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Published on: October 20, 2023
Prediction Model for Postoperative Pressure Injury in Patients with Acute Type A Aortic Dissection
Qiuji Wang1, Weiqi Feng, Wenhui Li
1Qiuji Wang, MS, is PhD Candidate, Department of Cardiac Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. Weiqi Feng, BS, is Master Candidate, School of Medicine, South China University of Technology, Guangzhou, China. Also at Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Wenhui Li, BS, and Shan Li, BS, are Nurse Practitioners, Department of Cardiac Surgery; Qiuyi Wu, BS, is Nurse, Department of Cardiac Surgery; Zhichang Liu, BS, is Nurse, Department of Cardiac Surgery Intensive Care Unit 1; Xin Li, MS, and Changjiang Yu, MS, are Deputy Chief Physicians, Department of Cardiac Surgery; Yunqing Cheng, BS, is Head Nurse, Department of Cardiac Surgery; and Ruixin Fan, PhD, is Chief, Department of Cardiac Surgery. Acknowledgments: Editorial support and funding for the publication charge for this research were supplied by the Science and Technology Planning Project of Guangdong Province (no. 2015A020214017). The authors have disclosed no other financial relationships related to this article. Submitted October 18, 2022; accepted in revised form March 16, 2023.
Objective:
To establish a risk assessment model to predict postoperative National Pressure Injury Advisory Panel stage 2 or higher pressure injury (PI) risk in patients undergoing acute type A aortic dissection surgery.
Methods:
This retrospective assessment included consecutive patients undergoing acute type A aortic dissection surgery in the authors' hospital from September 2017 to June 2021. The authors used LASSO (logistic least absolute shrinkage and selection operator) regression analysis to identify the most relevant variables associated with PI by running cyclic coordinate descent with 10-times cross-validation. The variables selected by LASSO regression analysis were subjected to multivariate logistic analysis. A calibration plot, receiver operating characteristic curve, and decision curve analysis were used to validate the model.
Results:
There were 469 patients in the study, including 94 (27.5%) with postoperative PI. Ten variables were selected from LASSO regression: body mass index, diabetes, Marfan syndrome, stroke, preoperative skin moisture, hemoglobin, albumin, serum creatinine, platelet, and d-dimer. Four risk factors emerged after multivariate logistic regression: Marfan syndrome, preoperative skin moisture, albumin, and serum creatinine. The area under the receiver operating characteristic curve of the model was 0.765. The calibration plot and the decision curve analysis both suggested that the model was suitable for predicting postoperative PI.
Conclusions:
This study built an efficient predictive model that could help identify high-risk patients.
