Nomogram Model to Predict Cardiorenal Syndrome Type 1 in Patients with Acute Heart Failure
Background/Aims: Cardiorenal syndrome type 1(CRS1) is a serious clinical condition in patients with acute heart failure (AHF) associated with adverse clinical outcomes. Although several biomarkers for identifying CRS1 have been reported, early and accurate predicting CRS1 still remains a challenge....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Karger Publishers
2018-12-01
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| Series: | Kidney & Blood Pressure Research |
| Subjects: | |
| Online Access: | https://www.karger.com/Article/FullText/495815 |
| _version_ | 1818246371371646976 |
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| author | Zeyuan Fan Yang Li Hanhua Ji Xinwen Jian |
| author_facet | Zeyuan Fan Yang Li Hanhua Ji Xinwen Jian |
| author_sort | Zeyuan Fan |
| collection | DOAJ |
| description | Background/Aims: Cardiorenal syndrome type 1(CRS1) is a serious clinical condition in patients with acute heart failure (AHF) associated with adverse clinical outcomes. Although several biomarkers for identifying CRS1 have been reported, early and accurate predicting CRS1 still remains a challenge. This study was aimed to develop and validate an individualized predictive nomogram for the risk of CRS1 in patients with AHF. Methods: A total of 1235 AHF patients between 2013 and 2018 were included in this study. The patients were randomly classified into training set (n=823) and validation set (n=412). All data of the training set were used to screen the predictors of CRS1 via univariate and multivariate analyses. A nomogram was developed based on these predictors and validated by internal and external validation. The nomogram validation comprised discriminative ability determined by the area under the curve (AUC) of receiver-operating characteristic (ROC) curve and the predictive accuracy by calibration plots. Results: The overall incidence of CRS1 was 31.7%. Multivariate logistic regression revealed that age, diabetes, NYHA class, eGFR, hs-CRP and uAGT were independently associated with CRS1. A nomogram developed based on the six variables was with the AUC 0.885 and 0.823 on internal and external validation, respectively. Calibration plots showed that the predicted and actual CRS1 probabilities were fitted well on both internal and external validation. Conclusion: The proposed nomogram could predict the individualized risk of CRS1 with good accuracy, high discrimination, and potential clinical applicability in patients with AHF. |
| first_indexed | 2024-12-12T14:47:45Z |
| format | Article |
| id | doaj.art-bd6dc8dbe867474c8e1cb0807d789318 |
| institution | Directory Open Access Journal |
| issn | 1420-4096 1423-0143 |
| language | English |
| last_indexed | 2024-12-12T14:47:45Z |
| publishDate | 2018-12-01 |
| publisher | Karger Publishers |
| record_format | Article |
| series | Kidney & Blood Pressure Research |
| spelling | doaj.art-bd6dc8dbe867474c8e1cb0807d7893182022-12-22T00:21:05ZengKarger PublishersKidney & Blood Pressure Research1420-40961423-01432018-12-014361832184110.1159/000495815495815Nomogram Model to Predict Cardiorenal Syndrome Type 1 in Patients with Acute Heart FailureZeyuan FanYang LiHanhua JiXinwen JianBackground/Aims: Cardiorenal syndrome type 1(CRS1) is a serious clinical condition in patients with acute heart failure (AHF) associated with adverse clinical outcomes. Although several biomarkers for identifying CRS1 have been reported, early and accurate predicting CRS1 still remains a challenge. This study was aimed to develop and validate an individualized predictive nomogram for the risk of CRS1 in patients with AHF. Methods: A total of 1235 AHF patients between 2013 and 2018 were included in this study. The patients were randomly classified into training set (n=823) and validation set (n=412). All data of the training set were used to screen the predictors of CRS1 via univariate and multivariate analyses. A nomogram was developed based on these predictors and validated by internal and external validation. The nomogram validation comprised discriminative ability determined by the area under the curve (AUC) of receiver-operating characteristic (ROC) curve and the predictive accuracy by calibration plots. Results: The overall incidence of CRS1 was 31.7%. Multivariate logistic regression revealed that age, diabetes, NYHA class, eGFR, hs-CRP and uAGT were independently associated with CRS1. A nomogram developed based on the six variables was with the AUC 0.885 and 0.823 on internal and external validation, respectively. Calibration plots showed that the predicted and actual CRS1 probabilities were fitted well on both internal and external validation. Conclusion: The proposed nomogram could predict the individualized risk of CRS1 with good accuracy, high discrimination, and potential clinical applicability in patients with AHF.https://www.karger.com/Article/FullText/495815Cardiorenal syndrome type 1Acute heart failureNomogram |
| spellingShingle | Zeyuan Fan Yang Li Hanhua Ji Xinwen Jian Nomogram Model to Predict Cardiorenal Syndrome Type 1 in Patients with Acute Heart Failure Kidney & Blood Pressure Research Cardiorenal syndrome type 1 Acute heart failure Nomogram |
| title | Nomogram Model to Predict Cardiorenal Syndrome Type 1 in Patients with Acute Heart Failure |
| title_full | Nomogram Model to Predict Cardiorenal Syndrome Type 1 in Patients with Acute Heart Failure |
| title_fullStr | Nomogram Model to Predict Cardiorenal Syndrome Type 1 in Patients with Acute Heart Failure |
| title_full_unstemmed | Nomogram Model to Predict Cardiorenal Syndrome Type 1 in Patients with Acute Heart Failure |
| title_short | Nomogram Model to Predict Cardiorenal Syndrome Type 1 in Patients with Acute Heart Failure |
| title_sort | nomogram model to predict cardiorenal syndrome type 1 in patients with acute heart failure |
| topic | Cardiorenal syndrome type 1 Acute heart failure Nomogram |
| url | https://www.karger.com/Article/FullText/495815 |
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