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Optimizing risk stratification in cardiac rehabilitation with inclusion of a comorbidity index
Gilbert J Zoghbi1, Bonnie Sanderson, Jenny Breland
1Division of Cardiovascular Disease, University of Alabama at Birmingham, 35294-0007, USA. gzoghbi@cardio.dom.uab.edu
Insights
Noncardiac comorbidities are prevalent in cardiac rehabilitation patients and independently predict clinical events. Supplementing current risk stratification with comorbidity assessment improves event prediction, especially in women.
Area of Science:
- Cardiology
- Rehabilitation Medicine
- Clinical Risk Stratification
Background:
- Current cardiac rehabilitation (CR) risk stratification guidelines from the American Association of Cardiovascular and Pulmonary Rehabilitation focus on disease progression and short-term clinical events.
- These established criteria do not incorporate noncardiac comorbidities, potentially limiting their comprehensive risk assessment capabilities.
- Understanding the prevalence and impact of noncardiac comorbidities in CR patients is crucial for refining risk stratification algorithms.
Purpose of the Study:
- To determine the prevalence of noncardiac comorbidities among patients undergoing cardiac rehabilitation.
- To evaluate the relationship between noncardiac comorbidities and the existing risk stratification algorithm for predicting clinical events.
- To assess the combined predictive value of current criteria and comorbidity indices for clinical events.
Main Methods:
- Patients were stratified into high, intermediate, and low-risk groups using the American Association of Cardiovascular and Pulmonary Rehabilitation risk stratification criteria for clinical events (ARSE).
- Noncardiac comorbidities were quantified using a comorbidity index (CMI) and analyzed within each ARSE risk group.
- Logistic regression was employed to evaluate the association between clinical events and risk status determined by both ARSE and CMI.
Main Results:
- A significant comorbidity burden was observed in 490 ischemic heart disease patients entering CR, with a median of 2 comorbidities.
- While ARSE tended to identify patients with higher comorbidity, 38% of those with high CMI were not in the highest ARSE group.
- Both ARSE and CMI independently predicted clinical events, with combined assessment offering the best predictive accuracy. CMI was a stronger predictor in women.
Conclusions:
- Noncardiac comorbidities are common in CR patients and are significant independent predictors of clinical events.
- The current ARSE criteria should be augmented with noncardiac comorbidity assessment for a more comprehensive risk evaluation.
- Integrating comorbidity data enhances the prediction of clinical events, particularly highlighting its importance in female patients.
Purpose:
The risk stratification criteria of the American Association of Cardiovascular and Pulmonary Rehabilitation include guidelines to be used in stratifying cardiac rehabilitation (CR) patients for risk of disease progression (long term) and clinical events (short term). Noncardiac comorbidities are not included as indicators in these criteria. This study was designed to ascertain the prevalence of noncardiac comorbidities among CR patients, and to assess their relation to the current risk stratification algorithm for clinical events.
Methods:
Patients were stratified into high-, intermediate-, and low-risk groups according to the American Association of Cardiovascular and Pulmonary Rehabilitation risk stratification criteria for clinical events (ARSE) at program entry. Within each risk group, age, gender, race, and noncardiac comorbidities were ascertained. Comorbidities were summarized in a comorbidity index (CMI). The relation between clinical events and risk status by ARSE and CMI was evaluated by logistic regression.
Results:
Among 490 patients (age, 60 +/- 12 years; 35% women; 30% nonwhite) enrolled in CR with ischemic heart disease, the number of comorbidities ranged from 0 to 7 (median, 2; 75th percentile, 3). The patients categorized in the three ARSE groups differed significantly in age and comorbidities. Although ARSE tended to identify patients with a greater comorbidity burden, 38% of the patients with a comorbidity index exceeding the 75th percentile were not classified in the highest ARSE group. Clinical events increased across ARSE and CMI risk strata. Both ARSE and CMI were independent predictors of events in an age-, gender-, and race-adjusted logistic regression analysis (ARSE odds ratio [OR], 1.56; 95% confidence interval [CI], 1.14-2.12; CMI OR, 1.23, 95% CI, 1.03a-1.47). Events were predicted best when both classifications were combined. Exploratory gender-specific analyses suggested that ARSE performed better among men than among women, whereas CMI was a more important predictor among women.
Conclusions:
To appreciate more fully the overall complexity of disease among CR patients, ARSE should be supplemented not only with the inclusion of cardiac risk factors, as suggested in the current guidelines, but also with an assessment of noncardiac comorbidities.
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