Heart failure (HF) is a complicated clinical diagnosis that may vary in presentation and course from patient to patient. Identification of high-risk patients and timely intervention is a critical skill in the management of HF. “[HF] is a major cause of morbidity and mortality worldwide,” wrote Tariq Ahmad, MD, MPH, and colleagues in JAMA Cardiology. “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.” The purpose of this study was to determine whether having information from a risk prediction tool available to clinicians influences their management of HF, as well as subsequent patient outcomes. Indeed, the study was conducted to “evaluate whether prognostic information in [HF] 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,” wrote Dr. Ahmad and team.
In this trial, 3,124 patients were randomized; 50.9% were assigned to the control group and 49.1% to the alert group. The demographic makeup and baseline characteristics of the two groups were comparable at baseline. Of participants, 48.7% were estimated to have a low risk for 1-year mortality (5% to 15%), 20.0% were at medium risk (15% to 30% 1-year mortality) and 25.1% were in the very low risk group (<5% 1-year mortality). The median number of alerts viewed by each clinician was five over the course of the 1-year study period. The primary endpoint of 30-day readmission to hospital rate was 38.9% in the alert group and 39.3% in the control group. The 1-year mortality rates were also similar between the two groups: 27.1% in the alert group and 26.1% in the control group. There was no difference in the discharge management of patients in the alert versus control groups (ie, medication prescriptions, palliative care referrals), which would suggest that the alerts did not influence clinician decision making.
This randomized controlled trial by Dr. Ahmad, et al. demonstrated that additional prognostication information presented to clinicians while managing patients hospitalized with HF did not influence management decisions and outcomes as predicted. “Provision of 1-year mortality estimates during [HF] hospitalization did not affect hospitalization or mortality, nor did it affect clinical decisionmaking,” added Dr. Ahmad and colleagues. This is an interesting finding, as developing risk prediction tools can be a resource- and timeintensive process but may not be clinically useful in a real-world setting. A strength of this study was the pragmatic design, as was the large size of the trial. However, a primary drawback was the oversimplification of clinical decision making in this study (eg, based on interactions with an EMR alert); to overcome this aspect, qualitative data about clinicians’ experience with the alert information system may be helpful.
In Depth [Randomized Controlled Trial]:
A pragmatic, multi-center, randomized controlled trial was performed. This trial is known as the Risk Evaluation and its Impact on Clinical Decision-Making and Outcomes in Heart Failure (REVEAL-HF) study. Patients aged 18 or older who had an elevated N-terminal pro-brain natriuretic peptide (NT-pro BNP) greater than 500 pg/mL and received IV loop diuretics within 24 hours of admission were eligible. These patients were automatically identified through the EMR at participating hospitals. Patients were randomized to the alert group (treating clinicians would receive an “alert” on the patient’s EMR displaying the predicted 1-year mortality rate and other prognosticators, the risk score being previously developed in this population) or the control group (care as usual, no additional information provided). Physicians treating the alert group patients were able to interact with the risk estimate to determine whether they thought it was accurate or not.
Baseline characteristics were well-balanced between the two groups. The most common comorbid conditions were atrial fibrillation, diabetes, chronic kidney disease, chronic obstructive pulmonary disease, and depression. The risk prediction model achieved a mean area under the curve of 0.74 (standard deviation, 0.02), which was similar to the cutoff point described in the original validation study. There were no significant differences in outcomes between the intervention and control groups: the P value for 1-year mortality was 0.89 and for 30-day readmission was 0.39. The median lengths of hospital stay (interquartile range) were 4.4 days (2.2-9.4) in the alert group and 4.3 (2.1-9.0) in the control group (P=0.28).
In the analysis of clinician interaction with the alert tool, 63.6% of clinicians were “not sure” about their agreement with the alert’s risk assessment, 29.3% felt that it “seemed appropriate,” 5.3% thought it was “too low,” and 1.7% felt that it was “too high.” Clinician estimates of risk correlated well with the actual mortality of patients, particularly those in higher risk groups (P=0.05).
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