Development and Validation of a Simple-to-Use Nomogram for Predicting In-Hospital Mortality in Patients With Acute Heart Failure Undergoing Continuous Renal Replacement Therapy - Gao, Luyao; Bian, Yuan; Cao, Shengchuan; Sang, Wentao; Zhang, Qun; Yuan, Qiuhuan; Xu, Feng; Chen, Yuguo (Frontiers in Medicine, 2021)
Abstract Background: Patients with acute heart failure (AHF) who require continuous renal replacement therapy (CRRT) have a high risk of in-hospital mortality. It is clinically important to screen high-risk patients using a model or scoring system. This study aimed to develop and validate a simple-to-use nomogram consisting of independent prognostic variables for prediction of in-hospital mortality in patients with AHF undergoing CRRT. Methods: We collected clinical data for 121 patients with a diagnosis of AHF who underwent CRRT in an AHF unit between September 2011 and August 2020 and from 105 patients in the MIMIC-III database. The nomogram model was created using a visual processing logistic regression model and verified using the standard method. Results: Patient age, days after admission, lactic acid level, blood glucose concentration, and diastolic blood pressure were significant prognostic factors in logistic regression analyses and were included in our model (named D-GLAD) as predictors. The resulting model containing the above-mentioned five factors had good discrimination ability in both the training group (C-index, 0.829) and the validation group (C-index, 0.740). Calibration and clinical effectiveness showed the nomogram to be accurate for prediction of in-hospital mortality in both the training cohort and the validation cohort when compared with other models. The in-hospital mortality rates in the low-risk, moderate-risk, and high-risk groups were 14.46%, 40.74%, and 71.91%, respectively. Conclusion: The nomogram allowed optimal prediction of in-hospital mortality in adults with AHF undergoing CRRT. Using this simple-to-use model, the in-hospital mortality risk can be determined for an individual patient, so could be useful for early identification of high-risk patients. An online version of the D-GLAD model can be accessed at https://ahfcrrt–d-glad.shinyapps.io/DynNomapp/. Trial registration: The study was registered at clinicaltrials.gov (NCT0751838).