ObjectiveTo systematically review the predictive factors of new-onset conduction abnormalities(NOCAs) after transcatheter aortic valve replacement (TAVR) in bicuspid aortic valve (BAV) patients. MethodsThe CNKI, VIP, WanFang Data, PubMed, Cochrane Library and EMbase databases were electronically searched to collect the relevant studies on NOCAs after TAVR in patients with BAV from inception to December 5, 2022. Two researchers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.4 software. ResultsSix studies involving 758 patients with BAV were included. The results of the meta-analysis showed that age (MD=−1.48, 95%CI −2.73 to −0.23, P=0.02), chronic kidney disease (OR=0.14, 95%CI 0.06 to 0.34, P<0.01), preoperative left bundle branch block (LBBB) (OR=2.84, 95%CI 1.11 to 7.23, P=0.03), membranous septum length (MSL) (MD=0.93, 95%CI 0.05 to 1.80, P=0.04), implantation depth (ID) (MD=−2.06, 95%CI −2.96 to −1.16, P<0.01), the difference between MSL and ID (MD=3.05, 95%CI 1.92 to 4.18, P<0.01), and ID>MSL (OR=0.27, 95%CI 0.15 to 0.49, P<0.01) could be used as predictors of NOCAs. ConclusionCurrent evidence shows that age, chronic kidney disease, LBBB, MS, ID, the difference between MSL and ID, and ID>MSL could be used as predictors of NOCAs. Due to the limited quantity and quality of included studies, more high-quality studies are required to verify the above conclusion.
ObjectiveTo explore the risk factors for accompanying depression in patients with community type Ⅱ diabetes and to construct their risk prediction model. MethodsA total of 269 patients with type Ⅱ diabetes accompanied with depression and 217 patients with simple type Ⅱ diabetes from three community health service centers in two streets of Pingshan District, Shenzhen from October 2021 to April 2022 were included. The risk factors were analyzed and screened out, and a logistic regression risk prediction model was constructed. The goodness of fit and prediction ability of the model were tested by the Hosmer-Lemeshow test and the receiver operating characteristic (ROC) curve. Finally, the model was verified. ResultsLogistic regression analysis showed that smoking, diabetes complications, physical function, psychological dimension, medical coping for face, and medical coping for avoidance were independent risk factors for depressive disorder in patients with type Ⅱ diabetes. Modeling group Hosmer-Lemeshow test P=0.345, the area under the ROC curve was 0.987, sensitivity was 95.2% and specificity was 98.6%. The area under the ROC curve was 0.945, sensitivity was 89.8%, specificity was 84.8%, and accuracy was 86.8%, showing the model predictive value. ConclusionThe risk prediction model of type Ⅱ diabetes patients with depressive disorder constructed in this study has good predictive and discriminating ability.
ObjectiveTo summarise the application research progress of clinical prediction models in postoperative complications of gastric cancer, in order to reduce the risk of complications after gastric cancer surgery. MethodThe literature on the study of postoperative complications of gastric cancer at home and abroad was read and reviewed. ResultsAt present, the main way of treating gastric cancer was still radical resection, and the occurrence of complications after surgical treatment seriously affected the recovery and survival quality of patients. With the deepening of research, the prediction models of postoperative complications in gastric cancer were constantly constructed, and these models provided strong evidence for the early judgement of postoperative complications in gastric cancer, and provided a scientific basis for the improvement of patients’ life quality. ConclusionClinical predictive models are expected to become risk screening tools for predicting the risk of postoperative complications of gastric cancer with clinical utility.
Brain age prediction, as a significant approach for assessing brain health and early diagnosing neurodegenerative diseases, has garnered widespread attention in recent years. Electroencephalogram (EEG), an non-invasive, convenient, and cost-effective neurophysiological signal, offers unique advantages for brain age prediction due to its high temporal resolution and strong correlation with brain functional states. Despite substantial progress in enhancing prediction accuracy and generalizability, challenges remain in data quality and model interpretability. This review comprehensively examined the advancements in EEG-based brain age prediction, detailing key aspects of data preprocessing, feature extraction, model construction, and result evaluation. It also summarized the current applications of machine learning and deep learning methods in this field, analyzed existing issues, and explored future directions to promote the widespread application of EEG-based brain age prediction in both clinical and research settings.
Objective To analyze the risk factors and prognosis of acute gastrointestinal injury (AGI) early after acute type A aortic dissection (ATAAD) repair, and develop the Nomogram prediction model of AGI. Methods The patients who underwent ATAAD cardiopulmonary bypass surgery in our hospital from 2016 to 2021 were collected and divided into an AGI group and a non-AGI group. The clinical data of the two groups were compared. A Nomogram prediction model was established by using R language. Results A total of 188 patients were enrolled, including 166 males and 22 females, aged 22-70 (49.70±9.96) years. Through multivariate logistic regression analysis, the aortic dissection (AD) risk score, poor perfusion of superior mesenteric artery (SMA), duration of aortic occlusion and intraoperative infusion of red blood cells were the predictors for AGI (P<0.05). There were statistical differences in the ventilator-assisted duration, ICU stay time, liver dysfunction, renal insufficiency, parenteral nutrition, nosocomial infection and death within 30 days after the operation between the two groups (P<0.05). The Nomogram prediction model was established by using the prediction factors, and the C index was 0.888. Through internal verification, the C index was 0.848. The receiver operating characteristic curve was used to evaluate the discrimination of the model, and the area under the curve was 0.888. Conclusion The AD risk score after ATAAD, poor perfusion of SMA, duration of aortic occlusion and intraoperative infusion of red blood cells are independent predictors for AGI. The Nomogram model has good prediction ability.
Objective To investigate the predictors for carbapenem-resistant Acinetobacter baumannii, Enterobacteriaceae and Pseudomonas aeruginosa (CR-AEP) as the pathogens of bloodstream infection (BSI) for intensive care unit (ICU) patients. Methods A retrospective case-control study based on ICU- healthcare-associated infection (HAI) research database was carried out. The patients who have been admitted to the central ICU between 2015 and 2019 in the ICU-HAI research database of West China Hospital of Sichuan University were selected. The included patients were divided into two groups, of which the patients with ICU-acquired BSI due to CR-AEP were the case group and the patients with BSI due to the pathogens other than CR-AEP were the control group. The clinical features of the two groups of patients were compared. Logistic regression model was used to identify the predictors of BSI due to CR-AEP.ResultsA total of 197 patients with BSI were included, including 83 cases in the case group and 114 cases in the control group. A total of 214 strains of pathogenic bacteria were isolated from the 197 BSI cases, including 86 CR-AEP strains. The results of multivariate logistic regression analysis showed that previous use of tigecycline [odds ratio (OR)=2.490, 95% confidence interval (CI) (1.141, 5.436), P=0.022] was associated with higher possibility for CR-AEP as the pathogens of BSI in ICU patients with BSI, while previous use of antipseudomonal penicillin [OR=0.497, 95%CI (0.256, 0.964), P=0.039] was associated with lower possibility for that. Conclusion Previous use of tigecycline or antipseudomonal penicillin is the predictor for CR-AEP as the pathogens of BSI in ICU patients with BSI.
ObjectiveTo systematically evaluate the prognostic prediction models for Idiopathic Pulmonary Fibrosis (IPF). MethodsA computer-based search was conducted in the PubMed, Embase, Web of Science, and Cochrane Library databases for literature relevant to the research objective, with the search period ranging from database inception to Jun 2025. Two researchers independently screened the articles. Data were extracted according to the key assessment and data extraction checklist for systematic reviews of prediction models (CHARMS). The risk of bias and applicability of the models were assessed using the PROBAST (Prediction model Risk of Bias Assessment Tool). The quality of model reporting was evaluated using the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) checklist. ResultsA total of 49 studies were included, of which 26 (53.06%) reported both model development and validation. The most common predictors included gender, age, diffusing capacity for carbon monoxide, forced vital capacity (FVC), and FVC percentage of predicted value. In terms of bias risk, 32 studies (65.31%) were classified as high risk of bias, mainly due to factors related to study subjects and predictors. Regarding applicability, 26 studies (53.06%) were rated as high risk, 11 studies (22.45%) were rated as unclear, and only 12 studies (24.49%) were rated as low risk, suggesting limited clinical applicability of the models. As for reporting quality, existing models showed generally insufficient adherence to the TRIPOD statement, especially in key areas such as research methods and result reporting, where normative issues were prominent. Of the 22 signaling questions in the TRIPOD checklist, most studies achieved only moderate reporting quality, with 8 signaling questions (1, 5c, 6b, 7b, 8, 11e, 13a, 14a) showing key information omissions or vague descriptions. ConclusionExisting prognostic prediction models for IPF generally exhibit high methodological bias risk and reporting deficiencies. Future studies should control for modeling biases based on the PROBAST framework, adhere to the TRIPOD guidelines for transparent reporting, and optimize clinical applicability through external validation.
ObjectiveTo predict the total hospitalization expenses of bronchopneumonia inpatients in a tertiay hospital of Sichuan Province through BP neural network and support vector machine models, and analyze the influencing factors.MethodsThe home page information of 749 cases of bronchopneumonia discharged from a tertiay hospital of Sichuan Province in 2017 was collected and compiled. The BP neural network model and the support vector machine model were simulated by SPSS 20.0 and Clementine softwares respectively to predict the total hospitalization expenses and analyze the influencing factors.ResultsThe accuracy rate of the BP neural network model in predicting the total hospitalization expenses was 81.2%, and the top three influencing factors and their importances were length of hospital stay (0.477), age (0.154), and discharge department (0.083). The accuracy rate of the support vector machine model in predicting the total hospitalization expenses was 93.4%, and the top three influencing factors and their importances were length of hospital stay (0.215), age (0.196), and marital status (0.172), but after stratified analysis by Mantel-Haenszel method, the correlation between marital status and total hospitalization expenses was not statistically significant (χ2=0.137, P=0.711).ConclusionsThe BP neural network model and the support vector machine model can be applied to predicting the total hospitalization expenses and analyzing the influencing factors of patients with bronchopneumonia. In this study, the prediction effect of the support vector machine is better than that of the BP neural network model. Length of hospital stay is an important influencing factor of total hospitalization expenses of bronchopneumonia patients, so shortening the length of hospital stay can significantly lighten the economic burden of these patients.
Objective The management of pulmonary nodules is a common clinical problem, and this study constructed a nomogram model based on FUT7 methylation combined with CT imaging features to predict the risk of adenocarcinoma in patients with pulmonary nodules. Methods The clinical data of 219 patients with pulmonary nodules diagnosed by histopathology at the First Affiliated Hospital of Zhengzhou University from 2021 to 2022 were retrospectively analyzed. The FUT7 methylation level in peripheral blood were detected, and the patients were randomly divided into training set (n=154) and validation set (n=65) according to proportion of 7:3. They were divided into a lung adenocarcinoma group and a benign nodule group according to pathological results. Single-factor analysis and multi-factor logistic regression analysis were used to construct a prediction model in the training set and verified in the validation set. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the model, the calibration curve was used to evaluate the consistency of the model, and the clinical decision curve analysis (DCA) was used to evaluate the clinical application value of the model. The applicability of the model was further evaluated in the subgroup of high-risk CT signs (located in the upper lobe, vascular sign, and pleural sign). Results Multivariate logistic regression analysis showed that female, age, FUT7_CpG_4, FUT7_CpG_6, sub-solid nodules, lobular sign and burr sign were independent risk factors for lung adenocarcinoma (P<0.05). A column-line graph prediction model was constructed based on the results of the multifactorial analysis, and the area under the ROC curve was 0.925 (95%CI 0.877 - 0.972 ), and the maximum approximate entry index corresponded to a critical value of 0.562, at which time the sensitivity was 89.25%, the specificity was 86.89%, the positive predictive value was 91.21%, and the negative predictive value was 84.13%. The calibration plot predicted the risk of adenocarcinoma of pulmonary nodules was highly consistent with the risk of actual occurrence. The DCA curve showed a good clinical net benefit value when the threshold probability of the model was 0.02 - 0.80, which showed a good clinical net benefit value. In the upper lobe, vascular sign and pleural sign groups, the area under the ROC curve was 0.903 (95%CI 0.847 - 0.959), 0.897 (95%CI 0.848 - 0.945), and 0.894 (95%CI 0.831 - 0.956). Conclusions This study developed a nomogram model to predict the risk of lung adenocarcinoma in patients with pulmonary nodules. The nomogram has high predictive performance and clinical application value, and can provide a theoretical basis for the diagnosis and subsequent clinical management of pulmonary nodules.