We have located links that may give you full text access.
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. 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.
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.
Full text links
Related Resources
Trending Papers
Challenges in Septic Shock: From New Hemodynamics to Blood Purification Therapies.Journal of Personalized Medicine 2024 Februrary 4
Molecular Targets of Novel Therapeutics for Diabetic Kidney Disease: A New Era of Nephroprotection.International Journal of Molecular Sciences 2024 April 4
Perioperative echocardiographic strain analysis: what anesthesiologists should know.Canadian Journal of Anaesthesia 2024 April 11
The 'Ten Commandments' for the 2023 European Society of Cardiology guidelines for the management of endocarditis.European Heart Journal 2024 April 18
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app