摘要
Objective To explore the risk factors for intracranial infection in patients after neurosurgery,con-struct and validate a Nomogram prediction model.Methods Data of 978 patients who underwent neurosurgery in a hospital in Nanjing from January 1,2019 to December 31,2022 were retrospectively analyzed.Independent risk fac-tors were screened through logistic univariate and multivariate analyses.Modeling variables were screened through Lasso regression.A Nomogram model was constructed and internally validated by logistic regression.Effectiveness of the model was evaluated with receiver operating characteristic(ROC)curve,calibration curve and decision curve.Results Among 978 patients underwent neurosurgery,293 had postoperative intracranial infection,with an inci-dence of healthcare-associated infection of 29.96％.There was no significant difference in age,gender,proportion of coronary heart disease,cerebral infarction,diabetes and hypertension between the infected group and the non-in-fected group(all P＞0.05).Multivariate logistic analysis showed that postoperative intracranial hypertension,fe-ver,increased neutrophil percentage in blood routine examination,turbid cerebrospinal fluid,positive Pan's test,decreased glucose concentration,abnormal ratio of cerebrospinal fluid/serum glucose,positive microbial culture,absence of indwelling external ventricular drainage tubes,presence of indwelling lumbar cistern drainage tubes,use of immunosuppressive agents,and long duration of surgery were independent risk factors for postoperative intracra-nial infection in patients who underwent neurosurgery(all P＜0.05).Fifteen variables were screened out through Lasso regression.Fourteen variables were finally included for modeling after collinear screening,missing data impu-tation(random forest method)and checking pairwise interaction items.A Nomogram prediction model was con-structed,with the area under ROC curve,sensitivity,specificity,and accuracy of 0.885,0.578,0.896,and 0.704,respectively.Internal validation of the model was conducted.The modeling and validation groups presented similar effects.The calibration curve and decision curve also indicated that the model had good predictive efficacy.Conclusion The constructed Nomogram prediction model for postoperative intracranial infection after neurosurgery is scientific,and the prediction indicators are easy to obtain.The model presents with high stability,reliability,and application value,thus can provide reference for the assessment of postoperative intracranial infection after neuro-surgery.
摘要
<p><b>OBJECTIVE</b>To investigate the association of urinary albumin excretion rate (UAER) and hyperuricemia with macrovascular atherosclerosis in type 2 diabetic patients.</p><p><b>METHODS</b>Ninety-seven type 2 diabetic patients were divided into two groups according to the UAER, namely group A with UAER between 20 and 200 microg/min (n=63) and group B with UAER > or = 200 microg/min (n=34); the patients were also classified into hyperuricemia group (group C, n=59) and normal blood uric acid (BUA) group (group D, n=38). The disease course, BUA, fasting blood glucose (FBG), triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoproteins (HDL), UAER and arteria carotis intima-media thickness (IMT) were determined in these patients. The relationship of UAER and hyperuricemia with carotid arterial IMT was analyzed statistically.</p><p><b>RESULTS</b>The levels of TG, TC, LDL and HDL showed no significant differences between the 4 groups (P>0.05). The disease course, BUA, UAER, and FBG levels and IMT in groups A and C were significantly higher than those in groups C and D (P<0.05), but no such differences were found between groups A and C or between groups B and D (P>0.05). Arotid arterial IMT was independently correlated to the disease course, BUA and UAER (r=0.201, 0.1999, 0.211, respectively, P<0.05), and a significant positive correlation was noted between BUA and UAER (r=0.221, P<0.05).</p><p><b>CONCLUSION</b>Macrovascular atherosclerosis in type 2 diabetic patients is significantly correlated to the disease course, BUA and UAER levels, which can be used to evaluate and predict macrovascular atherosclerosis in type 2 diabetic patients.</p>