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A Matrix Metalloproteinase-2-Based Nomogram to Assess the Risk of Encapsulating Peritoneal Sclerosis in Peritoneal Dialysis Patients
BACKGROUND: Encapsulating peritoneal sclerosis (EPS) is a rare but serious complication of peritoneal dialysis (PD). So far, there is no biomarker-based prediction tool available for EPS. Matrix metalloproteinase-2 (MMP-2) is a protein involved in the breakdown of the extracellular matrix, and the e...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7837760/ https://www.ncbi.nlm.nih.gov/pubmed/33532492 http://dx.doi.org/10.1155/2021/6666441 |
Sumario: | BACKGROUND: Encapsulating peritoneal sclerosis (EPS) is a rare but serious complication of peritoneal dialysis (PD). So far, there is no biomarker-based prediction tool available for EPS. Matrix metalloproteinase-2 (MMP-2) is a protein involved in the breakdown of the extracellular matrix, and the effluent MMP-2 can be a potential biomarker of EPS. This study is aimed at developing a nomogram for EPS based on effluent MMP-2 levels. Patients and Methods. We enrolled 18 EPS patients and 90 gender-matched PD patients without EPS in this cross-sectional case-controlled study. The effluent MMP-2 levels and possible risk factors for EPS were analyzed using multivariable logistic regression, and a nomogram was developed. The nomogram was validated using 200 bootstrap resamples to reduce overfit bias. RESULTS: The effluent MMP-2 levels in EPS patients were significantly higher than those in normal PD patients (p < 0.001, Manny-Whitney U test). Effluent MMP-2 levels and PD duration were independently associated with EPS risks (p < 0.001 and p = 0.001) in multivariate logistic regression. A nomogram based on MMP-2 levels and PD duration was proposed. The AUC of MMP-2 was 0.824, and the AUC of the nomogram was 0.907 (p = 0.05). CONCLUSION: A nomogram based on effluent MMP-2 levels and PD duration may predict EPS with high accuracy. |
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