Alerting Clinicians to 1-Year Mortality Risk in Patients Hospitalized With Heart Failure

Abstract

Heart failure is a major cause of morbidity and mortality worldwide. The use of risk scores has the potential to improve targeted use of interventions by clinicians that improve patient outcomes, but this hypothesis has not been tested in a randomized trial.OBJECTIVE To evaluate whether prognostic information in heart failure translates into improved decisions about initiation and intensity of treatment, more appropriate end-of-life care, and a subsequent reduction in rates of hospitalization or death. DESIGN, SETTING, AND PARTICIPANTSThis was a pragmatic, multicenter, electronic health record-based, randomized clinical trial across the Yale New Haven Health System, comprising small community hospitals and large tertiary care centers. Patients hospitalized for heart failure who had N-terminal pro-brain natriuretic peptide (NT-proBNP) levels of greater than 500 pg/mL and received intravenous diuretics within 24 hours of admission were automatically randomly assigned to the alert (intervention) or usual-care groups. INTERVENTIONSThe alert group had their risk of 1-year mortality calculated using an algorithm that was derived and validated using similar historic patients in the electronic health record. This estimate, including a categorical risk assessment, was presented to clinicians while they were interacting with a patient's electronic health record. MAIN OUTCOMES AND MEASURESThe primary outcome was a composite of 30-day hospital readmissions and all-cause mortality at 1 year. RESULTSBetween November 27, 2019, through March 7, 2021, 3124 patients were randomly assigned to the alert (1590 [50.9%]) or usual-care (1534 [49.1%]) group. The alert group had a median (IQR) age of 76.5 (65-86) years, and 796 were female patients (50.1%). Patients from the following race and ethnicity groups were included: 13 Asian (0.8%), 324 Black (20.4%), 136 Hispanic (8.6%), 1448 non-Hispanic (91.1%), 1126 White (70.8%), 6 other ethnicity (0.4%), and 127 other race (8.0%). The usual-care group had a median (IQR) age of 77 (65-86) years, and 788 were female patients (51.4%). Patients from the following race and ethnicity groups were included: 11 Asian (1.4%), 298 Black (19.4%), 162 Hispanic (10.6%), 1359 non-Hispanic (88.6%), 1077 White (70.2%), 13 other ethnicity (0.9%), and 137 other race (8.9%). Median (IQR) NT-proBNP levels were 3826 (1692-8241) pg/mL in the alert group and 3867 (1663-8917) pg/mL in the usual-care group. A total of 284 patients (17.9%) and 270 patients (17.6%) were admitted to the intensive care unit in the alert and usual-care groups, respectively. A total of 367 patients (23.1%) and 359 patients (23.4%) had a left ventricular ejection fraction of 40% or less in the alert and usual-care groups, respectively. The model achieved an area under the curve of 0.74 in the trial population. The primary outcome occurred in 619 patients (38.9%) in the alert group and 603 patients (39.3%) in the usual-care group (P = .89). There were no significant differences between study groups in the prescription of heart failure medic...

Incremental prognostic value of functional impairment assessed by six minute walking test for the prediction of mortality in heart failure

“…Consistently, a recent study showed that, in patients with severe exercise limitation, the peak VO 2 measured by a portable device allowing breath-by-breath measurement of cardiorespiratory parameters during a standard 6MWT is similar to -or even higher than-that reached in CPET [37].…”

“…Two recently published randomized clinical trials performed in the acute care setting provided somewhat con icting results. The Risk Evaluation and its Impact on Clinical Decision Making and Outcomes in Heart Failure (REVEAL-HF) study, investigated whether provision of 1-year mortality estimates during HF hospitalization affects outcomes in patients hospitalized for HF [37]. There was no evidence that information about risk affected the rate of 30-day hospital readmissions and mortality, prescription of HF medications at discharge, or 1-year mortality.…”

Barriers and Opportunities Regarding Implementation of a Machine Learning-Based Acute Heart Failure Risk Stratification Tool in the Emergency Department

“…A novel, targeted EHR-based alerting system for outpatients with heart failure with reduced ejection fraction led to significantly higher rates of guideline-directed medical therapy at 30 days compared with usual care [ 17 ]. On the other hand, there were no significant differences in key patient outcomes (30-day hospitalization, 1-year mortality, or adherence to guideline-directed medical therapy) among patients who had a 1-year mortality risk prediction rate displayed to their inpatient providers [ 18 ]. This finding suggests that prognostic information alone (without paired decision support) does not change practice or outcomes.…”

“…Implementation of a new risk tool and clinical decision support (CDS) into a busy ED can be challenging [ 14 , 15 , 16 ]. EHR tools for patients with AHF have been successfully deployed, with limited impacts observed when decision support was not added to prognostic scores [ 17 , 18 ]. Prior studies highlight an underappreciation of the steps needed to translate model predictions into real-time use and improved health [ 19 , 20 ].…”

Improving Quality Improvement—From Aspiration Toward Empiricism

“…Improving Quality Improvement-From Aspiration Toward Empiricism Andrew S. Oseran, MD, MBA; Rishi K. Wadhera, MD, MPP, MPhil In this issue of JAMA Cardiology, Ahmad and colleagues 1 report the results of their pragmatic, multicenter, electronic health record (EHR)-based, randomized clinical trial The Risk Evaluation and Its Impact on Clinical Decision-making and Outcomes in Heart Failure (REVEAL-HF), which was designed to evaluate whether providing clinicians with patient-specific prognostic information improved care for individuals hospitalized with heart failure. The authors randomly assigned 3124 patients across 4 hospitals to either usual care or the intervention, an EHR alert directed at treating physicians that displayed predicted 1-year mortality rates.…”