European Journal of Cardiovascular Medicine

Acute kidney injury (AKI) manifests as a rapid fall in renal function, resulting in retention of metabolic waste and imbalance of fluids and electrolytes ^[1]. It is associated with significant morbidity, prolonged hospital stays, and increased healthcare costs ^[2]. Worldwide, AKI is seen in approximately 10–20% of hospitalized patients, and the prevalence rises substantially in critically ill individuals ^[3]. Despite advances in diagnostic modalities and supportive care, AKI remains an important contributor to in-hospital mortality ^[4].

In developing countries, including India, the burden of AKI is often underestimated due to delayed diagnosis, lack of uniform reporting, and limited access to healthcare resources ^[5]. Hospitalized patients are particularly vulnerable, as they may present with community-acquired AKI or develop hospital-acquired AKI secondary to infections, nephrotoxic drugs, sepsis, or major surgical procedures ^[6,7].

AKI presents with a wide range of severity, from a mild, asymptomatic rise in creatinine levels to profound oliguric failure necessitating dialysis ^[8]. Understanding the etiology, risk factors, staging, complications, and outcomes of AKI is critical for early recognition and management ^[9]. Moreover, knowledge of AKI patterns in a tertiary care hospital setting can help guide preventive strategies, improve prognosis, and reduce healthcare burden ^[10].

Although several studies have described the epidemiology of AKI worldwide, there is a paucity of prospective observational data from Indian tertiary care centers that systematically evaluate the clinical profile of AKI. Hence, this study was aimed to study various clinical, metabolic, biochemical, hematological and sonographic abnormalities among hospitalized patients who are diagnosed with AKI.

OBJECTIVES:

• To describe clinical course in AKI patients.
• To study comorbidities and risk factors in AKI.
• To study the need of RRT in AKI patients.

Study Design: Prospective Observational Study

Study Setting: Department of General Medicine, Sri Venkateswara Medical College, Tirupati, A.P.

Study Duration: 1 year.

Study Population: All adult patients (>18 years) admitted to the hospital who develop AKI during the study period.

Sample Size: A total of 74 subjects were included in our study within our study period.

Sampling Method: Convenient sampling method.

Inclusion Criteria:

• Adult patients (>18 years) fulfilling KDIGO criteria for AKI:
• Increase in serum creatinine by ≥0.3 mg/dl within 48 hours, OR
• Increase in serum creatinine to ≥1.5 times baseline within 7 days, OR
• Urine output <0.5 ml/kg/h for 6 hours.

Exclusion Criteria:

• Patients with known chronic kidney disease (CKD stage ≥3) prior to admission.
• Patients with history of renal transplant.
• Patients not consenting to participate.

Study tools and data collection method: Patients with acute kidney injury were selected. Patients having one or more risk factors for chronic kidney disease and those already diagnosed with chronic kidney disease were excluded. Patients with a history of alcohol abuse and chronic smoking were also excluded. Patients’ social, demographic, economic, and medical details were recorded in a predesigned proforma sheet. A detailed history regarding symptoms of acute kidney injury, such as decrease in urine output, was obtained. Baseline clinical examination of each patient was performed. Vital signs were recorded. All patients were subjected to clinical and laboratory evaluation as per the preformed proforma. Ultrasound abdomen was performed for all patients using an eSaote machine to assess renal size and texture, in order to rule out chronic kidney disease. Serum creatinine was estimated by the Jaffe rate method (kinetic alkaline picrate).

Statistical Analysis:

Data will be entered into Microsoft Excel and analyzed using SPSS software (version 21). Descriptive statistics: Mean, median, SD, frequencies, and percentages. Comparative analysis: Chi-square test for categorical variables, Student’s t-test/ANOVA for continuous variables. Logistic regression to identify predictors of poor outcome (mortality, need for dialysis). p-value <0.05 considered statistically significant.

Table 1: Descriptive statistics in the study population

Table 2: Sex distribution

Table 3: Urine Albumin distribution

Table 4: KDIGO stage distribution

Table 5: RRT by KDIGO stage

Requirement of dialysis was comparable between KDIGO groups, and statistical analysis did not show a significant association (p = 0.48).

Table 6: RRT by Urine Albumin

The need for RRT was similar across albumin groups (Absent, +, ++). There was no significant association between urine albumin and RRT (p = 0.744).

Table 7: Tests of normality for continuous variables

Age and Serum K are normal (p>0.05); HB is near normal. FBS, PPBS, Urea, Creatinine, and Serum Na are not normal (p<0.05).

Table 8: RRT vs continuous variables (non-normal) – Mann–Whitney ranks

FBS, PPBS, Urea, Creatinine, and Serum Na did not differ between RRT and non-RRT groups (all p>0.05). No meaningful shift in ranks was seen for these variables.

Table 9: Age, HB, and Serum K across KDIGO stages (ANOVA)

Age, HB, and Serum K did not differ across G2, G3, and G4 (Age p=0.225; HB p=0.239; Serum K p=0.213). Group means were broadly similar.

Table 10: KDIGO (G2–G4) vs Serum Na, FBS, PPBS, Urea, Creatinine, and Sodium grading (Kruskal - Wallis test)

PPBS (p=0.022) and Urea (p=0.006) differed by KDIGO stage; higher KDIGO showed higher ranks. Serum Na, FBS, Creatinine, and Sodium grading showed no significant difference.

Urine albumin distribution was similar across KDIGO stages. There was no significant association between KDIGO stage and urine albumin (p = 0.317).

Table 11: Pearson correlation matrix of study variables

FBS and PPBS showed a strong positive correlation and both increased as systolic BP increased. Creatinine and urea correlated with each other and with serum potassium, while serum sodium fell as sodium-grading increased.

Overall summary of results:

A total of 74 patients with acute kidney injury (AKI) were analyzed. The mean age was 54 years (range: 18–85 years), and 71.6% were male. Baseline laboratory parameters showed a mean hemoglobin of 9.6 g/dl, blood urea 121.7 mg/dl, serum creatinine 5.0 mg/dl, sodium 135.7 mmol/L, and potassium 4.2 mmol/L.

Clinical and Laboratory Findings: Urine albumin was absent in 71.6%, mild (+) in 20.3%, and moderate (++) in 8.1%. All patients had normal ECG and echocardiography. According to KDIGO staging, 45.9% were stage G2, 41.9% stage G3, and 12.2% stage G4.

Renal Replacement Therapy (RRT): 9 patients (12.2%) required RRT. The need for RRT was not significantly associated with KDIGO stage (p=0.48) or urine albumin (p=0.744). There were no significant differences in age, hemoglobin, or potassium between RRT and non-RRT groups. Similarly, FBS, PPBS, urea, creatinine, and sodium did not differ significantly between RRT groups.

KDIGO Stage Comparisons: Age, hemoglobin, and potassium did not vary across KDIGO stages. However, postprandial blood sugar (PPBS, p=0.022) and blood urea (p=0.006) were significantly higher in advanced KDIGO stages.

Correlation Analysis: FBS and PPBS were strongly correlated and also increased with systolic BP. Creatinine and urea correlated with each other and with potassium. Serum sodium decreased with increasing sodium-grading. Hemoglobin correlated negatively with creatinine and positively with sodium.

Key Findings:

• Most AKI patients were middle-aged males.
• Roughly 12% required dialysis, but RRT requirement was not linked to stage or urine albumin.
• Higher KDIGO stage patients had significantly higher PPBS and urea levels.
• Biochemical correlations reflected the interplay of renal function with electrolytes and glucose metabolism

Table 1: Descriptive statistics in the study population

Table 2: Sex distribution

Table 3: Urine Albumin distribution

Table 4: KDIGO stage distribution

Table 5: RRT by KDIGO stage

Requirement of dialysis was comparable between KDIGO groups, and statistical analysis did not show a significant association (p = 0.48).

Table 6: RRT by Urine Albumin

The need for RRT was similar across albumin groups (Absent, +, ++). There was no significant association between urine albumin and RRT (p = 0.744).

Table 7: Tests of normality for continuous variables

Age and Serum K are normal (p>0.05); HB is near normal. FBS, PPBS, Urea, Creatinine, and Serum Na are not normal (p<0.05).

Table 8: RRT vs continuous variables (non-normal) – Mann–Whitney ranks

FBS, PPBS, Urea, Creatinine, and Serum Na did not differ between RRT and non-RRT groups (all p>0.05). No meaningful shift in ranks was seen for these variables.

Table 9: Age, HB, and Serum K across KDIGO stages (ANOVA)

Age, HB, and Serum K did not differ across G2, G3, and G4 (Age p=0.225; HB p=0.239; Serum K p=0.213). Group means were broadly similar.

Table 10: KDIGO (G2–G4) vs Serum Na, FBS, PPBS, Urea, Creatinine, and Sodium grading (Kruskal - Wallis test)

PPBS (p=0.022) and Urea (p=0.006) differed by KDIGO stage; higher KDIGO showed higher ranks. Serum Na, FBS, Creatinine, and Sodium grading showed no significant difference.

Urine albumin distribution was similar across KDIGO stages. There was no significant association between KDIGO stage and urine albumin (p = 0.317).

Table 11: Pearson correlation matrix of study variables

FBS and PPBS showed a strong positive correlation and both increased as systolic BP increased. Creatinine and urea correlated with each other and with serum potassium, while serum sodium fell as sodium-grading increased.

Overall summary of results:

A total of 74 patients with acute kidney injury (AKI) were analyzed. The mean age was 54 years (range: 18–85 years), and 71.6% were male. Baseline laboratory parameters showed a mean hemoglobin of 9.6 g/dl, blood urea 121.7 mg/dl, serum creatinine 5.0 mg/dl, sodium 135.7 mmol/L, and potassium 4.2 mmol/L.

Clinical and Laboratory Findings: Urine albumin was absent in 71.6%, mild (+) in 20.3%, and moderate (++) in 8.1%. All patients had normal ECG and echocardiography. According to KDIGO staging, 45.9% were stage G2, 41.9% stage G3, and 12.2% stage G4.

Renal Replacement Therapy (RRT): 9 patients (12.2%) required RRT. The need for RRT was not significantly associated with KDIGO stage (p=0.48) or urine albumin (p=0.744). There were no significant differences in age, hemoglobin, or potassium between RRT and non-RRT groups. Similarly, FBS, PPBS, urea, creatinine, and sodium did not differ significantly between RRT groups.

KDIGO Stage Comparisons: Age, hemoglobin, and potassium did not vary across KDIGO stages. However, postprandial blood sugar (PPBS, p=0.022) and blood urea (p=0.006) were significantly higher in advanced KDIGO stages.

Correlation Analysis: FBS and PPBS were strongly correlated and also increased with systolic BP. Creatinine and urea correlated with each other and with potassium. Serum sodium decreased with increasing sodium-grading. Hemoglobin correlated negatively with creatinine and positively with sodium.

Key Findings:

• Most AKI patients were middle-aged males.
• Roughly 12% required dialysis, but RRT requirement was not linked to stage or urine albumin.
• Higher KDIGO stage patients had significantly higher PPBS and urea levels.
• Biochemical correlations reflected the interplay of renal function with electrolytes and glucose metabolism

This prospective study underscores that acute kidney injury (AKI) among hospitalized patients remains a multifaceted clinical challenge influenced not only by renal indices but also by metabolic, hemodynamic, and systemic factors. Our findings highlight that middle-aged men with diabetes and hypertension form the core high-risk group, and that postprandial hyperglycemia and elevated urea are closely linked with AKI severity. Importantly, the need for dialysis was relatively low, and KDIGO stage alone did not reliably predict RRT requirement, reaffirming that staging must be supplemented by metabolic and clinical markers.

Emerging evidence demonstrates that glycemic variability, AKI trajectory, and intradialytic complications such as hypotension are stronger determinants of outcomes than static laboratory parameters. This emphasizes the need to move beyond traditional staging towards dynamic and holistic risk stratification models that integrate metabolic control, comorbidity burden, and hemodynamic stability.

In the Indian tertiary care setting, where resource limitations can delay diagnosis and intervention, early detection, stringent glycemic control, and proactive hemodynamic monitoring are crucial to reduce morbidity and prevent progression to end-stage renal disease. Ultimately, improving outcomes in AKI will require multidisciplinary strategies, combining nephrology, critical care, endocrinology, and preventive medicine to transform AKI from a silent threat into a preventable and manageable condition.

Recommendations for Practice and Future Research:

Monitor and manage hyperglycaemia proactively—even postprandial glucose—with tight glycaemic control strategies. Develop and validate risk stratification tools that include biomarkers, metabolic parameters, and trajectory-based profiles to guide intervention thresholds. Incorporate hemodynamic risk factors (e.g., IDH) into RRT protocols to mitigate mortality. Larger, multicentred prospective studies are warranted to assess mortality, renal recovery, and long-term progression to CKD or CKD exacerbation.

1. Kellum JA, Lameire N; KDIGO AKI Guideline Work Group. Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary. Kidney Int. 2013;82(1):516–25.
2. Lewington AJ, Cerdá J, Mehta RL. Raising awareness of acute kidney injury: a global perspective of a silent killer. Kidney Int. 2013;84(3):457–67.
3. Susantitaphong P, Cruz DN, Cerda J, et al. World incidence of AKI: a meta-analysis. Clin J Am Soc Nephrol. 2013;8(9):1482–93.
4. Hoste EA, Kellum JA, Selby NM, et al. Global epidemiology and outcomes of acute kidney injury. Nat Rev Nephrol. 2018;14(10):607–25.
5. Jha V, Parameswaran S. Community-acquired acute kidney injury in Asia. Semin Nephrol. 2008;28(4):330–47.
6. Prakash J, Singh TB, Ghosh B, et al. Changing epidemiology of community-acquired acute kidney injury in developing countries: analysis of 2405 cases in 26 years from eastern India. Clin Kidney J. 2013;6(2):150–5.
7. Mehta RL, Cerdá J, Burdmann EA, et al. International Society of Nephrology’s 0by25 initiative for acute kidney injury (zero preventable deaths by 2025): a human rights case for nephrology. Lancet. 2015;385(9987):2616–43.
8. Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120(4):c179–84.
9. Bagshaw SM, George C, Bellomo R; ANZICS Database Management Committee. Early acute kidney injury and sepsis: a multicentre evaluation. Crit Care. 2008;12(2):R47.
10. Chawla LS, Bellomo R, Bihorac A, et al. Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup. Nat Rev Nephrol. 2017;13(4):241–57.
11. Girman CJ, Kovesdy CP, Hsu CY. Acute kidney injury in diabetic patients: an update. Clin Kidney J. 2023;16(3):389–97.
12. Marik PE, Bellomo R. Stress hyperglycemia: an essential survival response! Crit Care. 2013;17(2):305.
13. Shen Y, Cai R, Sun J, Dong X, Huang R, Tian S, et al. Admission hyperglycemia and acute kidney injury in hospitalized patients without diabetes: a retrospective cohort study. Diabetes Care. 2022;45(8):1886–93.
14. Chen Y, Zhang W, Yu C, Li Z, Li Y, Huang J, et al. Glycemic gap and stress hyperglycemia ratio are predictors of ICU mortality in patients with acute kidney injury. BMC Nephrol. 2023; 24:122.
15. Harer MW, Kashani K, Yadav H, Kashani KB. Epidemiology and trajectories of acute kidney injury in hospitalized patients: a multicenter analysis. Crit Care Med. 2024;52(4):e211–21.
16. Zhou H, Chen J, Wang X, Li Y, Chen Z, Wang Y, et al. Prediction of dialysis-requiring acute kidney injury after heart transplantation: development of a novel nomogram. BMC Surg. 2025; 25:34.
17. Arango-Gonzalez E, Casanova M, Bortolozzi L, Greloni G, Greloni A, Bratti G, et al. Predictors of mortality in critically ill patients with KDIGO stage 3 AKI requiring dialysis: the role of intradialytic hypotension. Kidney Int Rep. 2024;9(2):278–87.
18. Kothari N, Reddy L, Narasimhan S, Prasad N, Kuppachi S. Clinical profile and outcomes of community- vs hospital-acquired acute kidney injury: a prospective Indian cohort. J Med Evid. 2025;6(1):44–52