Acute kidney injury has high morbidity and mortality, especially in ICUs. For the purpose of early identification of AKI, researching on the risk prediction model of AKI is necessary. Existing models have many flaws. This model made up for some defects and proposed a few new risk prediction factors.
Clinically, if all the risk factors are identified and quantified to use to profile risk, 20%–30% AKI can be predicted and avoided . After admission to a hospital, early prediction of AKI can bring high opportunity to prevent patients from developing AKI. There have been a few prediction models for AKI risk using in ICUs, but there are still many researchers trying to develop risk prediction models because we still need more sensitive and more accurate models to apply to the clinic.
We used logistic regression to develop a model. Of all the candidate variables, only a few turned out to predict AKI (hypertension, chronic kidney disease, acute pancreatitis, cardiac failure, shock, pH ≤ 7.30, CK > 1000 U/L, hypoproteinemia, nephrotoxin exposure, and male). In our analysis, some variables had high significance in univariate analysis (age > 70 years, diabetes, chronic liver disease, coronary heart disease, cancer, trauma, respiratory failure, sepsis, and major surgery), but were not selected into the final model. This means there may be some false associations or indirect associations between these variables and independent predictors. These variables turned out not independent predictors in this model. However, there are previous reports showing their relationship with the development of AKI. We still need more research about these variables.
Using risk ranges, a total score of 0–4 points was associated with a low risk of AKI. Patients with a total score of 5–15 have a high risk to develop AKI. However, this is not absolute. In our study, 22% of low-risk patients also developed AKI, which indicates that this risk prediction score is not perfect yet. In the following work, more efforts should be pay to include more variables and perform more accurate verification to perfect it.
There are several strengths of our study. Firstly, this is a prospective observational study. We collected data prospectively to ensure the information detailed and reliable and to reduce the impact of bias. Secondly, we included a lot of candidate risk factors in our research. The risk factors we have included are very comprehensive. Most of those which reported in previous researches have been included. All of them are medical history data and clinical examinations easily to get, which are convenient for clinicians to apply. With only 10 variables in the risk prediction score, it is simple to calculate. Thirdly, two variables (acute pancreatitis and CK > 1000 U/L) in our risk prediction score have not been included in other models before. There is a study  which reported the relationship between acute pancreatitis and AKI but there is no study included acute pancreatitis into the model. The incidence of acute pancreatitis in China is relatively high and the main causes of acute pancreatitis are different from those in Europe and America. Our study raises the possibility that acute pancreatitis in China is highly correlated with the occurrence of AKI. There is also a study  suggesting the correlation between rhabdomyolysis and AKI but no study include incorporated rhabdomyolysis into the model. We considered that it might be that rhabdomyolysis’ diagnostic criteria led to this result. We relaxed the standard of CK to greater than 1000 and found it highly correlated with the occurrence of AKI. Finally, the risk factors we included are similar to other studies, but which incorporated into the model were different. That may indicate that the risk factors for AKI differ depending on the regions and races. It provides a direction for future researchs.
However, our study also has several limitations. The most important one is that this is a monocenter study. We did not conduct externally verification and only verified internally. This may cause some problems with the extrapolation of our model and the results may not be widely generalizable in other regions and races. Secondly, recent studies emphasized the importance of AKI biomarkers to predict AKI [31, 32]. However, for ease of application, we did not include biomarkers because they are not yet widely used clinically. Thirdly, KDIGO criterion diagnoses AKI with both urine volume and serum creatinine. Nevertheless, we used serum creatinine only on account of the inaccuracy of urine volume data. Finally, In order to facilitate statistics, we represent most of the variables as binary variables, and the severity of each variable is not taken into account.
Prediction of AKI still has high importance because it is associated with high mortality and high morbidity. Our future studies will focus on improving our model by expanding the sample size and performing external validation. We may bring in some refined biomarkers as predictors.
There is no single intervention can improve the outcome of AKI patients, so a risk prediction model would most likely to be used as a measure to help those patients. On this road, we should all work harder because there is still a long way to go.