Bioactive adrenomedullin for assessment of venous congestion in heart failure.

AIMS: Bioactive adrenomedullin (bio-ADM) is a vascular-derived peptide hormone that has emerged as a promising biomarker for assessment of congestion in decompensated heart failure (HF). We aimed to evaluate diagnostic and prognostic performance of bio-ADM for HF in comparison to amino-terminal pro-B-type natriuretic peptide (NT-proBNP), with decision thresholds derived from invasive haemodynamic and population-based studies. METHODS AND RESULTS: Normal reference ranges for bio-ADM were derived from a community-based cohort (n = 5060). Correlations with haemodynamic data were explored in a cohort of HF patients undergoing right heart catheterization (n = 346). Mortality and decision cutoffs for bio-ADM was explored in a cohort of patients presenting in the ER with acute dyspnoea (n = 1534), including patients with decompensated HF (n = 570). The normal reference range was 8-39 pg/mL. The area under the receiver operating characteristic curve (AUROC) for discrimination of elevated mean right atrial pressure (mRAP) and pulmonary arterial wedge pressure (PAWP) was 0.74 (95% CI = 0.67-0.79) and 0.70 (95% CI = 0.64-0.75), respectively, with optimal bio-ADM decision cutoff of 39 pg/mL, concordant with cubic spline analyses. NT-proBNP discriminated PAWP slightly better than mRAP (AUROC 0.73 [95% CI = 0.68-0.79] and 0.68 [95% CI = 0.61-0.75]). Bio-ADM correlated with (mRAP, r = 0.55) while NT-proBNP correlated with PAWP. Finally, a bio-ADM decision cutoff of 39 pg/mL associated with 30 and 90 day mortality and conferred a two-fold increased odds of HF diagnosis, independently from NT-proBNP. CONCLUSIONS: Bio-ADM tracks with mRAP and associates with measures of systemic congestion and with mortality in decompensated HF independently from NT-proBNP. Our findings support utility of bio-ADM as a biomarker of systemic venous congestion in HF and nominate a decision threshold.

Validation of the modified Skåne emergency department assessment of patient load (mSEAL) model for emergency department crowding and comparison with international models; an observational study.

BACKGROUND: Emergency Department crowding is associated with increased morbidity and mortality but no measure of crowding has been validated in Sweden. We have previously derived and internally validated the Skåne Emergency Department Assessment of Patient Load (SEAL) score as a measure of crowding in Emergency Departments (ED) in a large regional healthcare system in Sweden. Due to differences in electronic health records (EHRs) between health care systems in Sweden, all variables in the original SEAL-score could not be measured reliably nationally. We aimed to derive and validate a modified SEAL (mSEAL) model and to compare it with established international measures of crowding. METHODS: This was an observational cross sectional study at four EDs in Sweden. All clinical staff assessed their workload (1-6 where 6 is the highest workload) at 5 timepoints each day. We used linear regression with stepwise backward elimination on the original SEAL dataset to derive and internally validate the mSEAL score against staff workload assessments. We externally validated the mSEAL at four hospitals and compared it with the National Emergency Department Overcrowding Score (NEDOCS), the simplified International Crowding Measure in Emergency Department (sICMED), and Occupancy Rate. Area under the receiver operating curve (AuROC) and coefficient of determination was used to compare crowding models. Crowding was defined as an average workload of 4.5 or higher. RESULTS: The mSEAL score contains the variables Patient Hours and Time to physician and showed strong correlation with crowding in the derivation (r2 = 0.47), internal validation (r2 = 0.64 and 0.69) and in the external validation (r2 = 0.48 to 0.60). AuROC scores for crowding in the external validation were 0.91, 0.90, 0.97 and 0.80 for mSEAL, Occupancy Rate, NEDOCS and sICMED respectively. CONCLUSIONS: The mSEAL model can measure crowding based on workload in Swedish EDs with good discriminatory capacity and has the potential to systematically evaluate crowding and help policymakers and researchers target its causes and effects. In Swedish EDs, Occupancy Rate and NEDOCS are good alternatives to measure crowding based on workload.