Acute kidney injury: Diagnosis, causes, and latest treatments- An updated review article

‏Sahar Adi Albogamy; ‏Ahmad Alhelo Alanazi; ‏Hussain Mahdi Aljawad; ‏Hala Abdulaziz Alzuhair; Mohammad Aljehani; Alaa Saud K Alanazi; Mohammed Saad Ali Al-Harbi; ‎Fouad Hamed Alamri

doi:10.53730/ijhs.v6nS10.15238

Authors

‏Sahar Adi Albogamy KSA, National Guard Health Affairs
‏Ahmad Alhelo Alanazi KSA, National Guard Health Affairs
‏Hussain Mahdi Aljawad KSA, National Guard Health Affairs
‏Hala Abdulaziz Alzuhair KSA, National Guard Health Affairs
Mohammad Aljehani KSA, National Guard Health Affairs
Alaa Saud K Alanazi KSA, National Guard Health Affairs
Mohammed Saad Ali Al-Harbi KSA, National Guard Health Affairs
‎Fouad Hamed Alamri KSA, National Guard Health Affairs

Keywords:

Acute kidney injury, biomarkers, machine learning, diagnosis, treatment, nephrology

Abstract

Background: Acute kidney injury (AKI) is a critical clinical syndrome characterized by a rapid decline in renal function, with various precipitating factors including heart failure, sepsis, and nephrotoxic drugs. The prevalence in hospitalized patients is concerning, particularly among those with COVID-19, where AKI incidence has reached approximately 36.6%. The current diagnostic criteria primarily rely on serum creatinine (SCR) levels and urine output (UO), which often fail to identify AKI early enough for effective intervention. Aim: This review aims to consolidate current knowledge on AKI, highlighting its diagnosis, causes, and the latest treatment approaches, with a focus on emerging technologies that improve early detection. Methods: The article reviews literature on AKI diagnostic criteria, imaging techniques, biomarkers, and the application of machine learning algorithms in predicting AKI. Emphasis is placed on novel biomarkers and biosensors that enhance early detection, as well as machine learning models that synthesize data from electronic health records. Results: Advances in biomarkers like NGAL and KIM-1, alongside biosensors, offer improved sensitivity for early AKI detection. Additionally, machine learning models have demonstrated high predictive accuracy, achieving area under the receiver operating characteristic curve (AUC) values exceeding 0.9 across various clinical contexts.

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