bluuebunny/crossref_metadata_2025_split huggingface dataset:
AI-ECG sex-estimation discordances predict cardiovascular events
Authors: Multinu G , Rajai N , Ozcan I , Medina-Inojosa B , Medina-Inojosa J , Attia Z , Lerman L O , Lopez-Jimenez F , Friedman P , Lerman A 
Published: October 2024

Abstract

Background
Cardiovascular disease remains the leading cause of death and burden globally, warranting the need to explore novel predictors of major adverse cardiovascular events (MACE) using new technologies. Artificial Intelligence (AI)-enhanced ECG (AI-ECG) algorithms can provide information on cardiovascular health of individuals, independent of conventional risk factors.

Purpose
The current study aimed to understand whether a previously described AI-ECG sex estimation algorithm could be applied for risk stratification of major adverse cardiovascular events (MACE), with a focus on a population without previous history of MACE.

Methods
Patients having 12-lead ECGs between 2018-2019 were included and followed up until August 2023. Exclusion criterion was a prior history of MACE. AI-ECG sex estimation was determined using a previously described algorithm predicting sex based on 12-lead ECGs. The algorithm generates outputs ranging from 0 to 1, representing the estimated probability of being male. Outputs above 0.5 were considered as AI-ECG-estimated male, and below as AI-ECG-estimated female. This was compared with biological sex to determine whether the two matched. Inconsistency between AI-ECG estimated sex and biological sex was defined as discordance. MACE was defined as myocardial infarction, coronary revascularization, non-fatal stroke, and all-cause death.

Results
Among 80,578 subjects (mean age 59.1 ± 16.1 years, 52.4% female), 9.7% exhibited sex discordance in AI-ECG estimation. Discordance was associated with a 56% higher risk of MACE (HR, 1.56, 95% CI 1.46-1.65, p &lt;0.001), and remained significant in multivariable analysis with traditional risk factors (HR 1.35, 95% CI 1.27-1.43, p &lt;0.001) (Figure 1). Sex-specific analysis showed increased MACE risk in both discordant males and females (Figure 2).

Conclusion
In patients with no history of MACE, discordance in AI-ECG sex estimation is associated with increased risk of MACE. These findings suggest the potential of AI-ECG sex estimation algorithm for risk assessment, especially in patients with no history of MACE.Figure 1.Cox proportional hazard modelFigure 2.Kaplan-Meier of MACE by sex
Predicting physical fitness levels from resting ECG data: a machine learning approach in cardiovascular assessment
Authors: Cohen-Shelly M , Cohen J , Zimlichman E , Rahman N , Klempfner R , Maor E , Segev A , Raanani E , Sabbag A , Masalha E 
Published: October 2024

Abstract

Background
Exercise Stress Testing (EST), an inexpensive noninvasive modality, is widely used for preliminary cardiovascular evaluation and ongoing monitoring of cardiac patients. The MET (Metabolic Equivalent of Task) score from EST, indicating physical fitness, is associated with overall mortality risk and is extremely helpful for preoperative evaluation. Artificial Intelligence (AI) advances, particularly Deep Learning (DL) for resting ECG analysis, have significantly improved cardiac diagnostic accuracy.

Objectives
This study explores AI's ability to predict MET scores from resting ECGs, aiming to streamline fitness assessments and detect early changes in fitness levels.

Methods
The current analysis encompasses 45,518 consecutive EST evaluations from 31,532 individual patients from 2017 to 2023. MET scores were automatically computed using an established formula, while interpretations by certified cardiologists provided a benchmark for the AI model. Poor fitness was defined as being within the lowest quartile of MET scores, adjusted for sex and age. A deep learning framework was developed to process a 10-second snippet of resting phase ECG in raw waveform, predicting the likelihood of diminished fitness and thus acting as a binary classifier. The efficacy of this Poor Fitness AI model (PF-AI model) was assessed through metrics such as the area under the receiver operating characteristic curve (AUROC), along with sensitivity, specificity, and accuracy rates.

Results
The average age of the study group was 55±10 years, predominantly male (68.8%, n = 31,337). Among them, 1,152 were identified as having poor fitness. Applying the model to the entire cohort through 5-fold cross-validation yielded a sensitivity of 78%, a specificity of 81%, and an AUROC of 0.87 in predicting poor fitness.

Conclusions
Leveraging merely 10 seconds of resting ECG data, the PF-AI model proves to be a robust tool in predicting poor fitness levels, comparable to the outcomes of comprehensive stress testing.
Revolutionising patient care: the role of AI-generated avatars in healthcare consultations
Authors: Loh B C S , Fong A Y Y , Ong T K , Then P H H 
Published: October 2024

Abstract

Background/Introduction
The healthcare sector faces an unprecedented challenge with the surging demand in both public and private settings, pushing them towards the brink of overload. This scenario underscores a critical knowledge gap in leveraging technology to alleviate the burden on healthcare services. Our study introduces an innovative solution through the development of avatars generated with artificial intelligence (AI) technologies, that replicate the likeness and expertise of healthcare professionals. These AI-generated avatars aim to bridge the gap in patient consultations, offering a scalable and efficient alternative to traditional care models.

Purpose
The primary objective of our study was to design and implement a virtual assistant capable of conducting patient consultations with the same reliability and personal touch as a trusted human medical professional. This was carried out through the integration of diverse AI technologies including generative models, large language models (LLMs), speech-to-text, and text-to-speech algorithms.

Methods
Our methodology encompassed the creation of three-components, the visual and auditory representation of the healthcare professional (avatar), the capability to process and generate speech (brain functions), and a database of information sourced from medical repositories as well as expert inputs (compiled knowledge). The development process involved capturing and generating the likeness and voice of a medical expert, training LLMs with the compiled knowledge, and engineering the system to function in real-time. This multifaceted approach ensured the AI-generated avatars were not only personalised but also intelligent and interactive.

Results
The successful execution of each component confirmed its feasibility, with the AI-generated avatars resembling their human counterparts in appearance and voice, effectively processing spoken inputs, and providing medical advice based on the compiled knowledge. The primary challenge identified was the integration of these components to operate seamlessly in real-time, indicating the need for further refinement in synchronisation and response speed.

Conclusion
This study has laid a foundational step towards revolutionising patient care through AI technologies. Our results demonstrate the potential of AI-generated avatars to alleviate workloads by providing personalised and efficient patient consultation services. Future work will focus on enhancing the real-time integration of system components, improving the scalability of the solution, and further personalising the patient experience. This direction not only intends to expand the capabilities of healthcare providers but also opens new avenues for patient engagement and care delivery.
Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages
Authors: Olszewski R , Brzezinski J , Watros K , Manczak M , Owoc J , Jeziorski K 
Published: October 2024

Abstract

Background
Amidst the backdrop of staff shortages in healthcare systems, patients and their families are increasingly turning to chatbots, powered by Large Language Models (LLMs), for information about their medical conditions. These AI-driven chatbots, capable of generating human-like responses across a broad range of topics, have become a prevalent tool in the healthcare landscape. Given the proliferation of these chatbots, it is crucial to evaluate the quality and accuracy of the responses they provide.

Methods
We selected five freely accessible chatbots (Bard, Microsoft Copilot, PiAI, ChatGPT, and ChatSpot) for our study. These chatbots were posed questions spanning three medical fields: cardiology, cardio-oncology, and cardio-rheumatology. The responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score. To assess the accuracy of the responses in accordance with the medical guidelines, two independent medical professionals rated them on a 3-point Likert scale (0 - incorrect, 1 - partially correct or incomplete, 2 - correct). This allowed us to gauge the compliance of the chatbot responses with the medical guidelines.

Results
Results: In our study, we posed a total of 45 questions to each of the chatbots. Out of the five chatbots, Microsoft Copilot, PiAI, and ChatGPT were able to respond to all the questions. The length of the responses varied, with PiAI providing the shortest average response length of 7.26 words, and Bard providing the longest at 18.9 words. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT). These results highlight the variability in the readability and complexity of responses generated by different chatbots.
Readability analysis is presented in table 1.

Conclusion
This study indicates that chatbots vary in length, quality and readability. They answer each question in their way, based on data they have pulled from the network. Our data suggests that people who want information from a chatbot need to be careful and verify the answers they get.
"I don't want hear about that": Treatment wishes/preferences of elderly patients regarding possible treatment goals in acute life-threatening illness or acute organ failure
Authors: Richter S , Filo J , Boukadida M , Winterhalter M , Krabs R , Ohlow M A 
Published: October 2024

Abstract

Background
In many people with acutely life-threatening illnesses, the will to undergo treatment is not known with sufficient precision. It is often unclear whether the person concerned would have consented to life-sustaining measures or would have preferred to die with palliative care. This applies in particular to the vulnerable group of elderly, chronically ill/fragile people. It is not uncommon for people to reject the risks/burdens of medical treatment, even if there is a chance of success. On the other hand, there are many people who fight to continue living, even if this can be associated with great burdens and the outcome is rather uncertain.
Aim of the study is to determine the characteristics of cardiologic patients who refuse to discuss treatment wishes/preferences in case of acute emergencies, temporary (e.g. in the context of intensive care unit (ICU) treatment), or permanent (vegetative state) inability to consent.

Methods
Prospective, questionnaire-based cross-sectional study including Hospital Anxiety and Depression Scale (HADS) and assessment of quality of life using the 12-item Short Form Health Survey (SF-12) on normal cardiology wards.

Results
455 cardiologic patients were interviewed (mean age 71.2 ± 12.9 years, 39.6% female), 76.1% had moderate-severe concomitant diseases according to the Carlsson Comorbidity Index (4.3 ± 2.4 points) (see Table I). 23.5 % of respondents did not wish to address the above-mentioned question, they were significantly older (74.6 ± 13.1 vs. 69.9 ± 12.8 years; p=0.0009), had more comorbidities, were less likely to have a high level of education (university degree: 13.0 % vs. 30.6 %; p=0.0021) and were less likely to be employed (11.7 % vs. 25.8 %; p=0.0026). Furthermore, these patients live alone more frequently (45.8% vs. 30.6%; p=0.0048) and are less likely to live in a partnership (52.0% vs. 68.0%; p=0.0043). Refusal of an advance directive was also more common (22.5% vs. 10.7%; p=0.05), quality of life was impaired (42.4 ± 9.8 vs. 45.9 ± 10.5 points; p=0.0022), and the HADS-score levels were higher (15.6 ± 7.3 vs. 12.7 ± 7.8 points; p=0.0007).

Conclusions
A relevant proportion of our patients refuse to discuss treatment wishes/preferences in case of acute emergencies, temporary or permanent inability to consent. Paradoxically, these were patients for whom it would make particular sense (older, sicker, and more socially isolated patients). Those patients are also prone to anxiety and depression, and have a reduced quality of life.
Distinguishing factors in regional variations of TAVI in Japan: the unique role of administrative facility standards compared to other procedures in a high-cost medical care subsidy system
Authors: Kanda M , Kuwabara H , Kobayashi Y , Inoue T 
Published: October 2024

Abstract

Background
Transcatheter aortic valve implantation (TAVI) is less invasive than surgical aortic valve replacement and is becoming increasingly popular worldwide, particularly among patients with aortic valve stenosis (AS) due to aging. However, regional differences are expected to exist not only between countries but also within a single country. Reports from Western countries indicate the presence of regional differences in TAVI and the reasons for these differences, which include differences in medical infrastructure, economic resources, health insurance systems and policies, geographic factors, and availability of specialized medical personnel. TAVI is expensive, but in Japan, the economic aspect is relatively unimportant due to the high-cost medical assistance system. Furthermore, there is little socioeconomic disparity between regions. In such a society, we have reported that the number of specialists and resources for PCI can explain regional differences in ablation and endovascular treatment (EVT). However, unlike those other cardiovascular procedures, there are specific requirements for facilities to perform TAVI, such as the number of transesophageal echocardiographies, as well as cardiologists and cardiovascular surgeons, and the equipment itself, such as hybrid operating rooms. We examined how these facility requirements, which are thought to be controlled by the government, are related to regional differences in TAVI implementation.

Methods and Results
We used the Japanese Ministry of Health, Labour and Welfare national database to determine the rate of TAVI for patients with AS performed per 100,000 population aged ≥40 years in all 47 prefectures in 2022. A linear regression model was developed to assess factors contributing to the regional medical supply, including the rates of PCI per 100,000 population aged ≥40 years and the numbers of cardiologists, cardiovascular surgeons, cardiovascular intervention and therapy training facilities, cardiovascular surgery base facilities, and TAVI facilities. The numbers in the highest and lowest regions were 26.6 and 3.9 (a 6.8-fold difference) for TAVI number. The number of TAVI facilities contributed most significantly to the TAVI volumes (P = 0.0105), while the rates of PCI and other factors did not contribute significantly. The number of TAVI facilities was positively correlated with the number of transesophageal echocardiographies the most (r = 0.353).

Conclusion
This study revealed large regional variations in TAVI volumes in Japan, of which administrative facility standards were a major determinant.
Association of ticagrelor vs clopidogrel with net adverse clinical events in NSTEMI patients with mild thrombocytopenia
Authors: Jiang C , Zhang Y K , Gu T S , Hu S T , Zhang J K , Wu X , Liu X , Liu T , Chen K Y 
Published: October 2024

Abstract
Backgrounds: Guidelines currently recommend aspirin plus ticagrelor as the preferred dual antiplatelet therapy (ADPT) for non-ST-elevation myocardial infarction (NSTEMI). However, there are no established recommendations for managing NSTEMI patients with mild thrombocytopenia, defined as a platelet count of 100~150×109/L.

Purpose
This study aims to investigate the association between ticagrelor and clopidogrel with ischemic and hemorrhagic events in NSTEMI patients with a platelet count of 100~150×109/L.

Methods
A retrospective cohort study was conducted using data from the Health and Medical Big Data Super Platform from January 2010 to June 2023. The study included NSTEMI patients with a platelet count of 100~150×109/L who received DAPT consisting of aspirin and a P2Y12 receptor antagonist (eithor clopidogrel or ticagrelor). Patients were followed up for at least one year. The inverse probability of treatment weighting (IPTW) approach based on propensity score was used to balance the observed confounders between treatments. The primary endpoint was net adverse events (NACE) at 1 year, composed of ischemic events (recurrent myocardial infarction or ischemic stroke), Bleeding Academic Research Consortium (BARC) classification ≥ 3, and cardiac death. Secondary outcomes included cardiac death, recurrent myocardial infarction and, ischemic stroke.

Results
The study cohort comprised 2969 participants, with a median age of 72 years and 29.2.0% female. After adjustment using inverse probability of treatment weighting, ticagrelor and clopidogrel showed similar rates of NACE (aHR, 0.84; 95%Cl, 0.58-1.21). Cardiac death (aHR, 0.99; 95%Cl, 0.54-1.83) and ischemic stroke (aHR, 0.94; 95%Cl, 0.49-1.81) did not significantly differ between the two groups. However, ticagrelor was associated with a lower rate of recurrent myocardial infarction (aHR, 0.44; 95%Cl, 0.21-0.93).

Conclusion
In routine clinical practice, among NSTEMI patients with a platelet count of 100~150×109/L, ticagrelor, did not showed a significant difference in the risk of NACE at 1 year compared with clopidogrel. However, ticagrelor was associated with a lower rate of recurrent myocardial infarction. Further research is warranted to confirm the this conclusion.
Factors associated with major adverse cardiovascular events among Arabs and Jews in Israel: a population-based cohort study
Authors: Kalter-Leibovici O , Agay N , Benderly M , Abu-Saad K , Kimron L , Ziv A 
Published: October 2024

Abstract

Introduction
Arabs in Israel are at higher risk of cardiovascular mortality; however, there are no data on factors associated with disparities in major adverse cardiovascular events (MACE) incidence among Arabs and Jews.

Purpose
To study factors associated with MACE incidence among Arabs and Jews in Israel.

Materials and Methods
This is a cohort study carried out in urban localities of Hadera District in Israel. The study sample included 1,100 participants, stratified in equal numbers by ethnicity (Arab and Jewish), sex, and five 10-year age group (mean age: 51 years, range: 26-77). Data collected via in-person interviews conducted between 2001-2006 included socio-demographic characteristics and health-related behaviors (smoking, physical activity, and diet). Data on baseline diabetes, hypertension, dyslipidemia, and cardiovascular disease were collected from interviews and participants' health records. Information on MACE occurring during follow-up was determined based on hospital discharge diagnoses, recorded in the Israel Ministry of Health hospital admission database. MACE was defined as an acute coronary, cerebrovascular or peripheral artery event, or invasive procedure for its treatment. Factors associated with the time to first MACE were tested in Cox proportional hazards models. Sensitivity analysis included death from any cause as a competing risk, using Fine and Gray’s model.

Results
During a median follow-up period of 20 years, there were 221 first MACE during follow-up; 91 among Jewish participants and 130 among Arab participants, hazard ratio (HR) Arab vs. Jewish: 1.59; 95% confidence interval (CI) 1.22, 2.09, adjusted for age and sex. Arab ethnicity was no longer significantly associated with MACE, following further adjustment for years of education; HR: 1.32; 95%CI 0.95, 1.84. The HR (95%CI) associated with ethnicity after additional adjustment for health-related behaviors, body weight and baseline chronic morbidity was 1.02 (95%CI: 0.73, 1.42). A subgroup analysis among 950 participants, who did not have a baseline diagnosis of cardiovascular disease, produced similar results. These participants experience 147 first MACE during follow-up. The age and sex-adjusted HR (95%CI) associated with Arab ethnicity was 1.72 (1.23, 2.40), compared to 1.08 (0.72, 1.64) in a fully adjusted model. Competing risk analysis did not materially change these results.

Conclusions
Arabs in Israel are at higher risk for MACE. Lower socioeconomic position and higher prevalence of unhealthy lifestyle and cardiovascular risk factors may explain the excess risk of MACE among Arabs. Improving cardiovascular health among Arabs should consider social determinants of health and cultural aspects.