Prediction of pork supply via the calculation of pig population based on population prediction model

Based on the perspective of pig population system prediction,and accorded to principle of pig months transfer,this paper refers to the modeling principle and method of discrete population quantity prediction model.Then the prediction model of pork supply is derived and established:Firstly,the recursive model of pig population system and estimation model of pork supply was established.Then this study estimated the sum of monthly mortality and culling rate of breeding sows.Furthermore,the method for new left gilts in each month and estimation of breeding sows at each month of age was established.Last,this research established the estimation method model of the initial state of pig population.On this basis,an example calculation is made to predict the monthly pork supply in Heilongjiang Province from January 2016 to March 2018 in the future.The results showed that the prediction model of pork supply based on the prediction of pig population system is an effective perspective to study the forecast of pork supply.In the prediction stage,the prediction accuracy of the number of slaughtered fattened hogs was 96.36%and 97.54%,and the prediction accuracy of pork supply was 98.08%and 93.82%.This study not only lay a theoretical foundation for further study on the balance between pork supply and demand,but also helps to guide pork producers and governments at all levels to make relevant production decisions and plans.

Risk factors for acute kidney injury following coronary artery bypass graft surgery in a Chinese population and development of a prediction model

BACKGROUND Acute kidney injury(AKI)after coronary artery bypass graft(CABG)surgery is associated with significant morbidity and mortality.This retrospective study aimed to establish a risk score for postoperative AKI in a Chinese population.METHODS A total of 1138 patients undergoing CABG were collected from September 2018 to May 2020 and divided into a derivation and validation cohort.AKI was defined according to the Kidney Disease Improving Global Outcomes(KDIGO)criteria.Multivariable logistic regression analysis was used to determine the independent predictors of AKI,and the predictive ability of the model was determined using a receiver operating characteristic(ROC)curve.RESULTS The incidence of cardiac surgery–associated acute kidney injury(CSA-AKI)was 24.17%,and 0.53%of AKI patients required dialysis(AKI-D).Among the derivation cohort,multivariable logistic regression showed that age≥70 years,body mass index(BMI)≥25 kg/m2,estimated glomerular filtration rate(eGFR)≤60 mL/min per 1.73 m2,ejection fraction(EF)≤45%,use of statins,red blood cell transfusion,use of adrenaline,intra-aortic balloon pump(IABP)implantation,postoperative low cardiac output syndrome(LCOS)and reoperation for bleeding were independent predictors.The predictive model was scored from 0 to32 points with three risk categories.The AKI frequencies were as follows:0-8 points(15.9%),9-17 points(36.5%)and≥18 points(90.4%).The area under of the ROC curve was 0.730(95%CI:0.691-0.768)in the derivation cohort.The predictive index had good discrimination in the validation cohort,with an area under the curve of 0.735(95%CI:0.655-0.815).The model was well calibrated according to the Hosmer-Lemeshow test(P=0.372).CONCLUSION The performance of the prediction model was valid and accurate in predicting KDIGO-AKI after CABG surgery in Chinese patients,and could improve the early prognosis and clinical interventions.