RESUMO
【Objective】 To explore the establishment of individualized prediction model of recurrence after percutaneous endoscopic lumbar discectomy （PELD） in patients with lumbar disc herniation （LDH）. 【Methods】 We selected 124 LDH patients treated with PELD in Department of Orthopedics， The First Affiliated Hospital of Xi’an Jiaotong University， from January 2017 to January 2020 as the research subjects. Their clinical data were retrospectively analyzed， and the independent risk factors affecting PELD recurrence in the LDH patients were screened by univariate analysis and Logistic regression analysis， respectively； the correlation histogram prediction model was established. 【Results】 Age， history of diabetes， course of disease， work intensity and IDDG were the risk factors for the recurrence of PELD in LDH patients （P<0.05）. Based on the risk factors screened out， the prediction model of the histogram was established， and the model was verified. The results showed that the C-index of the modeling set and the validation set was 0.944 （95% CI： 0.902-0.963） and 0.969 （95% CI： 0.911-0.978）， respectively. The correction curves of both groups were well fitted with the standard curves. The areas under the ROC curve （AUC） in the two groups were 0.944 and 0.969， respectively， which proved that the model had good prediction accuracy. 【Conclusion】 LDH patients have many independent risk factors for recurrence after PELD， and the model based on risk factors with good predictive ability can be useful in preoperative evaluation， appropriate patient selection， and decrease of recurrence rate after PELD.
RESUMO
@#Objective To explore the risk factors and risk mapping model of post-stroke cognitive dysfunction (PSCD) in patients with Acute stroke (AI).Methods Two hundred and sixty cases of AI patients were divided into PSCD group and PSCD group according to whether the patients had PSCD.Independent risk factors affecting the occurrence of PSCD in AI patients were screened by Logistic regression analysis.R software was used to establish a risk prediction model for the screened independent risk factors,and the predictive and accuracy of the model were verified.Results Logistic regression analysis showed that age,education level,hypertension,diabetes,physical exercise and cerebrovascular stenosis were independent risk factors for PSCD in AI patients (P<0.05),which were all highly correlated with PSCD.Based on the above 6 influencing factors,a nomogram model was established to predict PSCD in AI patients.The verification results show that the model has a high predictive performance with a C-index of 0.808 (95%CI 0.747~0.869).Conclusion Comprehensive factors such as age,education level,hypertension,diabetes,physical exercise and cerebral vascular stenosis should be taken into account in time for AI patients,and the probability of PSCD occurrence in AI patients should be predicted through the individual line map model,and intervention measures should be taken as soon as possible to improve the prognosis of the patients.