ObjectiveTo analyze prognostic ability of inflammation-based Glasgow prognostic score (GPS) in patients with ST-segment elevation myocardial infarction (STEMI).MethodsWe retrospectively analyzed the clinical data of 289 patients with STEMI admitted to the Department of Emergency in West China Hospital from April 2015 to January 2016. All study subjects were divided into three groups: a group of GPS 0 (190 patients including 150 males and 40 females aged 62.63±12.98 years), a group of GPS 1 (78 patients including 58 males and 20 females aged 66.57±15.25 years), and a group of GPS 2 (21 patients including 16 males and 5 females aged 70.95±9.58 years). Cox regression analysis was conducted to analyze the independent risk factors of predicting long-term mortality of patients with STEMI.ResultsThere was a statistical difference in long-term mortality (9.5% vs. 23.1% vs. 61.9%, P<0.001) and in-hospital mortality (3.7% vs. 7.7% vs. 23.8%, P<0.001) among the three groups. The Global Registry of Acute Coronary Events (GRACE) scores and Gensini scores increased in patients with higher GPS scores, and the differences were statistically different (P<0.001). Multivariable Cox regression analysis showed that the GPS was independently associated with STEMI long-term all-cause mortality (1 vs. 0, HR: 2.212, P=0.037; 2 vs. 0, HR: 8.286, P<0.001).ConclusionGPS score is helpful in predicting the long-term and in-hospital prognosis of STEMI patients, and thus may guide clinical precise intervention by early risk stratification.
ObjectiveTo explore the influencing factors of cancer-specific survival of patients with large hepatocellular carcinoma, and draw a nomogram to predict the cancer-specific survival rate of large hepatocellular carcinoma patients.MethodsThe clinicopathological data of patients with large hepatocellular carcinoma during the period from 1975 to 2017 in the Surveillance, Epidemiology, and End Results (SEER) database were searched and randomly divided into training group and validation group at 1∶1. Using the training data, the Cox proportional hazard regression model was used to explore the influencing factors of cancer-specific survival and construct the nomogram; finally, the receiver operating characteristic curve (ROC curve) and the calibration curve were drawn to verify the nomogram internally and externally.ResultsThe results of the multivariate Cox proportional hazard regression model showed that the degree of liver cirrhosis, tumor differentiation, tumor diameter, T stage, M stage, surgery, and chemotherapy were independent influencing factors that affect the specific survival of patients with large hepatocellular carcinoma (P<0.05), and then these factors were enrolled into the nomogram of the prediction model. The areas under the 1, 3, and 5-year curves of the training group were 0.800, 0.827, and 0.814, respectively; the areas under the 1, 3, and 5-year curves of the validation group were 0.800, 0.824, and 0.801, respectively. The C index of the training group was 0.779, and the verification group was 0.777. The calibration curve of the training group and the verification group was close to the ideal curve of the actual situation.ConclusionThe nomogram of the prediction model drawn in this study can be used to predict the specific survival of patients with large hepatocellular carcinoma in the clinic.
Non-small cell lung cancer is the main cause of cancer death in the world, and its incidence is increasing year by year, seriously endangering human health. Early non-small cell lung cancer is generally difficult to be detected based on symptoms and signs. Therefore, accurate pathological diagnosis and accurate prediction of prognosis are crucial for formulating the best treatment plan for non-small cell lung cancer patients and improving their survival. The application of artificial intelligence in the diagnosis and treatment of non-small cell lung cancer has shown good performance and great potential effect. This paper introduces the research progress of artificial intelligence in predicting the classification, staging, genomics and prognosis of non-small cell lung cancer.