RÉSUMÉ
【Objective】 To explore the factors influencing the survival and prognosis of patients with bladder urothelial carcinoma (BUC) after surgical treatment, and to establish an artificial intelligence algorithm to predict the effects of different surgical regimens. 【Methods】 BUC patients treated with surgery during Jan.2007 and Jan.2019 in The Second Hospital of Dalian Medical University and Nanfang Hospital of Southern Medical University were enrolled. The complete clinical and follow-up data were collected. Deep neural network (DNN) was used to establish an artificial intelligence algorithm model. A prediction model of survival and prognosis was established, and the influencing factors of survival were explored and ranked by the artificial intelligence algorithm. 【Results】 A total of 832 patients were involved, including 438 (52.64%) treated in The Second Hospital of Dalian Medical University, and 394 (47.36%) treated in Nanfang Hospital of Southern Medical University. Of all cases, 579 (69.6%) were non-muscle invasive bladder cancer, and 253 (30.4%) were muscle invasive bladder cancer. Transurethral resection of bladder tumor was conducted in 539 (64.8%) cases, partial cystectomy in 66 (7.9%) cases, and total cystectomy in 227 (27.3%) cases. The data of patients treated in Second Hospital of Dalian Medical University were used for DNN modeling, and the data of patients treated in Nanfang Hospital of Southern Medical University were used for external verification after modeling. Finally, it was concluded that the factors affecting survival and prognosis were T stage, pathological grade, hypertension or cardiovascular and cerebrovascular disease, hemoglobin, blood calcium, smoking, albumin, lymphocytes, age, ratio of albumin/globulin, operation method, N stage, and creatinine clearance rate in descending order. The model could be used for preoperative prediction. 【Conclusion】 Through DNN modeling and external verification, the influencing factors of postoperative survival can be predicted for patients with bladder cancer, and the surgical effects can also be predicted before operation. The model can provide artificial intelligence algorithm support for the selection of surgical methods and postoperative follow-up plans.
RÉSUMÉ
Objective To evaluate the rationality, safety and efficiency of the time-limited rescue angioplasty following thrombolytic therapy in acute myocardial infarction (AMI).Methods Among the patients within 6 hours from the onset of symptoms of AMI, forty-four cases (group A) underwent primary coronary angioplasty and fifty-eight cases (group B) underwent firstly intravenous thrombolytic therapy. According to clinical early reperfusion indication within 90 minutes following thrombolytic therapy, group B was divided into two subgroups, the patients with early reperfusion (subgroup C) underwent delayed interventional examination 7～10 days after AMI and that with non-reperfusion (subgroup D) underwent rescue angioplasty. The reperfusion rates and complications in different groups were analyzed and compared. Cardiac function (left ventricular ejection fraction, LVEF) was evaluated by echocardiograph 4 weeks after AMI. Results The results showed that the rate of reperfusion, in group A, was 95.45% (42/44),that of subgroup C was 32.76 % (19/58) within 90 minutes following thrombolytic therapy (16 of subgroup C underwent delayed interventional examination and 12 of them underwent PTCA+stent) and that of subgroup D was 97.43% (38/39); There were no serious complications that occurred in subgroups C and D. The LVEFs in group A, subgroups C and D were not significantly different, but there was a significant difference between reperfusion within 6 hours and beyond after AMI (62.7% vs 56.8%, P