Background and Aims: Although non-optimum ambient temperature is a major non-traditional risk factor for acute myocardial infarction, there is no prior knowledge on whether non-optimum ambient temperature could differentially affect myocardial infarction with obstructive coronary artery disease (MI-CAD) and myocardial infarction with non-obstructive coronary arteries (MINOCA). Methods: Using the Chinese Cardiovascular Association database-Chest Pain Center Registry, a nationwide, time-stratified, case-crossover investigation was conducted from 2015 to 2021. Meteorological data were obtained from an established satellite-based model, and daily exposures were assigned according to the onset of myocardial infarction in each patient. A conditional logistic regression model combined with distributed lag non-linear models (10 days) was used to estimate the exposure–response relationships. Results: A total of 83 784 MINOCA patients and 918 730 MI-CAD patients were included. The risk of MINOCA and MI-CAD associated with low temperature occurred at lag 2 day and lasted to 1 week. Extremely low temperature was associated with a substantially greater odds ratio (OR) of MINOCA [OR 1.58, 95% confidence interval (CI) 1.31–1.90] than MI-CAD (unmatched: OR 1.32, 95% CI 1.23–1.43; equally matched by age and sex: OR 1.25, 95% CI 1.04–1.50), compared with the corresponding reference temperatures (30°C, 35°C, and 30°C). Stronger associations were observed for patients who were aged ≥65 years, female, or resided in the south. There was no significant difference for the impacts of high temperature on MINOCA and MI-CAD. Conclusions: This nationwide study highlights the particular susceptibility of MINOCA patients to ambient low temperature compared with that of MI-CAD patients.

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image recognition in the medical field, which requires large-scale and high-quality training datasets consisting of raw images and annotated images. However, suitable experimental datasets for cervical spine X-ray are scarce. We fill the gap by providing an open-access Cervical Spine X-ray Atlas (CSXA), which includes 4963 raw PNG images and 4963 annotated images with JSON format (JavaScript Object Notation). Every image in the CSXA is enriched with gender, age, pixel equivalent, asymptomatic and symptomatic classifications, cervical curvature categorization and 118 quantitative parameters. Subsequently, an efficient algorithm has developed to transform 23 keypoints in images into 77 quantitative parameters for cervical spine disease diagnosis and treatment. The algorithm’s development is intended to assist future researchers in repurposing annotated images for the advancement of machine learning techniques across various image recognition tasks. The CSXA and algorithm are open-access with the intention of aiding the research communities in experiment replication and advancing the field of medical imaging in cervical spine.

Background: Contrast induced nephropathy (CIN) is a common complication in patients receiving intravascular contrast media. In 2020, the American College of Radiology and the National Kidney Foundation issued a new contrast induced acute kidney injury (CI-AKI) criteria. Therefore, we aimed to explore the potential risk factors for CIN under the new criteria, and develop a predictive model for patients with coronary artery disease (CAD) with relatively normal renal function (NRF).Methods: Patients undergoing coronary angiography or percutaneous coronary intervention at Zhongshan Hospital, Fudan University between May 2019 and April 2020 were consecutively enrolled. Eligible candidates were selected for statistical analysis. Univariate and multivariate logistic regression analyses were used to identify the predictive factors. A stepwise method and a machine learning (ML) method were used to construct a model based on the Akaike information criterion. The performance of our model was evaluated using the area under the receiver operating characteristic curves (AUC) and calibration curves. The model was further simplified into a risk score.Results: A total of 2,009 patients with complete information were included in the final statistical analysis. The results showed that the incidence of CIN was 3.2 and 1.2% under the old and new criteria, respectively. Three independent predictors were identified: baseline uric acid level, creatine kinase-MB level, and log (N-terminal pro-brain natriuretic peptide) level. Our stepwise model had an AUC of 0.816, which was higher than that of the ML model (AUC = 0.668, P = 0.09). The model also achieved accurate predictions regarding calibration. A risk score was then developed, and patients were divided into two risk groups: low risk (total score < 10) and high risk (total score ≥ 10).Conclusions: In this study, we first identified important predictors of CIN in patients with CAD with NRF. We then developed the first CI-AKI model on the basis of the new criteria, which exhibited accurate predictive performance. The simplified risk score may be useful in clinical practice to identify high-risk patients.

Background: Contemporary risk assessment in patients with coronary atherosclerotic disease (CAD) often relies on invasive angiography. However, we aimed to explore the potential of metabolomic biomarkers in reflecting residual risk in patients with CAD after moderate lipid‐lowering therapy. Methods and Results: We analyzed serum metabolomic profile among 2560 patients with newly diagnosed CAD undergoing moderate lipid‐lowering therapy, through nuclear magnetic resonance spectroscopy and quantified 175 metabolites, predominantly lipoproteins and their components. CAD severity was evaluated using Gensini score for plaque burden and circulating cardiac troponin T levels for plaque instability. The association of metabolites with CAD severity was examined using multivariate linear regression, and the underlying potential causality was explored using a 2‐sample Mendelian randomization approach. Two composite metabolomic indices were constructed to reflect CAD severity using least absolute shrinkage and selection operator linear regression, and their associations with risk of major adverse cardiac events during a median follow‐up of 3.8 years were evaluated using Cox models. Our investigation revealed that triglycerides and apolipoprotein B in low‐density lipoprotein particles displayed stronger associations with CAD severity compared with the clinically used low‐density lipoprotein cholesterol marker. In large high‐density lipoprotein, components like cholesterol, cholesterol esters, triglyceride, apolipoprotein A1/A2 showed inverse associations with CAD severity. Certain metabolites, including apolipoprotein B and dihydrothymine, showed a putative causal link with Gensini score. Notably, per standard deviation increase in Gensini score–based metabolomic index was associated with 14.8% higher major adverse cardiac event risk (hazard ratio, 1.148 [95% CI, 1.018–1.295]) independent of demographic factors, medication use, and disease status. Conclusions: Our findings highlight the potential of nuclear magnetic resonance–based metabolomics in identifying novel biomarkers of plaque burden and instability. Metabolites related to plaque burden may facilitate noninvasive assessment of CAD prognosis.