CHRONIC KIDNEY DISEASE AND STAGES PREDICTION WITH RECOMMENDATION OF SUITABLE DIET PLAN
Author(s):
Ankit Kumar Singh, Banupriya B Naik, Akshath H Kandlur, K.Deepa Shree
Keywords:
Batchs, Clickstream, Data.
Abstract
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
Article Preview & Download


Share This Article

Conference Alert

NCSST-2023

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2023

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2023

Latest Publication

Go To Issue



Call For Paper

Volume 10 Issue 1

Last Date for paper submitting for March Issue is 25 June 2023

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies

Important Links

Browse Rsearch papers