A nomogram to predict the in-hospital mortality of patients with congestive heart failure and chronic kidney disease

Abstract

Aims: Patients with congestive heart failure (CHF) may also suffer from chronic kidney disease (CKD), and the two conditions may interact to increase the risk of death. The purpose of this study was to investigate the risk factors contributing to in-hospital mortality in patients with CHF and CKD and to develop a nomogram to predict the risk of in-hospital mortality.

Methods and results: This retrospective study used data from the Marketplace for Medical Information in Intensive Care (MIMIC-IV, version 1.0). Patients diagnosed with CHF and CKD in MIMIC-IV were included in this study. The least absolute shrinkage and selection operator (LASSO) logistic regression is used to select risk variables for the nomogram model, and bootstrap is used for internal validation. Simplified Acute Physiology Score II (SAPS II) and Logistic Organ Dysfunction Score (LODS) were compared with the nomogram model by the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). A total of 4638 adult patients with CHF and CKD were included in the final cohort; of them, 707 (15.2%) died and 3931 (84.8%) survived during hospitalization. Our final model included the following 13 variables: age, acute kidney injury, myocardial infarction, anaemia, heart rate ≥ 100 b.p.m., systolic blood pressure ≥ 130 mmHg, anion gap (AG) ≥ 20 mEq/L, sodium ≥ 145 mEq/L, red blood cell distribution width (RDW) ≥ 15.5%, white blood cell count ≥ 10 K/μL, continuous renal replacement therapy (CRRT), angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, and beta-blocker. The corrected C-statistic of the nomogram was 0.767, and the calibration curve indicating good concordance between the predicted and observed values. The nomogram demonstrated good accuracy for predicting the in-hospital mortality with an AUC of 0.771 (95% CI: 0.752-0.790), while the AUC for SAPS II and LODS was 0.747 (95% CI: 0.726-0.767) and 0.752 (95% CI: 0.730-0.773), respectively. DCA found that when the threshold probability was 0.05 to 0.41, the nomogram model could provide a greater net benefit than SAPS II.

Conclusions: In this retrospective cohort analysis of patients with CHF and CKD, we identified 13 independent variables associated with in-hospital mortality using LASSO logistic regression. RDW, AG, and CRRT were reported to play a significant role in in-hospital mortality among patients with CHF and CKD for the first time. Based on a simplified model including 13 variables, a nomogram was drawn to predict the risk of in-hospital mortality. In comparison with SAPS II and LODS, the nomogram model performed well.

Keywords: Anion gap; Chronic kidney disease; Congestive heart failure; Continuous renal replacement therapy; MIMIC-IV; Nomogram; Red blood cell distribution width.

Figure 1

Flow chart of inclusion and exclusion process of patients admitted with CHF and CKD. CHF, congestive heart failure; CKD, chronic kidney disease; ICU, intensive care unit.

Figure 2

Nomogram for predicting hospital mortality among patients with congestive heart failure (CHF) and chronic kidney disease (CKD). ACEI/ARB, angiotensin‐converting enzyme inhibitors/angiotensin receptor blockers; CRRT, continuous renal replacement therapy; AKI, acute kidney injury; MI, myocardial infarction; HR, heart rate; SBP, systolic blood pressure; AG, anion gap; RDW, red blood cell distribution width; WBC, white blood cell count.

Figure 3

Calibration curves for internal validation of the nomogram model.

Figure 4

Comparison of the nomogram with SAPS II and LODS. (A) Receiver operating characteristic curves, (B) decision curve analysis.