External Validation of the MEESSI Acute Heart Failure Risk Score: A Cohort Study

Diagnostic and prognostic utility of bone morphogenetic protein 10 in acute dyspnea: a cohort study

BACKGROUND AND AIM

The possible clinical utility of Bone Morphogenetic Protein 10 (BMP10), a novel atrial-specific biomarker, is incompletely understood. We aimed to test the hypothesis that BMP10 has high diagnostic and prognostic accuracy in patients presenting with acute dyspnea.

METHODS AND RESULTS

In a multicenter diagnostic study, BMP10, high-sensitivity cardiac troponin T (hs-cTnT), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentrations were determined in patients presenting with acute dyspnea to the emergency department. The final diagnosis was centrally adjudicated by two independent cardiologists blinded to BMP10. Diagnostic accuracy for acute heart failure (AHF) was quantified using the area under the receiver operating characteristic curve (AUC). 720-day all-cause mortality and the composite of all-cause mortality or AHF rehospitalization were prognostic endpoints. Among 933 consecutive patients, 54% were adjudicated to have AHF. Patients with AHF had higher BMP10 concentrations (median 3.34 [IQR 2.55-4.35] ng/mL) compared to patients with other causes of acute dyspnea (2.04 [1.74-2.45] ng/mL, p < 0.001). The AUC of BMP10 was 0.85 (95%CI, 0.82-0.87), versus 0.79 (95%CI, 0.76-0.82, p < 0.001) for hs-cTnT and 0.91 (95%CI, 0.90-0.93, p < 0.001) for NT-proBNP. The combination of BMP10 with NT-proBNP (AUC 0.92, 95%CI, 0.90-0.94) did not significantly increase the AUC versus NT-proBNP alone. BMP10 was a powerful predictor of death and AHF rehospitalization, but did not provide incremental value to models including NT-proBNP.

CONCLUSION

BMP10 had a high diagnostic accuracy for AHF and high prognostic accuracy for death and AHF rehospitalization. However, it did not provide relevant incremental value to the current gold standard NT-proBNP.

Artificial intelligence algorithms permits rapid acute kidney injury risk classification of patients with acute myocardial infarction

Objective

This study aimed to develop and validate several artificial intelligence (AI) models to identify acute myocardial infarction (AMI) patients at an increased risk of acute kidney injury (AKI) during hospitalization.

Methods

Included were patients diagnosed with AMI from the Medical Information Mart for Intensive Care (MIMIC) III and IV databases. Two cohorts of AMI patients from Changzhou Second People's Hospital and Xuzhou Center Hospital were used for external validation of the models. Patients' demographics, vital signs, clinical characteristics, laboratory results, and therapeutic measures were extracted. Totally, 12 AI models were developed. The area under the receiver operating characteristic curve (AUC) were calculated and compared.

Results

AKI occurred during hospitalization in 1098 (28.3 %) of the 3882 final enrolled patients, split into training (3105) and test (777) sets randomly. Among them, Random Forest (RF), C5.0 and Bagged CART models outperformed the other models in both the training and test sets. The AUCs for the test set were 0.754, 0.734 and 0.730, respectively. The incidence of AKI was 9.8 % and 9.5 % in 2202 patients in the Changzhou cohort and 807 patients in the Xuzhou cohort with AMI, respectively. The AUCs for patients in the Changzhou cohort were RF, 0.761; C5.0, 0.733; and bagged CART, 0.725, respectively, and Xuzhou cohort were RF, 0.799; C5.0, 0.808; and bagged CART, 0.784, respectively.

Conclusion

Several machines learning-based prediction models for AKI after AMI were developed and validated. The RF, C5.0 and Bagged CART model performed robustly in identifying high-risk patients earlier.

Clinical trial approval statement

This Trial was registered in the Chinese clinical trials registry: ChiCTR1800014583. Registered January 22, 2018 (http://www.chictr.org.cn/searchproj.aspx).

Acute Heart Failure: From The Emergency Department to the Intensive Care Unit

Acute heart failure (AHF) is a frequent cause of hospitalization around the world and is associated with high in-hospital and post-discharge morbidity and mortality. This review summarizes data on diagnosis and management of AHF from the emergency department to the intensive care unit. While more evidence is needed to guide risk stratification and care of patients with AHF, hospitalization is a key opportunity to optimize evidence-based medical therapy for heart failure. Close linkage to outpatient care is essential to improve post-hospitalization outcomes.

Impact of the MEESSI-AHF tool to guide disposition decision-making in patients with acute heart failure in the emergency department: a before-and-after study

OBJECTIVES

To determine the impact of risk stratification using the MEESSI-AHF (Multiple Estimation of risk based on the Emergency department Spanish Score In patients with acute heart failure) scale to guide disposition decision-making on the outcomes of ED patients with acute heart failure (AHF), and assess the adherence of emergency physicians to risk stratification recommendations.

METHODS

This was a prospective quasi-experimental study (before/after design) conducted in eight Spanish EDs which consecutively enrolled adult patients with AHF. In the pre-implementation stage, the admit/discharge decision was performed entirely based on emergency physician judgement. During the post-implementation phase, emergency physicians were advised to 'discharge' patients classified by the MEESSI-AHF scale as low risk and 'admit' patients classified as increased risk. Nonetheless, the final decision was left to treating emergency physicians. The primary outcome was 30-day all-cause mortality. Secondary outcomes were days alive and out of hospital, in-hospital mortality and 30-day post-discharge combined adverse event (ED revisit, hospitalisation or death).

RESULTS

The pre-implementation and post-implementation cohorts included 1589 and 1575 patients, respectively (median age 85 years, 56% females) with similar characteristics, and 30-day all-cause mortality was 9.4% and 9.7%, respectively (post-implementation HR=1.03, 95% CI=0.82 to 1.29). There were no differences in secondary outcomes or in the percentage of patients entirely managed in the ED without hospitalisation (direct discharge from the ED, 23.5% vs 24.4%, OR=1.05, 95% CI=0.89 to 1.24). Adjusted models did not change these results. Emergency physicians followed the MEESSI-AHF-based recommendation on patient disposition in 70.9% of cases (recommendation over-ruling: 29.1%). Physicians were more likely to over-rule the recommendation when 'discharge' was recommended (56.4%; main reason: need for hospitalisation for a second diagnosis) than when 'admit' was recommended (12.8%; main reason: no appreciation of severity of AHF decompensation by emergency physician), with an OR for over-ruling the 'discharge' compared with the 'admit' recommendation of 8.78 (95% CI=6.84 to 11.3).

CONCLUSIONS

Implementing the MEESSI-AHF risk stratification tool in the ED to guide disposition decision-making did not improve patient outcomes.

What are the Data for Current Prognostic Tools Used to Determine the Risk of Short-Term Adverse Events in Patients with Acute Heart Failure?

BACKGROUND

Acute heart failure (AHF) is a common condition evaluated in the emergency department (ED). Patients may present with a wide range of signs and symptoms, comorbidities, exacerbating factors, and ability to follow-up. Having a decision tool to objectively assess the risk of near-term events would help guide disposition decisions in these patients.

CLINICAL QUESTION

What are the data for current tools used to determine the short-term risk of adverse events of patients with AHF in the ED setting?

EVIDENCE REVIEW

Studies retrieved included six prospective studies and three retrospective cohort studies that evaluated the following five different risk scores that may predict the risk of serious adverse events in those with AHF: Ottawa Heart Failure Risk Score (OHFRS), Emergency Heart Failure Mortality Risk Grade (EHMRG), EHMRG at 30 days with addition of an ST depression variable (EHMRG30-ST), Multiple Estimation of Risk Based on the Emergency Department Spanish 40 Score in Patients with AHF Score (MEESSI-AHF), and the Improving Heart Failure Risk Stratification in the ED (STRATIFY) tool.

CONCLUSIONS

Based on the available literature, risk scores, including the OHFRS; EHMRG; EHMRG30-ST; MEESSI-AHF; and STRATIFY, can help identify short-term risk of adverse events, but are insufficient in isolation. Clinicians should use these tools in conjunction with other factors, such as the patient's symptom trajectory, hemodynamics, and access to follow-up care.

MEESSI-AHF score to estimate short-term prognosis of acute heart failure patients in the Emergency Department: a prospective and multicenter study

BACKGROUND

The assessment of acute heart failure (AHF) prognosis is primordial in emergency setting. Although AHF management is exhaustively codified using mortality predictors, there is currently no recommended scoring system for assessing prognosis. The European Society of Cardiology (ESC) recommends a comprehensive assessment of global AHF prognosis, considering in-hospital mortality, early rehospitalization rates and the length of hospital stay.

OBJECTIVE

We aimed to prospectively evaluate the performance of the Multiple Estimation of risk based on the Emergency department Spanish Score In patients with AHF (MEESSI-AHF) score in estimating short prognosis according to the ESC guidelines.

DESIGN, SETTINGS AND PATIENTS

A multicenter study was conducted between November 2020, and June 2021. Adult patients who presented to eleven French hospitals for AHF were prospectively included.

OUTCOME MEASURES AND ANALYSIS

According to MEESSI-AHF score, patients were stratified in four categories corresponding to mortality risk: low-, intermediate-, high- and very high-risk groups. The primary outcome was the number of days alive and out of the hospital during the 30-day period following admission to the Emergency Department (ED).

RESULTS

In total, 390 patients were included. The number of days alive and out of the hospital decreased significatively with increasing MEESSI-AHF risk groups, ranging from 21.2 days (20.3-22.3 days) for the low-risk, 20 days (19.3-20.5 days) for intermediate risk,18.6 days (17.6-19.6 days) for the high-risk and 17.9 days (16.9-18.9 days) very high-risk category.

CONCLUSION

Among patients admitted to ED for an episode of AHF, the MEESSI-AHF score estimates with good performance the number of days alive and out of the hospital.

Emergency department risk assessment and disposition of acute heart failure patients: existing evidence and ongoing challenges

Heart failure (HF) is a global public health burden, characterized by frequent emergency department (ED) visits and hospitalizations. Identifying successful strategies to avoid admissions is crucial for the management of acutely decompensated HF, let alone resource utilization. The primary challenge for ED management of patients with acute heart failure (AHF) lies in the identification of those who can be safely discharged home instead of being admitted. This is an elaborate decision, based on limited objective evidence. Thus far, current biomarkers and risk stratification tools have had little impact on ED disposition decision-making. A reliable definition of a low-risk patient profile is warranted in order to accurately identify patients who could be appropriate for early discharge. A brief period of observation can facilitate risk stratification and allow for close monitoring, aggressive treatment, continuous assessment of response to initial therapy and patient education. Lung ultrasound may represent a valid bedside tool to monitor cardiogenic pulmonary oedema and determine the extent of achieved cardiac unloading after treatment in the observation unit setting. Safe discharge mandates multidisciplinary collaboration and thoughtful assessment of socioeconomic and behavioural factors, along with a clear post-discharge plan put forward and a close follow-up in an outpatient setting. Ongoing research to improve ED risk stratification and disposition of AHF patients may mitigate the tremendous public health challenge imposed by the HF epidemic.

Emergency department direct discharge compared to short-stay unit admission for selected patients with acute heart failure: analysis of short-term outcomes

Short stay unit (SSU) is an alternative to conventional hospitalization in patients with acute heart failure (AHF), but the prognosis is not known compared to direct discharge from the emergency department (ED). To determine whether direct discharge from the ED of patients diagnosed with AHF is associated with early adverse outcomes versus hospitalization in SSU. Endpoints, defined as 30-day all-cause mortality or post-discharge adverse events, were evaluated in patients diagnosed with AHF in 17 Spanish EDs with an SSU, and compared by ED discharge vs. SSU hospitalization. Endpoint risk was adjusted for baseline and AHF episode characteristics and in patients matched by propensity score (PS) for SSU hospitalization. Overall, 2358 patients were discharged home and 2003 were hospitalized in SSUs. Discharged patients were younger, more frequently men, with fewer comorbidities, had better baseline status, less infection, rapid atrial fibrillation and hypertensive emergency as the AHF trigger, and had a lower severity of AHF episode. While their 30-day mortality rate was lower than in patients hospitalized in SSU (4.4% vs. 8.1%, p < 0.001), 30-day post-discharge adverse events were similar (27.2% vs. 28.4%, p = 0.599). After adjustment, there were no differences in the 30-day risk of mortality of discharged patients (adjusted HR 0.846, 95% CI 0.637-1.107) or adverse events (1.035, 0.914-1.173). In 337 pairs of PS-matched patients, there were no differences in mortality or risk of adverse event between patients directly discharged or admitted to an SSU (0.753, 0.409-1.397; and 0.858, 0.645-1.142; respectively). Direct ED discharge of patients diagnosed with AHF provides similar outcomes compared to patients with similar characteristics and hospitalized in a SSU.

What drives performance in machine learning models for predicting heart failure outcome?

Aims

The development of acute heart failure (AHF) is a critical decision point in the natural history of the disease and carries a dismal prognosis. The lack of appropriate risk-stratification tools at hospital discharge of AHF patients significantly limits clinical ability to precisely tailor patient-specific therapeutic regimen at this pivotal juncture. Machine learning-based strategies may improve risk stratification by incorporating analysis of high-dimensional patient data with multiple covariates and novel prediction methodologies. In the current study, we aimed at evaluating the drivers for success in prediction models and establishing an institute-tailored artificial Intelligence-based prediction model for real-time decision support.

Methods and results

We used a cohort of all 10 868 patients AHF patients admitted to a tertiary hospital during a 12 years period. A total of 372 covariates were collected from admission to the end of the hospitalization. We assessed model performance across two axes: (i) type of prediction method and (ii) type and number of covariates. The primary outcome was 1-year survival from hospital discharge. For the model-type axis, we experimented with seven different methods: logistic regression (LR) with either L1 or L2 regularization, random forest (RF), Cox proportional hazards model (Cox), extreme gradient boosting (XGBoost), a deep neural-net (NeuralNet) and an ensemble classifier of all the above methods. We were able to achieve an area under receiver operator curve (AUROC) prediction accuracy of more than 80% with most prediction models including L1/L2-LR (80.4%/80.3%), Cox (80.2%), XGBoost (80.5%), NeuralNet (80.4%). RF was inferior to other methods (78.8%), and the ensemble model was slightly superior (81.2%). The number of covariates was a significant modifier (P < 0.001) of prediction success, the use of multiplex-covariates preformed significantly better (AUROC 80.4% for L1-LR) compared with a set of known clinical covariates (AUROC 77.8%). Demographics followed by lab-tests and administrative data resulted in the largest gain in model performance.

Conclusions

The choice of the predictive modelling method is secondary to the multiplicity and type of covariates for predicting AHF prognosis. The application of a structured data pre-processing combined with the use of multiple-covariates results in an accurate, institute-tailored risk prediction in AHF.

Prognostic Value of Self-Reported Subjective Exercise Capacity in Patients With Acute Dyspnea

Background

Self-reported exercise capacity is a well-established prognostic measure in stable ambulatory patients with cardiac and pulmonary disease.

Objectives

The authors aimed to directly compare the prognostic accuracy of quantified self-reported exercise capacity using the Duke Activity Status Index (DASI) with the established objective disease-severity marker B-type natriuretic peptide (BNP) in patients presenting with acute dyspnea to the emergency department.

Methods

The DASI was obtained in a prospective multicenter diagnostic study recruiting unselected patients presenting with acute dyspnea to the emergency department. The prognostic accuracy of DASI and BNP for 90-day and 720-day all-cause mortality was evaluated using C-index.

Results

Among 1,019 patients eligible for this analysis, 75 (7%) and 297 (29%) patients died within 90 and 720 days after presentation, respectively. Unadjusted hazard ratios (HRs) and multivariable adjusted hazard ratios (aHRs) for 90- and 720-day mortality increased continuously from the fourth (best self-reported exercise capacity) to the first DASI quartile (worst self-reported exercise capacity). For 720-day mortality the HR of the first quartile vs the fourth was 9.1 (95% CI, 5.5-14.9) vs (aHR: 6.1, 95% CI: 3.7-10.1), of the second quartile 6.4 (95% CI: 3.9-10.6) vs (aHR: 4.4, 95% CI: 2.6-7.3), while of the third quartile the HR was 3.2 (95% CI: 1.9-5.5) vs (aHR: 2.4, 95% CI: 1.4-4.0). The prognostic accuracy of the DASI score was high, and higher than that of BNP concentrations (720-day mortality C-index: 0.67 vs 0.62; P = 0.024).

Conclusions

Quantification of self-reported subjective exercise capacity using the DASI provides high prognostic accuracy and may aid physicians in risk stratification. (Basics in Acute Shortness of Breath EvaLuation [BASEL V] Study [BASEL V]; NCT01831115).

Quantifying inflammation using interleukin-6 for improved phenotyping and risk stratification in acute heart failure

AIMS

Systemic inflammation may be central in the pathophysiology of acute heart failure (AHF). We aimed to assess the possible role of systemic inflammation in the pathophysiology, phenotyping, and risk stratification of patients with AHF.

METHODS AND RESULTS

Using a novel Interleukin-6 immunoassay with unprecedented sensitivity (limit of detection 0.01 ng/L), we quantified systemic inflammation in unselected patients presenting with acute dyspnoea to the emergency department in a multicentre study. One-year mortality was the primary prognostic endpoint. Among 2042 patients, 1026 (50.2%) had an adjudicated diagnosis of AHF, 83.7% of whom had elevated interleukin-6 concentrations (>4.45 ng/L). Interleukin-6 was significantly higher in AHF patients compared to patients with other causes of dyspnoea (11.2 [6.1-26.5] ng/L vs. 9.0 [3.2-32.3] ng/L, p < 0.0005). Elevated interleukin-6 concentrations were independently predicted by increasing N-terminal pro-B-type natriuretic peptide and high-sensitivity cardiac troponin T, as well as the clinical diagnosis of infection. Among the different AHF phenotypes, interleukin-6 concentrations were highest in patients with cardiogenic shock (25.7 [14.0-164.2] ng/L) and lowest in patients with hypertensive AHF (9.3 [4.8-21.6] ng/L, p = 0.001). Inflammation as quantified by interleukin-6 was a strong and independent predictor of 1-year mortality both in all AHF patients, as well as those without clinically overt infection at presentation (adjusted hazard ratio [95% confidence interval] 1.45 [1.15-1.83] vs. 1.48 [1.09-2.00]). The addition of interleukin-6 significantly improved the discrimination of the BIOSTAT-CHF risk score.

CONCLUSION

An unexpectedly high percentage of patients with AHF have subclinical systemic inflammation as quantified by interleukin-6, which seems to contribute to AHF phenotype and to the risk of death.

Clinical profile and 90 day outcomes of 10 851 heart failure patients across India: National Heart Failure Registry

AIMS

Limited data on the uptake of guideline-directed medical therapies (GDMTs) and the mortality of acute decompensated HF (ADHF) patients are available from India. The National Heart Failure Registry (NHFR) aimed to assess clinical presentation, practice patterns, and the mortality of ADHF patients in India.

METHODS AND RESULTS

The NHFR is a facility-based, multi-centre clinical registry of consecutive ADHF patients with prospective follow-up. Fifty three tertiary care hospitals in 21 states in India participated in the NHFR. All consecutive ADHF patients who satisfied the European Society of Cardiology criteria were enrolled in the registry. All-cause mortality at 90 days was the main outcome measure. In total, 10 851 consecutive patients were recruited (mean age: 59.9 years, 31% women). Ischaemic heart disease was the predominant aetiology for HF (72%), followed by dilated cardiomyopathy (18%). Isolated right HF was noted in 62 (0.6%) participants. In eligible HF patients, 47.5% received GDMT. The 90 day mortality was 14.2% (14.9% and 13.9% in women and men, respectively) with a re-admission rate of 8.4%. An inverse relationship between educational class based on years of education and 90 day mortality (high mortality in the lowest educational class) was observed in the study population. Patients with HF with reduced ejection fraction and HF with mildly reduced ejection fraction who did not receive GDMT experienced higher mortality (log-rank P < 0.001) than those who received GDMT. Baseline educational class, body mass index, New York Heart Association functional class, ejection fraction, dependent oedema, serum creatinine, QRS > 120 ms, atrial fibrillation, mitral regurgitation, haemoglobin levels, serum sodium, and GDMT independently predicted 90 day mortality.

CONCLUSION

One of seven ADHF patients in the NHFR died during the first 90 days of follow-up. One of two patients received GDMT. Adherence to GDMT improved survival in HF patients with reduced and mildly reduced ejection fractions. Our findings call for innovative quality improvement initiatives to improve the uptake of GDMT among HF patients in India.

Performance of current risk stratification models for predicting mortality in patients with heart failure: a systematic review and meta-analysis

AIMS

There are several risk scores designed to predict mortality in patients with heart failure (HF). This study aimed to assess performance of risk scores validated for mortality prediction in patients with acute HF (AHF) and chronic HF.

METHODS AND RESULTS

MEDLINE and Scopus were searched from January 2015 to January 2021 for studies which internally or externally validated risk models for predicting all-cause mortality in patients with AHF and chronic HF. Discrimination data were analysed using C-statistics, and pooled using generic inverse-variance random-effects model. Nineteen studies (n = 494 156 patients; AHF: 24 762; chronic HF mid-term mortality: 62 000; chronic HF long-term mortality: 452 097) and 11 risk scores were included. Overall, discrimination of risk scores was good across the three subgroups: AHF mortality [C-statistic: 0.76 (0.68-0.83)], chronic HF mid-term mortality [1 year; C-statistic: 0.74 (0.68-0.79)], and chronic HF long-term mortality [≥2 years; C-statistic: 0.71 (0.69-0.73)]. MEESSI-AHF [C-statistic: 0.81 (0.80-0.83)] and MARKER-HF [C-statistic: 0.85 (0.80-0.89)] had an excellent discrimination for AHF and chronic HF mid-term mortality, respectively, whereas MECKI had good discrimination [C-statistic: 0.78 (0.73-0.83)] for chronic HF long-term mortality relative to other models. Overall, risk scores predicting short-term mortality in patients with AHF did not have evidence of poor calibration (Hosmer-Lemeshow P > 0.05). However, risk models predicting mid-term and long-term mortality in patients with chronic HF varied in calibration performance.

CONCLUSIONS

The majority of recently validated risk scores showed good discrimination for mortality in patients with HF. MEESSI-AHF demonstrated excellent discrimination in patients with AHF, and MARKER-HF and MECKI displayed an excellent discrimination in patients with chronic HF. However, modest reporting of calibration and lack of head-to-head comparisons in same populations warrant future studies.

Evaluation of the HEFESTOS scale to predict outcomes in emergency department acute heart failure patients

The HEFESTOS scale was developed in 14 Spanish primary care centres and validated in 9 primary care centres of other European countries. It showed good performance to predict death/hospitalisation during the first 30 days after an episode of acute heart failure (AHF), with c-statistics of 0.807/0.730 in the derivation/validation cohorts. We evaluated this scale in the emergency department (ED) setting, comparing it to the EHMRG and MEESSI scales in the ED and the EFFECT and GWTG scales in hospitalised patients, to predict 30-day outcomes, including death and hospitalisation. Consecutive AHF patients were enrolled in 34 Spanish EDs in January-February 2016, 2018, and 2019 with variables needed to calculate outcome scores. Thirty-day hospitalisation/death (together and separately) and post-discharge combined adverse event (ED revisit or hospitalisation for AHF or all-cause death) were determined for patients discharged home after ED care. Predictive capacity was assessed by c-statistic with 95% confidence intervals. Of 10,869 patients, 4,044 were included (median age: 83 years, 54% women). The performance of HEFESTOS was modest for 30-day hospitalisation/death, c-statistic=0.656 (0.637-0.675), hospitalisation, 0.650 (0.631-0.669), and death, 0.610 (0.576-0.644). Of 1,034 patients with scores for the 5 scales, HEFESTOS had the numerically highest c-statistic for hospitalisation/death at 30 days, 0.666 (0.627-0.704), vs. MEESSI= 0.650 (0.612-0.687, p=0.51), EFFECT=0.633 (0.595-0.672, p=0.21), GWTG=0.618 (0.578-0.657, p=0.06) and EHMRG=0.617 (0.577-0.704, p=0.07). Similar modest performances were observed for predicting hospitalisation [ranging from HEFESTOS=0.656 (0.618-0.695) to GWTG=0.603 (0.564-0.643)]. Conversely, prediction of 30-day death was good with the MEESSI=0.787 (0.728-845), EFFECT=0.754 (0.691-0.818) and GWTG=0.749 (0.689-0.809) scales, and modest with EHMRG=0.649 (0.581-0.717) and HEFESTOS=0.610 (0.538-0.683). Although the HEFESTOS scale was numerically better for predicting 30-day hospitalisation/death in ED AHF patients, its modest performance precludes routine use. Only 30-day mortality was adequately predicted by some scales, with the MEESSI achieving the best results.

Original articleAnalysis of the effectiveness and safety of short-stay units in the hospitalization of patients with acute heart failure. Propensity Score SSU-EAHFE

OBJECTIVES

This work aims to analyze if hospitalization in short-stay units (SSU) of patients diagnosed in the emergency department with acute heart failure (AHF) is effective in terms of the length of hospital stay and if it is associated with differences in short-term progress.

METHOD

Patients from the EAHFE registry diagnosed with AHF who were admitted to the SSU (SSU group) were included and compared to those hospitalized in other departments (non-SSU group) from all hospitals (comparison A) and, separately, those from hospitals with an SSU (comparison B) and without an SSU (comparison C). For each comparison, patients in the SSU/non-SSU groups were matched by propensity score. The length of hospital stay (efficacy), 30-day mortality, and post-discharge adverse events at 30 days (safety) were compared.

RESULTS

A total of 2,003 SSU patients and 12,193 non-SSU patients were identified. Of them, 674 pairs of patients were matched for comparison A, 634 for comparison B, and 588 for comparison C. The hospital stay was significantly shorter in the SSU group in all comparisons (A: median 4 days (IQR = 2-5) versus 8 (5-12) days, p < 0.001; B: 4 (2-5) versus 8 (5-12), p < 0.001; C: 4 (2-5) versus 8 (6-12), p < 0.001). Admission to the SSU was not associated with differences in mortality (A: HR = 1.027, 95%CI = 0.681-1.549; B: 0.976, 0.647-1.472; C: 0.818, 0.662-1.010) or post-discharge adverse events (A: HR = 1.002, 95%CI = 0.816-1.232; B: 0.983, 0.796-1.215; C: 1.135, 0.905-1.424).

CONCLUSION

The hospitalization of patients with AHF in the SSU is associated with shorter hospital stays but there were no differences in short-term progress.

Development and validation of a decision support tool for the diagnosis of acute heart failure: systematic review, meta-analysis, and modelling study

OBJECTIVES

To evaluate the diagnostic performance of N-terminal pro-B-type natriuretic peptide (NT-proBNP) thresholds for acute heart failure and to develop and validate a decision support tool that combines NT-proBNP concentrations with clinical characteristics.

DESIGN

Individual patient level data meta-analysis and modelling study.

SETTING

Fourteen studies from 13 countries, including randomised controlled trials and prospective observational studies.

PARTICIPANTS

Individual patient level data for 10 369 patients with suspected acute heart failure were pooled for the meta-analysis to evaluate NT-proBNP thresholds. A decision support tool (Collaboration for the Diagnosis and Evaluation of Heart Failure (CoDE-HF)) that combines NT-proBNP with clinical variables to report the probability of acute heart failure for an individual patient was developed and validated.

MAIN OUTCOME MEASURE

Adjudicated diagnosis of acute heart failure.

RESULTS

Overall, 43.9% (4549/10 369) of patients had an adjudicated diagnosis of acute heart failure (73.3% (2286/3119) and 29.0% (1802/6208) in those with and without previous heart failure, respectively). The negative predictive value of the guideline recommended rule-out threshold of 300 pg/mL was 94.6% (95% confidence interval 91.9% to 96.4%); despite use of age specific rule-in thresholds, the positive predictive value varied at 61.0% (55.3% to 66.4%), 73.5% (62.3% to 82.3%), and 80.2% (70.9% to 87.1%), in patients aged <50 years, 50-75 years, and >75 years, respectively. Performance varied in most subgroups, particularly patients with obesity, renal impairment, or previous heart failure. CoDE-HF was well calibrated, with excellent discrimination in patients with and without previous heart failure (area under the receiver operator curve 0.846 (0.830 to 0.862) and 0.925 (0.919 to 0.932) and Brier scores of 0.130 and 0.099, respectively). In patients without previous heart failure, the diagnostic performance was consistent across all subgroups, with 40.3% (2502/6208) identified at low probability (negative predictive value of 98.6%, 97.8% to 99.1%) and 28.0% (1737/6208) at high probability (positive predictive value of 75.0%, 65.7% to 82.5%) of having acute heart failure.

CONCLUSIONS

In an international, collaborative evaluation of the diagnostic performance of NT-proBNP, guideline recommended thresholds to diagnose acute heart failure varied substantially in important patient subgroups. The CoDE-HF decision support tool incorporating NT-proBNP as a continuous measure and other clinical variables provides a more consistent, accurate, and individualised approach.

STUDY REGISTRATION

PROSPERO CRD42019159407.

Diurnal Variations in Natriuretic Peptide Levels: Clinical Implications for the Diagnosis of Acute Heart Failure

BACKGROUND

Current guidelines recommend interpreting concentrations of NPs (natriuretic peptides) irrespective of the time of presentation to the emergency department. We hypothesized that diurnal variations in NP concentration may affect their diagnostic accuracy for acute heart failure.

METHODS

In a secondary analysis of a multicenter diagnostic study enrolling patients presenting with acute dyspnea to the emergency department and using central adjudication of the final diagnosis by 2 independent cardiologists, the diagnostic accuracy for acute heart failure of BNP (B-type NP), NT-proBNP (N-terminal pro-B-type NP), and MR-proANP (midregional pro-atrial NP) was compared among 1577 daytime presenters versus 908 evening/nighttime presenters. In a validation study, the presence of a diurnal rhythm in BNP and NT-proBNP concentrations was examined by hourly measurements in 44 stable individuals.

RESULTS

Among patients adjudicated to have acute heart failure, BNP, NT-proBNP, and MR-proANP concentrations were comparable among daytime versus evening/nighttime presenters (all P=nonsignificant). Contrastingly, among patients adjudicated to have other causes of dyspnea, evening/nighttime presenters had lower BNP (median, 44 [18-110] versus 74 [27-168] ng/L; P<0.01) and NT-proBNP (median, 212 [72-581] versus 297 [102-902] ng/L; P<0.01) concentrations versus daytime presenters. This resulted in higher diagnostic accuracy as quantified by the area under the curve of BNP and NT-proBNP among evening/nighttime presenters (0.97 [95% CI, 0.95-0.98] and 0.95 [95% CI, 0.93-0.96] versus 0.94 [95% CI, 0.92-0.95] and 0.91 [95% CI, 0.90-0.93]) among daytime presenters (both P<0.01). These differences were not observed for MR-proANP. Diurnal variation of BNP and NT-proBNP with lower evening/nighttime concentration was confirmed in 44 stable individuals (P<0.01).

CONCLUSIONS

BNP and NT-proBNP, but not MR-proANP, exhibit a diurnal rhythm that results in even higher diagnostic accuracy among evening/nighttime presenters versus daytime presenters.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifiers: NCT01831115, NCT02091427, and NCT02210897.

Prevalence, Related Factors and Association of Left Bundle Branch Block With Prognosis in Patients With Acute Heart Failure: a Simultaneous Analysis in 3 Independent Cohorts

OBJECTIVES

To determine the prevalence, characteristics and association with prognosis of left bundle branch block (LBBB) in 3 different cohorts of patients with acute heart failure (AHF).

METHODS AND RESULTS

We retrospectively analyzed 12,950 patients with AHF who were included in the EAHFE (Epidemiology Acute Heart Failure Emergency), RICA (National Heart Failure Registry of the Spanish Internal Medicine Society), and BASEL-V (Basics in Acute Shortness of Breath Evaluation of Switzerland) registries. We independently analyzed the relationship between baseline and clinical characteristics and the presence of LBBB and the potential association of LBBB with 1-year all-cause mortality and a 90-day postdischarge combined endpoint (Emergency Department reconsultation, hospitalization or death). The prevalence of LBBB was 13.5% (95% confidence interval: 12.9%-14.0%). In all registries, patients with LBBB more commonly had coronary artery disease and previous episodes of AHF, were taking chronic spironolactone treatment, had lower left ventricular ejection fraction and systolic blood pressure values and higher NT-proBNP levels. There were no differences in risk for patients with LBBB in any cohort, with adjusted hazard ratios (95% confidence interval) for 1-year mortality in EAHFE/RICA/BASEL-V cohorts of 1.02 (0.89-1.17), 1.15 (0.95-1.38) and 1.32 (0.94-1.86), respectively, and for 90-day postdischarge combined endpoint of 1.00 (0.88-1.14), 1.14 (0.92-1.40) and 1.26 (0.84-1.89). These results were consistent in sensitivity analyses.

CONCLUSIONS

Less than 20% of patients with AHF present LBBB, which is consistently associated with cardiovascular comorbidities, reduced left ventricular ejection fraction and more severe decompensations. Nonetheless, after taking these factors into account, LBBB in patients with AHF is not associated with worse outcomes.

Disposition of acute decompensated heart failure from the emergency department: An evidence-based review

INTRODUCTION

Acute heart failure (HF) exacerbation is a serious and common condition seen in the Emergency Department (ED) that has significant morbidity and mortality. There are multiple clinical decision tools that Emergency Physicians (EPs) can use to reach an appropriate evidence-based disposition for these patients.

OBJECTIVE

This narrative review is an evidence-based discussion of clinical decision-making tools aimed to assist EPs risk stratify patients with AHF and determine disposition.

DISCUSSION

Risk stratification in patients with AHF exacerbation presenting to the ED is paramount in reaching an appropriate disposition decision. High risk features include hypotension, hypoxemia, elevated brain natriuretic peptide (BNP) and/or troponin, elevated creatinine, and hyponatremia. Patients who require continuous vasoactive infusions, respiratory support, or are initially treatment-resistant generally require intensive care unit admission. In most instances, new-onset AHF patients should be admitted for further evaluation. Other AHF patients in the ED can be risk stratified with the Ottawa HF Risk Score (OHFRS), the Multiple Estimation of Risk Based on Spanish Emergency Department Score (MEESSI), or the Emergency HF Mortality Risk Grade (EHFMRG). These tools take various factors into account such as mode of arrival to the ED, vital signs, laboratory values like troponin and pro-BNP, and clinical course. If used appropriately, these scores can predict patients at low risk for adverse outcomes.

CONCLUSION

This article discusses evidence-based disposition of patients in acute decompensated HF presenting to the ED. Knowledge of these factors and risk tools can assist emergency clinicians in determining appropriate disposition of patients with HF.

Development and external validation of a prognostic model for ischaemic stroke after surgery

BACKGROUND

There is an under-recognised patient cohort at elevated risk of postoperative ischaemic stroke. We aimed to develop and validate a prognostic model for the identification of such patients at high risk of ischaemic stroke within 1 yr after noncardiac surgery.

METHODS

This was a hospital registry study of adult patients undergoing noncardiac surgery between 2005 and 2017 at two independent healthcare networks in Massachusetts, USA without a preoperative indication for therapeutic anticoagulation. Logistic regression was used to fit a model from a priori defined candidate predictors for the outcome 1 yr postoperative ischaemic stroke. To enhance clinical applicability, the model was simplified to a scoring system and externally validated.

RESULTS

In the development (n=107 756) and validation (n=141 724) cohorts, 1.4% and 0.5% of patients had an ischaemic stroke up to 1 yr postoperatively. The final model included 13 variables (patient characteristics, comorbidities, procedural factors), considering sub-models conditional on a previous history of ischaemic stroke. Areas under the curve were 0.89 (95% confidence interval 0.89-0.90) and 0.88 (95% confidence interval 0.86-0.89) in the development and validation cohorts. Decision curve analysis indicated positive net benefits superior to other prediction instruments.

CONCLUSIONS

Stroke after surgery (STRAS) screening can reliably identify patients with a high risk for ischaemic stroke during the first year after surgery. A STRAS-guided risk stratification may inform the recruitment to future randomised trials testing the efficacy of treatments for the prevention of postoperative ischaemic stroke.

Safety of diuretic administration during the early management of dyspnea patients who are not finally diagnosed with acute heart failure

OBJECTIVES

Investigating whether it is safe or not to administrate diuretics to patients arriving at emergency departments in a stage of acute dyspnea but without a final diagnosis of acute heart failure.

METHODS

We analyzed an unselected multinational sample of patients with dyspnea without a final diagnosis of acute heart failure from Global Research on Acute Conditions Team (France, Lithuania, Tunisia) and Basics in Acute Shortness of Breath Evaluation (Switzerland) registries. Thirty-day all-cause mortality and 30-day postdischarge all-cause readmission rate of treated patients with diuretics at emergency departments were compared with untreated patients by unadjusted and adjusted hazard and odds ratios. Interaction and stratified analyses were performed.

RESULTS

We included 2505 patients. Among them, 365 (14.6%) received diuretics in emergency departments. Thirty-day mortality was 4.5% (treated/untreated = 5.2%/4.3%, hazard ratio: 1.22; 95% confidence interval, 0.75-2.00) and 30-day readmission rate was 11.3% (14.7%/10.8%, odds ratio: 1.41; 95% confidence interval, 0.95-2.11). After adjustment, no differences were found between two groups in mortality (hazard ratio: 0.86; 95% confidence interval, 0.51-1.44) and readmission (odds ratio: 1.15; 95% confidence interval, 0.72-1.82). Age significantly interacted with the use of diuretics and readmission (P = 0.03), with better prognosis when used in patients >80 years (odds ratio: 0.27; 95% confidence interval, 0.07-1.03) than in patients ≤80 years (odds ratio: 1.56; 95% confidence interval, 0.94-2.63).

CONCLUSIONS

Diuretic administration to patients presenting to emergency departments with dyspnea while they were undiagnosed and in whom acute heart failure was finally excluded was not associated with 30-day all-cause mortality and 30-day postdischarge all-cause readmission rate.

Assessment of acute heart failure prognosis: the promising role of prognostic models and biomarkers

Numerous models and biomarkers have been proposed to estimate prognosis and improve decision-making in patients with acute heart failure (AHF). The present literature review provides a critical appraisal of externally validated prognostic models in AHF, combining clinical data and biomarkers. We perform a literature review of clinical studies, using the following terms: "acute heart failure," "acute decompensated heart failure," "prognostic models," "risk scores," "mortality," "death," "hospitalization," "admission," and "biomarkers." We searched MEDLINE and EMBASE databases from 1990 to 2020 for studies documenting prognostic models in AHF. External validation of each prognostic model to another AHF cohort, containing at least one biomarker, was prerequisites for study selection. Among 358 initially screened studies, 9 of them fulfilled all searching criteria. The majority of prognostic models were simplified, including a narrow number of variables (up to 10), with good performance regarding calibration and discrimination (c-statistics > 0.65). Unfortunately, the derived and validated cohorts showed a wide variety in patients' characteristics (e.g., cause of AHF and therapy). Moreover, the prognostic models used various time-points and a plethora of combinations of variables determining different cut-off values. Although the application of valid prognostic models in AHF population is quite promising, a precise methodological approach should be set for the derivation and validation of prognostic models in AHF with unified characteristics to establish an effective performance in clinical practice.

Risk stratification scores for patients with acute heart failure in the Emergency Department: A systematic review

AIMS

This study aimed to systematically identify and summarise all risk scores evaluated in the emergency department setting to stratify acute heart failure patients.

METHODS AND RESULTS

A systematic review of PubMed and Web of Science was conducted including all multicentre studies reporting the use of risk predictive models in emergency department acute heart failure patients. Exclusion criteria were: (a) non-original articles; (b) prognostic models without predictive purposes; and (c) risk models without consecutive patient inclusion or exclusively tested in patients admitted to a hospital ward. We identified 28 studies reporting findings on 19 scores: 13 were originally derived in the emergency department (eight exclusively using acute heart failure patients), and six in emergency department and hospitalised patients. The outcome most frequently predicted was 30-day mortality. The performance of the scores tended to be higher for outcomes occurring closer to the index acute heart failure event. The eight scores developed using acute heart failure patients only in the emergency department contained between 4-13 predictors (age, oxygen saturation and creatinine/urea included in six scores). Five scores (Emergency Heart Failure Mortality Risk Grade, Emergency Heart Failure Mortality Risk Grade 30 Day mortality ST depression, Epidemiology of Acute Heart Failure in Emergency department 3 Day, Acute Heart Failure Risk Score, and Multiple Estimation of risk based on Emergency department Spanish Score In patients with Acute Heart Failure) have been externally validated in the same country, and two (Emergency Heart Failure Mortality Risk Grade and Multiple Estimation of risk based on Emergency department Spanish Score In patients with Acute Heart Failure) further internationally validated. The c-statistic for Emergency Heart Failure Mortality Risk Grade to predict seven-day mortality was between 0.74-0.81 and for Multiple Estimation of risk based on Emergency department Spanish Score In patients with Acute Heart Failure to predict 30-day mortality was 0.80-0.84.

CONCLUSIONS

There are several scales for risk stratification of emergency department acute heart failure patients. Two of them are accurate, have been adequately validated and may be useful in clinical decision-making in the emergency department i.e. about whether to admit or discharge.

Synergistic Impact of Systolic Blood Pressure and Perfusion Status on Mortality in Acute Heart Failure

BACKGROUND

Physical examination remains the cornerstone in the assessment of acute heart failure. There is a lack of adequately powered studies assessing the combined impact of both systolic blood pressure (SBP) and hypoperfusion on short-term mortality.

METHODS

Patients with acute heart failure from 41 Spanish emergency departments were recruited consecutively in 3 time periods between 2011 and 2016. Logistic regression models were used to assess the association of 30-day mortality with SBP (<90, 90-109, 110-129, and ≥130 mm Hg) and with manifestations of hypoperfusion (cold skin, cutaneous pallor, delayed capillary refill, livedo reticularis, and mental confusion) at admission.

RESULTS

Among 10 979 patients, 1143 died within the first 30 days (10.2%). There was an inverse association between 30-day mortality and initial SBP (35.4%, 18.9%, 12.4%, and 7.5% for SBP<90, SBP 90-109, SBP 110-129, and SBP≥130 mm Hg, respectively; P<0.001) and a positive association with hypoperfusion (8.0%, 14.8%, and 27.6% for those with none, 1, ≥2 signs/symptoms of hypoperfusion, respectively; P<0.001). After adjustment for 11 risk factors, the prognostic impact of hypoperfusion on 30-day mortality varied across SBP categories: SBP≥130 mm Hg (odds ratio [OR]=1.03 [95% CI, 0.77-1.36] and OR=1.18 [95% CI, 0.86-1.62] for 1 and ≥2 compared with 0 manifestations of hypoperfusion), SBP 110 to 129 mm Hg (OR=1.23 [95% CI, 0.86-1.77] and OR=2.18 [95% CI, 1.44-3.31], respectively), SBP 90 to 109 mm Hg (OR=1.29 [95% CI, 0.79-2.10] and OR=2.24 [95% CI, 1.36-3.66], respectively), and SBP<90 mm Hg (OR=1.34 [95% CI, 0.45-4.01] and OR=3.22 [95% CI, 1.30-7.97], respectively); P-for-interaction =0.043.

CONCLUSIONS

Hypoperfusion confers an incremental risk of 30-day all-cause mortality not only in patients with low SBP but also in normotensive patients. On admission, physical examination plays a major role in determining prognosis in patients with acute heart failure.

What Factors Predict Adverse Discharge Disposition in Patients Older Than 60 Years Undergoing Lower-extremity Surgery? The Adverse Discharge in Older Patients after Lower-extremity Surgery (ADELES) Risk Score

BACKGROUND

Adverse discharge disposition, which is discharge to a long-term nursing home or skilled nursing facility is frequent and devastating in older patients after lower-extremity orthopaedic surgery. Predicting individual patient risk allows for preventive interventions to address modifiable risk factors and helps managing expectations. Despite a variety of risk prediction tools for perioperative morbidity in older patients, there is no tool available to predict successful recovery of a patient's ability to live independently in this highly vulnerable population.

QUESTIONS/PURPOSES

In this study, we asked: (1) What factors predict adverse discharge disposition in patients older than 60 years after lower-extremity surgery? (2) Can a prediction instrument incorporating these factors be applied to another patient population with reasonable accuracy? (3) How does the instrument compare with other predictions scores that account for frailty, comorbidities, or procedural risk alone?

METHODS

In this retrospective study at two competing New England university hospitals and Level 1 trauma centers with 673 and 1017 beds, respectively; 83% (19,961 of 24,095) of patients 60 years or older undergoing lower-extremity orthopaedic surgery were included. In all, 5% (1316 of 24,095) patients not living at home and 12% (2797 of 24,095) patients with missing data were excluded. All patients were living at home before surgery. The mean age was 72 ± 9 years, 60% (11,981 of 19,961) patients were female, 21% (4155 of 19,961) underwent fracture care, and 34% (6882 of 19,961) underwent elective joint replacements. Candidate predictors were tested in a multivariable logistic regression model for adverse discharge disposition in a development cohort of all 14,123 patients from the first hospital, and then included in a prediction instrument that was validated in all 5838 patients from the second hospital by calculating the area under the receiver operating characteristics curve (ROC-AUC).Thirty-eight percent (5360 of 14,262) of patients in the development cohort and 37% (2184 of 5910) of patients in the validation cohort had adverse discharge disposition. Score performance in predicting adverse discharge disposition was then compared with prediction scores considering frailty (modified Frailty Index-5 or mFI-5), comorbidities (Charlson Comorbidity Index or CCI), and procedural risks (Procedural Severity Scores for Morbidity and Mortality or PSS).

RESULTS

After controlling for potential confounders like BMI, cardiac, renal and pulmonary disease, we found that the most prominent factors were age older than 90 years (10 points), hip or knee surgery (7 or 8 points), fracture management (6 points), dementia (5 points), unmarried status (3 points), federally provided insurance (2 points), and low estimated household income based on ZIP code (1 point). Higher score values indicate a higher risk of adverse discharge disposition. The score comprised 19 variables, including socioeconomic characteristics, surgical management, and comorbidities with a cutoff value of ≥ 23 points. Score performance yielded an ROC-AUC of 0.85 (95% confidence interval 0.84 to 0.85) in the development and 0.72 (95% CI 0.71 to 0.73) in the independent validation cohort, indicating excellent and good discriminative ability. Performance of the instrument in predicting adverse discharge in the validation cohort was superior to the mFI-5, CCI, and PSS (ROC-AUC 0.72 versus 0.58, 0.57, and 0.57, respectively).

CONCLUSION

The Adverse Discharge in Older Patients after Lower Extremity Surgery (ADELES) score predicts adverse discharge disposition after lower-extremity surgery, reflecting loss of the ability to live independently. Its discriminative ability is better than instruments that consider frailty, comorbidities, or procedural risk alone. The ADELES score identifies modifiable risk factors, including general anesthesia and prolonged preoperative hospitalization, and should be used to streamline patient and family expectation management and improve shared decision making. Future studies need to evaluate the score in community hospitals and in institutions with different rates of adverse discharge disposition and lower income. A non-commercial calculator can be accessed at www.adeles-score.org.

LEVEL OF EVIDENCE

Level III, diagnostic study.

Cardiac myosin-binding protein C in the diagnosis and risk stratification of acute heart failure

AIMS

Cardiac myosin-binding protein C (cMyC) seems to be even more sensitive in the quantification of cardiomyocyte injury vs. high-sensitivity cardiac troponin, and may therefore have diagnostic and prognostic utility.

METHODS AND RESULTS

In a prospective multicentre diagnostic study, cMyC, high-sensitivity cardiac troponin T (hs-cTnT), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) plasma concentrations were measured in blinded fashion in patients presenting to the emergency department with acute dyspnoea. Two independent cardiologists centrally adjudicated the final diagnosis. Diagnostic accuracy for acute heart failure (AHF) was quantified by the area under the receiver operating characteristic curve (AUC). All-cause mortality within 360 days was the prognostic endpoint. Among 1083 patients eligible for diagnostic analysis, 51% had AHF. cMyC concentrations at presentation were higher among AHF patients vs. patients with other final diagnoses [72 (interquartile range, IQR 39-156) vs. 22 ng/L (IQR 12-42), P < 0.001)]. cMyC's AUC was high [0.81, 95% confidence interval (CI) 0.78-0.83], higher than hs-cTnT's (0.79, 95% CI 0.76-0.82, P = 0.081) and lower than NT-proBNP's (0.91, 95% CI 0.89-0.93, P < 0.001). Among 794 AHF patients eligible for prognostic analysis, 28% died within 360 days; cMyC plasma concentrations above the median indicated increased risk of death (hazard ratio 2.19, 95% CI 1.66-2.89; P < 0.001). cMyC's prognostic accuracy was comparable with NT-proBNP's and hs-cTnT's. cMyC did not independently predict all-cause mortality when used in validated multivariable regression models. In novel multivariable regression models including medication, age, left ventricular ejection fraction, and discharge creatinine, cMyC remained an independent predictor of death and had no interactions with medical therapies at discharge.

CONCLUSION

Cardiac myosin-binding protein C may aid physicians in the rapid triage of patients with suspected AHF.

The FAST-FURO study: effect of very early administration of intravenous furosemide in the prehospital setting to patients with acute heart failure attending the emergency department

AIMS

The effect of early administration of intravenous (IV) furosemide in the emergency department (ED) on short-term outcomes of acute heart failure (AHF) patients remains controversial, with one recent Japanese study reporting a decrease of in-hospital mortality and one Korean study reporting a lack of clinical benefit. Both studies excluded patients receiving prehospital IV furosemide and only included patients requiring hospitalization. To assess the impact on short-term outcomes of early IV furosemide administration by emergency medical services (EMS) before patient arrival to the ED.

METHODS AND RESULTS

In a secondary analysis of the Epidemiology of Acute Heart Failure in Emergency Departments (EAHFE) registry of consecutive AHF patients admitted to Spanish EDs, patients treated with IV furosemide at the ED were classified according to whether they received IV furosemide from the EMS (FAST-FURO group) or not (CONTROL group). In-hospital all-cause mortality, 30-day all-cause mortality, and prolonged hospitalization (>10 days) were assessed. We included 12 595 patients (FAST-FURO = 683; CONTROL = 11 912): 968 died during index hospitalization [7.7%; FAST-FURO = 10.3% vs. CONTROL = 7.5%; odds ratio (OR) = 1.403, 95% confidence interval (95% CI) = 1.085-1.813; P = 0.009], 1269 died during the first 30 days (10.2%; FAST-FURO = 13.4% vs. CONTROL = 9.9%; OR = 1.403, 95% CI = 1.146-1.764; P = 0.004), and 2844 had prolonged hospitalization (22.8%; FAST-FURO = 25.8% vs. CONTROL = 22.6%; OR = 1.189, 95% CI = 0.995-1.419; P = 0.056). FAST-FURO group patients had more diabetes mellitus, ischaemic cardiomyopathy, peripheral artery disease, left ventricular systolic dysfunction, and severe decompensations, and had a better New York Heart Association class and had less atrial fibrillation. After adjusting for these significant differences, early IV furosemide resulted in no impact on short-term outcomes: OR = 1.080 (95% CI = 0.817-1.427) for in-hospital mortality, OR = 1.086 (95% CI = 0.845-1.396) for 30-day mortality, and OR = 1.095 (95% CI = 0.915-1.312) for prolonged hospitalization. Several sensitivity analyses, including analysis of 599 pairs of patients matched by propensity score, showed consistent findings.

CONCLUSION

Early IV furosemide during the prehospital phase was administered to the sickest patients, was not associated with changes in short-term mortality or length of hospitalization after adjustment for several confounders.

Analysis of standards of quality for outcomes in acute heart failure patients directly discharged home from emergency departments and their relationship with the emergency department direct discharge rate

OBJECTIVE

Experts recommended that direct discharge without hospitalization (DDWH) for emergency departments (EDs) able to observe acute heart failure (AHF) patients should be >40%, and these discharged patients should fulfil the following outcome standards: 30-day all-cause mortality <2% (outcome A); 7-day ED revisit due to AHF < 10% (outcome B); and 30-day ED revisit/hospitalization due to AHF < 20% (outcome C). We investigated these outcomes in a nationwide cohort and their relationship with the ED DDWH percentage.

METHODS

We analyzed the EAHFE registry (includes about 15% of Spanish EDs), calculated DDWH percentage of each ED, and A/B/C outcomes of DDWH patients, overall and in each individual ED. Relationship between ED DDWH and outcomes was assessed by linear and quadratic regression models, non-weighted and weighted by DDWH patients provided by each ED.

RESULTS

Among 17,420 patients, 4488 had DDWH (25.8%, median ED stay = 0 days, IQR = 0-1). Only 12.9% EDs achieved DDWH > 40%. Considering DDWH patients altogether, outcomes A/C were above the recommended standards (4.3%/29.4%), while outcome B was nearly met (B = 10.1%). When analyzing individual EDs, 58.1% of them achieved the outcome B standard, while outcomes A/C standards were barely achieved (19.3%/9.7%). We observed clinically relevant linear/quadratic relationships between higher DDWH and worse outcomes B (weighted R² = 0.184/0.322) and C (weighted R² = 0.430/0.624), but not with outcome A (weighted R² = 0.002/0.022).

CONCLUSIONS

The EDs of this nationwide cohort do not fulfil the standards for AHF patients with DDWH. High DDWH rates negatively impact ED revisit or hospitalization but not mortality. This may represent an opportunity for improvement in better selecting patients for early ED discharge and in ensuring early follow-up after ED discharge.

MEESSI-AHF risk score performance to predict multiple post-index event and post-discharge short-term outcomes

BACKGROUND

The multiple estimation of risk based on the emergency department Spanish score in patients with acute heart failure (MEESSI-AHF) is a risk score designed to predict 30-day mortality in acute heart failure patients admitted to the emergency department. Using a derivation cohort, we evaluated the performance of the MEESSI-AHF risk score to predict 11 different short-term outcomes.

METHODS

Patients with acute heart failure from 41 Spanish emergency departments (n=7755) were recruited consecutively in two time periods (2014 and 2016). Logistic regression models based on the MEESSI-AHF risk score were used to obtain c-statistics for 11 outcomes: three with follow-up from emergency department admission (inhospital, 7-day and 30-day mortality) and eight with follow-up from discharge (7-day mortality, emergency department revisit and their combination; and 30-day mortality, hospital admission, emergency department revisit and their two combinations with mortality).

RESULTS

The MEESSI-AHF risk score strongly predicted mortality outcomes with follow-up starting at emergency department admission (c-statistic 0.83 for 30-day mortality; 0.82 for inhospital death, P=0.121; and 0.85 for 7-day mortality, P=0.001). Overall, mortality outcomes with follow-up starting at hospital discharge predicted slightly less well (c-statistic 0.80 for 7-day mortality, P=0.011; and 0.75 for 30-day mortality, P<0.001). In contrast, the MEESSI-AHF score predicted poorly outcomes involving emergency department revisit or hospital admission alone or combined with mortality (c-statistics 0.54 to 0.62).

CONCLUSIONS

The MEESSI-AHF risk score strongly predicts mortality outcomes in acute heart failure patients admitted to the emergency department, but the model performs poorly for outcomes involving hospital admission or emergency department revisit. There is a need to optimise this risk score to predict non-fatal events more effectively.

High-sensitivity cardiac troponin T 30 days all-come mortality in patients with acute heart failure. A Propensity Score-Matching Analysis Based on the EAHFE Registry. TROPICA4 Study

BACKGROUND

Acute heart failure (AHF) patients with high troponin levels have a worse prognosis. High-sensitive troponin T (hs-TnT) has been used as a tool to stratify prognosis in many scales but always as a qualitative and not as a quantitative variable.

OBJECTIVES

The main objective of this study was to determine the best hs-TnT cut-off for prediction of 30-day all-cause mortality.

METHODS

The EAHFE registry, a prospective follow-up cohort of patients with AHF, was analysed. We performed a propensity score analysis of the optimal hs-TnT cut-off point previously determined by receiver operating characteristic (ROC) curve analysis.

RESULTS

Of the 13 791 patients in the EAHFE cohort, we analysed 3190 patients in whom hs-TnT determination was available. The area under the ROC curve for 30-day all-cause mortality was 0.70 (CI95% 0.68 to 0.71; P < .001), establishing an optimal cut-off of hs-TnT of 35 ng/L. The sensitivity and specificity of this cut-off were 76.2 and 55.5%, respectively, with a negative predictive value (NPV) of 95.3%. A propensity score was made with 34 variables showing differences based on the cut-off of 35 ng/L for hs-TnT. In the analysis of the population obtained with the propensity score, patients with hs-TnT > 35 ng/L showed a greater 30-day all-cause mortality, with a HR of 2.95 (CI95% 1.83-4.75; P < .001). External validation reported similar results.

CONCLUSIONS

An hs-TnT value of 35 ng/L is an adequate cut-off to evaluate the prediction of 30-day all-cause mortality with a NPV of 95.3%.

Editor's Choice- Impact of identifying precipitating factors on 30-day mortality in acute heart failure patients

BACKGROUND

The aim of this study was to describe the prevalence and prognostic value of the most common triggering factors in acute heart failure.

METHODS

Patients with acute heart failure from 41 Spanish emergency departments were recruited consecutively in three time periods between 2011 and 2016. Precipitating factors were classified as: (a) unrecognized; (b) infection; (c) atrial fibrillation; (d) anaemia; (e) hypertension; (f) acute coronary syndrome; (g) non-adherence; and (h) two or more precipitant factors. Unadjusted and adjusted logistic regression models were used to assess the association between 30-day mortality and each precipitant factor. The risk of dying was further evaluated by week intervals over the 30-day follow-up to assess the period of higher vulnerability for each precipitant factor.

RESULTS

Approximately 69% of our 9999 patients presented with a triggering factor and 1002 died within the first 30 days (10.0%). The most prevalent factors were infection and atrial fibrillation. After adjusting for 11 known predictors, acute coronary syndrome was associated with higher 30-day mortality (odds ratio (OR) 1.87; 95% confidence interval (CI) 1.02-3.42), whereas atrial fibrillation (OR 0.75; 95% CI 0.56-0.94) and hypertension (OR 0.34; 95% CI 0.21-0.55) were significantly associated with better outcomes when compared to patients without precipitant. Patients with infection, anaemia and non-compliance were not at higher risk of dying within 30 days. These findings were consistent across gender and age groups. The 30-day mortality time pattern varied between and within precipitant factors.

CONCLUSIONS

Precipitant factors in acute heart failure patients are prevalent and have a prognostic value regardless of the patient's gender and age. They can be managed with specific treatments and can sometimes be prevented.

Departments involved during the first episode of acute heart failure and subsequent emergency department revisits and rehospitalisations: an outlook through the NOVICA cohort

OBJECTIVES

We investigated the natural history of patients after a first episode of acute heart failure (FEAHF) requiring emergency department (ED) consultation, focusing on: the frequency of ED visits and hospitalisations, departments admitting patients during the first and subsequent hospitalisations, and factors associated with difficult disease control.

METHODS AND RESULTS

We included consecutive patients diagnosed with FEAHF (either with or without previous heart failure diagnosis) in four EDs during 5 months in three different time periods (2009, 2011, 2014). Diagnosis was adjudicated by local principal investigators. The clinical characteristics of the index event were prospectively recorded, and all post-discharge ED visits and hospitalisations [related/unrelated to acute heart failure (AHF)], as well as departments involved in subsequent hospitalisations were retrospectively ascertained. 'Uncontrolled disease' during the first year after FEAHF was considered if patients were attended at ED (≥ 3 times) or hospitalised (≥ 2 times) for AHF or died. Overall, 505 patients with FEAHF were included and followed for a mean of 2.4 years. In-hospital mortality was 7.5%. Among 467 patients discharged alive, 288 died [median survival 3.9 years, 95% confidence interval (CI) 3.5-4.4], 421 (90%) revisited the ED (2342 ED visits; 42.4% requiring hospitalisation, 34.0% AHF-related) and 357 (77%) were hospitalised (1054 hospitalisations; 94.1% through ED, 51.4% AHF-related). AHF-related hospitalisations were mainly in internal medicine (28.0%), short-stay unit (26.3%), cardiology (20.8%), and geriatrics (14.1%). Only 47.4% of AHF-related hospitalisations were in the same department as the FEAHF, and internal medicine involvement significantly increased with subsequent hospitalisations (P = 0.01). Uncontrolled disease was observed in 31% of patients, which was independently related to age > 80 years [odds ratio (OR) 1.80, 95% CI 1.17-2.77], systolic blood pressure < 110 mmHg at ED arrival (OR 2.61, 95% CI 1.26-5.38) and anaemia (OR 2.39, 95% CI 1.51-3.78).

CONCLUSION

In the present aged cohort of AHF patients from Barcelona, Spain, the natural history after FEAHF showed different patterns of hospital department involvement. Advanced age, low systolic blood pressure and anaemia were factors related to uncontrolled disease during the year after debut.

Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of - usually interactive and online available - tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

Risk assessment have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of - usually interactive and online available - tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of – usually interactive and online available – tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.