Prognostic Value of Leukocyte-Based Risk Model for Acute Kidney Injury Prediction in Critically Ill Acute Exacerbation of Chronic Obstructive Pulmonary Disease Patients

Abstract

Purpose: Acute kidney injury (AKI) is a common complication of acute exacerbation of chronic obstructive pulmonary disease (AECOPD), and inflammation is the potential link between AKI and AECOPD. However, little is known about the incidence and risk stratification of AKI in critically ill AECOPD patients. In this study, we aimed to establish risk model based on white blood cell (WBC)-related indicators to predict AKI in critically ill AECOPD patients.

Material and methods: For the training cohort, data were taken from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) database, and for the validation cohort, data were taken from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. The study employed logistic regression analysis to identify the major predictors of WBC-related biomarkers on AKI prediction. Subsequently, a risk model was developed by multivariate logistic regression, utilizing the identified significant indicators.

Results: Finally, 3551 patients were enrolled in training cohort, 926 patients were enrolled in validation cohort. AKI occurred in 1206 (33.4%) patients in training cohort and 521 (56.3%) patients in validation cohort. According to the multivariate logistic regression analysis, four WBC-related indicators were finally included in the novel risk model, and the risk model had a relatively good accuracy for AKI in the training set (C-index, 0.764, 95% CI 0.749-0.780) as well as in the validation set (C-index, 0.738, 95% CI: 0.706-0.770). Even after accounting for other models, the critically ill AECOPD patients in the high-risk group (risk score > 3.44) still showed an increased risk of AKI (odds ratio: 4.74, 95% CI: 4.07-5.54) compared to those in low-risk group (risk score ≤ 3.44). Moreover, the risk model showed outstanding calibration capability as well as therapeutic usefulness in both groups for AKI and ICU mortality and in-hospital mortality of critical ill AECOPD patients.

Conclusion: The novel risk model showed good AKI prediction performance. This risk model has certain reference value for the risk stratification of AECOPD complicated with AKI in clinically.

Keywords: acute exacerbation of chronic obstructive pulmonary disease; acute kidney injury; prediction; risk model; white blood cell.

Figure 1

The flow chart of this study.

Figure 2

The correlations between risk score with other serum inflammatory biomarkers, severity score, and clinical outcomes in the training set (A) and in the validation set (B). *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 3

The waterfall plots and forest plots of the high-risk group and low risk group for the prediction of acute kidney injury and acute respiratory failure for patients in the training set. The waterfall plot of risk score for each patient of acute kidney injury (A) and acute respiratory failure (B). The subgroup analysis of the risk score in individuals for acute kidney injury and acute respiratory failure (C).

Figure 4

The waterfall plots and forest plots of the high-risk group and low risk group for the prediction of in-hospital mortality and ICU mortality in the training set. The waterfall plot of risk score for each patient of in-hospital mortality (A) and ICU mortality (B). The subgroup analysis of the risk score in individuals for in-hospital mortality and ICU mortality (C).

Figure 5

The risk score was established to detect the ICU mortality of patients in the training set. All patients were distinguished into high and low risk based on the risk score (A), the relationship between survival time and prognosis of patients in the two corresponding groups (B), and the heatmap of WBC-based markers between the two groups (C). Receiver operating characteristic (ROC) curve analysis of the risk score for ICU mortality (D), Decision curve analysis of the risk score for ICU mortality (E). Kaplan–Meier curves showing the ICU mortality of groups with different risk (F).

Figure 6

The risk score was established to detect the in-hospital mortality of patients in the training set. All patients were distinguished into high and low risk based on the risk score (A), the relationship between survival time and prognosis of patients in the two corresponding groups (B), and the heatmap of WBC-based markers between the two groups (C). Receiver operating characteristic (ROC) curve analysis of the risk score for in-hospital mortality (D), Decision curve analysis of the risk score for in-hospital mortality (E). Kaplan–Meier curves showing the in-hospital mortality of groups with different risk (F).