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<p>Chronic Kidney Disease (CKD) is a leading non-communicable disease with rapidly rising prevalence, posing substantial clinical and economic burdens globally. This study employs predictive modeling to identify key determinants of CKD, contributing to Sustainable Development Goals (SDGs) related to NCD mortality reduction, universal health coverage and financial risk protection. Although diabetes and hypertension are well-established contributors to CKD, this study explores a broader spectrum of clinical, metabolic and symptomatic predictors. A dataset of 1,659 anonymized patient records was analyzed using Spearman’s correlation and multivariable logistic regression. The association matrix revealed that 14 variables had statistically significant associations with CKD (<i>p</i> &lt; 0.05). Key correlates included serum creatinine, blood pressure, fasting glucose and HbA1c.The multivariable logistic regression model identified 11 independent predictors, accounting for 23.5% of the variability in CKD risk as measured by Nagelkerke R², highlighting the multifactorial nature of disease prediction. These findings underscore the multi-dimensional nature of CKD progression and reinforce the need for multifactorial screening protocols. The results provide evidence that combining metabolic (fasting glucose, HbA1c), renal (serum creatinine, GFR, BUN) and symptom-based indicators (itching, cramps) into a single profiling model can strengthen CKD risk assessments beyond traditional reliance on diabetes and hypertension alone. From a policy perspective, these findings highlight the potential value of community-based screening programs that include a broader panel of symptom-based and biochemical indicators, while emphasizing the need for validation in larger, multi-centric studies before incorporation into guidelines.</p>

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Strengthening Chronic Kidney Disease Prevention Through Predictive Profiling: Implications for Public Health Policy

  • 期刊论文

  • 发表日期：

    2026年1月22日

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  • Namsit Khandelwal,
  • Raunak Shikhar,
  • Sujoy Chakraborty,
  • Vivek Kumar,
  • Aakarsh Chandra,
  • Poornima Suryanath Singh

摘要

Chronic Kidney Disease (CKD) is a leading non-communicable disease with rapidly rising prevalence, posing substantial clinical and economic burdens globally. This study employs predictive modeling to identify key determinants of CKD, contributing to Sustainable Development Goals (SDGs) related to NCD mortality reduction, universal health coverage and financial risk protection. Although diabetes and hypertension are well-established contributors to CKD, this study explores a broader spectrum of clinical, metabolic and symptomatic predictors. A dataset of 1,659 anonymized patient records was analyzed using Spearman’s correlation and multivariable logistic regression. The association matrix revealed that 14 variables had statistically significant associations with CKD (p < 0.05). Key correlates included serum creatinine, blood pressure, fasting glucose and HbA1c.The multivariable logistic regression model identified 11 independent predictors, accounting for 23.5% of the variability in CKD risk as measured by Nagelkerke R², highlighting the multifactorial nature of disease prediction. These findings underscore the multi-dimensional nature of CKD progression and reinforce the need for multifactorial screening protocols. The results provide evidence that combining metabolic (fasting glucose, HbA1c), renal (serum creatinine, GFR, BUN) and symptom-based indicators (itching, cramps) into a single profiling model can strengthen CKD risk assessments beyond traditional reliance on diabetes and hypertension alone. From a policy perspective, these findings highlight the potential value of community-based screening programs that include a broader panel of symptom-based and biochemical indicators, while emphasizing the need for validation in larger, multi-centric studies before incorporation into guidelines.