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.
ObjectiveThis study aims to analyze the trends in Parkinson’s disease incidence rates among the elderly population in China from 1990 to 2021 and to forecast incidence growth over the next 20 years, providing. MethodsJoinpoint regression and age-period-cohort models were employed to analyze temporal trends in Parkinson’s disease incidence, and the Nordpred model was used to predict case numbers and incidence rates among the elderly in China from 2022 to 2044. ResultsFindings indicated a significant increase in Parkinson’s disease incidence among China’s elderly population from 1990 to 2021, with crude and age-standardized incidence rates rising from 95.37 per 100 000 and 111.05 per 100 000 to 170.52 per 100 000 and 183.91 per 100 000, respectively. Predictions suggested that by 2044, the number of cases will rise to approximately 878 264, with the age-standardized incidence rate reaching 223.4 per 100 000, and men showing significantly higher incidence rates than women. The rapid increase in both cases and incidence rates indicated that Parkinson’s disease will continue to impose a heavy disease burden on China’s elderly population. ConclusionThe burden of Parkinson’s disease in China’s elderly population has grown significantly and is expected to worsen. To address the rising incidence rates effectively, it is recommended to enhance early screening and health education for high-risk groups, improve diagnostic and treatment protocols, and prioritize resource allocation to Parkinson’s disease prevention and care services to reduce future public health burdens.
ObjectiveTo review individual treatment effect (ITE) models developed from randomized controlled trials, with the aim of systematically summarizing the current state of model development and assessing the risk of bias. MethodsPubMed and Embase databases were searched for studies published between 1990 and 14 June 2024. Data were extracted using the CHARMS inventory, and the PROBAST risk of bias tool was used to assess model quality. ResultsA total of 11 publications were included, containing 19 ITE models. The ITE modelling methods were regression models with interaction terms (n=8, 42.1%), dual-range models (n=5, 26.3%) and machine learning (n=6, 31.6%). The ITE models had a reporting rate of 78.9%, 73.2% and 10.5% for differentiation, calibration and clinical validity, respectively. Fourteen models were assessed as having a high risk of bias (73.7%), particularly in the area of statistical analysis, due to inappropriate handling of missing data (n=15, 78.9%), inappropriate consideration of model fit issues (n=5, 26.3%), etc. ConclusionCommon approaches to ITE model development include constructing interaction terms, dual procedure theory, and machine learning, but suffer from a low number of model developments, more complex modeling methods, and non-standardized reporting. In the future, emphasis should be placed on further exploration of ITE models, promoting diversified modeling methods and standardized reporting to improve the clinical promotion and practical application value of the models.
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.
ObjectiveTo explore the risk factors for accompanying depression in patients with community type Ⅱ diabetes and to construct their risk prediction model. MethodsA total of 269 patients with type Ⅱ diabetes accompanied with depression and 217 patients with simple type Ⅱ diabetes from three community health service centers in two streets of Pingshan District, Shenzhen from October 2021 to April 2022 were included. The risk factors were analyzed and screened out, and a logistic regression risk prediction model was constructed. The goodness of fit and prediction ability of the model were tested by the Hosmer-Lemeshow test and the receiver operating characteristic (ROC) curve. Finally, the model was verified. ResultsLogistic regression analysis showed that smoking, diabetes complications, physical function, psychological dimension, medical coping for face, and medical coping for avoidance were independent risk factors for depressive disorder in patients with type Ⅱ diabetes. Modeling group Hosmer-Lemeshow test P=0.345, the area under the ROC curve was 0.987, sensitivity was 95.2% and specificity was 98.6%. The area under the ROC curve was 0.945, sensitivity was 89.8%, specificity was 84.8%, and accuracy was 86.8%, showing the model predictive value. ConclusionThe risk prediction model of type Ⅱ diabetes patients with depressive disorder constructed in this study has good predictive and discriminating ability.
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.
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 evaluate the risk prediction model of knee osteoarthritis (KOA). MethodsThe CNKI, WanFang Data, VIP, PubMed, Embase, Web of Science and Cochrane Library databases were electronically searched to collect relevant studies on KOA’s risk prediction model from inception to April, 2024. After study screening and data extraction by two independent researchers, the PROBAST bias risk assessment tool was used to evaluate the bias risk and applicability of the risk prediction model. ResultsA total of 12 studies involving 21 risk prediction models for KOA were included. The number of predictors ranged from 3 to 12, and the most common predictors were age, sex, and BMI. The range of modeling AUC included in the model was 0.554-0.948, and the range of testing AUC was 0.6-0.94. The overall predictive performance of the models was mediocre and the risk of overall bias was high, and more than half of the models were not externally verified. ConclusionAt present, the overall quality and applicability of the KOA morbidity risk prediction model still have great room for improvement. Future modeling should follow the CHARMS and PROBAST to reduce the risk of bias, explore the combination of multiple modeling methods, and strengthen the external verification of the model.
ObjectiveTo explore the long-term seizure outcome and prognostic factors of patients with frontal lobe epilepsy after surgery, so as to guide the evaluation of treatment and provide clinical reference.Methods This study retrospectively analyzed the clinical data of patients with frontal lobe epilepsy undergoing surgical treatment by multimodal epilepsy surgical evaluation system in the functional neurosurgery of the Second Hospital of Lanzhou University from January 2016 to February 2020, there were 17 males and 13 females, the age of onset of epileptic seizures was (16.30±10.65) years, the age at the time of surgical treatment was (23.98±11.04) years, and the duration of seizures was (7.68±6.37) years. The multimodal epilepsy surgical evaluation system includes phase I non-invasive evaluation and phase II invasive evaluation. The collected research variables were analyzed by descriptive statistics and multivariate logistic regression analysis to evaluate the importance of each research variable to the prognosis of epilepsy surgery, and to determine the potential prognostic factors, so as to explore the epilepsy control situation of patients with frontal lobe epilepsy after surgery and the influence of each variable that may affect the prognosis of epilepsy on the prognosis.ResultsThe analysis of the clinical data of 30 patients with frontal lobe epilepsy showed that the good prognosis rate after FLE surgery (Engel I) was 70%, and the average follow-up was (29.9±14.1) months. The results of multivariate logistic regression analysis showed that the duration of epilepsy, the frequency of seizures, the presence or absence of stereotactic EEG (SEEG) monitoring, whether the lesion was completely removed, and whether acute postoperative seizures occurred were independent predictors of prognosis (P<0.05).ConclusionThis study found that the long duration of epilepsy, frequent seizures, and postoperative acute seizures are significantly related to poor prognosis. The application of SEEG and complete resection of epileptic lesions can significantly improve the prognosis of FLE surgery.
ObjectiveTo investigate the value of plasma microRNA-216 (miR-216) in patients with acute pancreatitis as a clinical biomarker to early identify severe acute pancreatitis (SAP).MethodsPatients with acute pancreatitis who admitted to the hospital within 48 hours after the onset of disease between September and November 2014 were enrolled in this study. Plasam and clinical data of all the patients were collected. MiR-216 in the plasma was detected using quantitative real time-polymerase chain reaction.ResultsA total of 25 patients were enrolled. The Ct value of plasma miR-216 in SAP patients (32.40±1.43) was significantly upregulated than mild acute pancreatitis (MAP) (35.85±1.91, P<0.05) and moderately severe acute pancreatitis (MSAP) patients (35.90±2.44,P<0.05), respectively. The area under receiver operating characteristic curve for plasmamiR-216 in predicting SAP was 0.792 (P<0.05), which did not differ much from other conventional parameters such as C-reactive protein, urinary nitrogen, and cytokines (P>0.05).ConclusionPlasma miR-216 is significantly upregulated in SAP patients compared with MAP and MSAP, but it shows no inferior efficiency than the investigated conventional predictors in predicting SAP.