Nomogram Model to Predict Cardiorenal Syndrome Type 1 in Patients with Acute Heart Failure

Abstract

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.

Value of Plasma NGAL and Creatinine on First Day of Admission in the Diagnosis of Cardiorenal Syndrome Type 1

“…With the plasma NGAL concentration on the 1st day, we will have the corresponding score and creatinine D1 concentration will have the corresponding score and the total score will correspond to the risk of CRS1. This result was different from the research results of Zeyuan Fan et al; the nomogram includes 6 variables: age, diabetes mellitus, hsCRP, eGFR, NYHA, and urine albumin/creatinine ratio [ 16 ]. The reason for this difference is that in our research, when building the optimal regression model, only two variables NGAL and creatinine were the best models.…”

A nomogram based on a patient-reported outcomes measure: predicting the risk of readmission for patients with chronic heart failure

“…Nomograms have been regarded as dependable instruments by creating simple intuitive diagrams of the predictive models that quantify the risk of clinical adverse events [ 20 , 21 ]. In recent years, nomograms have also been used to predict the prognosis of HF [ 22 – 24 ]. This study, as far as we know, is the first attempt to construct a prognostic nomogram of chronic heart failure (CHF) based on the Chinese PROM data of 454 patients with CHF, to predict the possibility of readmission.…”

A Nomogram based on a Patient-reported Outcomes Measure: Predicting the Risk of Readmission for Patients with Chronic Heart Failure

“…[20,21] In recent years, nomograms have also been used to predict the prognosis of HF. [22][23][24] This study, as far as we know, is the first attempt to construct a prognostic nomogram of chronic heart failure (CHF) based on the Chinese PROM data of 454 patients with CHF, to predict the possibility of readmission.…”