Prediction of ICU admission after orthopedic surgery in elderly patients

Author:

Tang Yongzhong,Li Hao,Guo Ziyi

Abstract

Objectives: Prediction of ICU admission after surgery are important for rational decision-making for different patients in clinical practice. Little information is available about the risk factors of postoperative ICU admission in elderly patients undergoing orthopedic surgery. This study aimed to identify risk factors and develop a predictive model for postoperative ICU admission in elderly patients undergoing orthopedic surgery. Methods: A total of 2826 cases of elderly patients receiving orthopedic surgery from October 2010 to September 2016 were retrospectively collected and analyzed. Logistic regression was used to evaluate the impacts of covariates. Support vector machine (SVM) was employed to develop a predictive model based on all pre-operative covariates and the demographic information. Results: There were 256 patients transferred to ICU after surgery. ASA III or IV and emergency surgery were found to be independent risk factors while neuraxial anesthesia and joint surgery were protective factors. In addition, a SVM-based predictive model was developed, which had a sensitivity of 90.99%, a specificity of 99.10% and an area under ROC curve of 0.9678. Conclusions: Our study revealed that emergency surgery, anesthesia method, surgery type and ASA grade were risk factors to predict postoperative ICU admission in elderly orthopedic patients. doi: https://doi.org/10.12669/pjms.37.4.3371 How to cite this:Tang Y, Li H, Guo Z. Prediction of ICU admission after orthopedic surgery in elderly patients. Pak J Med Sci. 2021;37(4):1179-1184.  doi: https://doi.org/10.12669/pjms.37.4.3371 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Publisher

Pakistan Journal of Medical Sciences

Subject

General Medicine

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