ObjectivesTo systematically review the delirium risk prediction models in intensive care unit (ICU) patients.MethodsThe Cochrane Library, PubMed, Web of Science, Ovid, VIP, WanFang Date and CNKI databases were electronically searched to collect studies on delirium risk prediction models in intensive care unit patients from inception to December, 2018. Two reviewers independently screened literature, extracted data, evaluated the included studies according to the CHARMS checklist, and then systematic review was performed to evaluate the risk prediction models.ResultsA total of 9 studies were included, of which 7 were prospective studies. Six models were internally validated. All studies reported the area under receiver operating characteristic curve (AUROC) over 0.7 (0.739-0.926). The reduction of cognitive reserve and increased blood urea nitrogen were the most commonly reported predisposing and precipitating factors of delirium among all prediction models. Methodologically, the absence or unreported of the blind method, to a certain extent, partially increase the risk of bias.ConclusionsNine prediction models all have great power in early identifying and screening patients who are at high risk of developing ICU delirium. On the basis of judiciously selecting a practical prediction model for clinical practice or carrying out a large sample-size prospective cohort study to construct the localized prediction model, stratified prevention strategies should be formulated and implemented according to the risk stratification results to reduce the incidence of ICU delirium and accelerate the rational allocation of medical resources for delirium prevention.
According to different experimental methods of induced diabetic retinopathy (DR) and different characteristics of the observed retinopathy, DR animal models can be divided into drug or dietary-induced models, oxygen-induced retinopathy (OIR) models, spontaneous inheritance models, and transgenic models. At present, induced model is one of the most commonly used animal model for DR study, which has the advantages of short modeling cycle, low cost, simple experimental operation and good repeatability. However, the drugs have certain side effects on various organs of animals and the risk of animal death is higher. OIR model has good repeatability, good stability and relatively low cost. However, due to the lack of metabolic changes of hyperglycemia in OIR mice, this model cannot accurately reflect the effects of metabolism on retina under hyperglycemia. The pathological changes of the spontaneous model are relatively stable, however, the application of this model is limited because the genetic homogeneity of diabetes differs from that of human and the cost is high. Transgenic model has definite etiology, however, its application is limited owing to the high cost and the high requirements of technology, operation and equipment. Therefore, researchers should comprehensively consider characteristics and limitations of different models while choosing suitable DR model based on research objectives, observation indicators, experimental conditions, and funds. In addition, animal models that can more accurately simulate DR need to be developed to provide more effective tools for studying the mechanism of DR and developing feasible prevention and treatment approaches.
Retinal ischemia is the common pathologic process in many ophthalmic diseases, including ischemic optic neuropathy, retinal artery and vein occlusion, carotid artery obstructive disease, retinopathy of prematurity, chronic diabetic retinopathy and glaucoma. It is very important to establish animal models to investigate pathology mechanism and explore the treatment of retinal ischemia disease. At present, the commonly used methods for establishing retinal ischemia animal models include increasing intraocular pressure, ligating of blood vessels, suture method, photochemical method, and drug injection etc. This article summarizes the methods to establish the animal models and analyzes the indication for each animal model. It is expected that the method of establishing a retinal ischemic animal model will be helpful to the experimental design of follow-up retinal ischemia studies.
This study introduced the construction of individualized risk assessment model based on Bayesian networks, comparing with traditional regression-based logistic models using practical examples. It evaluates the model's performance and demonstrates its implementation in the R software, serving as a valuable reference for researchers seeking to understand and utilize Bayesian network models.
ObjectiveThis study aims to conduct a bibliometric analysis of the dataset obtained from a systematic review of Model-Based Meta-Analysis (MBMA) studies to uncover research trends in MBMA. MethodsWe conducted a systematic search across databases including Embase, MEDLINE, Cochrane Library, Web of Science, CNKI, VIP, WanFang Data, and Sinomed. Relevant literature pertaining to MBMA was screened and included. Qualitative and quantitative analyses were performed to assess author contributions, temporal trends, national distribution, and disease prevalence. ResultsA total of 129 articles were included in the analysis, most published in being China (n=48), the United States (n=47), and the United Kingdom (n=7). The time span covered by these articles ranged from 2005 to 2023. A total of 531 researchers contributed to the included studies, with first authors from 15 different countries. The compilation encompassed 402 keywords, with the three most frequently used being “Meta-analysis” (n=28), “Model-based meta-analysis” (n=27), and “Pharmacokinetics” (n=14). The study covered 16 distinct disease categories, with the top three neoplasms (n=16), neurological disorders (n=14), and endocrine and metabolic diseases (n=13). ConclusionBibliometric analysis showed that the number of MBMA studies has increased significantly over the past three years, using a variety of key diseases as carriers. However, this new type of quantitative research has not yet attracted sufficient attention, and the research power is still concentrated in China and the United States; Moreover, a core group of authors has not yet been formed, it is necessary for scholars from various countries to strengthen multidisciplinary cooperation and communication to promote the production and translation of high-quality evidence.
ObjectiveTo verify the influence of different variable selection methods on the performance of clinical prediction models. MethodsThree sample sets were extracted from the MIMIC database (acute myocardial infarction group, sepsis group, and cerebral hemorrhage group) using the direct entry of COX regression, step by step forward, step by step backward, LASSO, and ridge regression, based on random forest. These existing six methods of variable importance algorithm, and the optimal variable set of different selected methods were used to construct the model. Through the C index, the area under the ROC curve (AUC value) and the calibration curve, and the results within and between groups were compared. ResultsThe variables and numbers selected by the six variable selection methods were different, however, whether it was within or between groups did not reflect which method had the advantage of significantly improving the performance of the model. ConclusionsPrior to using the variable selection method to establish a clinical prediction model, we should first clarify the research purpose and determine the type of data. Combining medical knowledge to select a method that can meet the data type and simultaneously achieve the research purpose.
ObjectiveTo investigate the expression of interleukin-18（IL-18）and signal transducers and activators of transcription 5（STAT5）in retina of 4-24-week-old diabetic rats, and explore the potential molecular mechanisms involved in diabetic retinopathy (DR).MethodsRetinal gene expression profile of healthy and 8-week-old diabetic rats was established with restriction fragment differential displaypolymerase chained reaction (RFDD-PCR), and the differences was analyzed by bioinformatics. IL-18 and STAT5 were filtrated as the candidate genes of DR. The expression of IL-18 and STAT5 in retina of diabetic rats with the age of 4, 8, and 24 weeks was observed by semi-quantitative reverse transcriptase-polymerase chain reaction（RT-PCR）.ResultsThe result of RFDD-PCR showed:expression of IL-18 was higher in healthy retina than that in diabetic one; expression of STAT5 was not found in healthy rats but in diabetic ones. The result of RT-PCR showed：compared with the normal, high expression of IL-18 was found in 4-week diabetic retina, reduced in 8-week one, and decreased to the lowest in 24-week one. The expression of STAT5 was not observed in healthy or 4week diabetic retina, but occurred in 8-week one, and increased in 24-week one. ConclusionThe expression of IL-18 and the activation of STAT5 may relate to the occurrance of DR. The expression of IL-18 doesn′t depend on the activation of STAT5. (Chin J Ocul Fundus Dis, 2005,21:258-260)
Some complex surgical procedures allow for day surgery benefited from the advancement of the concept of enhanced recovery after surgery and minimally invasive surgical techniques. Shanghai Chest Hospital has set up thoracic day surgery ward incorporating some lung tumor surgeries into day surgery. Through process innovation, model innovation and management innovation, relying on the full-process closed-loop day surgery management system, the day surgery ward runs efficiently. It can help effectively alleviate the difficulty of admission and operation, and improve hospital operation efficiency as well as reduce the economic burden of disease. At the same time, we hope to explore an innovative development path for the possibility of day surgery in complex thoracic surgery, and create a new mode of day surgery that can be replicated and promoted.
Pneumonectomy is known as an effective treatment of lung cancer, lung tuberculosis, and damaged lung. But the incidences of complications and mortality are significantly higher in patients undergoing pneumonectomy than those undergoing lobectomy. The complication rate within 30 days after pneumonectomy is 11%-49% and the mortality is 3%-25%. Mortality of right pneumonectomy is triple that of left pneumonectomy. Postpneumonectomy complications include cardiopulmonary failure, bronchopleural fistula and postpneumonectomy syndrome. Besides the symptomatic treatment, which includes flushing drainage, plugging and operation, observation and prompt diagnosis are necessary for prevention. This review is focused on the prevention and treatment of complications after pneumonectomy.
The accuracy of the clinical prediction model determines its extrapolation and application value. When the prediction model is applied to a new setting, the differences between the new population and the initially modeled population in terms of study time, population characteristics, region, and other factors could lead to a reduction in its predictive performance. Calibrating or updating the prediction model with appropriate statistical methods is important to improve the accuracy of the prediction model in new populations. The model updating methods mainly include regression coefficients updating, meta-model updating and dynamic model updating. However, due to the limitations of meta-model updating and dynamic model updating in practical applications, the regression coefficient updating method is still the most common method in model updating. This paper introducd several types of model updating methods, the regression coefficients updating methods for two common clinical prediction models based on Logistic regression and Cox regression, and provide corresponding R codes for reference of researchers.