ObjectiveTo analyze the burden of digestive diseases attributed to smoking in China from 1990 to 2019 and forecast its change in the next 10 years. MethodsThe Global Burden of Disease database 2019 was used to analyze the burden of digestive diseases attributed to smoking in China from 1990 to 2019. Joinpoint regression model was used to analyze the time variation trend. A time series model was used to predict the burden of digestive diseases attributable to smoking over the next 10 years. ResultsIn 2019, there were 12 900 deaths from digestive diseases attributed to smoking in China, with a DALY of 398 600 years, a crude death rate of 0.91/100 000 and a crude DALY rate of 28.02/100 000. The attributed standardized mortality rate was 0.69 per 100 000, and the standardized DALY rate was 19.79 per 100 000, which was higher than the global level. In 2019, the standardized mortality rate and DALY rate of males were higher than those of females (1.48/ 100 000 vs. 0.11/ 100 000, 38.42/ 100 000 vs. 293/100 000), and the standardized rates of males and females showed a downward trend over time. In 2019, both mortality and DALY rates from digestive diseases attributed to smoking increased with age. ARIMA predicts that over the next 10 years, the burden of disease in the digestive system caused by smoking will decrease significantly. ConclusionFrom 1990 to 2019, the burden of digestive diseases attributed to smoking showed a decreasing trend in China, and the problem of disease burden is more serious in men and the elderly population. A series of effective measures should be taken to reduce the smoking rate in key groups. The burden of digestive diseases caused by smoking will be significantly reduced in the next 10 years.
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.
Objective To explore the predicted precision of discharged patients number using curve estimation combined with trend-season model. Methods Curve estimation and trend-season model were both applied, and the quarterly number of discharged patients of 363 hospital from 2009 to 2015 was collected and analyzed in order to predict discharged patients in 2016. Relative error between predicted value and actual number was also calculated. Results An optimal quadratic regression equation Yt=3 006.050 1+202.350 8×t–3.544 4×t2 was established (Coefficient of determination R2=0.927, P<0.001), and a total of 23 462 discharged patients were predicted based on this equation combined with trend-season model, with a relative error of 1.79% compared to the actual number. Conclusion The curve estimation combined with trend-season model is a convenient and visual tool for predicting analysis. It has a high predicted accuracy in predicting the number of hospital discharged patients or outpatients, which can provide a reference basis for hospital operation and management.
ObjectiveTo construct a prediction model of diabetics distal symmetric polyneuropathy (DSPN) based on neural network algorithm and the characteristic data of traditional Chinese medicine and Western medicine. MethodsFrom the inpatients with diabetes in the First Affiliated Hospital of Anhui University of Chinese Medicine from 2017 to 2022, 4 071 cases with complete data were selected. The early warning model of DSPN was established by using neural network, and 49 indicators including general epidemiological data, laboratory examination, signs and symptoms of traditional Chinese medicine were included to analyze the potential risk factors of DSPN, and the weight values of variable features were sorted. Validation was performed using ten-fold crossover, and the model was measured by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC value. ResultsThe mean duration of diabetes in the DSPN group was about 4 years longer than that in the non-DSPN group (P<0.001). Compared with non-DSPN patients, DSPN patients had a significantly higher proportion of Chinese medicine symptoms and signs such as numbness of limb, limb pain, dizziness and palpitations, fatigue, thirst with desire to drink, dry mouth and throat, blurred vision, frequent urination, slow reaction, dull complexion, purple tongue, thready pulse and hesitant pulse (P<0.001). In this study, the DSPN neural network prediction model was established by integrating traditional Chinese and Western medicine feature data. The AUC of the model was 0.945 3, the accuracy was 87.68%, the sensitivity was 73.9%, the specificity was 92.7%, the positive predictive value was 78.7%, and the negative predictive value was 90.72%. ConclusionThe fusion of Chinese and Western medicine characteristic data has great clinical value for early diagnosis, and the established model has high accuracy and diagnostic efficacy, which can provide practical tools for DSPN screening and diagnosis in diabetic population.
ObjectiveTo explore the independent factors related to clinical severe events in community acquired pneumonia patients and to find out a simple, effective and more accurate prediction method.MethodsConsecutive patients admitted to our hospital from August 2018 to July 2019 were enrolled in this retrospective study. The endpoint was the occurrence of severe events defined as a condition as follows intensive care unit admission, the need for mechanical ventilation or vasoactive drugs, or 30-day mortality during hospitalization. The patients were divided into severe event group and non-severe event group, and general clinical data were compared between two groups. Multivariate logistic regression analysis was performed to identify the independent predictors of adverse outcomes. Receiver operating characteristic (ROC) curve was constructed to calculate and compare the area under curve (AUC) of different prediction methods.ResultsA total of 410 patients were enrolled, 96 (23.4%) of whom experienced clinical severe events. Age (OR: 1.035, 95%CI: 1.012 - 1.059, P=0.003), high-density lipoprotein (OR: 0.266, 95%CI: 0.088 - 0.802, P=0.019) and lactate dehydrogenase (OR: 1.006, 95%CI: 1.004 - 1.059, P<0.001) levels on admission were independent factors associated with clinical severe events in CAP patients. The AUCs in the prediction of clinical severe events were 0.744 (95%CI: 0.699 - 0.785, P=0.028) and 0.814 (95%CI: 0.772 - 0.850, P=0.025) for CURB65 and PSI respectively. CURB65-LH, combining CURB65, HDL and LDH simultaneously, had the largest AUC of 0.843 (95%CI: 0.804 - 0.876, P=0.022) among these prediction methods and its sensitivity (69.8%) and specificity (81.5%) were higher than that of CURB65 (61.5% and 76.1%) respectively.ConclusionCURB65-LH is a simple, effective and more accurate prediction method of clinical severe events in CAP patients, which not only has higher sensitivity and specificity, but also significantly improves the predictive value when compared with CURB65.
This paper introduced the fundamental theory, method advantages, application scenario and R software implementation method of the covariate-adjusted receiver operating characteristic (ROC) curve. Compared with the traditional univariate ROC curve, the covariate-adjusted ROC curve has distinct methodological advantages and wider application scenarios, which can help to evaluate the ability of markers to predict the targeted outcome more scientifically. It merits more widespread and prior adoption in practical research.
ObjectiveTo establish a forecasting model for inpatient cases of pediatric limb fractures and predict the trend of its variation.MethodsAccording to inpatient cases of pediatric limb fractures from January 2013 to December 2018, this paper analyzed its characteristics and established the seasonal auto-regressive integrated moving average (SARIMA) model to make a short-term quantitative forecast.ResultsA total of 4 451 patients, involving 2 861 males and 1 590 females were included. The ratio of males to females was 1.8 to 1, and the average age was 5.655. There was a significant difference in age distribution between males and females (χ2=44.363, P<0.001). The inpatient cases of pediatric limb fractures were recorded monthly, with predominant peak annually, from April to June and September to October, respectively. Using the data of the training set from January 2013 to May 2018, a SARIMA model of SARIMA (0,1,1)(0,1,1)12 model (white noise test, P>0.05) was identified to make short-term forecast for the prediction set from June 2018 to November 2018, with RMSE=8.110, MAPE=9.386, and the relative error between the predicted value and the actual value ranged from 1.61% to 8.06%.ConclusionsCompared with the actual cases, the SARIMA model fits well with good short-term prediction accuracy, and it can help provide reliable data support for a scientific forecast for the inpatient cases of pediatric limb fractures.
ObjectiveTo establish a predictive model of surgical site infection (SSI) following colorectal surgery using machine learning.MethodsMachine learning algorithm was used to analyze and model with the colorectal data set from Duke Infection Control Outreach Network Surveillance Network. The whole data set was divided into two parts, with 80% as the training data set and 20% as the testing data set. In order to improve the training effect, the whole data set was divided into two parts again, with 90% as the training data set and 10% as the testing data set. The predictive result of the model was compared with the actual infected cases, and the sensitivity, specificity, positive predictive value, and negative predictive value of the model were calculated, the area under receiver operating characteristic (ROC) curve was used to evaluate the predictive capacity of the model, odds ratio (OR) was calculated to tested the validity of evaluation with a significance level of 0.05.ResultsThere were 7 285 patients in the whole data set registered from January 15th, 2015 to June 16th, 2016, among whom 234 were SSI cases, with an incidence of SSI of 3.21%. The predictive model was established by random forest algorithm, which was trained by 90% of the whole data set and tested by 10% of that. The sensitivity, specificity, positive predictive value, and negative predictive value of the model were 76.9%, 59.2%, 3.3%, and 99.3%, respectively, and the area under ROC curve was 0.767 [OR=4.84, 95% confidence interval (1.32, 17.74), P=0.02].ConclusionThe predictive model of SSI following colorectal surgery established by random forest algorithm has the potential to realize semi-automatic monitoring of SSIs, but more data training should be needed to improve the predictive capacity of the model before clinical application.
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.
Diffuse choroidal retinal atrophy (DCA) is a type of myopic macular disease that presents with yellowish-white atrophic changes at the posterior pole of the eyeball. DCA is an important critical feature in the diagnosis of pathological myopia. Early intervention and treatment of this disease are of great significance in delaying the progression of pathological myopia and reducing the impairment of visual function. Ophthalmic imaging data can be used to diagnose the disease, and color fundus photography is the most simple and intuitive. Choroidal thickness is also a key indicator in the diagnosis of DCA, but the diagnostic critical value of choroidal thickness has not been clearly defined. With the development and popularization of artificial intelligence technology, the analysis of lesion imaging data is more objective and accurate. In the future, it is expected to actively establish a standard quantitative evaluation system for DCA by means of artificial intelligence to achieve early detection, early diagnosis and early treatment of pathological myopia.