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.
In recent years, wearable devices have seen a booming development, and the integration of wearable devices with clinical settings is an important direction in the development of wearable devices. The purpose of this study is to establish a prediction model for postoperative pulmonary complications (PPCs) by continuously monitoring respiratory physiological parameters of cardiac valve surgery patients during the preoperative 6-Minute Walk Test (6MWT) with a wearable device. By enrolling 53 patients with cardiac valve diseases in the Department of Cardiovascular Surgery, West China Hospital, Sichuan University, the grouping was based on the presence or absence of PPCs in the postoperative period. The 6MWT continuous respiratory physiological parameters collected by the SensEcho wearable device were analyzed, and the group differences in respiratory parameters and oxygen saturation parameters were calculated, and a prediction model was constructed. The results showed that continuous monitoring of respiratory physiological parameters in 6MWT using a wearable device had a better predictive trend for PPCs in cardiac valve surgery patients, providing a novel reference model for integrating wearable devices with the clinic.
ObjectiveTo evaluate the value of parathyroid hormone (PTH) in predicting hypocalcemia at different time after thyroidectomy. MethodsThe literatures in CBM, WanFang, CNKI, VIP in Chinese, and OVID, PUBMED, EMBASE, and MEDLINE in English were searched. Hand searches and additional searches were also conducted. The studies of predicting hypocalcemia after thyroidectomy by detecting postoperative PTH at different time were selected, and the quality and tested the heterogeneity of included articles were assessed. Then the proper effect model to calculate pooled weighted sensitivity (SEN), specificity (SPE), positive likelihood ratio (LR+), and negative likelihood ratio (LR-) were selected. The summary receiver operating characteristic (SROC) curve was performed and the area under the curve (AUC) was computed. ResultsTwenty-three articles entered this systematic review, 21 articles were English and 2 articles were Chinese. Fifteen of 23 articles were designed to be prospective cohort study (PC) and 8 of 23 articles were retrospective study (Retro). These articles were divided into two groups. Group 1 was the studies of detecting postoperative PTH in 1 hour, which included 2 012 cases (494 of them occurred hypocalcemia). Group 2 was the studies of detecting postoperative PTH between 4-12 hours, which included 693 cases (266 of them occurred hypocalcemia). The publication bias of 2 groups were smaller that founded through the literature funnel. Meta analysis showed that in addition to merge SEN, between the 2 groups with merge SPE, LR+, LR-, and AUC differences were statistically significant (P < 0.01);the forecast effect of group 1 was better than group 2, and the AUC was the largest area when the PTH value in 1 hour after operation was below 16 ng/L. ConclusionDetection of postoperative PTH value is an effective method for predicting postoperative hypocalcemia. The 1 hour after operation for detecting PTH value below 16 ng/L to predict postoperative hypocalcemia have the best effect.
Objective To provide a comprehensive overview of model performance and predictive efficacy of machine learning techniques to predict septic shock in children, in order to target and improve the quality and predictive power of models for future studies. MethodsTo systematically review all studies in four databases (PubMed, Embase, Web of Science, ScienceDirect, CNKI, WanFang Data) on machine learning prediction of septic shock in children before April 1, 2024. Two investigators independently conducted literature screening, literature data extraction and bias assessment, and conducted a systematic review of basic information, research data, study design and prediction models. Model discrimination, which area under the curve (AUC), was pooled using a random-effects model and meta-analysis was performed. Subgroup analyses were performed according to sample sizes, machine learning models, types of predictors, number of predictors, etc. And publication bias and sensitivity analyses were performed for the included literature. Results A total of 11 studies were included, of which 2 were at low risk of bias, 7 were at unknown risk of bias, and 2 were at high risk of bias. The data used in the included studies included both public and non-public electronic medical record databases, and the machine learning models used included logistic regression, random forest, support vector machine, and XGBoost, etc. The predictive models constructed based on different databases appeared to have different results in terms of the characteristic variables, so identifying the key variables of the predictive models requires further validation on other datasets. Meta-analysis showed the pooled AUC of 0.812 (95%CI 0.763 to 0.860, P<0.001), and further subgroup analyses showed that larger sample sizes (≥1 000) and predictor variable types significantly improved the predictive effect of the model, and the difference in AUC was statistically significant (95%CI not overlapping). The funnel plot showed that there was publication bias in the study, and when the extreme AUC values were excluded, the meta-analysis yielded a total AUC of 0.815 (95%CI 0.769 to 0.861, P<0.001), indicating that the extreme AUC values were insensitive. ConclusionMachine learning technology has shown some potential in predicting septic shock in children, but the quality of existing research needs to be strengthened, and future research work should improve the quality of research and improve the prediction effect of the model by expanding the sample size.
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 evaluate the value of blood urea nitrogen to creatinine ratio (UCR) in predicting the condition and prognosis of severe pneumonia patients.MethodsA total of 408 patients with severe pneumonia hospitalized in the intensive care unit (ICU) of Fangcun branch of Guangdong Provincial Hospital of traditional Chinese medicine from January 1, 2017 to August 1, 2020 were retrospectively collected. The patients were divided into a survival group (320 cases) and a death group (88 cases) according to the outcome of hospitalization. This study analyzed the relationship between UCR level and general information, condition, and treatment needs of severe pneumonia patients; and compared UCR, the value of neutrophil to lymphocyte ratio, the levels of hematocrit, C-reactive protein, procalcitonin and D-dimer, and the scores of Acute Physiology and Chronic Health EvaluationⅡ and Pneumonia Severity Index between the survival group and the death group. Receiver operating characteristic (ROC) curve was used to analyze the prognostic value of the above indicators. Logistic regression was used to analyze the risk factors of death of severe pneumonia.ResultsThe age of the patients died of severe pneumonia was higher than that of the survival patients (P<0.05); The mortality rate of severe hospital acquired pneumonia was higher than that of severe community acquired pneumonia (P<0.05); The level of UCR was higher in the patients over 70 years old (P<0.05); UCR level of the severe pneumonia patients with acute exacerbation of chronic obstructive pulmonary disease or multiple organ dysfunction syndrome during hospitalization was higher (P<0.05); The UCR level was higher in the patients with severe pneumonia whose ICU stay was more than 10 days (P<0.05); The UCR level of the severe pneumonia patients with mechanical ventilation longer than 180 hours was higher (P<0.05); UCR level of the severe pneumonia patients who died during hospitalization was higher than that of the survival group (P<0.05); The area under ROC curve of UCR for predicting death in the patients with severe pneumonia was 0.648 (95%CI 0.576 - 0.719), the cut-off value was 108.74, the sensitivity was 47.7%, and the specificity was 77.8% (P<0.05). PSI > level 3 (OR=4.297, 95%CI 2.777 - 6.651) and UCR > 108.74 (OR=0.545, 95%CI 0.332 - 0.896) were independent risk factors for death in the patients with severe pneumonia (P<0.05).ConclusionUCR has certain value in evaluating the condition and prognosis of severe pneumonia patients.
Abstract： Objective To validate the value of Cleveland Clinical Score to predict acute renal failure（ARF） requiring renal replacement therapy （RRT） and in-hospital death in Chinese adult patients after cardiac surgery. Methods A retrospective analysis was conducted for all the patients who underwent cardiac surgery from January 2005 to December 2009 in Renji Hospital of School of Medicine, Shanghai Jiaotong University. A total of 2 153 adult patients, 1 267 males and 886 females，were included. Their age ranged from 18 to 99 years with an average age of 58.70 years. Cleveland Clinical Score was used to predict ARF after cardiac surgery. ARF was defined as the need for RRT. Based on Cleveland Clinical Score, the patients were divided into four risk categories of increasing severity：0 to 2 point（n=979）, 3 to 5 point （n=1 116）, 6 to 8 point（n=54）, 9 to 13 point（n=4）. The rates of ARF, multiple organ system failure （MOSF）, and mortality were compared among the 4 categories. The predictive accuracy of postoperative ARF and hospital mortality was assessed by area under the receiver operating characteristic curve （AUC-ROC）. Results In the four categories, the rate of postoperative ARF was 0.92%, 1.88%, 12.96%, and 25.00%, respectively； MOSF rate was 1.23%, 1.88%, 3.70%, and 25.00%, respectively; mortality was 0.92%, 4.21%, 25.93%, and 50.00%, respectively. There was significant dif ference among the four categories in ARF rate （χ2=55.635, P=0.000），MOSF rate（χ2=16.080, P=0.001）, and mortality （χ2=71.470, P=0.000）. The AUC-ROC for Cleveland Clinical Score predicting ARF rate and hospital mortality was 0.775 （95%CI 0.713 to 0.837, P=0.000）and 0.764（95%CI, 0.711 to 0.817, P=0.000）, respectively. Conclusion Cleveland Clinical Score can accurately predict postoperative ARF and hospital mortality in a large, unselected Chinese cohort of adult patients after cardiac surgery. It can be used to provide evidence for effective preventive measures for patients at high risk of postoperative ARF.
Objective To evaluate the predicted value of APACHEⅡ score at admission for deep fungal infection（DFI） in patients with severe acute pancreatitis （SAP）.Methods The clinical data of 132 patients with SAP from January 2006 to June 2011 in our hospital were analyzed retrospectively. The receiver operating characteristic curve （ROC） was used for evaluating the predicted value.Results Thirty-nine patients with SAP infected DFI （29.5%）,of which 36 patients （92.3%） infected with Candida albicans,2 patients （5.1%） with Candida tropicalis,1 patient （2.6%） with pearl bacteria.And,among these 39 patients,27 patients （69.2%） infected at single site,12 patients （30.8%） infected at multi-site. The APACHEⅡ score in 39 patients with DFI was higher than that of 93 patients without DFI （17.1±3.8 versus 9.7±2.1, t=14.316,P=0.000）.The ROC for APACHEⅡ score predicting DFI was 0.745（P=0.000）, 95%CI was 0.641－0.849.When the cut off point was 15,it showed the best forecast performance,with specificity 0.81, sensitivity 0.72,Youden index 0.53. Conclusions The APACHEⅡ score at admission can preferably predict DFI in patients with SAP； when the APACHEⅡ score is greater than 15,it prompts highly possible of DFI,so preventive anti-fungal treatment may be necessary.
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.