ObjectiveTo observe the relationship between ventilator-associated pneumonia (VAP) and changes in bronchial mucosa and sputum in critically ill patients. A prediction model for SEH score was developed according to the abnormal degrees of airway sputum , mucosal edema and mucosal hyperemia , as well as to analyze the diagnostic value of the SEH scores for VAP during bronchoscopy. MethodsA collection of general data and initial bronchoscopy results was conducted for patients admitted to the department of intensive care unit at West China Hospital from March 1, 2024, to July 1, 2024. Patients were divided into infection group (n=138) and non-infection group (n=227) according to diagnostic criteria for VAP based on the date of their first bronchoscopy. T-tests were used to compare baseline data between groups, while analysis of variance was employed to assess differences in airway mucosal and sputum lesions. A binary logistic regression model was constructed using the SEH scores for predicting VAP risk, with receiver operating characteristic curve area under the curve (AUC) utilized to evaluate model accuracy. ResultsA total of 365 patients were included in this study, among which 138 cases (37.8%) were diagnosed with VAP. The AUC for using SEH scores in diagnosing VAP was found to be 0.81 [95% confidence interval (CI) 0.76-0.85], with an optimal cutoff value set at 6.5. The sensitivity and specificity of SEH scores for diagnosing VAP were determined as 79.7% (95% CI: 72.2%-85.6%) and 73.1% (95% CI:67.0%-78.5%). Patients with SEH scores over 6.5 exhibited a significantly higher rate of VAP infection (64.3% vs.14.4%, P<0.0001), elevated white blood cell count levels (WBC) [(13.3±7.5 vs.1.8±6.2), P=0.04], as well as increased hospital mortality rates (39.8 % vs.24.2 %, P=0.002). ConclusionsThe SEH scores has a certain efficacy in the diagnosis of VAP in patients with mechanical ventilation. Compared with the traditional VAP diagnostic criteria, SEH scores is easier to obtain in clinical practice, and has certain clinical application value.
ObjectiveTo establish a hypertension prediction model for middle-aged and elderly people in China and to use the basic public health service database for performance validation. MethodsThe literature related to hypertension was retrieved from the internet. Using meta-analysis to assess the effect value of influencing factors. Statistically significant factors, which were also combined in the database, were extracted as the predictors of the models. The predictors’ effect values were logarithmarithm-transformed as the parameters of the Logit function model and the risk score model. Participants who were never diagnosed with hypertension at the physical examination of health service project of Hongguang Town Health Center in Pidu District of Chengdu from January 1, 2017, to January 1, 2022, were considered as the external validation group. ResultsA total of 15 original studies were involved in the meta-analysis and 11 statistically significant influencing factors for hypertension were identified, including age, female, systolic blood pressure, diastolic blood pressure, BMI, central obesity, triglyceride, smoking, drinking, history of diabetes and family history of hypertension. Of 4997 qualified participants, 684 individuals were identified with hypertension during the five-years follow-up. External validation indicated an AUC of 0.571 for the Logit function model and an AUC of 0.657 for the risk score model. ConclusionIn this study, we developed two different prediction models based on the results of meta-analysis. National basic public health service database is used to verify the models. The risk score model has a better prediction performance, which may help quickly stratify the risk class of the community crowd and strengthen the primary-level assistance system.
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.
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.
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.
Objective To systematically review the performance of postpartum hemorrhage risk prediction models, and to provide references for the future construction and application of effective prediction models. Methods The CNKI, WanFang Data, VIP, CBM, PubMed, EMbase, The Cochrane Library, Web of Science, and CINAHL databases were electronically searched to identify studies reporting risk prediction models for postpartum hemorrhage from database inception to March 20th, 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias and applicability of the included studies. Results A total of 39 studies containing 58 postpartum hemorrhage risk prediction models were enrolled. The area under the curve of 49 models was over 0.7. All but one of the models had a high risk of bias. Conclusion Models for predicting postpartum hemorrhage risk have good predictive performance. Given the lack of internal and external validation, and the differences in study subjects and outcome indicators, the clinical value of the models needs to be further verified. Prospective cohort studies should be conducted using uniform predictor assessment methods and outcome indicators to develop effective prediction models that can be applied to a wider range of populations.
With the rapid development of artificial intelligence (AI) and machine learning technologies, the development of AI-based prediction models has become increasingly prevalent in the medical field. However, the PROBAST tool, which is used to evaluate prediction models, has shown growing limitations when assessing models built on AI technologies. Therefore, Moons and colleagues updated and expanded PROBAST to develop the PROBAST+AI tool. This tool is suitable for evaluating prediction model studies based on both artificial intelligence methods and regression methods. It covers four domains: participants and data sources, predictors, outcomes, and analysis, allowing for systematic assessment of quality in model development, risk of bias in model evaluation, and applicability. This article interprets the content and evaluation process of the PROBAST+AI tool, aiming to provide references and guidance for domestic researchers using this tool.
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 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 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.