Objective To investigate the predictive factors of clinical progression and short-term prognosis of cerebral infarction caused by large artery atherosclerosis (LAA). MethodsPatients with acute LAA cerebral infarction who were hospitalized in the Department of Neurology, Lianyungang Hospital of Traditional Chinese Medicine between January 2016 and May 2019 were included. On admission, the patients’ medical history was collected. The degree of neurological deficit was assessed, blood pressure, blood glucose, blood lipids, plasma homocysteine, lipoprotein-associated phospholipase A2 (Lp-PLA2) were measured, and intracranial and extracranial blood vessels related test results were collected. Within 72 hours of onset, the Scandinavian Stroke Scale (SSS) was used to determine whether the patients’ condition progressed. The modified Rankin scale was used to evaluate the short-term prognosis at 30 days of onset. The related factors of clinical progression and short-term prognosis of LAA cerebral infarction were analyzed. Results Finally, 100 patients were included. According to the SSS assessment results within 72 hours of onset, 27 cases were divided into the progression group and 73 cases in the non-progression group. There was no significant difference in gender and age between the two groups (P>0.05). According to the evaluation results of the modified Rankin scale at 30 days of onset, they were divided into 31 cases in the poor prognosis group and 69 cases in the good prognosis group. There was no significant difference in gender and age between the two groups (P>0.05). Logistic regression analysis showed that plasma Lp-PLA2 [odds ratio (OR)=1.013, 95% confidence interval (CI) (1.007, 1.018), P<0.001], SSS score [OR=0.910, 95%CI (0.842, 0.985), P=0.019], and history of hypertension [OR=5.527, 95%CI (1.241, 24.613), P=0.025] were the predictors of disease progression within 72 hours. SSS score [OR=0.849, 95%CI (0.744, 0.930), P<0.001], carotid artery stenosis [OR=9.536, 95%CI (1.395, 65.169), P=0.021] and progressive stroke [OR=8.873, 95%CI (1.937, 40.640), P=0.005] were the predictors of short-term prognosis of LAA cerebral infarction. Conclusions History of hypertension and high levels of plasma Lp-PLA2 are predictors of early progression of cerebral infarction. Carotid artery stenosis and progressive stroke are predictors of adverse outcomes in the acute phase of cerebral infarction. Neurological scores on admission was a predictor for short-term adverse outcomes in the early and acute phases.
Vascular dementia is one of the most common types of dementia in China. How to better prevent and treat vascular dementia is still an unresolved problem, and the risk predictor of vascular dementia may help provide clinical targeted prevention measures to intervene in the development process of vascular dementia early. This article reviews the current research status of vascular dementia predictors from four aspects: blood markers, predictors based on disease characteristics, predictors based on assessment tools and neuropsychological tests, and predictors based on activity dysfunction. It aims to provide a basis for establishing a risk prediction model for patients with vascular dementia suitable for China’s conditions in the future.
ObjectiveTo systematically evaluate the methodological quality and predictive performance of acute kidney injury (AKI) prediction models following coronary artery bypass grafting (CABG), aiming to identify reliable tools for clinical practice and provide evidence-based guidance for developing higher-quality models in future. MethodsA systematic literature search was conducted across CNKI, Wanfang Data, VIP, SinoMed, PubMed, Web of Science, EMbase, and Cochrane Library databases from inception to October 2025. Two independent reviewers screened studies, extracted data, and performed prediction model risk of bias assessment. Qualitative synthesis was followed by meta-analysis using STATA 15.0 software. ResultsA total of 21 studies involving 55 prediction models were included. The majority of the studies demonstrated good applicability, but exhibited high overall risk of bias. The models showed favorable discriminative ability, with areas under the receiver operating characteristic curves ranging from 0.707 to 0.958 in training cohorts, and a pooled area under the curve of 0.79 [95%CI (0.76, 0.82)]. The area under the receiver operating characteristic curve in the validation set ranged from 0.55 to 0.90, with a pooled area under the curve of 0.80 [95%CI (0.78, 0.81)]. Most models were presented as Nomograms. Common predictors included age, serum creatinine, estimated glomerular filtration rate, hemoglobin, uric acid, cardiopulmonary bypass, and intra-aortic balloon pump. ConclusionCurrent prediction models demonstrate satisfactory discrimination performance but are limited by single-center development, insufficient external validation, and methodological biases. Future multicenter prospective studies should optimize variable processing and model validation strategies to enhance clinical applicability and generalizability of predictive tools.
Objective To systematically review the angiographic predictors of chronic total occlusion (CTO) percutaneous coronary intervention (PCI). Methods The PubMed, EMbase, Cochrane Library, Web of Science, CBM, WanFang Data, and CNKI databases were electronically searched to collect observational studies on the angiographic predictors of CTO-PCI from inception to December 18, 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was performed using RevMan 5.4 software. Results A total of 36 studies were included. The results of meta-analysis showed that the angiographic predictors of CTO-PCI included calcification (OR=1.92, 95%CI 1.49 to 2.47, P<0.01), occlusion length≥20mm (OR=1.80, 95%CI 1.26 to 2.57, P<0.01), bending>45° (OR=2.19, 95%CI 1.56 to 3.08, P<0.01), blunt stump (OR=1.53, 95%CI 1.08 to 2.16, P<0.01), ostial lesions (OR=2.27, 95%CI 1.34 to 3.85, P<0.01), proximal cap ambiguity (OR=2.27, 95%CI 1.40 to 3.68, P<0.01), side branch at proximal cap (OR=1.65, 95%CI 1.27 to 2.16, P<0.01), and J-CTO score≥3 (OR=2.53, 95%CI 1.53 to 4.16, P<0.01). Conclusion Current evidence indicates that calcification, occlusion length ≥20mm, bending>45°, blunt stump, ostial lesions, proximal cap ambiguity, side branch at proximal cap, and J-CTO score≥3 are the angiographic predictors of CTO-PCI. Due to the limited quantity and quality of the included studies, more high-quality studies are needed to verify the above conclusion.
ObjectiveTo develop and validate a prediction model for postoperative hepatic dysfunction after Stanford type A aortic dissection (TAAD), providing a reference for early identification and intervention. MethodsWe retrospectively enrolled the patients with TAAD who underwent surgical treatment at Renmin Hospital of Wuhan University from August 2022 to August 2025 and at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from December 2022 to June 2025. The dataset was randomly divided into a training set and a validation set at a ratio of 7.5 : 2.5. Independent predictors were identified using univariate analysis and multivariate logistic regression. Predictive models were developed using logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost). Model performance was assessed using receiver operating characteristic (ROC) curves, DeLong tests, calibration curves, and decision curve analysis (DCA). A nomogram was constructed based on the optimal model. ResultsA total of 482 patients were included, comprising 368 males and 114 females, with a median age of 54 (45, 62) years. Among them, 214 (44.4%) patients developed postoperative hepatic dysfunction. Multivariable analysis identified five independent predictors: preoperative serum creatinine level, pericardial effusion, postoperative mechanical ventilation duration, procalcitonin, and total bilirubin (all P<0.05). In the validation cohort, the area under the curve (AUC) values of the LR, RF, and XGBoost models were 0.741, 0.725, and 0.712, respectively, with the LR model demonstrating the best overall performance. The DeLong test showed no significant difference in AUC among the three models (P>0.05). Calibration curves demonstrated better agreement for the LR model. The DCA indicated that the LR model provided greater net benefit across a wider range of threshold probabilities. ConclusionMachine learning models do not outperform traditional LR in predicting postoperative hepatic dysfunction after TAAD. The LR model demonstrats more stable predictive performance and greater clinical utility. The nomogram developed based on this model may provide a reference for individualized clinical risk assessment.