Introduction

We aimed to assess whether machine learning models are superior at predicting acute kidney injury (AKI) compared to logistic regression (LR), a conventional prediction model.

Methods

Eligible studies were identified using PubMed and Embase. A total of 24 studies consisting of 84 prediction models met inclusion criteria. Independent samples t-test was performed to detect mean differences in area under the curve (AUC) between ML and LR models. One-way ANOVA and post-hoc t-tests were performed to assess mean differences in AUC between ML methods.

Results

AUC data were similar between ML (0.736 ± 0.116) and LR (0.748 ± 0.057) models (p = 0.538). However, specific ML models, such as gradient boosting (0.838 ± 0.077), exhibited superior performance at predicting AKI as compared to other ML models in the literature (p < 0.05). Creatinine and urine output, standard variables assessed for AKI staging, were classified as significant predictors across multiple ML models, although the majority of significant predictors were unique and study specific.

Conclusions

These data suggest that ML models perform equally to that of LR, however ML models exhibit variable performance with some ML models displaying exceptional performance. The variability in ML prediction of AKI can be attributed, in part, to the specific ML model utilized, variable selection and processing, study and subject characteristics, and the steps associated with model training, validation, testing, and calibration.

中文翻译：

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机器学习和逻辑回归模型在预测急性肾损伤中的比较：系统评价和荟萃分析

介绍

我们旨在评估机器学习模型在预测急性肾损伤（AKI）方面是否优于传统的预测模型logistic回归（LR）。

方法

使用PubMed和Embase鉴定了合格的研究。由84个预测模型组成的总共24项研究符合纳入标准。进行独立样本t检验以检测ML和LR模型之间曲线下面积（AUC）的平均差异。进行单向方差分析和事后t检验以评估ML方法之间AUC的平均差异。

结果

ML（0.736±0.116）和LR（0.748±0.057）模型之间的AUC数据相似（p = 0.538）。但是，与文献中的其他ML模型相比，特定的ML模型（例如梯度增强（0.838±0.077））在预测AKI时表现出了优越的性能（p <0.05）。尽管肌酐和尿液的排泄量是AKI分期的标准变量，但在大多数ML模型中均被归类为重要的预测指标，尽管大多数重要的预测指标都是独特的且针对特定研究而定。

结论

这些数据表明ML模型的性能与LR相同，但是ML模型表现出可变的性能，而某些ML模型则表现出出众的性能。AKI ML预测的可变性可以部分归因于所使用的特定ML模型，变量选择和处理，研究和主题特征，以及与模型训练，验证，测试和校准相关的步骤。