Establishing a risk prediction model for acute kidney injury: methodology is important

Abstract

[...]the multivariate regression analysis was used for the identification of risk factors of AKI. If an important risk factor is missed, the multivariate adjustment for the odd ratio of the measured outcome can be biased and even a spurious association between the intervention and measured outcome may be obtained. [...]we argue that not taking emergent PCI into the model would have tampered with the inferences of multivariate regression analysis for risk factors of AKI and their adjusted odds ratios. [5] Finally, an important ignore by the authors was that the statistical validation of their model was not performed. Because the predictive model was developed by multivariate regression analysis using demographic, clinical, and other variables to generate outcome estimates, overfitting is a common issue, especially when the number of predictors and interaction terms are large, and the number of events is small.