Ankit Kumar Singh, Banupriya B Naik, Akshath H Kandlur, K.Deepa Shree
Batchs, Clickstream, Data.
Early identification of chronic kidney disease is essential due to the ongoing rise in the number of individuals with end-stage renal disease (CKD). The objective of the current study was to use a diagnostic algorithm created by the working group to identify CKD in its earliest stages in a population that was chosen at random. Methods: To identify patients with CKD who need additional nephrological care, a diagnostic algorithm was developed. Adult residents of a city with a 60,000-person population were chosen at random to take part in this study. Microalbuminuria dipstick testing was done as part of the screening process, along with blood pressure readings and a medical questionnaire. The technique was utilised to further diagnose CKD using the estimated glomerular filtration rate (eGFR), albumin concentration in urine, urinalysis, and ultrasound examination. In order to determine correlations between participant characteristics and albuminuria, multivariate logistic regression was used. Results: 2,471 people took part in the PolNef study out of a total of 9,700 invited participants. Using the dipstick test, albuminuria was discovered in 15.6% of the population under investigation, and 11.9% of those cases were later verified using the turbidimetric approach. Male sex, diabetes, nocturia, and hypertension were found to be independent predictors of albuminuria in the modelling of multivariate logistic regression. Detection of albuminuria was independently predicted by nocturia in patients without diabetes or hypertension. 96% of the 481 patients who saw a nephrologist during their consultation were diagnosed with CKD. The suggested diagnostic criteria appears to be an effective tool for locating those who are at CKD risk. Further research should be done on the function of nocturia as an independent predictor of albuminuria, both in the general population and in those without diabetes or high blood pressure.
Article Details
Unique Paper ID: 157765

Publication Volume & Issue: Volume 9, Issue 8

Page(s): 144 - 148
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