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Renal Failure Volume 46, 2024 - Issue 2
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Acute Kidney Injury

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

Mukta Sharmaa Graduate Institute, Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, TaiwanView further author information
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Chia-Lung Tsaib Department of Electronic Engineering, Chang Gung University, Taoyuan, Taiwan;c Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Taoyuan, Taiwan;d Department of Electronic Engineering, Ming Chi University of Technology, New Taipei City, TaiwanCorrespondence[email protected]
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Ying-Chang Lia Graduate Institute, Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, TaiwanView further author information
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Cheng-Chia Leee Department of Nephrology, Kidney Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan;f Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, TaiwanCorrespondence[email protected]
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Yu-Li Hsiehg Department of Electrical and Electronic Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan, TaiwanView further author information
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Chih-Hsiang Change Department of Nephrology, Kidney Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan;f Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, TaiwanView further author information
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Shao-Wei Chenf Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan;h Department of Cardiothoracic and Vascular Surgery, Chang Gung Memorial Hospital, Taoyuan, TaiwanView further author information
&
Liann-Be Changb Department of Electronic Engineering, Chang Gung University, Taoyuan, Taiwan;i Green Technology Research Center, Chang Gung University, Taoyuan, TaiwanView further author information
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Article: 2375741 | Received 02 Jan 2024, Accepted 29 Jun 2024, Published online: 12 Jul 2024

Figures & data

Table 1. Demographic table for all cardiac surgery patients.

Figure 1. The mean Raman spectra of urine of acute kidney injury (AKI), and non-AKI group.

Figure 1. The mean Raman spectra of urine of acute kidney injury (AKI), and non-AKI group.

Figure 2. The mean Raman spectra of urine of acute kidney injury (AKI) stage 1, and non-AKI group.

Figure 2. The mean Raman spectra of urine of acute kidney injury (AKI) stage 1, and non-AKI group.

Figure 3. The mean Raman spectra of urine of acute kidney injury (AKI) stage 2, and non-AKI group.

Figure 3. The mean Raman spectra of urine of acute kidney injury (AKI) stage 2, and non-AKI group.

Figure 4. The mean Raman spectra of urine of acute kidney injury (AKI) stage 3, and non-AKI group.

Figure 4. The mean Raman spectra of urine of acute kidney injury (AKI) stage 3, and non-AKI group.

Figure 5. Screen plot of the cumulative percentages of the first 15 PLS components (a) with the percentage variance of x (predictors) and y (response variables) (b) with the accuracy curve of PLS-SVM algorithm, the inset shows the PLS-scatter plot.

Figure 5. Screen plot of the cumulative percentages of the first 15 PLS components (a) with the percentage variance of x (predictors) and y (response variables) (b) with the accuracy curve of PLS-SVM algorithm, the inset shows the PLS-scatter plot.

Table 2. Classification parameters of AKI and non-AKI group using PLS-SVM classifier: SEN (sensitivity), SPE (specificity), AC (accuracy), F1 (F1-score), BAC (balanced accuracy), and MCC (Matthew’s correlation coefficient).

Table 3. Stage bias classification parameters of AKI and non-AKI group using PLS-SVM classifier: SEN (sensitivity), SPE (specificity), AC (accuracy), precision (PRE), F1 (F1-score), BAC (balanced accuracy), and MCC (Matthew’s correlation coefficient).

Figure 6. ROC curves for classification results using PLS-SVM: (a) AKI vs. non-AKI factors and (b) non-AKI vs. individual AKI stages.

Figure 6. ROC curves for classification results using PLS-SVM: (a) AKI vs. non-AKI factors and (b) non-AKI vs. individual AKI stages.
Supplemental material

The_clean_editable_revised_paper.7z

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Data availability statement

Data are available from the corresponding author upon reasonable request.

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