A Novel Nomogram for Predicting Warfarin-Related Bleeding: A Retrospective Cohort Study.

Warfarin is a widely used anticoagulant, and bleeding complications are the main reason why patients discontinue the drug. Currently, there is no nomogram model for warfarin-associated bleeding risk. The aim of this study was to develop a risk-prediction nomogram model for warfarin-related major and clinically relevant non-major (CRNM) bleeding. A total of 280 heart disease outpatients taking warfarin were enrolled, 42 of whom experienced major or CRNM bleeding at the one-year follow-up. The Least Absolute Shrinkage and Selection Operator regression model was employed to identify potential predictors. Backward stepwise selection with the Akaike information criterion was used to establish the optimal predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration plot, Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. The nomogram consisted of four predictors: female (OR = 1.85; 95% CI: 0.91-3.94), TIA (OR = 6.47; 95% CI: 1.85-22.7), TTR (OR = 0.99; 95% CI: 0.97-1.00), and anemia (OR = 2.30; 95% CI: 1.06-4.84). The model had acceptable discrimination (area under the ROC curve = 0.68, 95% CI: 0.59-0.78), and was significantly better than the existing nine warfarin-related bleeding prediction scoring systems. The calibration plot and Hosmer-Lemeshow test (χ² = 7.557; P = .478) indicated well-calibrated data in the model. The DCA demonstrated good clinical utility. In this study, we developed a nomogram to predict the risk of warfarin-related major or CRNM bleeding. The model has good performance, allows rapid risk stratification of warfarin users, and provides a basis for personalized treatment.

Development and Validation of a Risk Prediction Model for In-Hospital Mortality in Patients With Acute Upper Gastrointestinal Bleeding.

Acute upper gastrointestinal bleeding (UGIB) is a common life-threatening clinical emergency with a poor prognosis. The aim of this study was to develop a risk prediction model for in-hospital mortality in patients with UGIB. We performed a post hoc analysis of a publicly available retrospective clinical data. A total of 360 patients with UGIB were included in this study. The least absolute shrinkage and selection operator regression was used to screen predictors and a restricted cubic spline function was used to investigate the assumption of linear relationships between continuous predictors and the risk of in-hospital mortality. Backward stepwise selection with the Akaike information criterion was used to identify variables for the best prediction model. A nomogram was developed based on the results of the best prediction model. The receiver operating characteristic curve, GiViTI calibration plot, and decision curve analysis were used to evaluate the performance of the nomogram. The optimal prediction model consisting of 4 predictors: red cell distribution width (odds ratio [OR] = 8.44; 95% confidence interval [CI]: 1.77-89.10), platelet count (OR = 0.99; 95% CI: 0.99-1.00), pulse rate (OR = 1.03; 95% CI: 1.01-1.05), and SpO2 (OR = 0.92; 95% CI: 0.86-0.96). The nomogram model had good discrimination (area under the curve = 0.86, 95% CI: 0.78-0.95), calibration, and clinical usefulness. In this study, we developed a nomogram model for predicting death during hospitalization in patients with UGIB based on blood biomarkers and baseline vital signs at the time of admission. The model has good performance, allowing rapid risk stratification of patients with UGIB.

Risk factors and prediction nomogram model for 1-year readmission for major adverse cardiovascular events in patients with STEMI after PCI.

To identify risk factors and develop a risk-prediction nomogram model for 1-year readmission due to major adverse cardiovascular events (MACEs) in patients with acute ST-segment elevation myocardial infarction (STEMI) after primary percutaneous coronary intervention (PCI). This was a single-center, retrospective cohort study. A total of 526 eligible participants were enrolled, which included 456 non-readmitted and 70 readmitted patients. Multivariate logistical regressions were performed to identify the independent risk factors for readmission, and a prediction nomogram model was developed based on the results of the regression analysis. The receiver operating characteristic curve, Hosmer-Lemeshow test, calibration plot, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Female (OR = 2.426; 95% CI: 1.395-4.218), hypertension (OR = 1.898; 95% CI: 1.100-3.275), 3-vessel disease (OR = 2.632; 95% CI: 1.332-5.201), in-hospital Ventricular arrhythmias (VA) (OR = 3.143; 95% CI: 1.305-7.574), peak cTnI (OR = 1.003; 95% CI: 1.001-1.004) and baseline NT-proBNP (OR = 1.001; 95% CI: 1.000-1.002) were independent risk factors for readmission (all P < 0.05). The nomogram exhibited good discrimination with the area under the curve (AUC) of 0.723, calibration (Hosmer-Lemeshow test; χ2 = 15.396, P = 0.052), and clinical usefulness. Female gender, hypertension, in-hospital VA, 3-vessel disease, baseline NT-proBNP, and peak cTnI were independent risk factors for readmission. The nomogram helped clinicians to identify the patients at risk of readmission before their hospital discharge, which may help reduce readmission rates.