Background: The formal risk assessment is essential in the management of acute coronary syndrome (ACS). In this study, we develop a risk model for the prediction of 3-year mortality for Chinese ACS patients with machine learning algorithms.Methods: A total of 2174 consecutive patients who underwent angiography with ACS were enrolled. The missing data among baseline characteristics were imputed using the MissForest algorithm based on random forest method. In model development, a least absolute shrinkage and selection operator (LASSO) derived Cox regression with internal tenfold cross-validation was used to identify the predictors for 3-year mortality. The clinical performance was assessed with decision curve analysis.Results: The average follow-up period was 27.82 ± 13.73 months; during the 3 years of follow up, 193 patients died (mortality rate 8.88%). The Kaplan–Meier estimate of 3-year mortality was 0.91 (95% confidence interval (CI): 0.890.92). After feature selection, 6 predictors were identified: Age,” “Creatinine,” “Hemoglobin,” “Platelets,” “aspartate transaminase (AST)” and “left ventricular ejection fraction (LVEF)”. At tenfold internal validation, our risk model performed well in both discrimination (area under curve (AUC) of receiver operating characteristic (ROC) analysis was 0.768) and calibration (calibration slope was approximately 0.711). As a comparison, the AUC and calibration slope were 0.701 and 0.203 in Global Registry of Acute Coronary Events (GRACE) risk score, respectively. Additionally, the highest net benefit of our model within the entire range of threshold probability for clinical intervention by decision curve analysis demonstrated the superiority of it in daily practice.Conclusion: Our study developed a prediction model for 3-year morality in Chinese ACS patients. The methods of missing data imputation and model derivation base on machine learning algorithms improved the ability of prediction. .Trial registrationChiCTR, ChiCTR-OOC-17010433. Registered 17 February 2017–Retrospectively registered

The role of triglyceride (TG) in secondary prevention of patients with coronary artery disease (CAD) was debated. In the present study, we assessed the association between admission TG levels and long-term mortality risk in CAD patients. A retrospective analysis was conducted from a single registered database. 3061 consecutive patients with CAD confirmed by coronary angiography were enrolled and were grouped into 3 categories by the tertiles of admission serum TG levels. The primary end point in this study was all-cause mortality and the secondary end point was cardiovascular mortality. The mean follow-up time was 26.9 ± 13.6 months and death events occurred in 258 cases and cardiovascular death events occurred in 146 cases. Cumulative survival curves indicated that the risk of all-cause death decreased with increasing TG level (Tertile 1 vs. Tertile 2 vs. Tertile 3 = 10.3% vs. 8.6% vs. 6.3%, log rank test for overall p = 0.001). Cox regression analysis showed an independent correlation between TG level and risk of all-cause mortality [hazard ratio (HR) 0.71, 95% confidence interval (CI) 0.58–0.86] and cardiovascular mortality (HR 0.67, 95% CI 0.51–0.89) in total patients with CAD. Subgroup analysis found the similar results in patients with acute coronary syndrome and acute myocardial infarction. This study found an inverse association between TG levels and mortality risk in CAD patients, which suggests that the “TG paradox” may exist in CAD patients. ChiCTR, ChiCTR-OOC-17010433 . Registered 17 February 2017 - Retrospectively registered.

A formal risk assessment for identifying high-risk patients is essential in clinical practice and promoted in guidelines for the management of anterior acute myocardial infarction. In this study, we sought to evaluate the performance of different machine learning models in predicting the 1-year mortality rate of anterior ST-segment elevation myocardial infarction (STEMI) patients and to compare the utility of these models to the conventional Global Registry of Acute Coronary Events (GRACE) risk scores. We enrolled all of the patients aged >18 years with discharge diagnoses of anterior STEMI in the Western China Hospital, Sichuan University, from January 2011 to January 2017. A total of 1244 patients were included in this study. The mean patient age was 63.8±12.9 years, and the proportion of males was 78.4%. The majority (75.18%) received revascularization therapy. In the prediction of the 1-year mortality rate, the areas under the curve (AUCs) of the receiver operating characteristic curves (ROCs) of the six models ranged from 0.709 to 0.942. Among all models, XGBoost achieved the highest accuracy (92%), specificity (99%) and f1 score (0.72) for predictions with the full variable model. After feature selection, XGBoost still obtained the highest accuracy (93%), specificity (99%) and f1 score (0.73). In conclusion, machine learning algorithms can accurately predict the rate of death after a 1-year follow-up of anterior STEMI, especially the XGBoost model.

Context: Hypothalamic obesity (HO) is a severe complication following craniopharyngioma, but studies regarding the sequelae in adult-onset patients with craniopharyngioma are sparse.Objective: The objective of the study was to describe weight changes after surgical treatment in adult-onset craniopharyngioma patients and to analyze risk factors for postoperative weight gain and HO.Subjects and Method: A retrospective analysis was conducted of 120 adult-onset patients who underwent surgery for craniopharyngioma and follow-up at the institution of the authors between January 2018 and September 2020. Clinical characteristics, anthropometric data, image features, treatment modalities, and endocrine indices were collected. Multivariable logistic regression analysis was used to identify independent risk factors for postoperative weight gain and HO.Results: Forty-nine (40.8%) patients had clinically meaningful weight gain (≥5%) in a median follow-up time of 12.0 months (range 1.0–41.0 months) after surgery. The mean postoperative weight gain in this subgroup was 17.59 ± 12.28 (%). Weight gain continued in the first year following surgery. Patients with lower preoperative BMI [OR 0.78, 95% CI (0.67–0.90), P = 0.001] and the adamantinomatous subtype [OR 3.46, 95% CI (1.02–11.76), P = 0.047] were more likely to experience postoperative weight gain ≥5%. The prevalence of HO was 19.2% preoperatively and increased to 29.2% at last follow-up postoperatively. Only preoperative BMI [OR 2.51, 95% CI (1.64–3.85), P < 0.001] was identified as an independent risk factor for postoperative HO.Conclusions: HO is a common complication in patients with adult-onset craniopharyngioma. Patients with higher preoperative BMI had a greater risk for developing HO postoperatively.