Evidence map›Paper›PMID 38994782›Full record

ArticleRenal failure2024

Utilizing Raman spectroscopy for urinalysis to diagnose acute kidney injury stages in cardiac surgery patients.

Mukta Sharma, Chia-Lung Tsai, Ying-Chang Li, Cheng-Chia Lee, Yu-Li Hsieh, Chih-Hsiang Chang, Shao-Wei Chen, Liann-Be Chang

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Article in Renal failure, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Mukta SharmaGraduate Institute, Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taiwan.
Chia-Lung TsaiDepartment of Electronic Engineering, Chang Gung University, Taoyuan, Taiwan.
Ying-Chang LiGraduate Institute, Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taiwan.
Cheng-Chia LeeDepartment of Nephrology, Kidney Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Yu-Li HsiehDepartment of Electrical and Electronic Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan, Taiwan.
Chih-Hsiang ChangDepartment of Nephrology, Kidney Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Shao-Wei ChenGraduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Liann-Be ChangDepartment of Electronic Engineering, Chang Gung University, Taoyuan, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe successful treatment and improvement of acute kidney injury (AKI) depend on early-stage diagnosis. However, no study has differentiated between the three stages of AKI and non-AKI patients following heart surgery. This study will fill this gap in the literature and help to improve kidney disease management in the future.

methodsIn this study, we applied Raman spectroscopy (RS) to uncover unique urine biomarkers distinguishing heart surgery patients with and without AKI. Given the amplified risk of renal complications post-cardiac surgery, this approach is of paramount importance. Further, we employed the partial least squares-support vector machine (PLS-SVM) model to distinguish between all three stages of AKI and non-AKI patients.

resultsWe noted significant metabolic disparities among the groups. Each AKI stage presented a distinct metabolic profile: stage 1 had elevated uric acid and reduced creatinine levels; stage 2 demonstrated increased tryptophan and nitrogenous compounds with diminished uric acid; stage 3 displayed the highest neopterin and the lowest creatinine levels. We utilized the PLS-SVM model for discriminant analysis, achieving over 90% identification rate in distinguishing AKI patients, encompassing all stages, from non-AKI subjects.

conclusionsThis study characterizes the incidence and risk factors for AKI after cardiac surgery. The unique spectral information garnered from this study can also pave the way for developing an

Indexed as

Acute Kidney InjuryBiomarkersCardiac Surgical ProceduresSpectrum Analysis, RamanUrinalysisAgedCreatinineFemaleHumansLeast-Squares AnalysisMaleMiddle AgedPostoperative ComplicationsRisk FactorsSupport Vector MachineUric AcidBiomarkersCreatinineUric AcidAcute kidney injurycardiac surgerypartial least squaresRaman spectroscopysupport vector machineurine

Identifiers

PMID38994782
PMCPMC11249162

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.