Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health
Chronic Kidney Disease is a condition involving the gradual loss of kidney function; your kidneys filter blood to remove waste and toxins which in turn helps control blood pressure and maintain red blood cell function and bone health so their ability to function properly is clearly very important. Chronic Kidney disease is caused by presence of diabetes, high blood pressure, obstruction of the urinary tract and range of other conditions including glomerulonephritis, interstitial nephritis, polycystic kidney disease, vesicoureteral and pyelonephritis (Mayo Clinic). Many patients do not realize they have Chronic Kidney Disease until it has progressed quite far in the 5 stages but when symptoms do show up, they include itching, muscle cramps, lack of appetite, nausea, unusual swelling, changes in frequency of urination and trouble breathing or sleeping. Once diagnosed, the disease is managed by slowing the progression of kidney damage to prevent end-stage kidney failure which necessitates dialysis or a kidney transplant. Currently, 15% of the American population (37 million people) has Chronic Kidney Disease but many of them do not know it – this is a frightening data point given that 340 people begin dialysis treatment every that and that kidney disease is the 9th leading cause of death in the United States (CDC). Additionally, UC San Francisco has calculated that CKD costs $79 billion dollars for Medicare patients and predicts that 16.7% of the population will contract CKD by 2030 which shows a clear need for further research in this area (Kent).
Key Data Points
Several factors increase a patients’ risk of chronic kidney disease including presence of diabetes or hypertension, heart disease, smoking activity, obesity, race (African Americans, Native Americans and Asian Americans are all higher risk race groups), family history and age (Mayo Clinic). These factors are often factored into predictive algorithms along with blood panels which hold key variables such as Albumin to Creatinine Ration (ACR), Serum Creatinine, Blood Urea Nitrogen (BUN) and Glomerular Filtration Rate (GFR). Urine tests can also measure relevant variables such as Urine Protein, Microalbuminuria and Creatine Clearance Rate. All of these variables measure kidney function and can predict the onset or stage of Chronic Kidney Disease.
An important study out of Cairo University utilized multiple algorithms to study the importance of physical variables in class identification of CKD. The study used probabilistic neural networks, multilayer perceptron, support vector machine and radial basis function algorithms to identify which algorithm would most accurately identify a patients’ stage of CKD. The study found that the probabilistic neural network algorithm yielded the highest classification accuracy at 96.7% and used that information to add weight to each considered variable and improve the prediction performance of CKD stage diagnosis. This study showed that each variable was, indeed, not weighted equally. In fact, there was a significant difference between the 100% importance of serum creatinine and a 9.256% importance of hypertension in diagnosis. This is important in identifying at risk groups because, clearly, not everyone with hypertension will have CKD but those at high risk serum creatinine levels are very likely to need treatment (Rady). Research conducted in the United States around CKD draws from the following databases for information: The National Health and Nutrition Examination Survey; United States Renal Data System; Kaiser Permanente; and Veterans Affairs Healthcare System. These databases are essential to the use of artificial intelligence and machine learning techniques because they can provide ranges for many of the physical variables listed above. However, outside of physical variables, research has also been done on nonconventional risk factors of CKD. For example, several studies have evaluated air pollution using “of land-use regression and spatiotemporal models that utilized satellite remote-sensing aerosol optical depth data” to associate air pollution with incidence of CKD in a population. These studies have concluded that increased air pollution could be correlated with incidence of CKD and decrease of glomerular filtration rate. Another study using artificial intelligence used clinical notes to evaluate predictors of CKD and found high-dose ascorbic acid and fast food consumption to be novel predictors (NCBI). Artificial intelligence can actually do most of the heavy lifting in studies like these in which we can gain insight into the impact of factors that we may have never otherwise considered to be relevant in the study of Chronic Kidney Disease.
Chronic Kidney Disease affects (and will continue to affect) a significant number of the population and it is clear that more research needs to be done in this area. To make that possible, some things need to change. For example, accessibility to medical data needs to be made easier so that research can happen at various levels, i.e. medical, academic and corporate. This ensures that those who want to research these topics can do so without the time constraints of existing rules and regulations so that developments can be made mainstream to patients and providers in the timeliest matter. Additionally, federal funding could be redirected to research in this area to improve data processing techniques which are currently fragmented and hinder the success rate of the existing multidimensional algorithms.
The necessary steps for preventing Chronic Kidney Disease are very much in line with leading a generally healthy life. Mayo Clinic recommends that one maintain a healthy weight through physical exercise and calorie reduction, not smoke and follow responsible usage guidelines for over-the-counter medications as abusing pain relievers can cause kidney damage. Furthermore, if you are at risk, it is important to check in with your physician frequently to track and manage symptoms of Chronic Kidney Disease (Mayo Clinic). If you are unsure about whether or not you might be at risk of contracting kidney disease, you may consider using the CDC’s Chronic Kidney Disease Risk Calculator at: https://nccd.cdc.gov/CKD/Calculators.aspx#tab-Bang.
“Chronic Kidney Disease Basics.” Centers for Disease Control and Prevention, Centers for Disease Control and Prevention, 7 Feb. 2020, www.cdc.gov/kidneydisease/basics.html.
“Chronic Kidney Disease.” Mayo Clinic, Mayo Foundation for Medical Education and Research, 15 Aug. 2019, www.mayoclinic.org/diseases-conditions/chronic-kidney-disease/symptoms-causes/syc-20354521.
Kent, Jessica. “Chronic Kidney Disease Patients Face Significant Care Disparities.” HealthITAnalytics, HealthITAnalytics, 17 July 2019, healthitanalytics.com/news/chronic-kidney-disease-patients-face-significant-care-disparities.
Rady, El-Houssainy A., and Ayman S. Anwar. “Prediction of Kidney Disease Stages Using Data Mining Algorithms.” Informatics in Medicine Unlocked, Elsevier, 7 Apr. 2019, www.sciencedirect.com/science/article/pii/S2352914818302387.
Zeng, Xiao-Xi, et al. “Big Data Research in Chronic Kidney Disease.” Chinese Medical Journal, Medknow Publications & Media Pvt Ltd, 20 Nov. 2018, www.ncbi.nlm.nih.gov/pmc/articles/PMC6247601/.