Objective To scoping review the risk prediction models for sarcopenia in China was conducted, and provide reference for scientific prevention and treatment of the disease and related research. Methods We systematically searched PubMed, Web of Science, Cochrane Library, Embase, China Knowledge Network, China Biomedical Literature Database, Wanfang Database, and Weipu Database for literature related to myasthenia gravis prediction models in China, with a time frame from the construction of the database to April 30, 2024 for the search. The risk of bias and applicability of the included literature were assessed, and information on the construction of myasthenia gravis risk prediction models, model predictors, model presentation form and performance were extracted. Results A total of 25 literatures were included, the prevalence of sarcopenia ranged from 12.16% to 54.17%, and the study population mainly included the elderly, the model construction methods were categorized into two types: logistic regression model and machine learning, and age, body mass index, and nutritional status were the three predictors that appeared most frequently. Conclusion Clinical caregivers should pay attention to the high-risk factors for the occurrence of sarcopenia, construct models with accurate predictive performance and high clinical utility with the help of visual model presentation, and design prospective, multicenter internal and external validation methods to continuously improve and optimize the models to achieve the best predictive effect.
Objective To investigate the risk factors for Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections, and construct a clinical model for predicting the risk of CRKP infections. Methods A retrospective analysis was performed on Klebsiella pneumoniae infection patients hospitalized in the Third Hospital of Hebei Medical University from May 2020 to May 2021. The patients were divided into a CRKP group (117 cases) and a Carbapenem-sensitive Klebsiella pneumoniae (CSKP) group (191 cases). The predictors were screened by full subset regression using R software (version 4.3.1). The truncation values of continuous data were determined by Youden index. Nomogram and score table model for CRKP infections risk prediction was constructed based on binary logistic regression. The receiver operator characteristic (ROC) curve and area under curve (AUC) were used to evaluate the accuracy of models. Calibration curve and decision curve were used to evaluate the performance of models. Results308 patients with Klebsiella pneumoniae infections were included. A total of 8 predictors were selected by using full subset regression and truncation values were determined according to Youden index: intensive care unit (ICU) stay at time of infection>2 days, male, acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) score>15 points, hospitalization stay at time of infection>10 days, any history of Gram-negative bacteria infection in the last 6 months, heart disease, lung infection, antibiotic exposure history in the last 6 months. The AUC of CRKP prediction risk curve model was 0.811 (95%CI 0.761 - 0.860). When the optimal cut-off value of the constructed CRKP prediction risk rating table was 6 points, the AUC was 0.723 (95%CI 0.672 - 0.774). The Bootstrap method was used for internal repeated sampling for 1000 times for verification. The model calibration curve and Hosmer-Lemeshow test (P=0.618) showed that these models have good calibration degree. The decision curve showed that these models have good clinical effectiveness. Conclusion The prediction model of CRKP infections based on the above 8 risk factors can be used as a risk prediction tool for clinical identification of CRKP infections.
ObjectiveTo discuss the risk factors of acute respiratory distress syndrome (ARDS) in patients with severe pneumonia.MethodsData of 80 patients with severe pneumonia admitted in our ICU were analyzed retrospectively, and they were divided into two groups according to development of ARDS, which was defined according to the Berlin new definition. The age, gender, weight, Acute Physiology and Chronic Health EvaluationⅡscore, lactate, PSI score and LIPS score, etc. were collected. Statistical significance results were evaluated by multivariate logistic regression analysis after univariate analysis. Receiver operating characteristic (ROC) curve was plotted to analyze the predictive value of the parameter for ARDS after severe pneumonia.ResultsForty patients with severe pneumonia progressed to ARDS, there were 4 moderate cases and 36 severe cases according to diagnostic criteria. Univariate analysis showed that procalcitonin (t=4.08, P<0.001), PSI score (t=10.67, P<0.001), LIPS score (t=5.14, P<0.001), shock (χ2=11.11, P<0.001), albumin level (t=3.34, P=0.001) were related to ARDS. Multivariate logistic regression analysis showed that LIPS ［odds ratio (OR) 0.226, 95%CI=4.62-5.53, P=0.013］ and PSI (OR=0.854, 95%CI=132.2-145.5, P=0.014) were independent risk factors for ARDS. The predictive value of LIPS and PSI in ARDS occurrence was significant. The area under ROC curve (AUC) of LIPS was 0.901, the cut-off value was 7.2, when LIPS ≥7.2, the sensitivity and specificity were both 85.0%. AUC of PSI was 0.947, the cut-off value was 150.5, when PSI score ≥150.5, the sensitivity and specificity were 87.5% and 90.0% respectively.ConclusionsPSI and LIPS are independent risk factors of ARDS in patients with severe pneumonia, which may be references for guiding clinicians to make an early diagnosis and treatment plan.
Objective To systematically evaluate the prognostic prediction model for chronic heart failure patients in China, and provide reference for the construction, application, and promotion of related prognostic prediction models. Methods A comprehensive search was conducted on the studies related to prognostic prediction model for Chinese patients with chronic heart failure published in The Cochrane Library, PubMed, EMbase, Web of Science, CNKI, VIP, Wanfang, and the China Biological Medicine databases from inception to March 31, 2023. Two researchers strictly followed the inclusion and exclusion criteria to independently screen literature and extract data, and used the prediction model risk of bias assessment tool (PROBAST) to evaluate the quality of the models. Results A total of 25 studies were enrolled, including 123 prognostic prediction models for chronic heart failure patients. The area under the receiver operating characteristic curve (AUC) of the models ranged from 0.690 to 0.959. Twenty-two studies mostly used random splitting and Bootstrap for internal model validation, with an AUC range of 0.620-0.932. Seven studies conducted external validation of the model, with an AUC range of 0.720-0.874. The overall bias risk of all models was high, and the overall applicability was low. The main predictive factors included in the models were the N-terminal pro-brain natriuretic peptide, age, left ventricular ejection fraction, New York Heart Association heart function grading, and body mass index. Conclusion The quality of modeling methodology for predicting the prognosis of chronic heart failure patients in China is poor, and the predictive performance of different models varies greatly. For developed models, external validation and clinical application research should be vigorously carried out. For model development research, it is necessary to comprehensively consider various predictive factors related to disease prognosis before modeling. During modeling, large sample and prospective studies should be conducted strictly in accordance with the PROBAST standard, and the research results should be comprehensively reported using multivariate prediction model reporting guidelines to develop high-quality predictive models with strong scalability.
ObjectiveTo summarise the application research progress of clinical prediction models in postoperative complications of gastric cancer, in order to reduce the risk of complications after gastric cancer surgery. MethodThe literature on the study of postoperative complications of gastric cancer at home and abroad was read and reviewed. ResultsAt present, the main way of treating gastric cancer was still radical resection, and the occurrence of complications after surgical treatment seriously affected the recovery and survival quality of patients. With the deepening of research, the prediction models of postoperative complications in gastric cancer were constantly constructed, and these models provided strong evidence for the early judgement of postoperative complications in gastric cancer, and provided a scientific basis for the improvement of patients’ life quality. ConclusionClinical predictive models are expected to become risk screening tools for predicting the risk of postoperative complications of gastric cancer with clinical utility.
Objective To analyze the epidemic trend of prostate cancer in China from 1992 to 2021, and predict its epidemic trends from 2022 to 2032. Methods Based on the data of Chinese population and prostate cancer incidence and mortality from Global Burden of Disease Database, the Joinpoint log-linear model was used to analyze the trends of prostate cancer incidence and mortality, use the age-period-cohort model to analyze the effects of age, period and cohort on changes in incidence and mortality, and the gray prediction model was used to predict the trends of prostate cancer. Results From 1992 to 2021, the incidence and mortality of prostate cancer in China showed an upward trend, with AAPC of 5.652% (P<0.001) and 3.466% (P<0.001), and the AAPC of age-standardized incidence decreased to 1.990% (P<0.001), the age-standardized mortality showed a downward trend and was not statistically significant. The results of the age-period-cohort model showed that the net drift values of prostate cancer incidence and mortality were 3.03% and −1.06%, respectively, and the risk of incidence and mortality gradually increased with age and period. The results of the grey prediction model showed that the incidence and mortality of prostate cancer showed an upward trend from 2022 to 2032, and the incidence trend was more obvious. Conclusion The incidence and mortality of prostate cancer in China showed an increasing trend, with a heavy disease burden and severe forms of prevention and control, so it is necessary to do a good job in monitoring the incidence and mortality of prostate cancer, and strengthen the efficient screening, early diagnosis and treatment of prostate cancer.
The widespread application of low-dose computed tomography (LDCT) has significantly increased the detection of pulmonary small nodules, while accurate prediction of their growth patterns is crucial to avoid overdiagnosis or underdiagnosis. This article reviews recent research advances in predicting pulmonary nodule growth based on CT imaging, with a focus on summarizing key factors influencing nodule growth, such as baseline morphological parameters, dynamic indicators, and clinical characteristics, traditional prediction models (exponential and Gompertzian models), and the applications and limitations of radiomics-based and deep learning models. Although existing studies have achieved certain progress in predicting nodule growth, challenges such as small sample sizes and lack of external validation persist. Future research should prioritize the development of personalized and visualized prediction models integrated with larger-scale datasets to enhance predictive accuracy and clinical applicability.
In recent years, the potential value of clinical big data have been gradually realized, and disease prediction models have begun to become a hot spot in clinical research. Predictive models of different types of diseases play an increasingly important role in individual risk assessment. However, due to the lack of reporting specifications for studies on disease prediction model, the structure and quality of reports are mostly mixed. In 2015, BMJ published a paper entitled "Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement" stated that there should be a uniform study of predictive models for disease diagnosis and prognosis. This article interprets key contents of the statement to promote research and understanding of the report specification.
Aiming at the problem of scaffold degradation in bone tissue engineering, we studied the feasibility that controlls bone defect repair effect with the inhomogeneous structure of scaffold. The prediction model of bone defect repair which contains governing equations for bone formation and scaffold degradation was constructed on the basis of analyzing the process and main influence factors of bone repair in bone tissue engineering. The process of bone defect repair and bone structure after repairing can be predicted by combining the model with finite element method (FEM). Bone defect repair effects with homogenous and inhomogeneous scaffold were simulated respectively by using the above method. The simulation results illustrated that repair effect could be impacted by scaffold structure obviously and it can also be controlled via the inhomogeneous structure of scaffold with some feasibility.
Objective To evaluate the predictive effect of three machine learning methods, namely support vector machine (SVM), K-nearest neighbor (KNN) and decision tree, on the daily number of new patients with ischemic stroke in Chengdu. Methods The numbers of daily new ischemic stroke patients from January 1st, 2019 to March 28th, 2021 were extracted from the Third People’s Hospital of Chengdu. The weather and meteorological data and air quality data of Chengdu came from China Weather Network in the same period. Correlation analyses, multinominal logistic regression, and principal component analysis were used to explore the influencing factors for the level of daily number of new ischemic stroke patients in this hospital. Then, using R 4.1.2 software, the data were randomly divided in a ratio of 7∶3 (70% into train set and 30% into validation set), and were respectively used to train and certify the three machine learning methods, SVM, KNN and decision tree, and logistic regression model was used as the benchmark model. F1 score, the area under the receiver operating characteristic curve (AUC) and accuracy of each model were calculated. The data dividing, training and validation were repeated for three times, and the average F1 scores, AUCs and accuracies of the three times were used to compare the prediction effects of the four models. Results According to the accuracies from high to low, the prediction effects of the four models were ranked as SVM (88.9%), logistic regression model (87.5%), decision tree (85.9%), and KNN (85.1%); according to the F1 scores, the models were ranked as SVM (66.9%), KNN (62.7%), decision tree (59.1%), and logistic regression model (57.7%); according to the AUCs, the order from high to low was SVM (88.5%), logistic regression model (87.7%), KNN (84.7%), and decision tree (71.5%). Conclusion The prediction result of SVM is better than the traditional logistic regression model and the other two machine learning models.