A simple discharge risk model for predicting 1-year mortality in hospitalised acute decompansated heart failure patients with reduced ejection fraction

Do we need a simplified model to predict outcomes in patients hospitalized with Acute Decompensated Heart Failure? Results from The Role of Sodium in Heart Failure Outcomes Prediction ('SHOUT-PREDICTION') study

Context

Hyponatremia is associated with high in-hospital mortality in patients with acute decompensated Heart Failure (ADHF) and is one of the components in various risk scores in heart failure (HF). However, some risk scores predict outcomes in these patients without using hyponatremia as its component.

Aim

The study was aimed to evaluate the relationship between serum sodium levels at admission and clinical outcomes during the in-hospital course and three months’ follow-up, in patients admitted in the intensive cardiac care unit (ICCU) with ADHF.

Methods and material

This was a single-center prospective, observational study in which 130 consecutive patients admitted with ADHF were observed for clinical characteristics and blood investigation at admission and their clinical outcomes during the in-hospital course and follow-up of 3 months.

Results

Hyponatremia and systolic blood pressure (SBP) both were found to be the independent predictor of in-hospital mortality. The SXS score (calculated as a product of SBP and serum sodium, divided by 1000) as a new prediction variable was significantly associated with in-hospital mortality and was compared with the Get with the guideline HF (GWTG-HF) score and ADHF national registry (ADHERE) score. The SXS score showed the best overall accuracy in predicting in-hospital mortality [area under the curve (AUC) = 0.899] as compared to the ADHERE (AUC = 0.780) and the GWTG (AUC = 0.815).

Conclusions

A score derived from the product of serum sodium and SBP (SXS score) had a significant association with in-hospital mortality, and better predictive value as compared to GWTG and ADHERE risk score in these patients.

Predicting the 1-Year All-Cause Mortality After Hospitalisation for an Acute Heart Failure Event: A Real-World Derivation Cohort for the Development of the S2PLiT-UG Score

BACKGROUND

Acute heart failure (AHF) is a complex syndrome associated with high morbidity and mortality. This study aimed to derive a simple risk score with which to identify AHF patients at high risk for an all-cause death event during the first year after hospital discharge.

METHODS

Three hundred AHF patients from the Heart Failure registry were included in the analysis. Cox regression with a forward-conditional algorithm and bootstrapping procedure was used to build the prognostic score, while c-statistic was used to assess the prognostic performance of the score.

RESULTS

Seven variables were independently associated with an all-cause mortality event during the 1-year follow-up (FU): estimated glomerular filtration rate of 40-60; estimated glomerular filtration rate <40 mL/min/1.73 m²; uric acid >450 μmol/L; left-ventricular ejection fraction <45%; sodium <136 mmol/L; systolic blood pressure <115 mmHg; and a positive history of previous heart failure-related decompensation event(s). The score derived from significant variables enabled classification of patients into three risk categories: low (0-2 points), intermediate (3 points), and high (4-6 points). Observed all-cause mortality rates during the 1-year FU were 6.1%, 30.5%, and 80.9% across the three risk categories, respectively. The score demonstrated a high level of discrimination for an all-cause death event in the derivation cohort with the c-statistic value of 0.907 (95% CI, 0.867-0.939; p < 0.0001) and adequate calibration.

CONCLUSIONS

The S₂PLiT-UG score is a simple tool with potential for facilitating risk stratification and therapeutic decision-making during the first year after hospitalisation for an AHF event. Future external validation studies are required to confirm its prognostic performance.