Role of Baseline Creatinine in Diagnosing Aki in Costal Rural India & It S Significance in Defining the Outcome
DOI:
https://doi.org/10.52783/jns.v14.2374Keywords:
AKI registry, Community-acquired AKI, Socioeconomic status, Sepsis-associated AKIAbstract
Aim: The aim of the present study was to evaluate the role of baseline creatinine in diagnosing AKI in costal rural India & it's significance in defining the outcome.
Methods: This research adopted a prospective observational study design. The target sample size was 65 patients, accounting for a 10% non-response rate. Initially, 59 patients were to be included, with an additional 6 patients to cover any dropouts or nonrespondents, thus ensuring a robust sample size of 65. The study spanned a period of 18 months, allowing for comprehensive data collection and follow-up.
Results: In our analysis, 52.3% of the cases had pre-renal causes. Renal or intrinsic causes were responsible for 43.1% of AKI cases. Post-renal obstruction accounted for 4.6% of the cases in our study. In our investigation, the results of AKI differed greatly, with 43.1% of patients achieving full recovery, 33.8% experiencing partial recovery, and 23.1% unfortunately succumbing to the condition. Common symptoms among AKI patients in this study included edema (41.5%), oliguria (24.6%), and hypotension (23.1%). Symptoms such as oliguria and hypotension were significantly associated with higher mortality (P < 0.001). Elevated total leukocyte count (TLC) and the levels of serum creatinine were associated with greater mortality in our study which was statically significant (P<0.01), emphasizing the importance of these parameters in assessing the severity and prognosis of AKI.
Conclusion: In conclusion, this study shows that ambiguous definitions for baseline can have major consequences on the AKI diagnosis in patients. Incorrect definition of baseline may result in misdiagnosis of AKI patients with suboptimal decisions for treatment and medication as result. Clinicians, as well as researchers and developers of automatic diagnostic tools such as clinical decision support systems should take these considerations into account when aiming to diagnose AKI in clinical and research settings.
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