Abstract
Background: Improving recognition of patients at increased risk of acute kidney injury in the community may facilitate earlier detection and implementation of proactive prevention measures that mitigate the impact of acute kidney injury (AKI). The aim of this study was to develop and externally validate a practical risk score to predict the risk of acute kidney injury 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 United Kingdom 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 one 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, United Kingdom and externally validated in two populations; 218,091 individuals from Kent, United Kingdom and 1,173,607 individuals from Alberta, Canada. Four variables were independent predictors for acute kidney injury 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%CI 0.80-0.81) in the development cohort and 0.71 (0.70-0.72) in Kent, UK external validation cohort and 0.76 (0.75- 0.76) in the Canadian validation cohort.
Conclusion: We have devised and externally validated simple risk score from routinely collected data which can aid both primary and secondary care physicians in identifying patients at high risk of AKI.
Methods: Routinely collected linked datasets from Tayside, Scotland, were used to develop the risk score, and datasets from Kent in the United Kingdom 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 one 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, United Kingdom and externally validated in two populations; 218,091 individuals from Kent, United Kingdom and 1,173,607 individuals from Alberta, Canada. Four variables were independent predictors for acute kidney injury 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%CI 0.80-0.81) in the development cohort and 0.71 (0.70-0.72) in Kent, UK external validation cohort and 0.76 (0.75- 0.76) in the Canadian validation cohort.
Conclusion: We have devised and externally validated simple risk score from routinely collected data which can aid both primary and secondary care physicians in identifying patients at high risk of AKI.
| Original language | English |
|---|---|
| Pages (from-to) | 402-412 |
| Number of pages | 11 |
| Journal | Clinical Kidney Journal |
| Volume | 13 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 16 Jul 2020 |
Keywords
- acute kidney injury
- risk score
- epidemiology
