A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment
Background Acute kidney injury (AKI) is not a rare complication during anti-tuberculosis treatment in some patients with pulmonary tuberculosis (PTB). We aimed to develop a risk prediction model for early recognition of patients with PTB at high risk for AKI during anti-TB treatment.Methods This ret...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2022.2058405 |
| _version_ | 1828913863641268224 |
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| author | Zhi Xiang Du Fang Qun Chang Zi Jian Wang Da Ming Zhou Yang Li Jiang Hua Yang |
| author_facet | Zhi Xiang Du Fang Qun Chang Zi Jian Wang Da Ming Zhou Yang Li Jiang Hua Yang |
| author_sort | Zhi Xiang Du |
| collection | DOAJ |
| description | Background Acute kidney injury (AKI) is not a rare complication during anti-tuberculosis treatment in some patients with pulmonary tuberculosis (PTB). We aimed to develop a risk prediction model for early recognition of patients with PTB at high risk for AKI during anti-TB treatment.Methods This retrospective cohort study assessed the clinical baseline, and laboratory test data of 315 inpatients with active PTB who were screened for predictive factors from January 2019 to June 2020. The elements were analyzed by logistic regression analysis. A nomogram was established by the results of the logistic regression analysis. The prediction model discrimination and calibration were evaluated by the concordance index (C-index), ROC curve, and Hosmer-Lemeshow analysis.Results A total of 315 patients with PTB were enrolled (67 patients with AKI and 248 patients without AKI). Seven factors, including microalbuminuria, hematuria, cystatin-C (CYS-C), albumin (ALB), creatinine-based estimated glomerular filtration rates (eGFRs), body mass index (BMI), and CA-125 were acquired to develop the predictive model. According to the logistic regression, microalbuminuria (OR = 3.038, 95%CI 1.168–7.904), hematuria (OR = 3.656, 95%CI 1.325–10.083), CYS-C (OR = 4.416, 95%CI 2.296–8.491), and CA-125 (OR = 3.93, 95%CI 1.436–10.756) were risk parameter, while ALB (OR = 0.741, 95%CI 0.650–0.844) was protective parameter. The nomogram demonstrated good prediction in estimating AKI (C-index= 0.967, AUC = 0.967, 95%CI (0.941–0.984), sensitivity = 91.04%, specificity = 93.95%, Hosmer-Lemeshow analysis SD = 0.00054, and quantile of absolute error = 0.049).Conclusions Microalbuminuria, hematuria, ALB reduction, elevated CYS-C, and CA-125 are predictive factors for the development of AKI in patients with PTB during anti-TB treatments. The predictive nomogram based on five predictive factors is achieved good risk prediction for AKI during anti-TB treatments. |
| first_indexed | 2024-12-13T19:44:52Z |
| format | Article |
| id | doaj.art-2558887a95ee4b09b9b876c751d3b024 |
| institution | Directory Open Access Journal |
| issn | 0886-022X 1525-6049 |
| language | English |
| last_indexed | 2024-12-13T19:44:52Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj.art-2558887a95ee4b09b9b876c751d3b0242022-12-21T23:33:35ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492022-12-0144162563510.1080/0886022X.2022.2058405A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatmentZhi Xiang Du0Fang Qun Chang1Zi Jian Wang2Da Ming Zhou3Yang Li4Jiang Hua Yang5Department of Infectious Diseases, Taizhou People's Hospital, Taizhou, ChinaDepartment of Geriatric respiratory and critical illness, The First Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Infectious Diseases, Yijishan Hospital, Wannan Medical College, Wuhu, ChinaDepartment of Infectious Diseases, Taizhou People's Hospital, Taizhou, ChinaDepartment of Infectious Diseases, Taizhou People's Hospital, Taizhou, ChinaDepartment of Infectious Diseases, Yijishan Hospital, Wannan Medical College, Wuhu, ChinaBackground Acute kidney injury (AKI) is not a rare complication during anti-tuberculosis treatment in some patients with pulmonary tuberculosis (PTB). We aimed to develop a risk prediction model for early recognition of patients with PTB at high risk for AKI during anti-TB treatment.Methods This retrospective cohort study assessed the clinical baseline, and laboratory test data of 315 inpatients with active PTB who were screened for predictive factors from January 2019 to June 2020. The elements were analyzed by logistic regression analysis. A nomogram was established by the results of the logistic regression analysis. The prediction model discrimination and calibration were evaluated by the concordance index (C-index), ROC curve, and Hosmer-Lemeshow analysis.Results A total of 315 patients with PTB were enrolled (67 patients with AKI and 248 patients without AKI). Seven factors, including microalbuminuria, hematuria, cystatin-C (CYS-C), albumin (ALB), creatinine-based estimated glomerular filtration rates (eGFRs), body mass index (BMI), and CA-125 were acquired to develop the predictive model. According to the logistic regression, microalbuminuria (OR = 3.038, 95%CI 1.168–7.904), hematuria (OR = 3.656, 95%CI 1.325–10.083), CYS-C (OR = 4.416, 95%CI 2.296–8.491), and CA-125 (OR = 3.93, 95%CI 1.436–10.756) were risk parameter, while ALB (OR = 0.741, 95%CI 0.650–0.844) was protective parameter. The nomogram demonstrated good prediction in estimating AKI (C-index= 0.967, AUC = 0.967, 95%CI (0.941–0.984), sensitivity = 91.04%, specificity = 93.95%, Hosmer-Lemeshow analysis SD = 0.00054, and quantile of absolute error = 0.049).Conclusions Microalbuminuria, hematuria, ALB reduction, elevated CYS-C, and CA-125 are predictive factors for the development of AKI in patients with PTB during anti-TB treatments. The predictive nomogram based on five predictive factors is achieved good risk prediction for AKI during anti-TB treatments.https://www.tandfonline.com/doi/10.1080/0886022X.2022.2058405Risk prediction modelpulmonary tuberculosisacute kidney injurynomogram |
| spellingShingle | Zhi Xiang Du Fang Qun Chang Zi Jian Wang Da Ming Zhou Yang Li Jiang Hua Yang A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment Renal Failure Risk prediction model pulmonary tuberculosis acute kidney injury nomogram |
| title | A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment |
| title_full | A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment |
| title_fullStr | A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment |
| title_full_unstemmed | A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment |
| title_short | A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment |
| title_sort | risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti tuberculosis treatment |
| topic | Risk prediction model pulmonary tuberculosis acute kidney injury nomogram |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2022.2058405 |
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