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Reply to letter to the editor ‘Serum heart-type fatty acid-binding protein as a predictor for the development of sepsis-associated acute kidney injury: methodological issues’

We appreciate the author’s patient reading and detailed comments on our recent article titled ‘Serum heart-type fatty acidbinding protein (HFABP) as a predictor for the development of sepsis-associated acute kidney injury (SAKI) [1]’. The main point of the author’s letter was that associations do not mean predictability and invalidated models are unreliable. In recent years, the prediction model researches have been increasing rapidly, which indicates that machine learning and other analytical technologies are playing an increasingly significant role in big data analysis in clinical research. However, the quality problem of prediction models has also been exposed. Some reviews assessed the quality of published reports describing the development or validation of prediction models. The results showed that serious deficiencies were common in statistical methods, such as the use of small data sets, improper handling of missing data, and lack of validation [2–7]. There are several points that we would like to explain. First, the reason why we introduced the concept of the prediction model is to explore the potential value of the correlation between HFABP and SAKI by comparing the differentiation of the with the other two risk models. Second, the results of our study did not confirm the feasibility of the prediction model. Whether HFABP could become an influential characteristic variable of SAKI prediction model requires further research and more data analysis. Third, the focus of our study was to explore the potential correlation between HFABP and SAKI with retrospective analysis data, rather than to build a risk prediction model for SAKI. However, due to the small sample size and patient selection bias, we did not have plans and conditions for developing a prediction model for SAKI. In our study, logistic regression analysis was adopted to build the clinical risk model, which is the most commonly used method for short-term outcomes [8–10]. We indeed didn’t validate the calibration of the model, which seemed to be an insufficiently rigorous approach. Finally, to ensure the feasibility and reliability of the predictable model, validation is an essential step in the construction procedure. Funding