The results of this study indicate that blockade of CXCR1/2 may represent a promising therapeutic strategy for the treatment of sepsis-associated ALI through regulation of neuropeptides and necroptosis.
Although observational studies have shown that abnormal systemic iron status is associated with an increased risk of heart failure (HF), it remains unclear whether this relationship represents true causality. We aimed to explore the causal relationship between iron status and HF risk. Two-sample Mendelian randomisation (MR) was applied to obtain a causal estimate. Genetic summary statistical data for the associations (p < 5 × 10−8) between single nucleotide polymorphisms (SNPs) and four iron status parameters were obtained from the Genetics of Iron Status Consortium in genome-wide association studies involving 48,972 subjects. Statistical data on the association of SNPs with HF were extracted from the UK biobank consortium (including 1088 HF cases and 360,106 controls). The results were further tested using MR based on the Bayesian model averaging (MR-BMA) and multivariate MR (MVMR). Of the twelve SNPs considered to be valid instrumental variables, three SNPs (rs1800562, rs855791, and rs1799945) were associated with all four iron biomarkers. Genetically predicted iron status biomarkers were not causally associated with HF risk (all p > 0.05). Sensitivity analysis did not show evidence of potential heterogeneity and horizontal pleiotropy. Convincing evidence to support a causal relationship between iron status and HF risk was not found. The strong relationship between abnormal iron status and HF risk may be explained by an indirect mechanism.
BackgroundEarly prediction and classification of prognosis is essential for patients in the coronary care unit (CCU). We applied a machine learning (ML) model using the eXtreme Gradient Boosting (XGBoost) algorithm to prognosticate CCU patients and compared XGBoost with traditional classification models.MethodsCCU patients' data were extracted from the MIMIC-III v1.4 clinical database, and divided into four groups based on the time to death: <30 days, 30 days−1 year, 1–5 years, and ≥5 years. Four classification models, including XGBoost, naïve Bayes (NB), logistic regression (LR), and support vector machine (SVM) were constructed using the Python software. These four models were tested and compared for accuracy, F1 score, Matthews correlation coefficient (MCC), and area under the curve (AUC) of the receiver operating characteristic curves. Subsequently, Local Interpretable Model-Agnostic Explanations method was performed to improve XGBoost model interpretability. We also constructed sub-models of each model based on the different categories of death time and compared the differences by decision curve analysis. The optimal model was further analyzed using a clinical impact curve. At last, feature ablation curves of the XGBoost model were conducted to obtain the simplified model.ResultsOverall, 5360 CCU patients were included. Compared to NB, LR, and SVM, the XGBoost model showed better accuracy (0.663, 0.605, 0.632, and 0.622), micro-AUCs (0.873, 0.811, 0.841, and 0.818), and MCC (0.337, 0.317, 0.250, and 0.182). In subgroup analysis, the XGBoost model had a better predictive performance in acute myocardial infarction subgroup. The decision curve and clinical impact curve analyses verified the clinical utility of the XGBoost model for different categories of patients. Finally, we obtained a simplified model with thirty features.ConclusionsFor CCU physicians, the ML technique by XGBoost is a potential predictive tool in patients with different conditions, and it may contribute to improvements in prognosis.
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