A Prediction Model for Acute Kidney Injury in Adult Patients With Minimal Change Disease
BackgroundEarly prediction of acute kidney injury (AKI) can allow for timely interventions, but there are still few methods that are easy and convenient to apply in predicting AKI, specially targeted at patients with minimal change disease (MCD). Motivated by this, we aimed to develop a predicting m...
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Frontiers Media S.A.
2022-05-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2022.862160/full |
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| author | Chen Yang Chen Yang Chen Yang Shu-Peng Lin Pu Chen Jie Wu Jin-Ling Meng Shuang Liang Feng-Ge Zhu Yong Wang Zhe Feng Xiang-Mei Chen Guang-Yan Cai |
| author_facet | Chen Yang Chen Yang Chen Yang Shu-Peng Lin Pu Chen Jie Wu Jin-Ling Meng Shuang Liang Feng-Ge Zhu Yong Wang Zhe Feng Xiang-Mei Chen Guang-Yan Cai |
| author_sort | Chen Yang |
| collection | DOAJ |
| description | BackgroundEarly prediction of acute kidney injury (AKI) can allow for timely interventions, but there are still few methods that are easy and convenient to apply in predicting AKI, specially targeted at patients with minimal change disease (MCD). Motivated by this, we aimed to develop a predicting model for AKI in patients with MCD within the KDIGO criteria.MethodsData on 401 hospitalized adult patients, whose biopsy was diagnosed as MCD from 12/31/2010 to 15/7/2021, were retrospectively collected. Among these data, patients underwent biopsy earlier formed the training set (n = 283), while the remaining patients formed the validation set (n = 118). Independent risk factors associated with AKI were analyzed. From this, the prediction model was developed and nomogram was plotted.ResultsAKI was found in 55 of 283 patients (19%) and 15 of 118 patients (13%) in the training and validation cohorts, respectively. According to the results from lasso regression and logistic regression, it was found that four factors, including mean arterial pressure, serum albumin, uric acid, and lymphocyte counts, were independent of the onset of AKI. Incorporating these factors, the nomogram achieved a reasonably good concordance index of 0.84 (95%CI 0.77–0.90) and 0.75 (95%CI 0.62–0.87) in predicting AKI in the training and validation cohorts, respectively. Decision curve analysis suggested clinical benefit of the prediction models.ConclusionsOur predictive nomogram provides a feasible approach to identify high risk MCD patients who might develop AKI, which might facilitate the timely treatment. |
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| last_indexed | 2024-12-12T09:05:00Z |
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| series | Frontiers in Medicine |
| spelling | doaj.art-c3e8dac24485427c864ca643a4dab2532022-12-22T00:29:42ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2022-05-01910.3389/fmed.2022.862160862160A Prediction Model for Acute Kidney Injury in Adult Patients With Minimal Change DiseaseChen Yang0Chen Yang1Chen Yang2Shu-Peng Lin3Pu Chen4Jie Wu5Jin-Ling Meng6Shuang Liang7Feng-Ge Zhu8Yong Wang9Zhe Feng10Xiang-Mei Chen11Guang-Yan Cai12School of Medicine, Nankai University, Tianjin, ChinaDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese PLA, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, ChinaDepartment of Nephrology, Cangzhou Center Hospital, Cangzhou, ChinaDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese PLA, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, ChinaDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese PLA, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, ChinaDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese PLA, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, ChinaDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese PLA, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, ChinaDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese PLA, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, ChinaDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese PLA, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, ChinaDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese PLA, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, ChinaDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese PLA, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, ChinaDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese PLA, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, ChinaDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese PLA, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, ChinaBackgroundEarly prediction of acute kidney injury (AKI) can allow for timely interventions, but there are still few methods that are easy and convenient to apply in predicting AKI, specially targeted at patients with minimal change disease (MCD). Motivated by this, we aimed to develop a predicting model for AKI in patients with MCD within the KDIGO criteria.MethodsData on 401 hospitalized adult patients, whose biopsy was diagnosed as MCD from 12/31/2010 to 15/7/2021, were retrospectively collected. Among these data, patients underwent biopsy earlier formed the training set (n = 283), while the remaining patients formed the validation set (n = 118). Independent risk factors associated with AKI were analyzed. From this, the prediction model was developed and nomogram was plotted.ResultsAKI was found in 55 of 283 patients (19%) and 15 of 118 patients (13%) in the training and validation cohorts, respectively. According to the results from lasso regression and logistic regression, it was found that four factors, including mean arterial pressure, serum albumin, uric acid, and lymphocyte counts, were independent of the onset of AKI. Incorporating these factors, the nomogram achieved a reasonably good concordance index of 0.84 (95%CI 0.77–0.90) and 0.75 (95%CI 0.62–0.87) in predicting AKI in the training and validation cohorts, respectively. Decision curve analysis suggested clinical benefit of the prediction models.ConclusionsOur predictive nomogram provides a feasible approach to identify high risk MCD patients who might develop AKI, which might facilitate the timely treatment.https://www.frontiersin.org/articles/10.3389/fmed.2022.862160/fullnomogramacute kidney injury (AKI)minimal change diseaseprediction modelnephrotic syndrome |
| spellingShingle | Chen Yang Chen Yang Chen Yang Shu-Peng Lin Pu Chen Jie Wu Jin-Ling Meng Shuang Liang Feng-Ge Zhu Yong Wang Zhe Feng Xiang-Mei Chen Guang-Yan Cai A Prediction Model for Acute Kidney Injury in Adult Patients With Minimal Change Disease Frontiers in Medicine nomogram acute kidney injury (AKI) minimal change disease prediction model nephrotic syndrome |
| title | A Prediction Model for Acute Kidney Injury in Adult Patients With Minimal Change Disease |
| title_full | A Prediction Model for Acute Kidney Injury in Adult Patients With Minimal Change Disease |
| title_fullStr | A Prediction Model for Acute Kidney Injury in Adult Patients With Minimal Change Disease |
| title_full_unstemmed | A Prediction Model for Acute Kidney Injury in Adult Patients With Minimal Change Disease |
| title_short | A Prediction Model for Acute Kidney Injury in Adult Patients With Minimal Change Disease |
| title_sort | prediction model for acute kidney injury in adult patients with minimal change disease |
| topic | nomogram acute kidney injury (AKI) minimal change disease prediction model nephrotic syndrome |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2022.862160/full |
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