A Prediction Model for Assessing Prognosis in Critically Ill Patients with Sepsis-associated Acute Kidney Injury. Issue 4 (October 2021) - Hu, Hongbin; Li, Lulan; Zhang, Yuan; Sha, Tong; Huang, Qiaobing; Guo, Xiaohua; An, Shengli; Chen, Zhongqing; Zeng, Zhenhua
ABSTRACT: Background: Sepsis-associated acute kidney injury (SA-AKI) is a common problem in critically ill patients and is associated with high morbidity and mortality. Early prediction of the survival of hospitalized patients with SA-AKI is necessary, but a reliable and valid prediction model is still lacking. Methods: We conducted a retrospective cohort analysis based on a training cohort of 2, 066 patients enrolled from the Multiparameter Intelligent Monitoring in Intensive Care Database III (MIMIC III) and a validation cohort of 102 patients treated at Nanfang Hospital of Southern Medical University. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were used to identify predictors for survival. Areas under the ROC curves (AUC), the concordance index (C-index), and calibration curves were used to evaluate the efficiency of the prediction model (SAKI) in both cohorts. Results: The overall mortality of SA-AKI was approximately 18%. Age, admission type, liver disease, metastatic cancer, lactate, BUN/SCr, admission creatinine, positive culture, and AKI stage were independently associated with survival and combined in the SAKI model. The C-index in the training and validation cohorts was 0.73 and 0.72. The AUC in the training cohort was 0.77, 0.72, and 0.70 for the 7-day, 14-day, and 28-day probability of in-hospital survival, respectively, while in the external validation cohort, it was 0.83, 0.73, and 0.67. SAPSII and SOFA scores showed poorer performance. Calibration curves demonstrated a good consistency. Conclusions: Our SAKI model has predictive value for in-hospital mortality of SA-AKI in critically ill patients and outperforms generic scores. Abstract : Supplemental Digital Content is available in the text