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Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis
Published on: June 30, 2023
Predicting Outcomes Over Time in Patients With Heart Failure, Left Ventricular Systolic Dysfunction, or Both
Renato D Lopes1, Karen S Pieper2, Susanna R Stevens2
1Duke Clinical Research Institute, Duke University Medical Center, Durham, NC renato.lopes@duke.edu.
Insights
Updated patient data significantly improves mortality prediction after myocardial infarction (MI). Dynamic risk assessment, incorporating evolving clinical information, offers better prognostic accuracy than static baseline models for patients with heart failure (HF) or left ventricular dysfunction post-MI.
Area of Science:
- Cardiology
- Clinical Risk Assessment
- Outcomes Research
Background:
- Traditional myocardial infarction (MI) risk assessments are static, failing to capture dynamic changes in patient status and care.
- There is a need for dynamic risk models that account for evolving clinical events and processes post-MI.
Purpose of the Study:
- To identify predictors of mortality, cardiovascular death or nonfatal MI, and cardiovascular death or nonfatal heart failure (HF) over time in post-MI patients.
- To develop and validate dynamic risk models for predicting adverse cardiovascular outcomes in post-MI patients.
Main Methods:
- Utilized data from the VALsartan In Acute myocardial iNfarcTion (VALIANT) trial.
- Developed multivariable Cox proportional hazards models to assess risk over specific time intervals (e.g., hospital arrival to discharge, 30 days to 6 months, 6 months to 3 years).
- Compared the predictive accuracy of models using updated patient information versus baseline data.
Main Results:
- Age, baseline heart rate, and creatinine clearance were strong predictors of overall mortality in the baseline model.
- Updated patient information significantly improved the prediction of mortality, cardiovascular death/nonfatal MI, and cardiovascular death/nonfatal HF across all follow-up periods.
- Integrated Discrimination Improvement (IDI) and Net Reclassification Improvement (NRI) indices demonstrated significant enhancements with updated models.
Conclusions:
- Patient information assessed closer to the outcome event is more valuable for predicting mortality than data from the initial hospitalization.
- Employing updated patient information in risk models substantially improves prognostic accuracy compared to using only baseline data.
- Dynamic risk stratification offers a more precise approach to managing patients post-MI, particularly those with heart failure or left ventricular systolic dysfunction.
Background:
Most studies of risk assessment or stratification in patients with myocardial infarction (MI) have been static and fail to account for the evolving nature of clinical events and care processes. We sought to identify predictors of mortality, cardiovascular death or nonfatal MI, and cardiovascular death or nonfatal heart failure (HF) over time in patients with HF, left ventricular systolic dysfunction, or both post-MI.
Methods And Results:
Using data from the VALsartan In Acute myocardial iNfarcTion (VALIANT) trial, we developed models to estimate the association between patient characteristics and the likelihood of experiencing an event from the time of a follow-up visit until the next visit. The intervals are: hospital arrival to discharge or 14 days, whichever occurs first; hospital discharge to 30 days; 30 days to 6 months; and 6 months to 3 years. Models were also developed to predict the entire 3-year follow-up period using baseline information. Multivariable Cox proportional hazards modeling was used throughout with Wald chi-squares as the comparator of strength for each predictor. For the baseline model of overall mortality, the 3 strongest predictors were age (adjusted hazard ratio [HR], 1.35; 95% CI, 1.28-1.42; P<0.0001), baseline heart rate (adjusted HR, 1.17; 95% CI, 1.14-1.21; P<0.0001), and creatinine clearance (≤100 mL/min; adjusted HR, 0.86; 95% CI, 0.84-0.89; P<0.0001). According to the integrated discrimination improvement (IDI) and net reclassification improvement (NRI) indices, the updated model had significant improvement over the model with baseline covariates only in all follow-up periods and with all outcomes.
Conclusions:
Patient information assessed closest to the time of the outcome was more valuable in predicting death when compared with information obtained at the time of the index hospitalization. Using updated patient information improves prognosis over using only the information available at the time of the index event.
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