Heart valve disease is one of the three most common cardiac diseases,and the patients undergoing valve surgery have been increasing every year. Due to the high mortality,increasing number of valve surgeries,and increasing economic burdens on public health, a lot of risk models for valve surgery have been developed by various countries based on their own clinical data all over the world,which aimed to regulate the preoperative risk assessment and decrease the perioperative mortality. Over the last 10 years, a number of excellent risk models for valve surgery have finally been developed including the Society of Thoracic Surgeons(STS), the Society of Thoracic Surgeons’ National Cardiac Database (STS NCD),New York Cardiac Surgery Reporting System(NYCSRS),the European System for Cardiac Operative Risk Evaluation(EuroSCORE),the Northern New England Cardiovascular Disease Study Group(NNECDSG),the Veterans Affairs Continuous Improvement in Cardiac Surgery Study(VACICSP),Database of the Society of Cardiothoracic Surgeons of Great Britain and Ireland(SCTS), and the North West Quality Improvement Programme in Cardiac Interventions(NWQIP). In this article, we reviewed these risk models which had been developed based on the multicenter database from 1999 to 2009, and summarized these risk models in terms of the year of publication, database, valve categories, and significant risk predictors.
ObjectiveTo investigate the establishment of a risk nomogram model for predicting vagus excitatory response in patients with functional epilepsy after radiofrequency thermocoagulation.MethodsA total of 106 patients with epilepsy admitted to the neurosurgery department of our hospital from January 2016 to June 2020 were selected and divided into the Vagus excitatory response (VER) group and the non-VER group according to their occurrence or absence. Logistic regression analysis was used to screen out the risk factors of VER during SEEG-guided Percutaneous radiofrequency thermocoagulation (PRFT) in patients with functional epilepsy, and R software was used to establish a histogram model affecting VER in SEEG-guided PRFT. Bootstrap method was used for internal verification. C-index, correction curve and ROC curve were used to evaluate the prediction ability of the model.ResultsLogistic regression analysis showed that age [OR=0.235, 95%CI (0.564, 3.076)], preoperative fugl-meyer score [OR=4.356, 95%CI (1.537, 6.621)], depression [OR=0.995, 95%CI (1.068, 7.404)], and lesion range [OR=1.512, 95%CI (0.073, 3.453)] were independent risk factors for the occurrence of VER in PRFT under the guidance of SEEG (P<0.05), and were highly correlated with the occurrence of VER in PRFT. Based on the above six indicators, a SEEG-guided colograph model of VER risk in PRFT was established, and the model was validated internally. The results showed that the C-index of the modeling set and validation set were 0.779 [95%CI (0.689, 0.869)] and 0.782 [95%CI (0.692, 0.872)], respectively. The calibration curves of the two groups fit well with the standard curves. The areas under the ROC curve (AUC) of the two groups were 0.779 and 0.782 respectively, which proved that the model had good prediction accuracy.ConclusionFor patients with functional epilepsy requiring seeg-guided PRFT therapy, age, preoperative Fugl-meyer score, depression and lesion range should be taken into full consideration to comprehensively assess the incidence of VER, and early intervention measures should be taken to reduce and reduce the incidence, which has good clinical application value.
ObjectiveTo establish and preliminarily validate a nomogram model for predicting the risk of retinal vein occlusion (RVO). MethodsA retrospective clinical study. A total of 162 patients with RVO (RVO group) diagnosed by ophthalmology examination in The Second Affiliated Hospital of Xi'an Jiaotong University from January 2017 to April 2022 and 162 patients with age-related cataract (nRVO group) were selected as the modeling set. A total of 45 patients with branch RVO, 45 patients with central RVO and 45 patients with age-related cataract admitted to Xi 'an Fourth Hospital from January 2022 to February 2023 were used as the validation set. There was no significant difference in gender composition ratio (χ2=2.433) and age (Z=1.006) between RVO group and nRVO group (P=0.120, 0.320). Age, gender, blood routine (white blood cell count, hemoglobin concentration, platelet count, neutrophil count, monocyte count, lymphocyte count, erythrocyte volume, mean platelet volume, platelet volume distribution width), and four items of thrombin (prothrombin time, activated partial thrombin time, fibrinogen, and thrombin time) were collected in detail ), uric acid, blood lipids (total cholesterol, triglyceride, high-density lipoprotein, low-density lipoprotein, lipoprotein a), hypertension, diabetes mellitus, coronary heart disease, and cerebral infarction. Neutrophil/lymphocyte ratio and platelet/lymphocyte ratio were calculated. The single logistic regression was used to analyze the clinical parameters of the two groups of patients in the modeling set, and the stepwise regression method was used to screen the variables, and the column graph for predicting the risk of RVO was constructed. The Bootstrap method was used to repeated sample 1 000 times for internal and external verification. The H-L goodness-of-fit test and receiver operating characteristic (ROC) curve were used to evaluate the calibration and discrimination of the nomogram model. ResultsAfter univariate logistic regression and stepwise regression analysis, high density lipoprotein, neutrophil count and hypertension were included in the final prediction model to construct the nomogram. The χ2 values of the H-L goodness-of-fit test of the modeling set and the validation set were 0.711 and 4.230, respectively, and the P values were 0.701 and 0.121, respectively, indicating that the nomogram model had good prediction accuracy. The area under the ROC curve of the nomogram model for predicting the occurrence of post-stroke depression in the modeling set and the verification set was 0.741 [95% confidence interval (CI) 0.688-0.795] and 0.741 (95%CI 0.646-0.836), suggesting that the nomogram model had a good discrimination. ConclusionsLow high density lipoprotein level, high neutrophil count and hypertension are independent risk factors for RVO. The nomogram model established based on the above risk factors can effectively assess and quantify the risk of post-stroke depression in patients with cerebral infarction.