ObjectiveTo establish a forecasting model for inpatient cases of pediatric limb fractures and predict the trend of its variation.MethodsAccording to inpatient cases of pediatric limb fractures from January 2013 to December 2018, this paper analyzed its characteristics and established the seasonal auto-regressive integrated moving average (SARIMA) model to make a short-term quantitative forecast.ResultsA total of 4 451 patients, involving 2 861 males and 1 590 females were included. The ratio of males to females was 1.8 to 1, and the average age was 5.655. There was a significant difference in age distribution between males and females (χ2=44.363, P<0.001). The inpatient cases of pediatric limb fractures were recorded monthly, with predominant peak annually, from April to June and September to October, respectively. Using the data of the training set from January 2013 to May 2018, a SARIMA model of SARIMA (0,1,1)(0,1,1)12 model (white noise test, P>0.05) was identified to make short-term forecast for the prediction set from June 2018 to November 2018, with RMSE=8.110, MAPE=9.386, and the relative error between the predicted value and the actual value ranged from 1.61% to 8.06%.ConclusionsCompared with the actual cases, the SARIMA model fits well with good short-term prediction accuracy, and it can help provide reliable data support for a scientific forecast for the inpatient cases of pediatric limb fractures.
ObjectiveTo investigate the predictive factors of portal vein thrombosis (PVT) before and after splenectomy and gastroesophageal devascularization for liver cirrhosis with portal hypertension. MethodsSixty-one cases of liver cirrhosis with portal hypertension who underwent splenectomy and gastroesophageal devascularization were enrolled retrospectively. The patients were divided into PVT group and non-PVT group based on the presence or absence of postoperative PVT on day 7. The clinical factors related with PVT were analyzed. ResultsThere were 25 cases in the DVT group and 36 cases in the non-DVT group. The results of univariate analysis showed that the preoperative platelet (P=0.006), activated partial thromboplastin time (P=0.048), prothrombin time (P=0.028), and international normalized ratio (P=0.029), postoperative fibrin degradation product (P=0.002) and D-dimer (P=0.014) on day 1, portal venous diameter (P=0.050) had significant differences between the DVT group and non-DVT group. The results of logistic multivariate regression analysis showed that the preoperative platelet (OR=0.966, 95% CI 0.934-1.000, P=0.048) and postoperative fibrin degradation product on day 1(OR=1.055, 95% CI 1.011-1.103, P=0.017) were correlated with the PVT. The PVT might happen when preoperative platelet was less than 34.5×109/L (sensitibity 80.6%, specificity 60.0%) or postoperative fibrin degradation product on day 1 was more than 64.75 mg/L (sensitibity 48.0%, specificity 91.7%). ConclusionPreoperative platelet and postoperative fibrin degradation product on day 1 might predict PVT after splenectomy and gastroesophageal devascularization for liver cirrhosis with portal hypertension.
ObjectiveTo analyze the burden of digestive diseases attributed to smoking in China from 1990 to 2019 and forecast its change in the next 10 years. MethodsThe Global Burden of Disease database 2019 was used to analyze the burden of digestive diseases attributed to smoking in China from 1990 to 2019. Joinpoint regression model was used to analyze the time variation trend. A time series model was used to predict the burden of digestive diseases attributable to smoking over the next 10 years. ResultsIn 2019, there were 12 900 deaths from digestive diseases attributed to smoking in China, with a DALY of 398 600 years, a crude death rate of 0.91/100 000 and a crude DALY rate of 28.02/100 000. The attributed standardized mortality rate was 0.69 per 100 000, and the standardized DALY rate was 19.79 per 100 000, which was higher than the global level. In 2019, the standardized mortality rate and DALY rate of males were higher than those of females (1.48/ 100 000 vs. 0.11/ 100 000, 38.42/ 100 000 vs. 293/100 000), and the standardized rates of males and females showed a downward trend over time. In 2019, both mortality and DALY rates from digestive diseases attributed to smoking increased with age. ARIMA predicts that over the next 10 years, the burden of disease in the digestive system caused by smoking will decrease significantly. ConclusionFrom 1990 to 2019, the burden of digestive diseases attributed to smoking showed a decreasing trend in China, and the problem of disease burden is more serious in men and the elderly population. A series of effective measures should be taken to reduce the smoking rate in key groups. The burden of digestive diseases caused by smoking will be significantly reduced in the next 10 years.
Objective To systematically review prediction models of small for gestational age (SGA) based on machine learning and provide references for the construction and optimization of such a prediction model. Methods The PubMed, EMbase, Web of Science, CBM, WanFang Data, VIP and CNKI databases were electronically searched to collect studies on SGA prediction models from database inception to August 10, 2022. Two researchers independently screened the literature, extracted data, evaluated the risk of bias of the included studies, and conducted a systematic review. Results A total of 14 studies, comprising 40 prediction models constructed using 19 methods, such as logical regression and random forest, were included. The results of the risk of bias assessment from 13 studies were high; the area under the curve of the prediction models ranged from 0.561 to 0.953. Conclusion The overall risk of bias in the prediction models for SGA was high, and the predictive performance was average. Models built using extreme gradient boosting (XGBoost) demonstrated the best predictive performance across different studies. The stacking method can improve predictive performance by integrating different models. Finally, maternal blood pressure, fetal abdominal circumference, head circumference, and estimated fetal weight were important predictors of SGA.
As precision medicine continues to gain momentum, the number of predictive model studies is increasing. However, the quality of the methodology and reporting varies greatly, which limits the promotion and application of these models in clinical practice. Systematic reviews of prediction models draw conclusions by summarizing and evaluating the performance of such models in different settings and populations, thus promoting their application in practice. Although the number of systematic reviews of predictive model studies has increased in recent years, the methods used are still not standardized and the quality varies greatly. In this paper, we combine the latest advances in methodologies both domestically and abroad, and summarize the production methods and processes of a systematic review of prediction models. The aim of this study is to provide references for domestic scholars to produce systematic reviews of prediction models.
ObjectiveTo systematically review the risk prediction models for the occurrence of low anterior resection syndrome in patients with rectal cancer after surgery. MethodsThe PubMed, Web of Science, Embase, Cochrane Library, Scopus, CINHAL, CNKI, CBM, WanFang Data and VIP databases were electronically searched to collect studies related to the objectives from inception to June 13, 2023. Two reviewers independently screened the literature, extracted data using the critical appraisal and data extraction for systematic reviews of prediction modelling studies (CHARMS) checklist, and assessed quality of the included studies using prediction model risk of bias assessment tool (PROBAST). ResultsA total of 14 studies were included, all studies reported model discrimination, and 10 studies reported calibration. The models were internally validated in 8 studies, externally validated in 5 studies. The most common predictors included in the models were tumour distance from the anal verge, neoadjuvant therapy, anastomotic leak and BMI. Only 5 studies had good overall applicability, and all studies had a high risk of bias, with the risk of bias mainly stemming from the field of participants, outcomes and analysis. ConclusionThere are still many shortcomings in the risk prediction models for the occurrence of low anterior resection syndrome in patients with rectal cancer after surgery. Future studies may consider external validation and recalibration of existing models. New prediction models should be built and validated according to methodological guidelines.
Objective To evaluate the predicted value of APACHEⅡ score at admission for deep fungal infection（DFI） in patients with severe acute pancreatitis （SAP）.Methods The clinical data of 132 patients with SAP from January 2006 to June 2011 in our hospital were analyzed retrospectively. The receiver operating characteristic curve （ROC） was used for evaluating the predicted value.Results Thirty-nine patients with SAP infected DFI （29.5%）,of which 36 patients （92.3%） infected with Candida albicans,2 patients （5.1%） with Candida tropicalis,1 patient （2.6%） with pearl bacteria.And,among these 39 patients,27 patients （69.2%） infected at single site,12 patients （30.8%） infected at multi-site. The APACHEⅡ score in 39 patients with DFI was higher than that of 93 patients without DFI （17.1±3.8 versus 9.7±2.1, t=14.316,P=0.000）.The ROC for APACHEⅡ score predicting DFI was 0.745（P=0.000）, 95%CI was 0.641－0.849.When the cut off point was 15,it showed the best forecast performance,with specificity 0.81, sensitivity 0.72,Youden index 0.53. Conclusions The APACHEⅡ score at admission can preferably predict DFI in patients with SAP； when the APACHEⅡ score is greater than 15,it prompts highly possible of DFI,so preventive anti-fungal treatment may be necessary.
ObjectiveTo observe the relationship between ventilator-associated pneumonia (VAP) and changes in bronchial mucosa and sputum in critically ill patients. A prediction model for SEH score was developed according to the abnormal degrees of airway sputum , mucosal edema and mucosal hyperemia , as well as to analyze the diagnostic value of the SEH scores for VAP during bronchoscopy. MethodsA collection of general data and initial bronchoscopy results was conducted for patients admitted to the department of intensive care unit at West China Hospital from March 1, 2024, to July 1, 2024. Patients were divided into infection group (n=138) and non-infection group (n=227) according to diagnostic criteria for VAP based on the date of their first bronchoscopy. T-tests were used to compare baseline data between groups, while analysis of variance was employed to assess differences in airway mucosal and sputum lesions. A binary logistic regression model was constructed using the SEH scores for predicting VAP risk, with receiver operating characteristic curve area under the curve (AUC) utilized to evaluate model accuracy. ResultsA total of 365 patients were included in this study, among which 138 cases (37.8%) were diagnosed with VAP. The AUC for using SEH scores in diagnosing VAP was found to be 0.81 [95% confidence interval (CI) 0.76-0.85], with an optimal cutoff value set at 6.5. The sensitivity and specificity of SEH scores for diagnosing VAP were determined as 79.7% (95% CI: 72.2%-85.6%) and 73.1% (95% CI:67.0%-78.5%). Patients with SEH scores over 6.5 exhibited a significantly higher rate of VAP infection (64.3% vs.14.4%, P<0.0001), elevated white blood cell count levels (WBC) [(13.3±7.5 vs.1.8±6.2), P=0.04], as well as increased hospital mortality rates (39.8 % vs.24.2 %, P=0.002). ConclusionsThe SEH scores has a certain efficacy in the diagnosis of VAP in patients with mechanical ventilation. Compared with the traditional VAP diagnostic criteria, SEH scores is easier to obtain in clinical practice, and has certain clinical application value.
Abstract： Objective To evaluate the incidence and prognosis of postoperative acute kidney injury （AKI） in patients after cardiovascular surgery， and analyse the value of AKI criteria and classification using the Acute Kidney Injury Network （AKIN） definition to predict their in-hospital mortality. Methods A total of 1 056 adult patients undergoing cardiovascular surgery in Renji Hospital of School of Medicine， Shanghai Jiaotong University from Jan. 2004 to Jun. 2007 were included in this study. AKI criteria and classification under AKIN definition were used to evaluate the incidence and in-hospital mortality of AKI patients. Univariate and multivariate analyses were used to evaluate preoperative， intraoperative， and postoperative risk factors related to AKI. Results Among the 1 056 patients， 328 patients（31.06%） had AKI. In-hospital mortality of AKI patients was significantly higher than that of non-AKI patients （11.59% vs. 0.69%， P＜0.05）. Multivariate logistic regression analysis suggested that advanced age （OR=1.40 per decade）， preoperative hyperuricemia（OR=1.97）， preoperative left ventricular failure （OR=2.53）， combined CABG and valvular surgery （OR=2.79）， prolonged operation time （OR=1.43 per hour）， postoperative hypovolemia （OR=11.08） were independent risk factors of AKI after cardiovascular surgery. The area under the ROC curve of AKIN classification to predict in-hospital mortality was 0.865 （95% CI 0.801-0.929）. Conclusion Higher AKIN classification is related to higher in-hospital mortality after cardiovascular surgery. Advanced age， preoperative hyperuricemia， preoperative left ventricular failure， combined CABG and valvular surgery， prolonged operation time， postoperative hypovolemia are independent risk factors of AKI after cardiovascular surgery. AKIN classification can effectively predict in-hospital mortality in patients after cardiovascular surgery， which provides evidence to take effective preventive and interventive measures for high-risk patients as early as possible.
ObjectiveTo systematically review mortality risk prediction models for acute type A aortic dissection (AAAD). MethodsPubMed, EMbase, Web of Science, CNKI, WanFang Data, VIP and CBM databases were electronically searched to collect studies of mortality risk prediction models for AAAD from inception to July 31th, 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Systematic review was then performed. ResultsA total of 19 studies were included, of which 15 developed prediction models. The performance of prediction models varied substantially (AUC were 0.56 to 0.92). Only 6 studies reported calibration statistics, and all models had high risk of bias. ConclusionsCurrent prediction models for mortality and prognosis of AAAD patients are suboptimal, and the performance of the models varies significantly. It is still essential to establish novel prediction models based on more comprehensive and accurate statistical methods, and to conduct internal and a large number of external validations.