Prediction of inflammation in hemodialysis patients using neural network analysis

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Abstract

Background. Numerous hemodialysis patients (HD) suffer from severe, life-threatening inflammation that must be treated to prevent further complications. Early diagnosis of inflammation in HD is highly needed. The present study used matrix metalloproteinase-1 (MMP3) and tissue inhibitor of metalloproteinases-1 (TIMP1) to differentiate between patients with/without inflammation using the neural network analysis (NN).

Methods. The positive results of C-reactive protein were used as a criterion for the presence of inflammation in the patients (HD+CRP) versus the negative group (HD-CRP). The NN analysis was used to discriminate between groups using the measured biomarkers.

Results. HD+CRP patients have a higher duration of disease, MMP3 and lower calcium than the HD-CRP level is significantly higher, while vitamin D is significantly lower in the HD+CRP group compared with both other groups (all p<0.05). TIMP1 is significantly correlated with inorganic phosphate and CRP. In NN#1, the model for the prediction of HD+CRP from HD-CRP has an area under the curve (AUC) of the receiver operating characteristic (ROC) of 0.907 with a sensitivity and specificity 89.2% and a specificity of 100.0%. The top predicting variable for the prediction of HD+CRP is MMP3 (100%), followed by creatinine (87.1%). MMP3 is linked to the pathophysiology of HD, at least through their correlation with the inflammation in HD. In NN#2, the AUC of the ROC for predicting the kidney disease and subsequent HD was 98.9%, with a sensitivity of 100.0% and a specificity of 97.1%. The top four predicting variables for the prediction of high risk of inflammation in HD patients are urea (100%), creatinine (100%), MMP3 (59.7%), and vitamin D (57.1%).

Conclusion. The NN analysis may differentiate between HD patients with inflammation from the HD without inflammation. Also, the measured parameters, especially MMP3, TIMP1, and vitamin D are useful as a diagnostic tools for the kidney diseases and inflammation linked with the disease.

About the authors

Hadi H. Hadi

University of Kufa

Author for correspondence.
Email: hhadi0615@gmail.com

Researcher, Department of Chemistry, Faculty of Science

Iraq, Najaf

Hawraa H. Al-Mayali

Al-Furat Al-Awsat Technical University

Email: hawaraalmyaly1@gmail.com

Instructor

Iraq, Najaf

Habiba K. Abdalsada

Al-Muthanna University

Email: habiba.khdair@mu.edu.iq

Assistant Professor, College of Pharmacy

Iraq, Al-Muthanna

Shatha R. Moustafa

Hawler Medical University

Email: shatha003@yahoo.com

Professor, Clinical Analysis Department, College of Pharmacy

Iraq, Havalan

Abbas F. Almulla

The Islamic University

Email: abbass.chem.almulla1991@gmail.com

Assistant Professor, Medical Laboratory Technology Department, College of Medical Technology

Iraq, Najaf

Hussein K. Al-Hakeim

University of Kufa

Email: headm2010@yahoo.com
ORCID iD: 0000-0001-6143-5196

Professor, Department of Chemistry, Faculty of Science

Iraq, Najaf

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2. Figure 1. Results of neural network 1 (NN#1) (importance chart) with HD+CRP and HD–CRP as output variables and biomarkers as input variables Note. B.ur — Blood urea, Ca — calcium, eGFR — estimated glomerular filtration rate, Mg — Magnesium, Pi — inorganic phosphate, MMP3 — matrix metalloproteinase-3, S.Cr — serum creatinine, TIMP1 — tissue inhibitor of metalloproteinases-1, U.A. — uric acid.

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3. Figure 2. Results of neural network 2 (NN#2) (importance chart) with HD and healthy controls as output variables and biomarkers as input variables Note. B.ur — Blood urea, Ca — calcium, eGFR — estimated glomerular filtration rate, Mg — Magnesium, Pi — inorganic phosphate, MMP3 — matrix metalloproteinase-3, S.Cr — serum creatinine, TIMP1 — tissue inhibitor of metalloproteinases-1, U.A. — uric acid.

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Copyright (c) 2023 Hadi H.H., Al-Mayali H.H., Abdalsada H.K., Moustafa S.R., Almulla A.F., Al-Hakeim H.K.

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