B-Type Natriuretic Peptide and Non-Invasive Haemodynamics and Hydration Status Assessments in the Management of Patients with Acute Heart Failure in the Emergency Department

2010 ◽  
Vol 17 (4) ◽  
pp. 219-225 ◽  
Author(s):  
Rossella Marino ◽  
Laura Magrini ◽  
Enrico Ferri ◽  
Giulia Gagliano ◽  
Salvatore Di Somma
Keyword(s):  
Heart Failure ◽  
Emergency Department ◽  
Acute Heart Failure ◽  
Natriuretic Peptide ◽  
Hydration Status ◽  
Non Invasive

scholarly journals Relationship between Uric Acid Levels and Diagnostic and Prognostic Outcomes in Acute Heart Failure

2012 ◽  
Vol 5 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Queen Henry-Okafor ◽  
Sean P. Collins ◽  
Cathy A. Jenkins ◽  
Karen F. Miller ◽  
David J. Maron ◽  
...  
Keyword(s):  
Heart Failure ◽  
Emergency Department ◽  
Logistic Regression ◽  
Uric Acid ◽  
Clinical Outcomes ◽  
Acute Heart Failure ◽  
Natriuretic Peptide ◽  
Diagnostic Utility ◽  
Wilcoxon Rank Sum Test ◽  
Prognostic Utility

Objectives: We evaluated the association of plasma uric acid alone and in combination with b-type natriuretic peptide (BNP) for emergency department (ED) diagnosis and 30-day prognosis in patients evaluated for acute heart failure (AHF). Methods: We prospectively enrolled 322 adult ED patients with suspected AHF. Wilcoxon rank sum test, multivariable logistic regression and likelihood ratio (LR) tests were used for statistical analyses. Results: Uric acid's diagnostic utility was poor and failed to show significant associations with 30-day clinical outcomes. Uric acid also did not add significantly to BNP results. Conclusion: Among ED patients with suspected AHF, uric acid has poor diagnostic and prognostic utility

scholarly journals Hemodynamic profiles by non-invasive monitoring of cardiac index and vascular tone in acute heart failure patients in the emergency department: external validation and clinical outcomes

2021 ◽  
Author(s):  
Nicholas Eric Harrison ◽  
Sarah Meram ◽  
Xiangrui Li ◽  
Patrick Medado ◽  
Morgan B White ◽  
...  
Keyword(s):  
Heart Failure ◽  
Emergency Department ◽  
Cardiac Index ◽  
Acute Heart Failure ◽  
Vascular Tone ◽  
Primary Outcome ◽  
De Novo ◽  
Risk Scores ◽  
Secondary Outcome ◽  
Non Invasive

Abstract Background Non-invasive finger-cuff monitors measuring cardiac index and vascular tone (SVRI) classify emergency department (ED) patients with acute heart failure (AHF) into three otherwise-indistinguishable subgroups. Our goals were to validate these hemodynamic profiles in an external cohort and assess their association with clinical outcomes. Methods AHF patients (n=257) from five EDs were prospectively enrolled in the validation cohort (VC). Cardiac index and SVRI were measured with a ClearSight finger-cuff monitor (formerly NexFin, Edwards Lifesciences) as in a previous study (derivation cohort, DC, n=127). A control cohort (CC, n=127) of ED patients with sepsis was drawn from the same study as the DC. K-means cluster analysis previously derived two-dimensional (cardiac index and SVRI) hemodynamic profiles in the DC and CC (k=3 profiles each). The VC was subgrouped de novo into three analogous profiles by unsupervised K-means consensus clustering. PERMANOVA tested whether VC profiles 1-3 differed from profiles 1-3 in the DC and CC, by multivariate group composition of cardiac index and vascular tone. Profiles in the VC were compared by a primary outcome of 90-day mortality and a 30-day ranked composite secondary outcome (death, mechanical cardiac support, intubation, new/emergent dialysis, coronary intervention/surgery) as time-to-event (survival analysis) and binary events (odds ratio, OR). Descriptive statistics were used to compare profiles by two validated risk scores for the primary outcome, and one validated score for the secondary outcome. Results The VC had median age 60 years (interquartile range {49-67}), and was 45% (n=116) female. Multivariate profile composition by cardiac index and vascular tone differed significantly between VC profiles 1-3 and CC profiles 1-3 (p=0.001, R2=0.159). A difference was not detected between profiles in the VC vs. the DC (p=0.59, R2=0.016). VC profile 3 had worse 90-day survival than profiles 1 or 2 (HR = 4.8, 95%CI 1.4-17.1). The ranked secondary outcome was more likely in profile 1 (OR = 10.0, 1.2-81.2) and profile 3 (12.8, 1.7-97.9) compared to profile 2. Diabetes prevalence and blood urea nitrogen were lower in the high-risk profile 3 (p<0.05). No significant differences between profiles were observed for other clinical variables or the 3 clinical risk scores. Conclusions Hemodynamic profiles in ED patients with AHF, by non-invasive finger-cuff monitoring of cardiac index and vascular tone, were replicated de novo in an external cohort. Profiles showed significantly different risks of clinically-important adverse patient outcomes.

scholarly journals PL01: Creation of a risk scoring system for emergency department patients with acute heart failure

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S5-S5
Author(s):  
I. Stiell ◽  
J. Perry ◽  
C. Clement ◽  
S. Sibley ◽  
A. McRae ◽  
...  
Keyword(s):  
Heart Failure ◽  
Emergency Department ◽  
Acute Heart Failure ◽  
Care Hospital ◽  
Invasive Ventilation ◽  
Non Invasive ◽  
Non Invasive Ventilation ◽  
Risk Patients ◽  
Risk Scoring ◽  
Risk Scale

Introduction: Acute heart failure (AHF) is a common emergency department (ED) presentation and may be associated with poor outcomes. Conversely, many patients rapidly improve with ED treatment and may not need hospital admission. Because there is little evidence to guide disposition decisions by ED and admitting physicians, we sought to create a risk score for predicting short-term serious outcomes (SSO) in patients with AHF. Methods: We conducted prospective cohort studies at 9 tertiary care hospital EDs from 2007 to 2019, and enrolled adult patients who required treatment for AHF. Each patient was assessed for standardized real-time clinical and laboratory variables, as well as for SSO (defined as death within 30 days or intubation, non-invasive ventilation (NIV), myocardial infarction, coronary bypass surgery, or new hemodialysis after admission). The fully pre-specified, logistic regression model with 13 predictors (age, pCO2, and SaO2 were modeled using spline functions with 3 knots and heart rate and creatinine with 5 knots) was fitted to the 10 multiple imputation datasets. Harrell's fast stepdown procedure reduced the number of variables. We calculated the potential impact on sensitivity (95% CI) for SSO and hospital admissions and estimated a sample size of 170 SSOs. Results: The 2,246 patients had mean age 77.4 years, male sex 54.5%, EMS arrival 41.1%, IV NTG 3.1%, ED NIV 5.2%, admission on initial visit 48.6%. Overall there were 174 (7.8%) SSOs including 70 deaths (3.1%). The final risk scale is comprised of five variables (points) and had c-statistic of 0.76 (95% CI: 0.73-0.80): 1.Valvular heart disease (1) 2.ED non-invasive ventilation (2) 3.Creatinine 150-300 (1) ≥300 (2) 4.Troponin 2x-4x URL (1) ≥5x URL (2) 5.Walk test failed (2) The probability of SSO ranged from 2.0% for a total score of 0 to 90.2% for a score of 10, showing good calibration. The model was stable over 1,000 bootstrap samples. Choosing a risk model total point admission threshold of >2 would yield a sensitivity of 80.5% (95% CI 73.9-86.1) for SSO with no change in admissions from current practice (48.6% vs 48.7%). Conclusion: Using a large prospectively collected dataset, we created a concise and sensitive risk scale to assist with admission decisions for patients with AHF in the ED. Implementation of this risk scoring scale should lead to safer and more efficient disposition decisions, with more high-risk patients being admitted and more low-risk patients being discharged.

Acute dyspnoea in the emergency department

2021 ◽  
pp. 103-112
Author(s):  
Eleni Michou ◽  
Nikola Kozhuharov ◽  
Jasmin Martin ◽  
Christian Mueller
Keyword(s):  
Heart Failure ◽  
Emergency Department ◽  
Acute Heart Failure ◽  
Natriuretic Peptide ◽  
Venous Blood ◽  
Combined Treatment ◽  
Acute Chest Pain ◽  
Chronic Obstructive ◽  
Common Symptom ◽  
Acute Dyspnoea

Acute dyspnoea is a very common symptom in the acute cardiac care setting. In contrast to current beliefs, acute dyspnoea, as the leading symptom, is associated with about twice the mortality risk, compared to acute chest pain. Rapid and accurate identification of the cause of dyspnoea is critical to the initiation of specific and effective treatment. In most patients, a rapid and accurate diagnosis in the emergency department can be achieved by a combination of vital signs, including pulse oximetry, detailed patient history, physical examination, blood tests including B-type natriuretic peptide or N-terminal pro-B type natriuretic peptide, venous blood gases, and C-reactive protein in all patients, and D-dimers in selected patients, electrocardiograms, chest X-ray and pleural and/or lung ultrasound. It is key to remember that the prevalence of acute heart failure in unselected patients with acute dyspnoea is about 50%. Therefore, a high awareness for the presence of acute heart failure is mandatory. Acute heart failure, pneumonia, obstructive pulmonary diseases (chronic obstructive pulmonary disease and asthma), pulmonary embolism, and anxiety disorders represent more than 90% of all cases with acute dyspnoea in the emergency department. In about 10-15%, two acute causes (e.g. acute heart failure and pneumonia) may be present and require combined treatment. Transthoracic echocardiography should be immediately performed in all patients with acute dyspnoea and shock, and in those patients in whom the diagnosis remains uncertain, even after initial work-up

scholarly journals LO02: Development of the HEARTRISK6 Scale for emergency department patients with acute heart failure

CJEM ◽  
2019 ◽  
Vol 21 (S1) ◽  
pp. S7
Author(s):  
I. Stiell ◽  
A. McRae ◽  
B. Rowe ◽  
J. Dreyer ◽  
L. Mielniczuk ◽  
...  
Keyword(s):  
Heart Failure ◽  
Emergency Department ◽  
Acute Heart Failure ◽  
Tertiary Care ◽  
Care Hospital ◽  
Invasive Ventilation ◽  
Non Invasive ◽  
Non Invasive Ventilation ◽  
Risk Patients ◽  
Risk Scale

Introduction: We previously derived (N = 559) and validated (N = 1,100) the 10-item Ottawa Heart Failure Risk Scale (OHFRS), to assist with disposition decisions for patients with acute heart failure (AHF) in the emergency department (ED). In the current study we sought to use a larger dataset to develop a more concise and more accurate risk scale. Methods: We analyzed data from the prior two studies and from a new cohort. For all 3 groups we conducted prospective cohort studies that enrolled patients who required treatment for AHF at 8 tertiary care hospital EDs. Patients were followed for 30 days. The primary outcome was short-term serious outcome (SSO), defined as death within 30 days, intubation or non-invasive ventilation (NIV) after admission, myocardial infarction, or relapse resulting in hospital admission within 14 days. The fully pre-specified logistic regression model with 13 predictors (where age, pCO2, and SaO2 were modeled using spline functions) was fitted to 10 multiple imputation datasets. Harrell's fast stepdown procedure reduced the number of variables. We calculated the potential impact on sensitivity (95% CI) for SSO and hospital admissions, and estimated a sample size of 2,000 patients. Results: The 1,986 patients had mean age 77.3 years, male 54.1%, EMS arrival 41.2%, IV NTG 3.3%, ED NIV 5.4%, admission on initial visit 49.5%. Overall there were 236 (11.9%) SSOs including 61 deaths (3.1%), meaning that current admission practice sensitivity for SSO was only 59.7%. The final HEARTRISK6 scale is comprised of 6 variables (points) (C-statistic 0.68): Valvular heart disease (2) Antiarrhythmic medication (2) ED non-invasive ventilation (3) Creatinine 80–150 (1); ≥150 (3) Troponin ≥3x URL (2) Walk test failed (1). The probability of SSO ranged from 4.8% for a total score of 0 to 62.4% for a score of 10, showing good calibration. Choosing a HEARTRISK6 total point admission threshold of ≥3 would yield sensitivity of 70.8% (95%CI 64.5-76.5) for SSO with a slight decrease in admissions to 47.9%. Choosing a threshold of ≥2 would yield a sensitivity of 84.3% (95%CI 79.0-88.7) but require 66.6% admissions. Conclusion: Using a large prospectively collected dataset, we created a more concise and more sensitive risk scale to assist with admission decisions for patients with AHF in the ED. Implementation of the HEARTRISK6 scale should lead to safer and more efficient disposition decisions, with more high-risk patients being admitted and more low-risk patients being discharged.

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