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.
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.
Objective To conduct a systematic review of the construction methods, predictive factors, and model quality of risk prediction models for postoperative chronic pain in knee replacement surgery patients, providing evidence for the development of nursing-sensitive dynamic prediction models. Methods A systematic review of risk prediction models for postoperative chronic pain in knee replacement surgery patients was conducted by searching PubMed, Web of Science, Cochrane Library, CINAHL, SinoMed, CNKI, Wanfang Database, and VIP Database. The search period was from the establishment of the databases to February 28, 2025. Two researchers independently screened the literature, extracted data, and assessed the risk of bias and applicability of the included studies. Results A total of 10 studies involving 10 predictive models were included in this review. Among these, three models underwent internal validation, and one model underwent external validation. Commonly reported predictive factors included postoperative 24-hour Numerical Rating Scale scores, postoperative knee function scores, sleep disorders, preoperative depression, postoperative functional exercises, postoperative complications, preoperative pain, and postoperative C-reactive protein levels. All 10 studies had a high risk of bias and were generally applicable. Conclusions Existing risk prediction models generally rely on static indicators and lack dynamic monitoring of postoperative rehabilitation behaviors and psychosocial factors, with severe deficiencies in model validation. Future research should focus on developing nursing-led multidimensional dynamic models that incorporate functional exercise adherence data collected via wearable devices, standardize external model validation, and enhance clinical translation value.
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 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.
ObjectiveTo screen compounds or drugs can affect the hypoxia induced-gene expression of retinal vascular endothelial cell based on gene expression microarrays and connectivity map (CMAP) technology. MethodsTotally 326 up-regulated and down-regulated genes of hypoxic human embryonic retinal microvascular endothelial cells minduced by cobalt chloride in the previous study were converted into query signature format documents. Gene profile of the disease characteristics was then compared with that of control in CMAP website database, positive and negative compounds related to retinopathy of prematurity (ROP) were finally screened out. Results44 and 18 compounds or drugs have positive and negative relationship with ROP respectively by searching CMAP database with differentially expressed genes. Ciclopirox, cobalt chloride, gossypol and withaferin A have positive relationship with ROP. Cyclic adenosine monophosphate, harmalol, naringin and probenecid have a negative effect on ROP. ConclusionsCiclopirox, cobalt chloride, gossypol and withaferin A have a positive effect on ROP. However, cyclic adenosine monophosphate, harmalol, naringin and probenecid have a negative effect.
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.
Although the recent studies have concerned the pathogenesis and therapeutic strategies of acute kidney injury (AKI), the mortality of AKI is still terribly high, and it is still one of the most important death factors in the intensive care unit. There is no doubt that early verdict of AKI, is good for a more aggressive treatment and can promise an improved prognosis for AKI patients. Serum creatinine level, serving as the gold standard for diagnosis of kidney injury, cannot meet current clinical work in its sensitivity and specificity of diagnosis of early AKI. Over the past decades, researchers worked to find and verify novel AKI biomarkers, including neutrophil gelatinase associated lipocalin, interleukin-18, kidney injury molecule-1 and cystatin-C, which were proved to be the potential reliable predictor of AKI development and prognosis, and were of great importance to the early diagnosis and clinical monitoring of AKI. This paper reviews the main studies on these novel prognostic predictors of AKI over the decades and evaluates their roles and limitations in early diagnosis and clinical prognosis prediction.
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 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.