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.
Background: Rosacea is a facial chronic inflammatory skin disease with almost 5.5% prevalence. Although there are various scales of rosacea, they are objective and discordant among different dermatologists. Noninvasive objective measurements such as VISIA system might play essential roles in the diagnosis and evaluation of rosacea.Here, we intended to reveal the effectiveness of VISIA system in rosacea.
Materials and methods:A number of 563 participants diagnosed with facial rosacea were enrolled in study. They all received both full-face image-shoot by VISIA system with quantitative analysis software and physician's assessment via five different scales, including investigator global assessment (IGA), clinician erythema assessment (CEA), numerical score, the National Rosacea Society (NRS) grading system and telangiectasis.Results: Absolute score and percentile of red area had significant correlations with IGA and CEA, whereas red area had no significant correlation with numerical score, NRS and telangiectasis. Red area in erythematotelangiectatic rosacea patients demonstrated the highest correlation with IGA and CEA, especially in those aged between 51 and 60. Besides red area, pigmentation parameters in VISIA system (brown spot) also showed significant correlation with IGA and CEA.
Conclusion:VISIA system might be an effective measurement in the assessment of rosacea severity.
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