Development and external validation of an acute kidney injury risk score for use in the general population

Abstract

Background Improving recognition of patients at increased risk of acute kidney injury (AKI) in the community may facilitate earlier detection and implementation of proactive prevention measures that mitigate the impact of AKI. The aim of this study was to develop and externally validate a practical risk score to predict the risk of AKI in either hospital or community settings using routinely collected data. Methods Routinely collected linked datasets from Tayside, Scotland, were used to develop the risk score and datasets from Kent in the UK and Alberta in Canada were used to externally validate it. AKI was defined using the Kidney Disease: Improving Global Outcomes serum creatinine–based criteria. Multivariable logistic regression analysis was performed with occurrence of AKI within 1 year as the dependent variable. Model performance was determined by assessing discrimination (C-statistic) and calibration. Results The risk score was developed in 273 450 patients from the Tayside region of Scotland and externally validated into two populations: 218 091 individuals from Kent, UK and 1 173 607 individuals from Alberta, Canada. Four variables were independent predictors for AKI by logistic regression: older age, lower baseline estimated glomerular filtration rate, diabetes and heart failure. A risk score including these four variables had good predictive performance, with a C-statistic of 0.80 [95% confidence interval (CI) 0.80–0.81] in the development cohort and 0.71 (95% CI 0.70–0.72) in the Kent, UK external validation cohort and 0.76 (95% CI 0.75–0.76) in the Canadian validation cohort. Conclusion We have devised and externally validated a simple risk score from routinely collected data that can aid both primary and secondary care physicians in identifying patients at high risk of AKI.

Development and Validation of a Nomogram for Predicting Drug-Induced Acute Kidney Injury in Hospitalized Patients: A Case-Control Study Based on Propensity-Score Matching

“…Again, these six selected predictors have been shown to be strongly associated with kidney damage in several studies (Kane-Gill and Goldstein, 2015;Motwani et al, 2018;Zhou et al, 2018;Guan et al, 2019;Wang Q. et al, 2019). In addition, we found that in the initial population of this study, D-AKI patients were older than non-D-AKI patients (median 65 vs. 56 years; p < 0.001), and advanced age has become a major risk factor for AKI due to changes in renal structure and function in older adults, which has been confirmed and incorporated into many AKI risk models (Mehran et al, 2004;Zhou et al, 2018;Bell et al, 2020). In order to find more targeted D-AKI predictors, we balanced demographic data including age; length of hospitalization and duration of suspected drug exposure by propensity score matching.…”

“…Recent studies haves shown that both are mutual risk factors and risk factors for cardiovascular disease (Awdishu and Mehta, 2017). Similarly, eGFR, the main diagnostic indicator of CKD, has been used as an independent predictor by multiple AKI prediction models (Bell et al, 2020;Hu et al, 2020). We also found a significant correlation between alcohol abuse and D-AKI.…”

Development and Validation of a Nomogram For Predicting Drug-Induced Acute Kidney Injury in Hospitalized Patients

“…Again, these six selected predictors have been shown to be strongly associated with kidney damage in several studies [3,15,21,27,28] . In addition, we found that in the initial population of this study, D-AKI patients were older than non-D-AKI patients (median 65 vs. 56 years; P < 0.001), and advanced age has become a major risk factor for AKI due to changes in renal structure and function in older adults, which has been con rmed and incorporated into many AKI risk models [13,15,26] . In order to nd more targeted D-AKI predictors, we balanced demographic data including age; length of hospitalization and duration of suspected drug exposure by propensity score matching.…”

“…The D-AKI risk prediction nomogram was developed by the development group and veri ed in the validation group. Previous research experience has shown that an AUC value greater than 0.7 indicates good predictive performance of the model [21,26] . In our model, the AUC values for the development and validation groups were 0.787 (95%CI: 0.752-0.823) and 0.788 (95%CI: 0.736-0.840), respectively.…”

“…Recent studies haves shown that both are mutual risk factors and risk factors for cardiovascular disease [4] . Similarly, eGFR, the main diagnostic indicator of CKD, has been used as an independent predictor by multiple AKI prediction models [26,38] . We also found a signi cant correlation between alcohol abuse and D-AKI.…”