Construction and evaluation of early prediction model for severe acute pancreatitis complicated with acute renal injury / 中华胰腺病杂志

Objective:To establish an early prediction Nomogram model for severe acute pancreatitis(SAP) complicated with acute renal injury (AKI), and evaluate the prediction efficiency of the model.Methods:The clinical data of 295 SAP patients hospitalized in Zhejiang Rongjun Hospital from July 2017 to June 2021 were retrospectively analyzed, and the patients were divided into AKI group ( n=61) and non-AKI group ( n=234) according to whether complicated with AKI. The common characters, clinical data and laboratory examination results were compared. The risk factors for SAP complicated with AKI was analyzed by multivariate logistic regression analysis, and a nomogram prediction model was established by R software. The receiver operating characteristic (ROC) curve was drawn and the area under the curve (AUC) was calculated to evaluate its prediction performance. Results:The acute physiology and chronic health assessment Ⅱ (APACHEⅡ) and Ranson score, the incidence of abdominal compartment syndrome (ACS) and systemic inflammatory response syndrome (SIRS), the cases of shock and mechanical ventilation, and the levels of blood lactic acid (BLA), blood creatinine (Scr), urea nitrogen (BUN), C-reactive protein (CRP), procalcitonin (PCT) and cystatin C(Cys C) in peripheral blood were significantly higher in AKI group than those in non-AKI group, while the levels of blood calcium were lower than those in non-AKI group, and the differences were statistically significant (all P value <0.05). Multivariate logistic regression analysis showed that APACHEⅡ score ( OR=1.185, 95% CI 1.074-1.308, P=0.001), Ranson score ( OR=12.668, 95% CI 5.102-31.456, P<0.001), Scr ( OR=1.028, 95% CI 1.002-1.054, P=0.034), PCT ( OR=4.298, 95% CI 1.379-13.395, P<0.001) and Cys C ( OR=38738.38, 95% CI 43.190-347459.41, P<0.001) were independent risk factors for SAP complicated AKI. Serum calcium ( OR=0.0001, 95% 0.000-0.048, P<0.001) was an independent protective factor for SAP complicated AKI. A Nomogram prediction model based on the six factors above were established, and its AUC, sensitivity and specificity to predict AKI were 0.987, 99.0% and 98.5% in the training set, and were 0.976, 98.6% and 94.2% in the validation set. Conclusions:This study successfully established an early prediction model with high predict value for SAP complicated with AKI, which can efficiently predict the risk of SAP with concurrent AKI.

Influenza incidence prediction based on ARIMAX model including meteorological factors / 预防医学

Objective@#To evaluate the feasibility of autoregressive integrated moving average with explanatory variables ( ARIMAX ) model including meteorological factors on the prediction of influenza-like illness ( ILI ), so as to provide a basis for the monitoring and early warning of influenza.@*Methods@#The ILI data reported by four sentinel hospitals in Yuhang District of Hangzhou from the 1st week of 2014 to the 26th week of 2018 was collected, as well as the meteorological data during the same period. The ARIMAX model was established using the percentage of ILI cases in total outpatients ( ILI% ) data from the 1st week of 2014 to the 52nd week of 2017 and the meteorological factors selected by Lasso regression model. The ILI% from the 1st to 26th week of 2018 was predicted and compared with the actual values to verify the ARIMAX model.@*Results@#From the 1st week of 2014 to the 26th week of 2018, a total of 60 419 cases of ILI were reported by the four sentinel hospitals of Yuhang District, with ILI% of 1.29%. Lasso regression analysis showed that there was a positive correlation between weekly average absolute humidity and ILI% ( r=27.769 ), and a negative correlation between weekly average temperature and ILI% ( r=-0.117 ). The ARIMAX (1, 0, 0) ( 1, 0, 0 )12 with weekly average temperature and absolute humidity was selected as the optimal model, with the Bayesian information criterion (BIC) value of 81.30 and the mean absolute percentage error (MAPE) value of 15.77%. The MAPE value of the ARIMAX model predicting the ILI% from 1st to 26th week of 2018 were 43.75%.@*Conclusion@#The ARIMAX model including meteorological factors can be used to predict the prevalence of ILI, but the accuracy needs to be promoted.

Comparison of two prediction models for mortality ofacute paraquat poisoning / 预防医学

Objective@#To compare the effects of Cox proportional hazard regression model (Cox model) and extreme gradient boosting model ( XGBoost model ) on the prediction of the mortality of acute paraquat poisoning (APP).@*Methods@#The APP cases admitted to Qingdao Eighth People's Hospital and Shandong Provincial Hospital from January 1st of 2018 to December 1st of 2020 was recruited and divided into a training group and a verification group by a random number table. The Cox model and XGBoost model were established to select the predictors for APP mortality. Receiver operating characteristic ( ROC ) curve was drawn to analyze the predictive power of the two models, and the calibration was evaluated using Hosmer-Lemeshow test.@*Results@#Totally 150 APP cases were recruited. There were 75 cases each in the training group and in the verification group, with 52 and 55 cases died respectively, accounting for 69.33% and 73.33%. The Cox model showed that paraquat intake, the time from taking poison to seeing a doctor, the time for the first perfusion, the time for the first vomiting, aspartate aminotransferase, alanine aminotransferase, serum creatinine, blood urea nitrogen, white blood cell, lactic acid, creatine kinase isoenzymes, glucose, serum calcium and serum potassium were the predictors of APP mortality ( all P<0.05 ). The XGboost model showed that the predictive power of the factors in a descending order were the time from taking poison to seeing a doctor, the time for the first vomiting, the time for the first perfusion, lactic acid, white blood cell, paraquat intake, serum creatinine, serum potassium, serum calcium, creatine kinase isoenzymes, glucose, aspartate aminotransferase, blood urea nitrogen and alanine aminotransferase. The area under curve ( AUC ) of the XGBoost model for predicting was 0.972, which was greater than 0.921 of the Cox model ( P<0.05 ). The predicted results of the Cox model and XGBoost model were consistent with the actual situation ( P>0.05 ). @*Conclusion@#The Cox model and XGBoost model are consistent in predicting the mortality of APP, but the latter is better.

Correlation between CYP2C9, APOE gene polymorphisms and stable warfarin and model prediction dose / 中国胸心血管外科临床杂志

@#Objective    To investigate the effect of CYP2C9 and APOE on the dose of stable warfarin and model prediction in Hainan population. Methods    From August 2016 to July 2018, 368 patients who required heart valve replacement and agreed to take warfarin anticoagulation at the second department of cardiothoracic surgery in our hospital were enrolled, including 152 males aged 48.5–70.5 (60.03±10.18) years and 216 females aged 43.5–65.6 (54.24±11.35) years. CYP2C9 and APOE were amplified by polymerase chain reaction. The gene fragment was sequenced by the Single Nucleotide Polymorphisms (SNP) site. The patients' age, sex, weight, history of smoking and drinking, and the dose of stable warfarin were recorded. Regression analysis of these clinical data was made to construct a dose prediction model. Results    Among 368 patients, CYP2C9 genotype test results showed 301 patients (81.8%) with *1*1 genotype, and 67 patients (18.2%) with *1*3 type. For different CYP2C9 genotype patients, the difference was statistically significant in the dose of stable warfarin (P<0.05). The results of APOE genotype showed 93 patients (25.3%) with E2 genotype, 221 patients (60.1%) with E3 genotype, and 54 patients (14.7%) with E4 genotype; the dose of stable   warfarin in patients with different APOE genotypes was statistically significant (P<0.05). Multiple regression analysis showed that patients' age, body weight, and CYP2C9 and APOE genotypes were correlated with the dose of stable warfarin. The correlation coefficient R2 was 0.572, and the prediction model was statistically significant (P<0.05). Conclusion    CYP2C9 and APOE gene polymorphisms exist in Hainan population. There is significant difference in the dose of stable warfarin among different genotypes of patients. The model to predict stable warfarin can partly explain the difference of warfarin among different patients.

Mathematical model for predicting birth defect trend in Xi'an / 西安交通大学学报(医学版)

Objective: To understand the trend of birth defects in Xi'an by using gray model, ARIMA and NAR. Methods: The birth defects monitoring data of perinatal infants from 28-week pregnant women to 7 days after birth were collected from all the hospitals with obstetrical department in Xi'an during 2003 and 2015. The incidence of birth defects data from October 2003 to September 2015 in Xi'an City were used to construct the data model. We compared data with the actual birth defects rate from October 2003 to September 2015 to further optimize and make supplement for the model, and then predicted the incidence of birth defects in Xi'an from 2016 to 2017. Microsoft Excel 2003 was used for data input and SPSS version 16.0 was used for statistical analysis. Matlab was used for Gray Model and NAR prediction. ARIMA mathematical model was predicted by R software. Results: The grey prediction model suggested that the birth defects rate in the four quarters of 2016 and 2017 was 9.62‰, 9.67‰, 9.72‰, 9.77‰, 9.82‰, 9.87‰, 9.92‰ and 9.97‰, which was in slow increase. The ARIMA model predicted that the birth defects rate in Xi'an would still fluctuate at a high level in 2016 and 2017, and the rate in the four quarters was 11.98‰, 12.83‰, 11.28‰, 11.78‰, 12.23‰, 11.73‰, 11.80‰ and 12.00‰. The NAR model predicted that the birth defects rate in Xi'an was 13.24‰, 17.91‰, 10.55‰, 16.08‰, 16.47‰, 9.42‰, 11.99‰ and 11.68‰. The birth defects rate would reach the peak in 2016 and decrease in 2017. Comparison of the above three models showed that the root mean square error of grey prediction model, ARIMA model and NAR model was 1.353 009, 1.181 373 and 0.555 347, respectively. Conclusion: Based on the prediction by the above three mathematical models, it shows that NAR model is more accurate and reliable in predicting the data of this study, followed by ARIMA and grey model. Effective intervention measures for birth defects are still the key task of public health for a long time.

Predictive value of ODI, SBI and SF-36 in patients with recurrent lumbar disc herniation undergoing reoperation / 中国骨伤

OBJECTIVE@#To evaluate the predictive value of ODI, SBI and SF-36 in patients with recurrent lumbar disc herniation undergoing reoperation.@*METHODS@#The patients of recurrent lumbar disc herniation underwent surgical treatment from June 2013 to December 2015 were enrolled in the study. Patients were assigned to A, B, C groups according to the excellent, good, poor of clinical efficacy, and divided into training set and test set by 70:30 ratio according to random number table. we use ordered Logistic regression to construct prediction model, and test set to verify the effect of the model and calculate the accuracy of the model.@*RESULTS@#Both ODI and SBI were lower in group A and group B than group C, and the SF-36 scale was significantly higher than group C (<0.05). The predictive efficacy model by ordered Logistic regression construction showed that the ODI coefficient was 0.67, the SF-36 coefficient was -0.43, and the SBI coefficient was 0.52. In the group A with excellent clinical efficacy, the prediction accuracy rate of the model was 80%; in the group B with good clinical efficacy, the prediction accuracy rate was 76.92% and in the group C with poor clinical efficacy, the prediction accuracy rate was 44.44%.@*CONCLUSIONS@#Comprehensive consideration of ODI, SBI and SF-36 to construct a clinical prediction model for patients with recurrent intervertebral disc herniation after surgery can better predict patients' prognosis. It has a value for clinical application.

Medicine Procurement and Inventory Control Strategies Based on Inventory Model Approach / 中国药房

OBJECTIVE:To work out the optimal inventory levels in zero inventory management mode through model predic-tion and control strategy,by using the inventory upper & lower limits settings generally available in the information management system of health care institutions. METHODS:Multi-varieties joint ordering modelwas constructed by referring to operations management,time series analysis and quantitative approach to decision-making,that is,to make a prediction of upper&lower lim-its on medicine inventory based on historical data and applicable mathematical models(fixed order interval model and re-order mod-el,i.e. FOI and ROP),and compared with real results;based on above,specific medicine procurement and inventory control strat-egies would be developed and an evaluation of the application effects would be made. RESULTS:The error test and reproducibility test exhibited that the out-of-stock ratio remained under 3.36%,of which 71.24% could be effectively alarmed;under computer simulation and practical operation,the instant replenishment rate reduced by 9.33% and 13.03%,OOS ratio down by 11.11% and 27.45%,and average daily inventory turnover rate up by 30.19% and 15.85% respectively,all showing remarkable improvements compared to before the implementation of the mode. CONCLUSIONS:This model is of favorable accuracy and operability,there-fore it can lay foundation for rational and well-founded decisions in medicine procurement and inventory control in zero inventory management mode.

BIOLOGICAL BASIS OF TIME,DOSE AND FRACTIONATION IN FRACTIONATED RADIOTHERAPY / 中华放射肿瘤学杂志

The biology rationale for radiotherapy in the treatment of malignant disease is based on repair, repopulation,reoxygenation and cell cycle redistribution. Various aspects of the roles of the 4R' are discussed, including in determining the sensitivity of tumors and normal tissue tolerances. An improvement in therapeutic ratio may derive from reducing the dose per fraction and minimizing the overall treatmemt duration. Some methods have developed to predict the response of normal and tumor tissues before radiotherapy. The parameters of cell survival at 2Gy(SF2) was correlated with clinical outcome. There is reasons to suppose that the pretreatment tumor LI and Tpot mat be good predictors for tumor repopulation kinetics. This review also discussed the rationale for the use of LQ model in fractionated radiotherapy.