Acute kidney injury (AKI) is a complication with high morbidity and mortality after cardiac surgery. In order to predict the incidence of AKI after cardiac surgery, many risk prediction models have been established worldwide. We made a detailed introduction to the composing features, clinical application and predictive capability of 14 commonly used models. Among the 14 risk prediction models, age, congestive heart failure, hypertension, left ventricular ejection fraction, diabetes, cardiac valve surgery, coronary artery bypass grafting (CABG) combined with cardiac valve surgery, emergency surgery, preoperative creatinine, preoperative estimated glomerular filtration rate (eGFR), preoperative New York Heart Association (NYHA) score>Ⅱ, previous cardiac surgery, cadiopulmonary bypass (CPB) time and low cardiac output syndrome (LCOS) are included in many risks prediction models (>3 times). In comparison to Mehta and SRI models, Cleveland risk prediction model shows the best discrimination for the prediction of renal replacement therapy (RRT)-AKI and AKI in the European. However, in Chinese population, the predictive ability of the above three risk prediction models for RRT-AKI and AKI is poor.
Objective To explore the risk factors for long-term death of patients with acute myocardial infarction (AMI) and reduced left ventricular ejection fraction (LVEF), and develop and validate a prediction model for long-term death. Methods This retrospective cohort study included 1013 patients diagnosed with AMI and reduced LVEF in West China Hospital of Sichuan University between January 2010 and June 2019. Using the RAND function of Excel software, patients were randomly divided into three groups, two of which were combined for the purpose of establishing the model, and the third group was used for validation of the model. The endpoint of the study was all-cause mortality, and the follow-up was until January 20th, 2021. Cox proportional hazard model was used to evaluate the risk factors affecting the long-term death, and then a prediction model based on those risk factors was established and validated. Results During a median follow-up of 1377 days, 296 patients died. Multivariate Cox regression analysis showed that age≥65 years [hazard ratio (HR)=1.842, 95% confidence interval (CI) (1.067, 3.179), P=0.028], Killip class≥Ⅲ[HR=1.941, 95%CI (1.188, 3.170), P=0.008], N-terminal pro-brain natriuretic peptide≥5598 pg/mL [HR=2.122, 95%CI (1.228, 3.665), P=0.007], no percutaneous coronary intervention [HR=2.181, 95%CI (1.351, 3.524), P=0.001], no use of statins [HR=2.441, 95%CI (1.338, 4.454), P=0.004], and no use of β-blockers [HR=1.671, 95%CI (1.026, 2.720), P=0.039] were independent risk factors for long-term death. The prediction model was established and patients were divided into three risk groups according to the total score, namely low-risk group (0-2), medium-risk group (4-6), and high-risk group (8-12). The results of receiver operating characteristic curve [area under curve (AUC)=0.724, 95%CI (0.680, 0.767), P<0.001], Hosmer-Lemeshow test (P=0.108), and Kaplan-Meier survival curve (P<0.001) showed that the prediction model had an efficient prediction ability, and a strong ability in discriminating different groups. The model was also shown to be valid in the validation group [AUC=0.758, 95%CI (0.703, 0.813), P<0.001]. Conclusions In patients with AMI and reduced LVEF, age≥65 years, Killip class≥Ⅲ, N-terminal pro-brain natriuretic peptide≥5598 pg/mL, no percutaneous coronary intervention, no use of statins, and no use of β-blockers are independent risk factors for long-term death. The developed risk prediction model based on these risk factors has a strong prediction ability.
Objective To explore the risk factors of chronic postoperative inguinal pain (CPIP) after transabdominal preperitoneal hernia repair (TAPP), establish and verify the risk prediction model, and then evaluate the prediction effectiveness of the model. Methods The clinical data of 362 patients who received TAPP surgery was retrospectively analyzed and divided into model group (n=300) and validation group (n=62). The risk factors of CPIP in the model group were screened by univariate analysis and multivariate logistic regression analysis, and the risk prediction model was established and tested. Results The incidence of CPIP at 6 months after operation was 27.9% (101/362). Univariate analysis showed that gender (χ2= 12.055, P=0.001), age (t=–4.566, P<0.01), preoperative pain (χ2=44.686, P<0.01) and early pain at 1 week after operation (χ2=150.795, P<0.01) were related to CPIP. Multivariate logistic regression analysis showed that gender, age, preoperative pain, early pain at 1 week after operation, and history of lower abdominal surgery were independent risk predictors of CPIP. The area under curve (AUC) of the receiver operating characteristic (ROC) of the risk prediction model was calculated to be 0.933 [95%CI (0.898, 0.967)], and the optimal cut-off value was 0.129, while corresponding specificity and sensitivity were 87.6% and 91.5% respectively. The prediction accuracy, specificity and sensitivity of the model were 91.9% (57/62), 90.7% and 94.7%, respectively when the validation group data were substituted into the prediction model. Conclusion Female, age≤64 years old, preoperative pain, early pain at 1 week after operation and without history of lower abdominal surgery are independent risk factors for the incidence of CPIP after TAPP, and the risk prediction model established on this basis has good predictive efficacy, which can further guide the clinical practice.
ObjectiveTo systematically evaluate the risk prediction model of anastomotic fistula after radical resection of esophageal cancer, and to provide objective basis for selecting a suitable model. MethodsA comprehensive search was conducted on Chinese and English databases including CNKI, Wanfang, VIP, CBM, PubMed, EMbase, Web of Science, The Cochrane Library for relevant studies on the risk prediction model of anastomotic fistula after radical resection of esophageal cancer from inception to April 30, 2023. Two researchers independently screened literatures and extracted data information. PROBAST tool was used to assess the risk of bias and applicability of included literatures. Meta-analysis was performed on the predictive value of common predictors in the model with RevMan5.3 software. ResultsA total of 18 studies were included, including 11 Chinese literatures and 7 English literatures. The area under the curve (AUC) of the prediction models ranged from 0.68 to 0.954, and the AUC of 10 models was >0.8, indicating that the prediction performance was good, but the risk of bias in the included studies was high, mainly in the field of research design and data analysis. ConclusionThe study on the risk prediction model of anastomotic fistula after radical resection of esophageal cancer is still in the development stage. Future studies can refer to the common predictors summarized by this study, and select appropriate methods to develop and verify the anastomotic fistula prediction model in combination with clinical practice, so as to provide targeted preventive measures for patients with high-risk anastomotic fistula as soon as possible.
Objective To construct a risk prediction score model for serious adverse event (SAE) after cardiac catheterization in patients with adult congenital heart disease (ACHD) and pulmonary hypertension (PH) and verify its predictive effect. Methods The patients with PH who underwent cardiac catheterization in Wuhan Asian Heart Hospital Affiliated to Wuhan University of Science and Technology from January 2018 to January 2022 were retrospectively collected. The patients were randomly divided into a model group and a validation group according to the order of admission. The model group was divided into a SAE group and a non-SAE group according to whether SAE occurred after the catheterization. The data of the two groups were compared, and the risk prediction score model was established according to the results of multivariate logistic regression analysis. The discrimination and calibration of the model were evaluated using the area under the receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow test, respectively. Results A total of 758 patients were enrolled, including 240 (31.7%) males and 518 (68.3%) females, with a mean age of 43.1 (18.0-81.0) years. There were 530 patients in the model group (47 patients in the SAE group and 483 patients in the non-SAE group) and 228 patients in the validation group. Univariate analysis showed statistical differences in age, smoking history, valvular disease history, heart failure history, N-terminal pro-B-type natriuretic peptide, and other factors between the SAE and non-SAE groups (P<0.05). Multivariate analysis showed that age≥50 years, history of heart failure, moderate to severe congenital heart disease, moderate to severe PH, cardiac catheterization and treatment, surgical general anesthesia, and N-terminal pro-B-type natriuretic peptide≥126.65 pg/mL were risk factors for SAE after cardiac catheterization for ACHD-PH patients (P<0.05). The risk prediction score model had a total score of 0-139 points and patients who had a score>50 points were high-risk patients. Model validation results showed an area under the ROC curve of 0.937 (95%CI 0.897-0.976). Hosmer-Lemeshow goodness-of-fit test: χ2=3.847, P=0.797. Conclusion Age≥50 years, history of heart failure, moderate to severe congenital heart disease, moderate to severe PH, cardiac catheterization and treatment, general anesthesia for surgery, and N-terminal pro-B-type natriuretic peptide≥126.65 pg/mL were risk factors for SAE after cardiac catheterization for ACHD-PH patients. The risk prediction model based on these factors has a high predictive value and can be applied to the risk assessment of SAE after interventional therapy in ACHD-PH patients to help clinicians perform early intervention.
Postoperative pulmonary complications (PPCs) risk prediction models can help healthcare professionals identify the probability of PPCs occurring in patients after surgery and provide a foundation for rapid decision-making by clinical healthcare professionals. This study evaluated PPCs of lung cancer models' merits, limitations, and challenges, covering construction methods, model performance, and clinical applications. The current risk prediction models for PPCs after lung cancer surgery have a certain predictive effect on the occurrence of PPCs. However, deficiencies persist in study design, clinical implementation, and reporting transparency. Future research should prioritize large-sample, prospective, multi-center studies for multiomics models, ensuring robust data for precise predictions, thereby facilitating clinical translation, adoption, and promotion.