Surgical risk prediction is to predict postoperative morbidity and mortality with internationally authoritative mathematical models. For patients undergoing high-risk cardiac surgery, surgical risk prediction is helpful for decision-making on treatment strategies and minimization of postoperative complications, which has gradually arouse interest of cardiac surgeons. There are many risk prediction models for cardiac surgery in the world, including European System for Cardiac Operative Risk Evaluation (EuroSCORE), Ontario Province Risk (OPR)score, Society of Thoracic Surgeons (STS)score, Cleveland Clinic risk score, Quality Measurement and Management Initiative (QMMI), American College of Cardiology/American Heart Association (ACC/AHA)Guidelines for Coronary Artery Bypass Graft Surgery, and Sino System for Coronary Operative Risk Evaluation (SinoSCORE). All these models are established from the database of thousands or ten thousands patients undergoing cardiac surgery in a specific region. As different sources of data and calculation imparities exist, there are probably bias and heterogeneities when the models are applied in other regions. How to decrease deviation and improve predicting effects had become the main research target in the future. This review focuses on the progress of risk prediction models for patients undergoing cardiac surgery.
Objective To summarize risk factors of pancreatic fistula after pancreaticoduodenectomy and to investigate clinical application of pancreatic fistula risk prediction system. Method The literatures of the risk factors and risk prediction of pancreatic fistula after the pancreaticoduodenectomy were collected to make a review. Results There were many risk factors for pancreatic fistula after pancreatoduodenectomy, including the patient’s own factors (gender, age, underlying diseases, etc.), disease related factors (pancreatic texture, diameter of pancreatic duct, pathological type, etc.), and surgical related factors (operation time, intraoperative blood loss, anastomosis, pancreatic duct drainage, etc.). The fistula risk prediction system after the pancreatoduodenectomy had a better forecast accuracy. Conclusions Occurrence of pancreatic fistula after pancreaticoduodenectomy is related to softness of pancreas and small diameter of pancreatic duct. Pancreatic fistula risk prediction system is helpful for prevention of pancreatic fistula after pancreaticoduodenectomy.
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.
ObjectiveTo summarize the current status and update of the use of medical imaging in risk prediction of pancreatic fistula following pancreaticoduodenectomy (PD).MethodA systematic review was performed based on recent literatures regarding the radiological risk factors and risk prediction of pancreatic fistula following PD.ResultsThe risk prediction of pancreatic fistula following PD included preoperative, intraoperative, and postoperative aspects. Visceral obesity was the independent risk factor for clinically relevant postoperative pancreatic fistula (CR-POPF). Radiographically determined sarcopenia had no significant predictive value on CR-POPF. Smaller pancreatic duct diameter and softer pancreatic texture were associated with higher incidence of pancreatic fistula. Besides the surgeons’ subjective intraoperative perception, quantitative assessment of the pancreatic texture based on medical imaging had been reported as well. In addition, the postoperative laboratory results such as drain amylase and serum lipase level on postoperative day 1 could also be used for the evaluation of the risk of pancreatic fistula.ConclusionsRisk prediction of pancreatic fistula following PD has considerable clinical significance, it leads to early identification and early intervention of the risk factors for pancreatic fistula. Medical imaging plays an important role in this field. Results from relevant studies could be used to optimize individualized perioperative management of patients undergoing PD.
ObjectiveTo investigate relationship of long non-coding RNA FoxP4-AS1 expression with lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC).MethodsReal time fluorescent quantitative polymerase chain reaction was used to detect the expression level of FoxP4-AS1 in 52 cases of PTC tissues and corresponding adjacent tissues, PTC cells (TPC-1, B-CPAP, K1), and normal thyroid follicular epithelial cells (Nthy-ori3-1). Univariate and multivariate analysis were used to identify the influencing factors of LNM in PTC. Receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of influencing factors of LNM in PTC.ResultsThe expression level of FoxP4-AS1 in the PTC tissues was significantly decreased as compared with the corresponding adjacent tissues (t=7.898, P<0.001), which in the different cells had statistical difference (F=29.866, P<0.001): expression levels in the TPC-1 and K1 cells were lower than Nthy-ori3-1 cells (P<0.05) and in the B-CPAP cells and Nthy-ori3-1 cells had no statistical difference (P>0.05) by multiple comparisons. Univariate analysis showed that the extraglandular invasion (χ2=4.205, P=0.040)and low expression of FoxP4-AS1 (χ2=7.144, P=0.008) were the influencing factors of LNM in PTC. Binary logistic regression analysis showed that extraglandular invasion [OR=9.455, 95%CI (1.120, 79.835), P=0.039] and low expression ofFoxP4-AS1[OR=5.437, 95%CI (1.488, 19.873), P=0.010] were risk factors for LNM of PTC. The area under the ROC curve ofFoxP4-AS1,extraglandular invasion alone, and combination of the two were 0.679, 0.656, and 0.785, respectively.ConclusionsFoxP4-AS1 is down-regulated in PTC. Low level of FoxP4-AS1 is a risk factor for LNM of PTC. Combined detection of expression level of FoxP4-AS1 and extraglandular invasion has a high predictive value for LNM of PTC.
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.
Objective To establish a machine learning based framework to rapidly screen out high-risk patients who may develop atrial fibrillation (AF) from patients with valvular heart disease and provide the information related to risk prediction to clinicians as clinical guidance for timely treatment decisions. Methods Clinical data were retrospectively collected from 1 740 patients with valvular heart disease at West China Hospital of Sichuan University and its branches, including 831 (47.76%) males and 909 (52.24%) females at an average age of 54 years. Based on these data, we built classical logistic regression, three standard machine learning models, and three integrated machine learning models for risk prediction and characterization analysis of AF. We compared the performance of machine learning models with classical logistic regression and selected the best two models, and applied the SHAP algorithm to provide interpretability at the population and single-unit levels. In addition, we provided visualization of feature analysis results. ResultsThe Stack model performed best among all models (AF detection rate 85.6%, F1 score 0.753), while XGBoost outperformed the standard machine learning models (AF detection rate 71.9%, F1 score 0.732), and both models performed significantly better than the logistic regression model (AF detection rate 65.2%, F1 score 0.689). SHAP algorithm showed that left atrial internal diameter, mitral E peak flow velocity (Emv), right atrial internal diameter output per beat, and cardiac function class were the most important features affecting AF prediction. Both the Stack model and XGBoost had excellent predictive ability and interpretability. ConclusionThe Stack model has the highest AF detection performance and comprehensive performance. The Stack model loaded with the SHAP algorithm can be used to screen high-risk patients for AF and reveal the corresponding risk characteristics. Our framework can be used to guide clinical intervention and monitoring of AF.
Objective To systematically evaluate risk prediction models for acute exacerbation of chronic obstructive pulmonary disease (COPD), and provide a reference for early clinical identification. Methods The literature on the risk prediction models of acute exacerbation of COPD published by CNKI, VIP, Cochrane, Embase and Web of Science database was searched in Chinese and English from inception to April 2022, and relevant studies were collected on the development of risk prediction models for acute exacerbations of COPD. After independent screening of the literature and extraction of information by two independent researchers, the quality of the included literature was evaluated using the PROBASTA tool. Results Five prospective studies, one retrospective case-control study and seven retrospective cohort studies were included, totally 13 papers containing 24 models. Twelve studies (92.3%) reported the area under the receiver operator characteristic curve ranging 0.66 to 0.969. Only five studies reported calibrated statistics, and three studies were internally and externally validated. The overall applicability of 13 studies was good, but there was a high risk of bias, mainly in the area of analysis. Conclusions The existing predictive risk models for acute exacerbations of COPD are unsatisfactory, with wide variation in model performance, inappropriate and incomplete inclusion of predictors, and a need for better ways to develop and validate high-quality predictive models. Future research should refine the study design and study report, and continue to update and validate existing models. Secondly medical staff should develop and implement risk stratification strategies for acute exacerbations of COPD based on predicted risk classification results in order to reduce the frequency of acute exacerbations and to facilitate the rational allocation of medical resources.
ObjectiveTo construct a demand model for electronic medical record (EMR) data quality in regards to the lifecycle in machine learning (ML)-based disease risk prediction, to guide the implementation of EMR data quality assessment. MethodsReferring to the lifecycle in ML-based predictive model, we explored the demand for EMR data quality. First, we summarized the key data activities involved in each task on predicting disease risk with ML through a literature review. Second, we mapped the data activities in each task to the associated requirements. Finally, we clustered those requirements into four dimensions. ResultsWe constructed a three-layer structured ring to represent the demand model for EMR data quality in ML-based disease risk prediction research. The inner layer shows the seven main tasks in ML-based predictive models: data collection, data preprocessing, feature representation, feature selection and extraction, model training, model evaluation and optimization, and model deployment. The middle layer is the key data activities in each task; and the outer layer represents four dimensions of data quality requirements: operability, completeness, accuracy, and timeliness. ConclusionThe proposed model can guide real-world EMR data governance, improve its quality management, and promote the generation of real-world evidence.