Machine learning to predict end stage kidney disease in chronic kidney disease | Scientific Reports –

With extensive utilization of electronic health record and recent progress in ML research, AI is expanding its impact on healthcare and has gradually changed the way clinicians pursue for problem-solving28. Instead of adopting a theory-driven strategy that requires a preformed hypothesis from prior knowledge, training an ML model typically follows a data-driven approach that allows the model to learn from experience alone. Specifically, the model improves its performance iteratively on a training set by comparing the predictions to the ground truths and adjusting model parameters so as to minimize the distance between the predictions and the truths. In nephrology, ML has demonstrated promising performances in predicting acute kidney injury or time to allograft loss from clinical features29,30, recognizing specific patterns in pathology slides31,32, choosing an optimal dialysis prescription33, or mining text in the electronic health record to find specific cases34,35. Additionally, a few recent studies were performed to predict the progression of CKD using ML methods. These models were developed to estimate the risk of short-term mortality following dialysis36, calculate the future eGFR values37, or assess the 24-h urinary protein levels18. To our best knowledge, there hasn’t been any attempt to apply ML methods to predict the occurrence of ESKD in CKD patients.

In the present study, a prediction model for ESKD in CKD patients was explored using ML techniques. Most classifiers demonstrated adequate performance based on easily accessible patient information that is convenient for clinical translation. In general, three ML models, including the logistic regression, naïve Bayes and random forest, showed non-inferior performance to the KFRE in this study. These findings imply ML as a feasible approach for predicting disease progression in CKD, which could potentially guide physicians in establishing personalized treatment plans for this condition at an early stage. These ML models with higher sensitivity scores may also be practically favored in patient screening over the KFRE.

To our best understanding, this study was also the first to validate the KFRE in CKD patients of Mainland China. The KFRE was initially developed and validated using North American patients with CKD stage 3–512. There were seven KFRE models that consisted of different combinations of predictor variables. The most commonly used KFRE included a 4-variable model (age, gender, eGFR and urine ACR) or an 8-variable model (age, gender, eGFR, urine ACR, serum calcium, phosphorous, bicarbonate, and albumin). Besides, there was a 3-variable model (age, gender, and eGFR) that required no urine ACR and still showed comparable performance to the other models in the original article. Despite its favorable performance in prediction for ESKD in patients of Western countries14,15,38,39, the generalizability of KFRE in Asian population remained arguable following the suboptimal results revealed by some recent papers13,40,41. In the current study, the KFRE was validated in a Chinese cohort with CKD stage 1–5 and showed an AUC of 0.80. This result indicated the KFRE was adequately applicable to the Chinese CKD patients and even earlier disease stages. In particular, the high specificity score (0.95) may favor the use of this equation in ruling in patients who require close monitoring of disease progression. On the other hand, a low sensitivity (0.47) at the default threshold may suggest it may be less desirable than the other models for ruling out patients.

Urine test is a critical diagnostic approach for CKD. The level of albuminuria (i.e. ACR) has also been regarded as a major predictor for disease progression and therefore used by most prognostic models. However, quantitative testing for albuminuria is not always available in China especially in rural areas, which precludes clinicians from using most urine-based models for screening patients. In this regard, several simplified models were developed to predict CKD progression without the need of albuminuria. These models were based on patient characteristics (e.g. age, gender, BMI, comorbidity) and/or blood work (e.g. creatinine/eGFR, BUN), and still able to achieve an AUC of 0.87–0.8912,18 or a sensitivity of 0.8837. Such performance was largely consistent with the findings of this study and comparable or even superior to some models incorporating urine tests16,42. Altogether, it suggested a reliable prediction for CKD progression may be obtained from routine clinical variables without urine measures. These models are expected to provide a more convenient screening tool for CKD patients in developing regions.

Missing data are such a common problem in ML research that they can potentially lead to a biased model and undermine the validity of study outcomes. Traditional methods to handle missing data include complete case analysis, missing indicator, single value imputation, sensitivity analyses, and model-based methods (e.g. mixed models or generalized estimating equations)43,44,45. In most scenarios, complete case analysis and single value imputation are favored by researchers primarily due to the ease of implementation45,46,47. However, these methods may be associated with significant drawbacks. For example, by excluding samples with missing data from analyses, complete case analysis can result in reduction of model power, overestimation of benefit and underestimation of harm43,46; Single value imputation replaces the missing data by a single value—typically the mean or mode of the complete cases, thereby increasing the homogeneity of data and overestimating the precision43,48. In this regard, multiple imputation solves these problems by generating several different plausible imputed datasets, which account for the uncertainty about the missing data and provide unbiased estimates of the true effect49,50. It is deemed effective regardless of the pattern of missingness43,51. Multiple imputation is now widely recognized as the standard method to deal with missing data in many areas of research43,45. In the current study, a 5-set multiple imputation method was employed to obtain reasonable variability of the imputed data. The performance of each model was analyzed on each imputed set and pooled for the final result. These procedures ensured that the model bias resulting from missing data was minimized. In the future, multiple imputation is expected to become a routine method for missing data handling in ML research, as the extra amount of computation associated with multiple imputation over those traditional methods can simply be fulfilled by the high level of computational power required by ML.

Although ML has been shown to outperform traditional statistics in a variety of tasks by virtue of the model complexity, some studies demonstrated no gain or even declination of performance compared to traditional regression methods52,53. In this study, the simple logistic regression model also yielded a comparable or even superior predictability for ESKD to other ML algorithms. The most likely explanation is that the current dataset only had a small sample size and limited numbers of predictor variables, and the ESKD+ cases were relatively rare. The lack of big data and imbalanced class distribution may have negative impact on the performance of complex ML algorithms, as they are typically data hungry54. On the other hand, this finding could imply simple interactions among the predictor variables. In other words, the risk of ESKD may be largely influenced by only a limited number of factors in an uncomplicated fashion, which is consistent with some previous findings12,18,55. The fact that the 3-variable KFRE, which is also a regression model, yielded equivalent outcomes to the best ML models in this study may further support this implication. It is therefore indicated that traditional regression models may continue to play a key role in disease risk prediction, especially when a small sample size, limited predictor variables, or an imbalanced dataset is encountered. The fact that some of the complex ML models are subject to the risk of overfitting and the lack of interpretability further favors the use of simple regression models, which can be translated to explainable equations.

Several limitations should be noted. First, this cohort consisted of less than 1000 subjects and ESKD only occurred in a small portion of them, both of which might have affected model performance as discussed earlier. Second, although this study aimed to assess the feasibility of a prediction model for ESKD without any urine variables, this was partially due to the lack of quantitative urine tests at our institute when this cohort was established. As spot urine tests become increasingly popular, urine features such as ACR will be as accessible and convenient as other lab tests. They are expected to play a critical role in more predictive models. Third, the KFRE was previously established on stages 3–5 CKD patients while the current cohort contained stages 1–5. This discrepancy may have affected the KFRE performance. Forth, the generalizability of this model has not been tested on any external data due to the lack of such resource in this early feasibility study. Therefore, additional efforts are required to improve and validate this model before any clinical translation. Finally, although a simple model without urine variables is feasible and convenient, model predictability may benefit from a greater variety of clinical features, such as urine tests, imaging, or biopsy. Future works should include training ML models with additional features using a large dataset, and validating them on external patients.

In conclusion, this study showed the feasibility of ML in evaluating the prognosis of CKD based on easily accessible features. Logistic regression, naïve Bayes and random forest demonstrated comparable predictability to the KFRE in this study. These ML models also had greater sensitivity scores that were potentially advantageous for patient screenings. Future studies include performing external validation and improving the model with additional predictor variables.

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