Challenges in predictive modelling of chronic kidney disease: A narrative review.

Sukhanshi Khandpur, Prabhaker Mishra, Shambhavi Mishra, Swasti Tiwari
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Abstract

The exponential rise in the burden of chronic kidney disease (CKD) worldwide has put enormous pressure on the economy. Predictive modeling of CKD can ease this burden by predicting the future disease occurrence ahead of its onset. There are various regression methods for predictive modeling based on the distribution of the outcome variable. However, the accuracy of the predictive model depends on how well the model is developed by taking into account the goodness of fit, choice of covariates, handling of covariates measured on a continuous scale, handling of categorical covariates, and number of outcome events per predictor parameter or sample size. Optimal performance of a predictive model on an independent cohort is desired. However, there are several challenges in the predictive modeling of CKD. Disease-specific methodological challenges hinder the development of a predictive model that is cost-effective and universally applicable to predict CKD onset. In this review, we discuss the advantages and challenges of various regression models available for predictive modeling and highlight those best for future CKD prediction.

慢性肾脏病预测建模的挑战:叙述性综述。
全球慢性肾脏病(CKD)负担的指数式增长给经济带来了巨大压力。慢性肾脏病的预测建模可以在发病前预测未来的疾病发生率,从而减轻这种负担。目前有多种基于结果变量分布的预测建模回归方法。然而,预测模型的准确性取决于模型的开发程度,要考虑到拟合度、协变量的选择、连续测量协变量的处理、分类协变量的处理以及每个预测参数的结果事件数量或样本大小。人们希望预测模型在独立队列中发挥最佳性能。然而,CKD 的预测建模面临着一些挑战。针对特定疾病的方法学挑战阻碍了具有成本效益且普遍适用于预测 CKD 发病的预测模型的开发。在这篇综述中,我们将讨论可用于预测建模的各种回归模型的优势和挑战,并重点介绍最适合未来 CKD 预测的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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