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.
ObjectivesTo explore the construction method of prediction model of absolute risk for breast cancer and provide personalized breast cancer management strategies based on the results.MethodsA case-control design was conducted with 2 747 individuals diagnosed as primary breast cancer by pathology in West China Hospital of Sichuan University from 2000 to 2017 and 6 307 healthy controls from Breast Cancer Screening Cohort in Sichuan Women and Children Center and Chengdu Shuangliu District Maternal and Child Health Hospital. Standardized questionnaires and information management systems in hospital were used to collect information. Decision trees, logistic regression, the formula in Gail model and registration data in China were used to estimate the probability of 5-year risk of breast cancer. Eventually a ROC (receiver operating characteristics) curve was drawn to identify optimal cut-off value, and the power was evaluated.ResultsThe decision tree exported 4 variables, which were urban or rural sources, number of live birth, age and age at menarche. The median 5-year risk and interquartile range of the controls was 0.027% and 0.137%, while the median 5-year risk and interquartile range of the cases was 0.219% and 0.256%. The ROC curve showed the cut-off value was 0.100%. Through verification, the sensitivity was 0.79, the specificity was 0.73, the accuracy was 0.75, and the AUC (area under the curve) was 0.79.ConclusionsThe methods used in our study based on 9 054 female individuals in Sichuan province could be used to predict the 5-year risk for breast cancer. Predictor variables include urban or rural sources, number of live birth, age, and age at menarche. If the 5-year risk is more than 0.100%, the person will be judged as a high risk individual.
ObjectiveTo construct and verify the nomogram prediction model of pregnant women's fear of childbirth. MethodsA convenient sampling method was used to select 675 pregnant women in tertiary hospital in Tangshan City, Hebei Province from July to September 2022 as the modeling group, and 290 pregnant women in secondary hospital in Tangshan City from October to December 2022 as the verification group. The risk factors were determined by logistic regression analysis, and the nomogram was drawn by R 4.1.2 software. ResultsSix predictors were entered into the model: prenatal education, education level, depression, pregnancy complications, anxiety and preference for delivery mode. The areas under the ROC curves of the modeling group and the verification group were 0.834 and 0.806, respectively. The optimal critical values were 0.113 and 0.200, respectively, with sensitivities of 67.2% and 77.1%, the specificities were 87.3% and 74.0%, and the Jordan indices were 0.545 and 0.511, respectively. The calibration charts of the modeling group and the verification group showed that the coincidence degree between the actual curve and the ideal curve was good. The results of Hosmer-Lemeshow goodness of fit test were χ2=6.541 (P=0.685) and χ2=5.797 (P=0.760), and Brier scores were 0.096 and 0.117, respectively. DCA in modeling group and verification group showed that when the threshold probability of fear of childbirth were 0.00 to 0.70 and 0.00 to 0.70, it had clinical practical value. ConclusionThe nomogram model has good discrimination, calibration and clinical applicability, which can effectively predict the risk of pregnant women's fear of childbirth and provide references for early clinical identification of high-risk pregnant women and targeted intervention.
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.
Clinical prediction models typically utilize a combination of multiple variables to predict individual health outcomes. However, multiple prediction models for the same outcome often exist, making it challenging to determine the suitable model for guiding clinical practice. In recent years, an increasing number of studies have evaluated and summarized prediction models using the systematic review/meta-analysis method. However, they often report poorly on critical information. To enhance the reporting quality of systematic reviews/meta-analyses of prediction models, foreign scholars published the TRIPOD-SRMA reporting guideline in BMJ in March 2023. As the number of such systematic reviews/meta-analyses is increasing rapidly domestically, this paper interprets the reporting guideline with a published example. This study aims to assist domestic scholars in better understanding and applying this reporting guideline, ultimately improving the overall quality of relevant research.
ObjectiveTo systematically evaluate postpartum depression risk prediction models in order to provide references for the construction, application and optimization of related prediction models. MethodsThe CNKI, VIP, WanFang Data, PubMed, Web of Science and EMbase were electronically searched to collect studies on predictive model for the risk of postpartum from January 2013 to April 2023. Two reviewers independently screened the literature, extracted data, and assessed the quality of the included studies based on PROBAST tool. ResultsA total of 10 studies, each study with 1 optimal model were evaluated. Common predictors included prenatal depression, age, smoking history, thyroid hormones and other factors. The area under the curve of the model was greater than 0.7, and the overall applicability was general. Overall high risk of bias and average applicability, mainly due to insufficient number of events in the analysis domain for the response variable, improper handling of missing data, screening of predictors based on univariate analysis, lack of model performance assessment, and consideration of model overfitting. ConclusionThe model is still in the development stage. The included model has good predictive performance and can help early identify people with high incidence of postpartum depression. However, the overall applicability of the model needs to be strengthened, a large sample, multi-center prospective clinical study should be carried out to construct the optimal risk prediction model of PPD, in order to identify and prevent PPD as soon as possible.
ObjectiveTo systematically review the research status of risk prediction models for gestational diabetes mellitus (GDM). MethodsThe CNKI, WanFang Data, VIP, CBM, PubMed, JBI EBP, Ovid MEDLINE, Embase, Web of Science and Cochrane Library databases were electronically searched to collect relevant literature on risk prediction models for GDM from inception to October 2022. Two researchers independently screened the literature, extracted data, and assessed the risk of bias of the included studies, and then qualitative description was performed. ResultsA total of 19 studies were included, involving 19 risk prediction models. The evaluation results showed that, in terms of the risk of bias, 18 studies were high risk, and 1 study was unclear. In terms of applicability, 14 studies were high risk, 2 studies were low risk, and 3 studies were unclear. The area under the receiver operating characteristic curve of the included models was 0.69 to 0.88. The most common predictors included age, weight, pre-pregnancy BMI, history of diabetes, family history of diabetes, and race. ConclusionThe overall performance of the risk prediction model for gestational diabetes mellitus is good, but the risk of bias of the model is high, and the clinical applicability of the model needs to be further verified.
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 systematically review the methodological quality of research on clinical prediction models of traditional Chinese medicine. Methods The PubMed, Embase, Web of Science, CNKI, WanFang Data, VIP and SinoMed databases were electronically searched to collect literature related to the research on clinical prediction models of traditional Chinese medicine from inception to March 31, 2023. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies based on prediction model risk of bias assessment tool (PROBAST). Results A total of 113 studies on clinical prediction models of traditional Chinese medicine (79 diagnostic model studies and 34 prognostic model studies) were included. Among them, 111 (98.2%) studies were rated at high risk of bias, while 1 (0.9%) study was rated at low risk of bias and risk of bias of 1 (0.9%) study was unclear. The analysis domain was rated with the highest proportion of high risk of bias, followed by the participants domain. Due to the widespread lack of reporting of specific study information, risk of bias of a large number of studies was unclear in both predictors and outcome domain. Conclusion Most existing researches on clinical prediction models of traditional Chinese medicine show poor methodological quality and are at high risk of bias. Factors contributing to risk of bias include non-prospective data source, outcome definitions that include predictors, inadequate modeling sample size, inappropriate feature selection, inaccurate performance evaluation, and incorrect internal validation methods. Comprehensive methodological improvements on design, conduct, evaluation, and validation of modeling, as well as reporting of all key information of the models are urgently needed for future modeling studies, aiming to facilitate their translational application in medical practice.
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.
