Development of a Nomogram Model for Predicting the Risk of In-Hospital Death in Patients with Acute Kidney Injury

Xiuying Yao,1,* Lixiang Zhang,2,* Lei Huang,1,* Xia Chen,2,* Li Geng,1 Xu Xu1 1Department of Intensive Care Unit, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230036, People’s Republic of China; 2Department of Nursing DepartmeThe First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230036, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiuying YaoDepartment of Intensive Care Unit, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 1, Swan Lake Road, Hefei, Anhui Province, People’s Republic of ChinaTel +86-18963782620Email xiuyingyao@126.comObjective: To analyze the risk factors of in-hospital death in patients with acute kidney injury (AKI) in the intensive care unit (ICU), and to develop a personalized risk prediction model.Methods: The clinical data of 137 AKI patients hospitalized in the ICU of Anhui provincial hospital from January 2018 to December 2020 were retrospectively analyzed. Patients were divided into two groups: those that survived to discharge (“survival” group, 100 cases) and those that died while in hospital (“death” group, 37 cases), and risk factors for in-hospital death analyzed.Results: The in-hospital mortality of AKI patients in the ICU was 27.01% (37/137). A multivariate logistic regression analysis indicated age, mechanical ventilation and vasoactive drugs were significant risk factors for in-hospital death in AKI patients, and a nomogram risk prediction model was developed. The Harrell’s C-index of the nomogram model was 0.891 (95% CI: 0.837– 0.945), and the area under the receiver operating characteristic (ROC) curve was 0.886 (95% CI: 0.823– 0.936) after internal validation, indicating that the nomogram model had good discrimination. The Hosmer–Lemeshow goodness of fit test and calibration curve indicated the predicted probability of the nomogram model was consistent with the actual frequency of death in ICU patients with AKI. The decision curve analysis (DCA) showed that the clinical net benefit level of the nomogram model is highest when the probability threshold of AKI is between 0.01 and 0.75.Conclusion: Patients in the ICU with AKI had high in-hospital mortality and were affected by a variety of risk factors. The nomogram prediction model based on the risk factors of AKI showed good prediction efficiency and clinical applicability, which could help medical staff in the ICU to identify AKI patients with high-risk, allowing early prevention, detection and intervention, and reducing the risk of in-hospital deaths in ICU patients with AKI.Keywords: AKI, ICU, inpatient mortality, risk factors, prediction risk mode