly at the individual level. SCD and myocardial infarction (MI) share similar pattern of characteristics and risk factors, making their specific prediction highly difficult. As a result, the current prediction models exhibit low specificity.

Methods

To estimate the specific risk of SCD over three months, we trained a machine learning model on electronic health record (EHR) data representing 8,566,229 drug prescriptions and 801,352 of hospital diagnoses up to five years prior to SCD. The data were obtained from a French cohort of 12,338 SCD and from a cohort of 12,338 controls from 2011 to 2015. We then validated the results on two external cohorts: one temporal cohort in the same area between 2016 and 2020 with 11,620 SCD cases and 11,620 controls and one geographical cohort from the USA with 892 SCD cases and 892 controls from 2013 to 2021. In order to address the specificity of our prediction model for SCD, SCD cases collected between 2016 and 2020 were also matched with a group of 35 000 controls who had myocardial infarction, with no SCD, following the same protocol as for the controls issued from the general population.

Findings: The analysis reveals three distinct patterns of distribution for SCD cases, controls, and MI cases. We observed a semi linear increase of SCD cases over the deciles of predicted risk, and our model detected 24% (2,908) of SCD cases with a predicted risk exceeding 90% (highest decile). We observed the opposite among the controls that decreased linearly with increasing deciles of predicted risk: most of the controls were accurately identified in the lowest deciles, and there were only 214 controls (2%) in the highest decile. Interestingly, we observed a relatively stable distribution of MI cases across the deciles, with 10% of MI cases in the highest decile (Figure 1).

Interpretation: We developed and validated a personalized prediction model that is able to identify subjects at high-risk for SCD specifically.

Figure 1: Histogram of predicted risks for sudden cardiac death cases, controls and myocardial infarction cases in the validation cohort.Figure 1
An artificial intelligence-enabled electrocardiogram model outperforms the revised cardiac risk index score for predicting major adverse cardiovascular events in patients undergoing noncardiac surgery
Authors: Sheffeh M , Estarda-Magana A , Medina-Inojosa J , Mangold K , Siontis K , Sher-Lu P , Jacob A , Chini E , Ortega Aviles L , Liu K , Noseworthy N , Attia Z , Lopez-Jiminez F
Published: October 2024

Background

Existing clinical preoperative risk assessment tools such as the Revised Cardiac Risk Index (RCRI) have been used extensively to predict perioperative major adverse cardiovascular events (MACE). Electrocardiogram (ECG) analysis using Deep Learning can identify hidden features that human eyes cannot identify and might help further risk stratify patients undergoing noncardiac surgery.

Objective

We aimed to develop a Deep Learning model that predicts perioperative MACE in patients undergoing elective noncardiac surgeries.

Methods

We included patients ≥ 18-year-old who underwent elective non-cardiac surgery between 2000-2021. MACE was validated in duplicate and were defined as a composite of in-hospital myocardial infarction, cardiac arrest or mortality. A convolutional neural network was developed using ECGs within 30 days before surgery. We randomly split subjects into training, internal validation, and testing datasets in a 7:1:2 ratio. Performance was evaluated using the area under the receiver operator characteristics curve (AUC) values on the testing dataset. Models trained included AI-ECG models to predict in-hospital MACE and to predict in-hospital mortality, and AI-ECG+RCRI models to predict in-hospital MACE and to predict in-hospital mortality. All models were validated using 30-day validated outcomes (MACE or all-cause mortality) in a community-based cohort and were compared with the Revised Cardiac Risk Index (RCRI) score using AUC values.

Findings: We included 195,214 patients who underwent a total of 241,999 surgeries. 169,214 ECGs were used in the training dataset, 24,234 in the internal validation dataset, and 48,551 in the testing dataset. In the test cohort, the AI-ECG algorithm discriminated risk for in-hospital MACE with an AUC of 0.82 (95% CI 0.79-0.85). The algorithm similarly discriminated 30-day MACE with an AUC value of 0.79 (95% CI 0.77-0.81) surpassing the discrimination of the RCRI score AUC of 0.69 (95% CI 0.66-0.72). All models’ performances are shown in Figure1A. The AI-ECG model to predict in-hospital mortality had an AUC of 0.83 (95% CI 0.80-0.86) and predicted 30-day mortality with an AUC of 0.80 (95% CI 0.77-0.82). Other models’ performances are shown Figure1B. Training the AI-ECG models including RCRI data did not improve the AI-ECG models’ performance.

Conclusion

AI-ECG models with no additional clinical data can improve perioperative risk prediction in non-cardiac surgery, outperforming a conventional and widely used risk stratification tool such as the RCRI.
The added value of device measured physical activity to the prediction of incident cardiovascular disease.
Authors: Sturge A , Harper C , Chan S , Van Duijvenboden S , Casadei B , Doherty A
Published: October 2024

Background

Conventional risk prediction models for Major Adverse Cardio/Cerebrovascular Events (MACCE) primarily rely on non-modifiable factors such as age, sex, and physiological measurements, including LDL/HDL cholesterol and blood pressure readings, that are prone to variations across medical practices and are often unavailable in many parts of the world. In this study, we investigate the potential of inexpensive wearable measurements of physical activity to enhance MACCE prediction when physiological measurements are unavailable.

Purpose

To assess the added value of physical activity measurements, collected via wrist-worn accelerometers, to established cardiovascular risk prediction models. Additionally, we seek to investigate whether physical activity measurements can substitute for common physiological measurements in established cardiovascular risk models.

Methods

We obtained accelerometer-measured physical activity over 7 days between 2013 and 2015 from 69,898 UK Biobank (UKB) participants without a prior history of MACCE, defined as death or hospitalisation due to (1) myocardial infarction, (2) heart failure, or (3) Stroke/transient ischaemic attack. We assessed the added value of daily step counts, sleep duration, and a deep learning-derived activity health score (Fig 1), calculated from complete 7-day accelerometer recordings, to established clinical risk models (SCORE2, QRISK3 and AHA) before and after excluding measurements of cholesterol and systolic blood pressure (SBP). The primary outcome was the first recorded MACCE within 6 years. Risk scores were developed in men and women using Cox proportional hazards models. We assessed predictive performance using Harrel’s C-index, net reclassification index (NRI), and net benefit at the recommended treatment threshold for each model.

Results

Of the 69,898 UKB participants in the study, 3,386 (4.8%) experienced a MACCE over a 6-year follow-up period. In place of HDL ratio and SBP, the addition of deep-learned activity health scores modestly improved performance in the best clinical baseline, QRISK3 for Female participants ΔC-index women:0.003 (95% CI 0.002-0.004); Δnet benefit 0.02(0.01-0.03); NRI 0.04, (0.02-0.07), and outperformed manually extracted steps and sleep measurements. We report no significant improvements for men. We find a greater increase in model performance when both HDL ratio and SBP, plus activity health scores, are included in the model. ΔC-index women:0.008, (0.007-0.009); men: 0.003 (0.002-0.004) (Fig 2).

Conclusion

Our findings indicate that in the absence of cholesterol and systolic blood pressure, the addition of device-measured physical activity modestly improves the performance of clinical risk scores among individuals without prior cardiovascular disease (CVD). These findings could further refine intervention strategies for targeted prevention of CVD, particularly in settings where physiological measurements may be unavailable.