Khandpur Sukhanshi, Mishra Prabhaker, Mishra Shambhavi, Tiwari Swasti
Department of Molecular Medicine & Biotechnology, Sanjay Gandhi Post Graduate Institute of Medical Science, Lucknow 226014, Uttar Pradesh, India.
Department of Biostatistics and Health Informatics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India.
World J Nephrol. 2024 Sep 25;13(3):97214. doi: 10.5527/wjn.v13.i3.97214.
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.
全球慢性肾脏病（CKD）负担呈指数级增长，给经济带来了巨大压力。CKD的预测模型可以通过在疾病发作前预测未来疾病发生情况来减轻这种负担。基于结果变量的分布，有各种回归方法用于预测建模。然而，预测模型的准确性取决于模型的构建质量，这包括考虑拟合优度、协变量的选择、连续尺度测量的协变量的处理、分类协变量的处理以及每个预测参数或样本量的结果事件数量。我们期望预测模型在独立队列中具有最佳性能。然而，CKD的预测建模存在几个挑战。特定疾病的方法学挑战阻碍了开发具有成本效益且普遍适用于预测CKD发作的预测模型。在本综述中，我们讨论了可用于预测建模的各种回归模型的优点和挑战，并强调了那些最适合未来CKD预测的模型。
