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Updated: Jan 24, 2026

A Mouse Model of Orthopedic Surgery to Study Postoperative Cognitive Dysfunction and Tissue Regeneration
Published on: February 27, 2018
Malondialdehyde on postoperative day 1 predicts postoperative cognitive dysfunction in elderly patients after hip
Chunxian Wu1, Bin Gao1, Yu Gui2
1Department of Anesthesiology, The Affiliated Hospital of Medical School, Ningbo University, No. 247 Renmin road, Ningbo City 315000, Zhejiang Province, China.
Abstract:
Background: Postoperative cognitive dysfunction (POCD) is a great problem for anesthetized subjects and is associated with poor short- and long-term outcomes. We explored promising predictors for POCD in elderly patients after hip fracture surgery.Methods: Elderly subjects (aged ≥65 years) undergoing surgery for hip fracture were consecutively recruited. Neuropsychological assessments were performed 1 day preoperatively (baseline) and 7 days postoperatively, and POCD was defined using the 'Z scores' method. Clinical data and laboratory tests were compared between patients with and without POCD development. Binary univariate and multivariate logistic regression analyses were conducted for risk factor assessment. Receiver operating characteristic (ROC) curve analysis was performed to investigate the predictive value of malondialdehyde (MDA) on postoperative day 1 (POD1) for POCD.Results: A total of 198 patients were finally enrolled in the analysis and 51 patients exhibited POCD within 7 postoperative days, with an incidence rate of 25.8%. MDA expression on POD1 (OR: 1.12, 95%CI: 1.03-1.23, P=0.017) was the only independent risk factor for POCD according to the final multivariate logistic regression analysis. ROC curve analysis indicated that MDA on POD1 was a predictor for POCD, with an area under the curve (AUC) of 0.683 and 95%CI of 0.590-0.775 (P<0.001).Conclusions: In conclusion, we demonstrated that MDA on POD1 was an independent risk factor for POCD in elderly subjects undergoing hip fracture surgery.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

