Development and validation of a nomogram for predicting gram-negative bacterial infections in patients with peritoneal dialysis-associated peritonitis
Background: This study aimed to develop a nomogram for predicting gram-negative bacterial (GNB) infections in patients with peritoneal dialysis-associated peritonitis (PDAP) to identify patients at high risk for GNB infections. Methods: In this investigation, hospitalization information was gathered...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023057596 |
| _version_ | 1827856197767659520 |
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| author | Guiling Liu Xunliang Li Wenman Zhao Rui Shi Yuyu Zhu Zhijuan Wang Haifeng Pan Deguang Wang |
| author_facet | Guiling Liu Xunliang Li Wenman Zhao Rui Shi Yuyu Zhu Zhijuan Wang Haifeng Pan Deguang Wang |
| author_sort | Guiling Liu |
| collection | DOAJ |
| description | Background: This study aimed to develop a nomogram for predicting gram-negative bacterial (GNB) infections in patients with peritoneal dialysis-associated peritonitis (PDAP) to identify patients at high risk for GNB infections. Methods: In this investigation, hospitalization information was gathered retrospectively for patients with PDAP from January 2016 to December 2021. The concatenation of potential biomarkers obtained by univariate logistic regression, LASSO analysis, and RF algorithms into multivariate logistic regression was used to identify confounding factors related to GNB infections, which were then integrated into the nomogram. The concordance index (C-Index) was utilized to assess the precision of the model's predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram. Results: The final study population included 217 patients with PDAP, and 37 (17.1%) patients had gram-negative bacteria due to dialysate effluent culture. After multivariate logistic regression, age, procalcitonin, and hemoglobin were predictive factors of GNB infections. The C-index and bootstrap-corrected index of the nomogram for estimating GNB infections in patients were 0.821 and 0.814, respectively. The calibration plots showed good agreement between the predictions of the nomogram and the actual observation of GNB infections. The AUC of the receiver operating characteristic curve was 0.821, 95% CI: 0.747–0.896, which indicates that the model has good predictive accuracy. In addition, the DCA curve showed that the nomogram had a high clinical value in the range of 1%–94%, which further demonstrated that the nomogram could accurately predict GNB infection in patients with PDAP. Conclusions: We have created a new nomogram for predicting GNB infections in patients with PDAP. The nomogram model may improve the identification of GNB infections in patients with PDAP and contribute to timely intervention to improve patient prognosis. |
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| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-12T12:22:16Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-0ae1660a0a864cc1941b99cc9d5f69972023-08-30T05:51:43ZengElsevierHeliyon2405-84402023-08-0198e18551Development and validation of a nomogram for predicting gram-negative bacterial infections in patients with peritoneal dialysis-associated peritonitisGuiling Liu0Xunliang Li1Wenman Zhao2Rui Shi3Yuyu Zhu4Zhijuan Wang5Haifeng Pan6Deguang Wang7Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China; Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China; Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China; Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China; Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China; Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China; Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaInstitute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China; Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, ChinaDepartment of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China; Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China; Corresponding author. Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.Background: This study aimed to develop a nomogram for predicting gram-negative bacterial (GNB) infections in patients with peritoneal dialysis-associated peritonitis (PDAP) to identify patients at high risk for GNB infections. Methods: In this investigation, hospitalization information was gathered retrospectively for patients with PDAP from January 2016 to December 2021. The concatenation of potential biomarkers obtained by univariate logistic regression, LASSO analysis, and RF algorithms into multivariate logistic regression was used to identify confounding factors related to GNB infections, which were then integrated into the nomogram. The concordance index (C-Index) was utilized to assess the precision of the model's predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram. Results: The final study population included 217 patients with PDAP, and 37 (17.1%) patients had gram-negative bacteria due to dialysate effluent culture. After multivariate logistic regression, age, procalcitonin, and hemoglobin were predictive factors of GNB infections. The C-index and bootstrap-corrected index of the nomogram for estimating GNB infections in patients were 0.821 and 0.814, respectively. The calibration plots showed good agreement between the predictions of the nomogram and the actual observation of GNB infections. The AUC of the receiver operating characteristic curve was 0.821, 95% CI: 0.747–0.896, which indicates that the model has good predictive accuracy. In addition, the DCA curve showed that the nomogram had a high clinical value in the range of 1%–94%, which further demonstrated that the nomogram could accurately predict GNB infection in patients with PDAP. Conclusions: We have created a new nomogram for predicting GNB infections in patients with PDAP. The nomogram model may improve the identification of GNB infections in patients with PDAP and contribute to timely intervention to improve patient prognosis.http://www.sciencedirect.com/science/article/pii/S2405844023057596Peritoneal dialysisPeritonitisGram-negative bacterialInfectionNomogram |
| spellingShingle | Guiling Liu Xunliang Li Wenman Zhao Rui Shi Yuyu Zhu Zhijuan Wang Haifeng Pan Deguang Wang Development and validation of a nomogram for predicting gram-negative bacterial infections in patients with peritoneal dialysis-associated peritonitis Heliyon Peritoneal dialysis Peritonitis Gram-negative bacterial Infection Nomogram |
| title | Development and validation of a nomogram for predicting gram-negative bacterial infections in patients with peritoneal dialysis-associated peritonitis |
| title_full | Development and validation of a nomogram for predicting gram-negative bacterial infections in patients with peritoneal dialysis-associated peritonitis |
| title_fullStr | Development and validation of a nomogram for predicting gram-negative bacterial infections in patients with peritoneal dialysis-associated peritonitis |
| title_full_unstemmed | Development and validation of a nomogram for predicting gram-negative bacterial infections in patients with peritoneal dialysis-associated peritonitis |
| title_short | Development and validation of a nomogram for predicting gram-negative bacterial infections in patients with peritoneal dialysis-associated peritonitis |
| title_sort | development and validation of a nomogram for predicting gram negative bacterial infections in patients with peritoneal dialysis associated peritonitis |
| topic | Peritoneal dialysis Peritonitis Gram-negative bacterial Infection Nomogram |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023057596 |
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