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.
ObjectiveTo systematically review the predictors of enteral nutrition feeding intolerance in critically ill patients. MethodsThe PubMed, Web of Science, Cochrane Library, Embase, CNKI, WanFang Data, VIP and CBM databases were searched to collect relevant observational studies from the inception to 6 August, 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was then performed using RevMan 5.4 software. ResultsA total of 18 studies were included, including 28 847 patients. The results of the meta-analysis showed that gender, age, severity of illness, hypo-albuminemia, length of stay, postpyloric feeding, mechanical ventilation and mechanical ventilation time, use of prokinetics, use of sedation drugs, use of vasoactive drugs and use of antibiotics were predictors of enteral nutrition feeding intolerance in critically ill patients, among which postpyloric feeding (OR=0.46, 95%CI 0.29 to 0.71, P<0.01) was a protective factor. ConclusionAccording to the influencing factors, the medical staff can formulate a targeted enteral nutrition program at the time of admission to the ICU to reduce the occurrence of feeding intolerance. Due to the limited quantity and quality of the included studies, more high-quality studies are needed to verify the above conclusion.
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.
ObjectiveTo investigate the clinical characteristics and predicting factors for death in critically ill patients with severe community-acquired pneumonia (CAP). MethodA total of 143 hospitalized patients with severe CAP between January 2009 and December 2012 were included and their clinical data were retrospectively analyzed. According to the clinical outcome, patients were divided into survival group and death group, and their clinical features and laboratory test results were compared, and multivariate regression analysis was conducted to search for predicting factors for death. ResultsIn this study, a total of 118 patients survived and 25 patients died, and the mortality rate was 17.5%. The number of underlying diseases in the two groups were different, and death group had more patients with 3 kinds of diseases than the survival group[76.0% (19/25) vs. 22.8% (13/57), P<0.05]. The intubation rate in the death group was significantly higher than that in the survival group[84.0% (21/25) vs. 33.1% (39/118), P<0.05], and the arterial blood pH value (7.15±0.52 vs. 7.42±0.17, P<0.05), HCO3- concentration[(18.07±6.25) vs. (25.07±5.44) mmol/L, P<0.05], PaO2[(58.92±35.18) vs. (85.92±32.19) mm Hg (1 mm Hg=0.133 kPa), P<0.05] and PaO2/FiO2[(118.23±98.02) vs. (260.17±151.22) mm Hg, P<0.05)] in the death group were significantly lower than those in the survival group. And multivariate regression analysis indicated that the number of underlying diseases[OR=0.202, 95%CI (0.198, 0.421), P=0.003], PaO2[OR=1.203, 95%CI (1.193, 1.294), P=0.011] and PaO2/FiO2[OR=0.956, 95%CI (0.927, 0.971), P=0.008] were independent predictors of death in the patients with severe pneumonia. ConclusionsPatients who died of severe pneumonia often had severe illnesses before admission, and the number of underlying diseases and PaO2 have highly predictive value for death.
Alveolar bone reconstruction simulation is an effective means for quantifying orthodontics, but currently, it is not possible to directly obtain human alveolar bone material models for simulation. This study introduces a prediction method for the equivalent shear modulus of three-dimensional random porous materials, integrating the first-order Ogden hyperelastic model to construct a computed tomography (CT) based porous hyperelastic Ogden model (CT-PHO) for human alveolar bone. Model parameters are derived by combining results from micro-CT, nanoindentation experiments, and uniaxial compression tests. Compared to previous predictive models, the CT-PHO model shows a lower root mean square error (RMSE) under all bone density conditions. Simulation results using the CT-PHO model parameters in uniaxial compression experiments demonstrate more accurate prediction of the mechanical behavior of alveolar bone under compression. Further prediction and validation with different individual human alveolar bone samples yield accurate results, confirming the generality of the CT-PHO model. The study suggests that the CT-PHO model proposed in this paper can estimate the material properties of human alveolar bone and may eventually be used for bone reconstruction simulations to guide clinical treatment.
ObjectiveConstructing a prediction model for seizures after stroke, and exploring the risk factors that lead to seizures after stroke. MethodsA retrospective analysis was conducted on 1 741 patients with stroke admitted to People's Hospital of Zhongjiang from July 2020 to September 2022 who met the inclusion and exclusion criteria. These patients were followed up for one year after the occurrence of stroke to observe whether they experienced seizures. Patient data such as gender, age, diagnosis, National Institute of Health Stroke Scale (NIHSS) score, Activity of daily living (ADL) score, laboratory tests, and imaging examination data were recorded. Taking the occurrence of seizures as the outcome, an analysis was conducted on the above data. The Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen predictive variables, and multivariate Logistic regression analysis was performed. Subsequently, the data were randomly divided into a training set and a validation set in a 7:3 ratio. Construct prediction model, calculate the C-index, draw nomogram, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the model's performance and clinical application value. ResultsThrough LASSO regression, nine non-zero coefficient predictive variables were identified: NIHSS score, homocysteine (Hcy), aspartate aminotransferase (AST), platelet count, hyperuricemia, hyponatremia, frontal lobe lesions, temporal lobe lesions, and pons lesions. Multivariate logistic regression analysis revealed that NIHSS score, Hcy, hyperuricemia, hyponatremia, and pons lesions were positively correlated with seizures after stroke, while AST and platelet count were negatively correlated with seizures after stroke. A nomogram for predicting seizures after stroke was established. The C-index of the training set and validation set were 0.854 [95%CI (0.841, 0.947)] and 0.838 [95%CI (0.800, 0.988)], respectively. The areas under the ROC curves were 0.842 [95%CI (0.777, 0.899)] and 0.829 [95%CI (0.694, 0.936)] respectively. Conclusion These nine variables can be used to predict seizures after stroke, and they provide new insights into its risk factors.
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.
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 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 explore the utilization of longitudinal data in constructing non-time-varying outcome prediction models and to compare the impact of different modeling approaches on prediction performance. MethodsClinical predictors were selected using univariate analysis and Lasso regression. Non-time-varying outcome prediction models were developed based on latent class trajectory analysis, the two-stage model, and logistic regression. Internal validation was performed using Bootstrapping resampling, and model performance was evaluated using ROC curves, PR curves, sensitivity, specificity and other relevant metrics. ResultsA total of 49 629 pregnant women were included in the study, with mean age of 31.42±4.13 years and pre-pregnancy BMI of 20.91±2.62kg/m². Fourteen predictors were incorporated into the final model. Prediction models utilizing longitudinal data demonstrated high accuracy, with AUROC values exceeding 0.90 and PR-AUC values greater than 0.47. The two-stage model based on late-pregnancy hemoglobin data showed the best performance, achieving AUROC of 0.93 (95%CI 0.92 to 0.94) and PR-AUC of 0.60 (95%CI 0.56 to 0.64). Internal validation confirmed robust model performance, and calibration curves indicated a good agreement between predicted and observed outcomes. ConclusionFor the longitudinal data, the two-stage model can well capture the dynamic change trajectory of the longitudinal data. For different clinical outcomes, the predictive value of repeated measurement data is different.