Design and rationale for the Acute Congestive Heart Failure Urgent Care Evaluation: The ACUTE Study

Trial of an Intervention to Improve Acute Heart Failure Outcomes

BACKGROUND

Patients with acute heart failure are frequently or systematically hospitalized, often because the risk of adverse events is uncertain and the options for rapid follow-up are inadequate. Whether the use of a strategy to support clinicians in making decisions about discharging or admitting patients, coupled with rapid follow-up in an outpatient clinic, would affect outcomes remains uncertain.

METHODS

In a stepped-wedge, cluster-randomized trial conducted in Ontario, Canada, we randomly assigned 10 hospitals to staggered start dates for one-way crossover from the control phase (usual care) to the intervention phase, which involved the use of a point-of-care algorithm to stratify patients with acute heart failure according to the risk of death. During the intervention phase, low-risk patients were discharged early (in ≤3 days) and received standardized outpatient care, and high-risk patients were admitted to the hospital. The coprimary outcomes were a composite of death from any cause or hospitalization for cardiovascular causes within 30 days after presentation and the composite outcome within 20 months.

RESULTS

A total of 5452 patients were enrolled in the trial (2972 during the control phase and 2480 during the intervention phase). Within 30 days, death from any cause or hospitalization for cardiovascular causes occurred in 301 patients (12.1%) who were enrolled during the intervention phase and in 430 patients (14.5%) who were enrolled during the control phase (adjusted hazard ratio, 0.88; 95% confidence interval [CI], 0.78 to 0.99; P = 0.04). Within 20 months, the cumulative incidence of primary-outcome events was 54.4% (95% CI, 48.6 to 59.9) among patients who were enrolled during the intervention phase and 56.2% (95% CI, 54.2 to 58.1) among patients who were enrolled during the control phase (adjusted hazard ratio, 0.95; 95% CI, 0.92 to 0.99). Fewer than six deaths or hospitalizations for any cause occurred in low- or intermediate-risk patients before the first outpatient visit within 30 days after discharge.

CONCLUSIONS

Among patients with acute heart failure who were seeking emergency care, the use of a hospital-based strategy to support clinical decision making and rapid follow-up led to a lower risk of the composite of death from any cause or hospitalization for cardiovascular causes within 30 days than usual care. (Funded by the Ontario SPOR Support Unit and others; COACH ClinicalTrials.gov number, NCT02674438.).

Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure

BACKGROUND

Although risk stratification of patients with acute decompensated heart failure (HF) is important, it is unknown whether machine learning (ML) or conventional statistical models are optimal. We developed ML algorithms to predict 7-day and 30-day mortality in patients with acute HF and compared these with an existing logistic regression model at the same timepoints.

METHODS

Patients presenting to one of 86 hospitals, who were either admitted to hospital or discharged home directly from the emergency department, were randomly selected using stratified random sampling. ML approaches, including neural networks, random forest, XGBoost, and the Lasso, were compared with a validated logistic regression model for discrimination and calibration.

RESULTS

Among 12,608 patients in our analysis, lasso regression (c-statistic 0.774; 95% CI, 0.743, 0.806) performed better than other ML models for 7-day mortality but did not outperform the baseline logistic regression model (0.794; 95% CI, 0.789, 0.800). For 30-day mortality, XGBoost performed better than other ML models (c-statistic 0.759; 95% CI; 0.740, 0.779), but was not significantly better than logistic regression (c-statistic 0.755; 95% CI, 0.750, 0.762). Logistic regression demonstrated better calibration at 7 days (calibration-in-the-large 0.017; 95% CI, -0.657, 0.692, and calibration slope 0.954; 95% CI,0.769, 1.139) and at 30 days (-0.026; 95% CI, -0.374, 0.322 and 0.964; 95% CI, 0.831, 1.098), and best Brier scores, compared to ML approaches.

CONCLUSIONS

Logistic regression was comparable to ML in discrimination, but was superior to ML algorithms in calibration overall. ML algorithms for prognosis should routinely report calibration metrics in addition to discrimination.

A Survival Prediction for Acute Heart Failure Patients via Web-Based Dynamic Nomogram with Internal Validation: A Prospective Cohort Study

Purpose

The current study aimed to develop a convenient and accurate prognostic dynamic nomogram model for the risk of all-cause death in acute heart failure (AHF) patients that incorporates clinical characteristics including N-terminal pro-brain natriuretic peptide (NT-pro BNP) and growth stimulation expresses gene 2 protein (ST2).

Patients and Methods

We prospectively studied 537 consecutive AHF patients and derived a clinical prediction model. The least absolute shrinkage and selection operator regression model combined with clinical characteristics were used for dimensional reduction and feature selection. Multivariate Cox proportional hazard analysis and “Dynnom” package were used to build the dynamic nomogram for prediction of 1-,2-,and 5-year overall survival for AHF. With bootstrap validation, the time-dependent concordance index (C-index) and calibration curves were used to assess predictive discrimination and accuracy. The contributions of NT-pro BNP and ST2 to the nomogram were evaluated using integrated discrimination improvement (IDI) and net reclassification improvement (NRI), while decision curve analysis (DCA) was used to assess clinical value.

Results

Patients were randomly divided into derivation (74.9%, n=402) and validation (25.1%, n=135) cohorts. Optimal independent prognostic factors for 1-,2-, and 5-year all-cause mortality were BS-ACMR (B: NT-pro BNP; S: ST2; A: age; C: complete right bundle branch block; M: mean arterial pressure; and R: red cell distribution width >14.5%); these were incorporated into the dynamic nomogram (https://bs-acmr-nom.shinyapps.io/dynnomapp/) with bootstrap validation. The C-indexes in the derivation (0.793) and validation (0.782) cohorts were consistent with comparable performance parameters. The calibration curve showed good agreement between the nomogram-predicted and actual survival. Adding NT-pro BNP and ST2 provided a significant net benefit and improved performance over other less adequate schemes in terms of DCA of survival probability compared to those neglecting either of these two factors.

Conclusion

The study constructed a dynamic BS-ACMR nomogram, which is a convenient, practical and effective clinical decision-making tool for providing accurate prognosis in AHF patients.

Rationale and design of the comparison of outcomes and access to care for heart failure (COACH) trial: A stepped wedge cluster randomized trial

BACKGROUND

Heart failure (HF) is an ambulatory care sensitive condition and a leading reason for emergency department (ED) visits and hospitalizations. Improved decision-making and care may enhance safety and efficiency for patients presenting to the ED with acute HF.

OBJECTIVES

We will evaluate an intervention comprised of 2 complementary components: (1) the Emergency Heart Failure Mortality Risk Grade simultaneous 7- and 30-day (EHMRG30-ST) risk scores, which will inform admission-discharge decisions, and (2) a rapid outpatient follow-up (RAPID-HF) clinic for low-to-intermediate risk patients on cardiovascular readmissions or death.

STUDY DESIGN

Stepped wedge cluster randomized trial with cross-sectional measurement at 10 acute care hospitals in Ontario, Canada. Patients presenting during control and intervention periods are eligible if they have a primary ED diagnosis of HF. In the intervention periods, access to the EHMRG30-ST web calculator will become available to hospitals' internet protocol (IP) addresses, and referral to the RAPID-HF clinic will be facilitated by a study nurse navigator. Patients with a high risk EHMRG30-ST score will be admitted to hospital. The RAPID-HF clinic will accept referrals for patients: (1) with low risk 7- and 30-day EHMRG30-ST scores who are discharged directly from the ED, or (2) intermediate risk patients with hospital length of stay < 72 hours. The RAPID-HF clinic, staffed by a nurse-clinician and cardiologist, will provide care during the 30-day transition after hospital separation.

CONCLUSION

This trial will determine whether novel risk stratification coupled with rapid ambulatory care achieves better outcomes than conventional decision-making and care for patients with HF.

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.

Effect of early physician follow-up on mortality and subsequent hospital admissions after emergency care for heart failure: a retrospective cohort study

BACKGROUND

The 1-year mortality rate in patients with heart failure who are discharged from an emergency department is 20%. We sought to determine whether early follow-up after discharge from the emergency department was associated with decreased mortality or subsequent admission to hospital.

METHODS

This retrospective cohort study conducted in Ontario, Canada, included adult patients who were discharged from 1 of 163 emergency departments between April 2007 and March 2014 with a primary diagnosis of heart failure. Using a propensity score-matched landmark analysis, we assessed follow-up in relation to mortality and admissions to hospital for cardiovascular conditions.

RESULTS

Of 34 519 patients, 16 274 (47.1%) obtained follow-up care within 7 days and 28 846 (83.6%) within 30 days. Compared with follow-up between day 8 and 30, patients with follow-up care within 7 days had a lower rate of mortality over 1 year (hazard ratio [HR] 0.92; 95% confidence interval [CI] 0.87-0.97), and a reduced rate of admission to hospital over 90 days (HR 0.87, 95% CI 0.80-0.94) and 1 year (HR 0.92; 95% CI 0.87-0.97); the mortality rate over 90 days in this group trended to a lower rate (HR 0.90, 95% CI 0.10-1.00). Follow-up care within 30 days, compared with patients without 30-day follow-up, was associated with a reduction in 1-year mortality (HR 0.89, 95% CI 0.82-0.97) but not admission to hospital (HR 1.02, 95% CI 0.94-1.10). In this group, there was a trend toward an increase in 90-day admission to hospital (HR 1.14, 95% CI 1.00-1.29).

INTERPRETATION

Follow-up care within 7 days of discharge from the emergency department was associated with lower rates of long-term mortality, as well as subsequent hospital admissions, and a trend to lower short-term mortality rates. Timely access to longitudinal care for patients with heart failure who are discharged from the emergency setting should be prioritized.

External Validation and Refinement of Emergency Heart Failure Mortality Risk Grade Risk Model in Patients With Heart Failure in the Emergency Department

Background

Emergency Heart Failure Mortality Risk Grade (EHMRG) assesses the risk of death within 7 days of emergency department (ED) presentation for patients with acute heart failure (AHF). We aimed to externally validate and refine the EHMRG model in patients who presented to the ED with AHF.

Methods

We performed a cohort study using administrative data for all ambulance-transported patients from Alberta (2012-2016) presenting to the ED with a primary diagnosis of AHF.

Results

Among 6708 patients with AHF, the 7-day mortality was 0.0%, 0.8%, 1.6%, 4.0%, 4.2%, and 12.0% across EHMRG risk categories (1-4, 5A and 5B). The EHMRG score had a c-index of 0.73 (95% confidence interval [CI], 0.71-0.76) for 7-day mortality and 0.71 (95% CI, 0.70-0.73) for 30-day mortality, but lower c-statistics for other outcomes (0.61-0.67). The inclusion of natriuretic peptides to the EHMRG model improved prediction (Net Reclassification Improvement, 0.268; 95% CI, 0.173-0.363; P < 0.01) for 7-day mortality, as did the addition of the Canadian Triage and Acuity Scale (Net Reclassification Improvement, 0.111; 95% CI, 0.005-0.218; P = 0.04).

Conclusion

The EHMRG model exhibited moderate discriminative ability in a large population-based cohort of patients with AHF in the ED. Revision of the EHMRG score through factor inclusion and exclusion could improve the model’s performance.