You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
Risk Factor Analysis and Construction of a Prognostic Model after Percutaneous Coronary Intervention in Patients with Chronic Total Occlusion
Copyright policy )Risk Factor Analysis and Construction of a Prognostic Model after Percutaneous Coronary Intervention in Patients with Chronic Total Occlusion
Background and Objective. Percutaneous coronary intervention (PCI) is expansively used in the treatment of chronic total occlusion (CTO), but there is a lack of effective means to predict the prognosis of CTO patients undergoing PCI. The aim of this study was to construct a risk model that could effectively predict disease progression after PCI treatment in patients with CTO and enrich the means of clinical prediction of prognosis. Methods. The clinical data of 82 patients with CTO who underwent PCI in Lianshui County People’s Hospital were collected in this study. The patients were divided into training set ( n = 54 ) and validation set ( n = 28 ) by random sampling method. Statistical difference test was performed for clinical features of patients. Univariate and multivariate Cox regression analyses were performed to determine the risk factors affecting progression of CTO. Nomogram was used to construct a prediction model for disease progression. C -index was calculated, and the accuracy of the model was tested by calibration curve. Results. No statistically significant differences were incorporated in baseline characteristics of included patients ( p > 0.05 ). There were 25 patients with adverse cardiac events during follow-up in the training set and 13 in the validation set. The results of multivariate Cox regression analysis demonstrated that the important factors affecting postoperative disease progression mainly came down to age, BMI, diabetes, creatinine clearance rate, and left ventricular fraction < 40 % . A nomogram was constructed and C -index was calculated. The calibration curve was then used to evaluate and predict risk model of disease progression. The result showed an internal validation C -index of 0.6219 and an external validation C -index of 0.6453, which indicated the good prediction performance of the model. Conclusion. The risk of disease progression in CTO patients treated with PCI can be effectively predicted by the risk model constructed in this study, which opens up a great possibility for enriching the means of predicting the prognosis of these patients in clinical practice.
Adult, Male, China, Article Subject, Computer applications to medicine. Medical informatics, R858-859.7, General Biochemistry, Genetics and Molecular Biology, Percutaneous Coronary Intervention, Postoperative Complications, Risk Factors, Humans, Aged, Proportional Hazards Models, General Immunology and Microbiology, Applied Mathematics, Computational Biology, General Medicine, Middle Aged, Prognosis, Nomograms, Coronary Occlusion, Heart Disease Risk Factors, Modeling and Simulation, Multivariate Analysis, Disease Progression, Female, Follow-Up Studies
Adult, Male, China, Article Subject, Computer applications to medicine. Medical informatics, R858-859.7, General Biochemistry, Genetics and Molecular Biology, Percutaneous Coronary Intervention, Postoperative Complications, Risk Factors, Humans, Aged, Proportional Hazards Models, General Immunology and Microbiology, Applied Mathematics, Computational Biology, General Medicine, Middle Aged, Prognosis, Nomograms, Coronary Occlusion, Heart Disease Risk Factors, Modeling and Simulation, Multivariate Analysis, Disease Progression, Female, Follow-Up Studies
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Popularity provided by BIP!Background and Objective. Percutaneous coronary intervention (PCI) is expansively used in the treatment of chronic total occlusion (CTO), but there is a lack of effective means to predict the prognosis of CTO patients undergoing PCI. The aim of this study was to construct a risk model that could effectively predict disease progression after PCI treatment in patients with CTO and enrich the means of clinical prediction of prognosis. Methods. The clinical data of 82 patients with CTO who underwent PCI in Lianshui County People’s Hospital were collected in this study. The patients were divided into training set ( n = 54 ) and validation set ( n = 28 ) by random sampling method. Statistical difference test was performed for clinical features of patients. Univariate and multivariate Cox regression analyses were performed to determine the risk factors affecting progression of CTO. Nomogram was used to construct a prediction model for disease progression. C -index was calculated, and the accuracy of the model was tested by calibration curve. Results. No statistically significant differences were incorporated in baseline characteristics of included patients ( p > 0.05 ). There were 25 patients with adverse cardiac events during follow-up in the training set and 13 in the validation set. The results of multivariate Cox regression analysis demonstrated that the important factors affecting postoperative disease progression mainly came down to age, BMI, diabetes, creatinine clearance rate, and left ventricular fraction < 40 % . A nomogram was constructed and C -index was calculated. The calibration curve was then used to evaluate and predict risk model of disease progression. The result showed an internal validation C -index of 0.6219 and an external validation C -index of 0.6453, which indicated the good prediction performance of the model. Conclusion. The risk of disease progression in CTO patients treated with PCI can be effectively predicted by the risk model constructed in this study, which opens up a great possibility for enriching the means of predicting the prognosis of these patients in clinical practice.