Wenqiang Li

Background: In recent years, advancements in our understanding of the pathophysiological mechanisms underlying coronary artery disease (CAD) have introduced new challenges regarding the clinical application of traditional risk scores. While studies suggest that machine learning (ML) algorithms surpass traditional statistical methods in risk prediction, their conclusions are often derived from heterogeneous datasets and varying model structures, which restrict their generalizability and persuasive power. Objective: This study aims to evaluate the clinical performance of the GRACE, ACEF, and TIMI risk scores, while also enhancing their predictive accuracy through redevelopment utilizing six ML algorithms. Methods: This retrospective study was undertaken at the First Hospital of Lanzhou University between January 1, 2019, and December 31, 2020. Six ML algorithms were employed to redevelop the original GRACE, TIMI, and ACEF risk scores. Model performance was evaluated using accuracy, sensitivity, precision, F1-score, the area under the receiver operating characteristic curve (AUROC), and the area under the precision-recall curve (AUPRC). Results: We retrospectively enrolled 1,682 patients diagnosed with acute myocardial infarction (AMI) who underwent emergency percutaneous coronary intervention (PCI). The derivation cohort from 2019 included 883 patients, among whom 48 (5.4%) experienced death during the follow-up period. The temporal validation cohort from 2020 consisted of 799 patients, with 26 (3.25%) reported deaths. The original GRACE, TIMI, and ACEF scores were validated using derivation data, resulting in AUROC values of 0.84, 0.68, and 0.69, and AUPRC values of 0.49, 0.19, and 0.34, respectively. The redevelopment of ML algorithms significantly enhanced predictive performance, with the ensemble algorithms yielding the most favorable results. The redeveloped GRACE, TIMI, and ACEF models achieved AUROC values of 0.89, 0.75, and 0.91, and AUPRC values of 0.55, 0.50, and 0.50, respectively. Demonstrating superior predictive performance for six-month mortality over the redeveloped ACEF and TIMI scores, the redeveloped GRACE score yielded an AUROC of 0.89 and an AUPRC of 0.32 in temporal validation. Conclusion: This study validated three established risk scores and found that the GRACE score exhibited superior predictive performance for six-month mortality compared to the ACEF and TIMI scores. The redevelopment of these scores using ML techniques significantly enhanced their predictive accuracy. This improvement reflects both the inherent strengths of ML algorithms and the recalibration of parameter weights to better align with contemporary clinical practice.

To develop and validate a prediction model for in-hospital cardiogenic shock (CS) after percutaneous coronary intervention (PCI) in patients with acute myo

Objective To develop and validate a machine learning (ML) model for predicting in-hospital cardiogenic shock (CS) in acute myocardial infarction (AMI) patients undergoing percutaneous coronary intervention. Methods We included AMI patients who admitted to the First Hospital of Lanzhou University. Key predictive variables were screened using LASSO regression, and six ML predictive models were constructed. Model performance was evaluated using accuracy, sensitivity, precision, F1 score, AUROC, and AUPRC, and an online predictive application was developed. Results 851 patients were included, with 43 (5.05%) was CS. They were randomly divided into a training set (n = 595) and a test set (n = 256) at a ratio of 7:3. LASSO regression initially screened out 14 relevant variables. The LightGBM model performed optimally. After GSCV optimization, eight core modeling variables were finally determined: heart rate, D-dimer, Killip classification, creatinine, lipoprotein a, alanine aminotransferase, diastolic blood pressure, and apolipoprotein B/A. We established an ML prediction model based on LightGBM + LASSO + GSCV, Its AUROC = 0.87 (95% CI: 0.77–0.96) and AUPRC = 0.64 (95% CI: 0.36–0.86). We developed an online risk prediction application based on the Streamlit framework. Conclusion This study developed and validated a risk prediction model for in-hospital CS in AMI patients, providing a reliable risk assessment tool for clinical decision-making.

BACKGROUND Left bundle branch block (LBBB) is not uncommon. In certain cases, patients with LBBB exhibit a deterioration of cardiac function, ultimately progressing to a clinical phenotype resembling cardiomyopathy in the absence of an alternative etiology, a condition referred to as left bundle branch block-induced cardiomyopathy (LIC). We present a case of a 57-year-old woman who developed LIC 1 year after the onset of new LBBB. Following conduction system pacing (CSP), her cardiac function was completely restored. CASE REPORT A 57-year-old woman presented with normal electrocardiograms (ECGs) and heart function 1 year prior. After developing LBBB, she experienced progressive dyspnea and reduced exercise tolerance over the subsequent year, during which her left ventricular ejection fraction (LVEF) declined from 55% to 31%. A comprehensive diagnostic workup revealed interventricular mechanical dyssynchrony and excluded ischemic or structural etiologies, which is consistent with LIC. Guideline-directed medical therapy was initiated; however, her symptoms persisted after 6 months of therapy. Following CSP for cardiac resynchronization therapy, the patient's LVEF improved to 54% at 6 months and further increased to 56% at 30 months, approaching normalization. CONCLUSIONS LIC is a recently identified condition wherein patients with LBBB experience left ventricular dysfunction and mechanical dyssynchrony without an alternative etiology. Emerging evidence indicates that CSP may effectively correct the electrical dyssynchrony associated with LBBB. We present a case of a patient diagnosed with LIC within 1 year of symptom onset, who exhibited significant improvement following CSP treatment. This case highlights the critical importance of early diagnosis and timely intervention.