Browsing by Autor "Subhasis Dasgupta"

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Performance of an Artificial Intelligence-Generated Risk Score for AKI Prediction
(American Society of Nephrology, 2024) Rolando Claure‐Del Granado; Juan C. Moya-Mamani; Rakesh Malhotra; Subhasis Dasgupta
The Utility of Urine Microscopy Score for Early Detection and Prediction of Acute Kidney Injury in At-Risk Patients
(Lippincott Williams & Wilkins, 2025) Rolando Claure‐Del Granado; Diego Torrico-Moreira; Jingyao Zhang; Jacqueline M. Breunig; Basmh Shamel; Vineet Gupta; Tushar Chopra; Subhasis Dasgupta; Rakesh Malhotra
Key Points Early recognition of subclinical AKI enables timely interventions that may mitigate progression and improve outcomes. This study evaluated the urine microscopy score as a tool for early detection of subclinical AKI and prediction of clinical AKI development in resource-limited settings. Background AKI is a global health concern associated with high morbidity and mortality. Early diagnosis and treatment of subclinical AKI (AKI-1S) are critical for mitigating adverse outcomes. Here, we evaluated whether the urine microscopy score (UMS), a simple and cost-effective method for detecting structural kidney injury, could serve as a substitute biomarker within the AKI Risk Assessment Model (ARA-F4) to identify AKI-1S and predict clinical AKI development. Methods A prospective cohort study was conducted, enrolling hospitalized adult patients (non-intensive care unit) at moderate to high risk of AKI according to ARA-F4 model. At admission, urine microscopy was performed, and patients with UMS ≥2 without concurrent serum creatinine elevation were classified as AKI-1S; those with UMS≤1 were classified as non-AKI. The primary outcomes were development of clinical AKI within 48 hours, the need for KRT, and mortality. The discriminative ability of the UMS for predicting AKI was assessed using the area under the receiver operating characteristic curve (area under the curve). Results A total of 103 patients were included in the study, with 39 (37.9%) classified as AKI-1S and 64 (62.1%) as non-AKI at admission. Among the AKI-1S group, 89.7% developed clinical AKI within 48 hours compared with 10.9% of non-AKI patients ( P < 0.05). The AKI-1S group had significantly higher requirement for KRT (10.3% versus 1.6%, P < 0.05) and increased mortality rate (43.6% versus 14.1%, P < 0.05). The UMS demonstrated good predictive performance for AKI development, with an area under the curve of 0.84 (95% confidence interval, 0.75 to 0.92). The sensitivity and specificity of the UMS were 74.5% and 92.9%, respectively. Conclusions The UMS can be used in the ARA-F4 model to identify patients with AKI-1S and predict the subsequent development of clinical AKI. Early recognition of AKI-1S using the UMS can facilitate timely interventions and may reduce the burden of AKI in low- and middle-income countries.