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Article analysis:

This article is an observational study that aims to develop and externally validate a model to predict renal function decline in Chinese patients with type 2 diabetic kidney disease (T2DKD). The authors have used Cox regression to establish the prediction model, which is represented by a nomogram and a risk table that can be easily applied in clinical practice using readily available clinical parameters. The performance of the prediction model was assessed by discrimination and calibration, with satisfactory results for both metrics in both the derivation cohort and external validation cohort.

The trustworthiness of this article is generally good, as it follows the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement23 and has been approved by the Ethics Committee of Zhongda Hospital, Southeast University (2019ZDSYLL057-P01). Furthermore, all data were collected from electronic medical records of these patients, which reduces potential bias due to recall errors or other sources of inaccuracy from self-reported data collection methods such as surveys or interviews. Additionally, missing data were imputed using random forests-based missing data imputation (R package missForest),27 further reducing potential bias due to incomplete data collection methods.

However, there are some potential biases that should be noted when interpreting the results of this study: firstly, this is an observational study rather than an experimental one; thus, it cannot prove causality between predictor variables and outcomes due to potential confounding factors that may not have been accounted for or measured accurately; secondly, since this is a retrospective study based on existing medical records rather than prospective data collection methods such as surveys or interviews, there may be some inaccuracies due to incomplete or inaccurate record keeping; thirdly, since this study only included hospitalized T2DKD patients from