Clinical characteristics and one-year mortality according to admission renal function in patients with a first acute heart failure hospitalization

One-year mortality after hospitalization for acute heart failure: Predicting factors (PRECIC study subanalysis)

INTRODUCTION AND OBJECTIVES

One-year mortality after hospitalization for heart failure (HF) is high. This study aims to identify predictive factors of one-year mortality.

METHODS

This is a retrospective, single-center and observational study. All patients hospitalized for acute HF for one-year were enrolled.

RESULTS

A total of 429 patients were enrolled, mean age of 79 years. The in-hospital and one-year all-cause mortality rates were 7.9% and 34.3%, respectively. In the univariable analysis, the factors significantly associated with higher one-year mortality risk were: age ≥80 years (odds ratio (OR)=2.05, 95% confidence interval (CI) 1.35-3.11, p=0.001); active cancer (OR=2.93, 95% CI 1.36-6.32, p=0.008); dementia (OR=2.84, 95% CI 1.81-4.47, p<0.001); functional dependency (OR=2.63, 95% CI 1.65-4.19, p<0.001); atrial fibrillation (OR=1.86, 95% CI 1.24-2.80, p=0.004); higher creatinine (OR=2.03, 95% CI 1.29-3.21, p=0.002), urea (OR=2.92, 95% CI 1.95-4.36, p<0.001) and red cell distribution width (RDW; 4thQ OR=5.59, 95% CI 3.03-10.32, p=0.001); and lower hematocrit (OR=0.94, 95% CI 0.91-0.97, p<0.001), hemoglobin (OR=0.83, 95% CI 0.75-0.92, p<0.001) and platelet distribution width (PDW; OR=0.89, 95% CI 0.82-0.97, p=0.005). In the multivariable analysis, the independent predictors of higher one-year mortality risk were: age ≥80 years (OR=2.05, 95% CI 1.21-3.48); active cancer (OR=2.70, 95% CI 1.03-7.01); dementia (OR=2.69, 95% CI 1.53-4.74); higher urea (OR=2.97, 95% CI 1.84-4.80) and RDW (4thQ OR=5.24, 95% CI 2.55-10.76); and lower PDW (OR=0.88, 95% CI 0.80-0.97).

CONCLUSIONS

Active cancer, dementia, and high values for urea and RDW at admission are predictors of one-year mortality in patients hospitalized for HF. These variables are readily available at admission and can support the clinical management of HF patients.

Palliative Care Needs and Clinical Features Related to Short-Term Mortality in Patients Enrolled in a Heart Failure Unit

(1) Background: Heart failure (HF) is a chronic and complex pathology requiring continuous patient management due to clinical instability, associated comorbidity, and extensive pharmacological treatment. Its unpredictable course makes the advanced stages challenging to recognize and raises the need for palliative care. This study aims to identify palliative care needs in HF patients and describe clinical features related to short-term mortality. (2) Methods: A descriptive, observational, cross-sectional, and retrospective study was carried out in an HF unit of a Spanish tertiary hospital. Patients’ socio-demographic and clinical data were collected from clinical records, and different instruments were used to establish mortality risks and patients’ needs for palliative care. Subsequently, univariate and bivariate descriptive analyses were performed. A binary logistic regression model helped to determine variables that could influence mortality 12 months after admission to the Unit. (3) Results: The studied population, sixty-five percent women, had an average age of 83.27 years. Among other clinical characteristics predominated preserved ejection fraction (pEF) and dyspnea NYHA (New York Heart Association) class II. The most prevalent comorbidities were hypertension and coronary heart disease. Forty-nine percent had a low–intermediate mortality risk in the following year, according to the PROFUND index. The NECPAL CCOMS-ICO© instrument identified subjects who meet the criteria for palliative care. This predictive model identified NECPAL CCOMS-ICO© results, using beta-blockers (BB) or AIIRA (Angiotensin II receptor antagonists) and low glomerular filtration rate (GFR) as explanatory variables of patients’ mortality in the following year. (4) Conclusions: The analysis of the characteristics of the population with HF allows us to identify patients in need of palliative care. The NECPAL CCOMS-ICO© instrument and the PROFUND have helped identify the characteristics of people with HF who would benefit from palliative management.

Renal function at admission as a prognostic marker for patients hospitalised for a first episode of heart failure. Results of the PREDICE study

BACKGROUND AND OBJECTIVE

Heart failure (HF) is a frequent condition that deteriorates quality of life and results in high morbidity and mortality. A considerable number of studies have been implemented in recent years to determine the factors that affect the prognosis of HF; however, few studies have assessed the prognosis of patients hospitalised for their first episode of HF. The aim of our study was to analyse the prognostic impact of renal function on patients hospitalised for a first episode of HF.

MATERIAL AND METHODS

We recruited 600 patients hospitalised for a first episode of HF in 3 tertiary Spanish hospitals. We analysed the mortality risk during the first year of follow-up according to renal function at the time of admission.

RESULTS

The patients with the highest degree of kidney failure at admission were older (P<.001), were more often women (p=.01) and presented a higher degree of dependence (P<.05), as well as a higher prevalence of arterial hypertension (P<.001), chronic renal failure (P<.001) and anaemia (P<.001). In the multivariate analysis, the degree of kidney failure at admission remained an independent predictor of increased mortality risk during the first year of follow-up.

CONCLUSIONS

The presence of kidney failure at admission was a marker of poor prognosis in our cohort of patients hospitalised for a first episode of HF.

Electronic medical record-based model to predict the risk of 90-day readmission for patients with heart failure

Background

Several heart failure (HF) risk models exist, however, most of them perform poorly when applied to real-world situations. This study aimed to develop a convenient and efficient risk model to identify patients with high readmission risk within 90 days of HF.

Methods

A multivariate logistic regression model was used to predict the risk of 90-day readmission. Data were extracted from electronic medical records from January 1, 2017 to December 31, 2017 and follow-up records of patients with HF within 3 months after discharge. Model performance was evaluated using a receiver operating characteristic curve. All statistical analysis was done using R version 3.5.0.

Results

A total of 350 patients met the inclusion criterion of being readmitted within in 90 days. All data sets were randomly divided into derivation and validation cohorts at a 7/3 ratio. The baseline data were fairly consistent among the derivation and validation cohorts. The variables most clearly related to readmission were logarithm of serum N-terminal pro b-type natriuretic peptide (NT-proBNP) level, red cell volume distribution width (RDW-CV), and Charlson comorbidity index (CCI). The model had good discriminatory ability (C-statistic = 0.73).

Conclusions

We developed and validated a multivariate logistic regression model to predict the 90-day readmission risk for Chinese patients with HF. The predictors included in the model are derived from electronic medical record (EMR) admission data, making it easier for physicians and pharmacists to identify high-risk patients and tailor more intensive precautionary strategies.