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1.
Eur Heart J Acute Cardiovasc Care ; 11(1): 43-52, 2022 Jan 12.
Article in English | MEDLINE | ID: mdl-34897402

ABSTRACT

AIMS: Hypochloraemia is common in patients hospitalized with heart failure (HF) and associated with a high risk of adverse outcomes during admission and following discharge. We assessed the significance of changes in serum chloride concentrations in relation to serum sodium and bicarbonate concentrations during admission in a cohort of 1002 consecutive patients admitted with HF and enrolled into an observational study based at a single tertiary centre in the UK. METHODS AND RESULTS: Hypochloraemia (<96 mmol/L), hyponatraemia (<135 mmol/L), and metabolic alkalosis (bicarbonate >32 mmol/L) were defined by local laboratory reference ranges. Outcomes assessed were all-cause mortality, all-cause mortality or all-cause readmission, and all-cause mortality or HF readmission. Cox regression and Kaplan-Meier curves were used to investigate associations with outcome. During a median follow-up of 856 days (interquartile range 272-1416), discharge hypochloraemia, regardless of serum sodium, or bicarbonate levels was associated with greater all-cause mortality [hazard ratio (HR) 1.44, 95% confidence interval (CI) 1.15-1.79; P = 0.001], all-cause mortality or all-cause readmission (HR 1.26, 95% CI 1.04-1.53; P = 0.02), and all-cause mortality or HF readmission (HR 1.41, 95% CI 1.14-1.74; P = 0.002) after multivariable adjustment. Patients with concurrent hypochloraemia and natraemia had lower haemoglobin and haematocrit, suggesting congestion; those with hypochloraemia and normal sodium levels had more metabolic alkalosis, suggesting decongestion. CONCLUSION: Hypochloraemia is common at discharge after a hospitalization for HF and is associated with worse outcome subsequently. It is an easily measured clinical variables that is associated with morbidity or mortality of any cause.


Subject(s)
Heart Failure , Hyponatremia , Heart Failure/complications , Heart Failure/epidemiology , Hospitalization , Hospitals , Humans , Hyponatremia/epidemiology , Hyponatremia/etiology , Patient Readmission , Prognosis
2.
Int J Cardiol ; 278: 167-172, 2019 Mar 01.
Article in English | MEDLINE | ID: mdl-30587417

ABSTRACT

BACKGROUND: Models for predicting the outcome of patients hospitalized for heart failure (HF) rarely take a holistic view. We assessed the ability of measures of frailty and social support in addition to demographic, clinical, imaging and laboratory variables to predict short-term outcome for patients discharged after a hospitalization for HF. METHODS: OPERA-HF is a prospective observational cohort, enrolling patients hospitalized for HF in a single center in Hull, UK. Variables were combined in a logistic regression model after multiple imputation of missing data to predict the composite outcome of death or readmission at 30 days. Comparisons were made to a model using clinical variables alone. The discriminative performance of each model was internally validated with bootstrap re-sampling. RESULTS: 1094 patients were included (mean age 77 [interquartile range 68-83] years; 40% women; 56% with moderate to severe left ventricular systolic dysfunction) of whom 213 (19%) had an unplanned re-admission and 60 (5%) died within 30 days. For the composite outcome, a model containing clinical variables alone had an area under the receiver-operating characteristic curve (AUC) of 0.68 [95% CI 0.64-0.72]. Adding marital status, support from family and measures of physical frailty increased the AUC (p < 0.05) to 0.70 [95% CI 0.66-0.74]. CONCLUSIONS: Measures of physical frailty and social support improve prediction of 30-day outcome after an admission for HF but predicting near-term events remains imperfect. Further external validation and improvement of the model is required.


Subject(s)
Frailty/diagnosis , Frailty/mortality , Heart Failure/diagnosis , Heart Failure/mortality , Patient Readmission/trends , Social Support , Aged , Aged, 80 and over , Cohort Studies , Female , Humans , Male , Predictive Value of Tests , Prospective Studies , Risk Factors , Time Factors
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