OBJECTIVE: In recent years, some studies have indicated that a novel marker described as the stress hyperglycemia ratio (SHR) can reflect true acute hyperglycemic status and is associated with the short-term poor prognosis in patients with acute myocardial infarction. In the current study we evaluated the association of SHR with adverse cardiovascular events among patients with acute coronary syndrome (ACS). RESEARCH DESIGN AND METHODS: We consecutively enrolled 5,562 ACS patients who underwent drug-eluting stent (DES) implantation. All subjects were divided into five groups according to SHR, which was determined by the following formula: ABG / [(28.7 × HbA1c %) − 46.7], where ABG is admission blood glucose level. The primary end point was major adverse cardiovascular and cerebrovascular events (MACCE) at the 2-year follow-up, and the secondary end point included major adverse cardiovascular events (MACE) at 2-year follow-up, cardiac death, and nonfatal myocardial infarction (MI) at 2-year follow-up and in-hospital cardiac death and nonfatal MI. RESULTS: A total of 643 MACCE were recorded during a median follow-up of 28.3 months. Kaplan-Meier survival analysis showed the lowest MACCE incidence in quintile 3 (P < 0.001). Moreover, the outcomes of restricted cubic spline analysis suggested that there was a U-shaped or J-shaped association between the SHR and early and late cardiovascular outcomes even after adjustment for other confounding factors. CONCLUSIONS: There were U-shaped associations of SHR with MACCE rate and MACE rate at 2-year follow-ups and J-shaped associations of SHR with in-hospital cardiac death and MI and that at 2-year follow-up in ACS patients who underwent DES implantation, and the inflection point of SHR for poor prognosis was 0.78.

Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs’ evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care.

The electrocardiogram (ECG) has always served as a crucial biomedical examination for cardiac diseases monitoring and diagnosing. Typical ECG measurement requires attaching electrodes to the body, which is inconvenient for long-term monitoring. Recent wireless sensing maps wireless signals reflected from human chest into electrical activities of heart so as to reconstruct ECG contactlessly. While making great progress, we find existing works are effective only for healthy populations with normal ECG, but fall short when confronted with the most desired usage scenario: reconstructing ECG accurately for people with cardiac diseases such as atrial fibrillation, premature ventricular beat. To bridge the gap, we propose AirECG, which moves forward to reconstruct ECG for both healthy people and even cardiac patients with morbid ECG, i.e., irregular rhythm and anomalous ECG waveform, via contactless millimeter-wave sensing. To realize AirECG, we first custom-design a cross-domain diffusion model that can perform multiple iteration denoising inference, in contrast with the single-step generative models widely used in previous works. In this way, AirECG is able to identify and eliminate the distortion due to the unstable and irregular cardiac activities, so as to synthesize ECG even during abnormal beats. Furthermore, we enhance the determinacy of AirECG, i.e., to generate high-fidelity ECG, by designing a calibration guidance mechanism to combat the inherent randomness issue of the probabilistic diffusion model. Empirical evaluation demonstrates AirECG's ability of ECG synthesis with Pearson correlation coefficient (PCC) of 0.955 for normal beats. Especially for abnormal beats, the PCC still exhibits a strong correlation of 0.860, with 15.0%~21.1% improvement compared with state-of-the-art approaches.

Arrhythmia is a common problem of irregular heartbeats, which may lead to serious complications such as stroke and even mortality. Due to the paroxysmal nature of arrhythmia, its long-term monitoring and early detection in daily household scenarios, instead of depending on ECG examination only available during clinical visits, are of critical importance. While ambulatory ECG Holter and wearables like smartwatches have been used, they are still inconvenient and interfere with users' daily activities. In this paper, we bridge the gap by proposing mmArrhythmia, which employs low-cost mmWave radar to passively sense cardiac motions and detect arrhythmia, in an unobtrusive contact-less way. Different from previous mmWave cardiac sensing works focusing on healthy people, mmArrhythmia needs to distinguish the minute and transient abnormal cardiac activities of arrhythmia patients. To overcome the challenge, we custom-design an encoder-decoder model that can perform arrhythmia feature encoding, sampling and fusion over raw IQ sensing data directly, so as to discriminate normal heartbeat and arrhythmia. Furthermore, we enhance the robustness of mmArrhythmia by designing multichannel ensemble learning to solve the model bias problem caused by unbalanced arrhythmia data distribution. Empirical evaluation over 79,910 heartbeats demonstrates mmArrhythmia's ability of robust arrhythmia detection, with 97.32% accuracy, 98.63% specificity, and 92.30% sensitivity.

Cuff-less methods, especially pulse wave analysis (PWA) techniques with PPG/mmWave sensing, have shown great potential for non-intrusive blood pressure (BP) monitoring. However, the state-of-the-art solutions are only validated on small-scale healthy subjects, neglecting patients with abnormal BP and thus a more urgent need for BP monitoring. To bridge the gap, we first build the largest mmWave-BP dataset to our knowledge, including 930 real patients with cardiovascular diseases, and perform extensive experiments, which reveals that all existing PWA methods exhibit far less satisfactory performance with standard deviation errors (STD) exceeding 16 mmHg for systolic BP (SBP) and 11mmHg for diastolic BP (DBP). An in-depth investigation shows that physiological factors have complex effect on vascular elasticity and structure, thus people with very different BP values may exhibit extremely similar pulse waveform, which leads to confusion in model learning. In this work, we propose BP³, which fuses physiological factors into sensing-data-driven deep-learning framework, so as to capture the intricate effect of physiological factors during the whole process of learning pulse waveforms. Evaluation results show that BP³ achieves the mean errors of-1.57 mmHg and -0.34 mmHg, STD of 9.77 mmHg and 7.93 mmHg for SBP and DBP, respectively. Moreover importantly, BP³ shows remarkable gain particularly for subjects with abnormal BP, achieving mean errors that are only 0.48% ~ 20.86% of the state-of-the-art solutions.