Development and validation of a dynamic online nomogram for predicting acute kidney injury in cirrhotic patients upon ICU admission

Lu-Huai Feng; Yang Lu; Shuang Ren; Hengkai Liang; Lu Wei; Jianning Jiang

doi:10.3389/fmed.2023.1055137

Development and validation of a dynamic online nomogram for predicting acute kidney injury in cirrhotic patients upon ICU admission

Front Med (Lausanne). 2023 Jan 27:10:1055137. doi: 10.3389/fmed.2023.1055137. eCollection 2023.

Authors

Lu-Huai Feng¹, Yang Lu¹, Shuang Ren², Hengkai Liang¹, Lu Wei¹, Jianning Jiang¹

Affiliations

¹ Department of Infectious Diseases, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
² Department of Comprehensive Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, China.

Abstract

Background: Acute kidney injury (AKI) is one of the most common and deadly complications among cirrhotic patients at intensive care unit (ICU) admission. We aimed to develop and validate a simple and clinically useful dynamic nomogram for predicting AKI in cirrhotic patients upon ICU admission.

Methods: We analyzed the admission data of 4,375 patients with liver cirrhosis in ICU from 2008 to 2019 in the intensive care unit IV (MIMIC-IV) database. The eligible cirrhotic patients were non-randomly divided into derivation (n = 2,188) and validation (n = 2,187) cohorts at a ratio of 1:1, according to the order of admission. The least absolute shrinkage and selection operator regression model was used to identify independent predictors of AKI in the derivation cohort. A dynamic online nomogram was built using multivariate logistic regression analysis in the derivation cohort and then validated in the validation cohort. The C-index, calibration curve, and decision curve analysis were used to assess the nomogram's discrimination, calibration, and clinical usefulness, respectively.

Results: The incidence of AKI in 4,375 patients was 71.3%. Ascites, chronic kidney disease, shock, sepsis, diuretic drugs, hepatic encephalopathy, bacterial infections, vasoactive drugs, admission age, total bilirubin, and blood urea nitrogen were identified using the multivariate logistic regression analysis as significant predictors of AKI upon ICU admission. In the derivation cohort, the model showed good discrimination (C-index, 0.786; 95% CI, 0.765-0.806) and good calibration. The model in the validation cohort yielded good discrimination (C-index, 0.774; 95% CI, 0.753-0.795) and good calibration. Decision curve analysis demonstrated that the dynamic online nomogram was clinically useful.

Conclusion: Our study presents a dynamic online nomogram that incorporates clinical predictors and can be conveniently used to facilitate the individualized prediction of AKI in cirrhotic patients upon ICU admission.

Keywords: acute kidney injury; dynamic online nomogram; intensive care unit; model; prediction.