Identifying quality of life outcome patterns to inform treatment choices in ischemic cardiomyopathy
Makoto Mori1, Daniel B Mark2, Rohan Khera3
1Division of Cardiac Surgery, Yale School of Medicine, New Haven, CT; Center for Outcomes Research and Evaluation, YaleNew Haven Hospital, New Haven, CT.
Insights
Coronary artery bypass grafting (CABG) surgery offers improved quality of life (QoL) for ischemic cardiomyopathy patients compared to guideline-directed medical therapy (GDMT). Patient-specific predictions can personalize treatment decisions for better QoL outcomes.
Area of Science:
- Cardiovascular Surgery
- Clinical Outcomes
- Quality of Life Research
Background:
- The Surgical Treatment for Ischemic Heart Failure (STICH) trial showed coronary artery bypass surgery (CABG) improved mean quality of life (QoL) over guideline-directed medical therapy (GDMT).
- Mean QoL scores lack personalized insights for high-risk treatment decisions in ischemic cardiomyopathy.
Purpose of the Study:
- To analyze QoL outcome patterns (phenotype trajectories) in STICH trial patients.
- To identify baseline predictors for QoL trajectories in CABG versus GDMT treatment groups.
- To provide patient-specific QoL outcome predictions for treatment choices.
Main Methods:
- Utilized Kansas City Cardiomyopathy Questionnaire (KCCQ) Overall Summary scores over 36 months.
- Applied mixture models to define QoL phenotype trajectories.
- Employed logistic regression to predict individual probabilities of achieving good QoL outcomes with CABG or GDMT.
Main Results:
- Identified 3 QoL phenotype trajectories, with 2 classified as "good QoL trajectories."
- Good QoL trajectories were observed in 84.1% of CABG patients and 73.9% of GDMT patients.
- 23.5% of patients had a >10% higher probability of good QoL with CABG, while 3.8% favored GDMT; 54.7% had similar probabilities.
Conclusions:
- QoL outcomes after CABG versus GDMT follow predictable phenotypic trajectories.
- Individual baseline features predict a patient's QoL trajectory pattern.
- Patient-specific QoL predictions offer a framework for personalized decision-making in treatment selection.
Background:
The Surgical Treatment for Ischemic Heart Failure (STICH) trial found that routine use of coronary artery bypass surgery (CABG) improved mean quality of life (QoL) scores relative to guideline-directed medical therapy (GDMT) in patients with ischemic cardiomyopathy. However, mean differences in QoL scores do not provide what patients want to know when facing a high-risk/high-benefit treatment choice.
Methods:
We analyzed Kansas City Cardiomyopathy Questionnaire (KCCQ) Overall Summary scores in CABG and GDMT patients over 36 months using a combination of statistical methods to group QoL data into clinically relevant outcome patterns (phenotype trajectories) and to then identify the main baseline predictors of each phenotype. QoL outcome phenotypes were developed using mixture models to define the dominant phenotype trajectories present in STICH QoL data. Logistic regression models were used to predict each patient's probability of achieving each outcome pattern with each treatment.
Results:
In STICH, 592 patients underwent CABG and 607 were managed with GDMT. Our analyses identified 3 phenotype trajectory patterns in both treatment groups. Two of the 3 trajectories showed improving patterns, and were classified as "good QoL trajectories," seen in 498 (84.1%) CABG and 449 (73.9%) GDMT patients. Defining a consequential CABG-GDMT treatment difference as a >10% higher absolute predicted probability of belonging to good QoL trajectories, 277 (23.5%) patients were more likely to have good outcome with CABG while 45 (3.8%) patients were more likely to have a good outcome with GDMT. For 644 (54.7%) patients, CABG and GDMT probabilities of a good outcome were within 5% of each other.
Conclusions:
The pattern of QoL outcomes after CABG compared with GDMT in STICH followed 3 main phenotypic trajectories, which could be predicted based on individual baseline features. Patient-specific predictions about expected QoL outcomes with different treatment choices provide an intuitive framework for personalizing patient decision making.
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