Development and Validation of a Risk Predictive Model for Adverse Postoperative Health Status of Elderly Patients Undergoing Major Abdominal Surgery Using Lasso-Logistic Regression

Abstract

Yu Wang,1,2,* Yufan Yang,1,2,* Wenting Li,3,* Yichan Wang,1,2 Jingjing Zhang,2,4 Jingjie Wan,1,2 Xiaowen Meng,1,2 Fuhai Ji1,2 1Department of Anesthesiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 2Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, People’s Republic of China; 3Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China; 4Department of Anesthesiology, Weifang Maternal and Child Health Hospital, Weifang, Shandong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Fuhai Ji; Xiaowen Meng, Department of Anesthesiology, First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou, Jiangsu, 215006, People’s Republic of China, Tel [phone], Email [email protected]; [email protected]: The postoperative health status of elderly patients has a substantial impact on both the individuals themselves and their families, and this impact became more pronounced with advancing age. The aim of this study was to identify risk factors that can predict the health status of patients aged 80 and over after major abdominal surgery and to establish a nomogram model.Methods: We conducted a retrospective study of elderly patients (aged 80+) who underwent major abdominal surgery at the First Affiliated Hospital of Soochow University from January 2017 to June 2023. Least absolute shrinkage and selection operator (lasso) regression analysis was employed to identify potential perioperative factors associated with the patients’ health status one year post-surgery. Subsequently, logistic regression was then used to refine these factors for the model. The nomogram’s performance was assessed through discriminative ability, calibration, and clinical utility in both training and validation datasets.Results: In total, 576 and 145 individuals were allocated to the training and validation sets, respectively. Lasso regression first identified 10 variables as candidate risk factors. After further screening through univariate and multivariate logistic regression, it was confirmed that seven variables, including tumor, operative duration, left ventricular ejection fraction (LVEF), blood transfusion, direct bilirubin, erythrocyte, and self-care, were included in the final nomogram model. The Hosmer–Lemeshow test, with a P-value of 0.835, indicates that the model was well-fitted. The area under the Receiver Operating Characteristic curve (ROC-AUC) for the model on the training set was 0.81 (95% CI 0.764– 0.855), and for the validation set, it was 0.83 (95% CI 0.751– 0.91). Additionally, the calibration curves and decision curve analyses in both the training and validation sets demonstrated the accuracy and clinical applicability of the predictive model.Conclusion: The nomogram has a good predictive ability for the health status of older patients aged 80 years and above after abdominal surgery for one year, which can help clinical doctors develop better treatment plans.Keywords: nomogram, postoperative, health status, aged, 80 and over, major abdominal surgery

Keywords

• nomogram
• postoperative
• health status
• aged
• 80 and over
• major abdominal surgery