Preprocedural Patient Factors Useful for Predicting Poor Outcome Following TAVR

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Newly developed predictive models may help identify patients at high risk for a poor outcome at 6 months and 1 year after transcatheter aortic valve replacement (TAVR), according to a study published online May 23, 2014, ahead of print in Circulation. The tools may help guide treatment choices, providing potential TAVR candidates realistic expectations regarding both survival and quality of life (QoL).

Death or Impaired QoL in One-Third at 6 Months

At 6 months, 704 patients (33%) had a poor outcome due to death (19%), poor QoL (12%), or worsened QoL (2%).

The most important predictors in both models were poor functional capacity (as assessed by the distance walked on the 6-minute walk test) and lower mean aortic valve gradients (table 1). Other predictors were oxygen-dependent chronic lung disease, renal dysfunction, and poorer baseline cognitive function. The Society of Thoracic Surgeons mortality risk score was not a predictor in either model.

Table 1. Predictors of Poor Outcome after TAVR

P < .001 for all.

The 6-month model demonstrated moderate discrimination (c-index = .66) and good calibration with the observed data, and performed similarly in a separate validation cohort (c-index = .64).

To better understand its ability to inform clinical decisions, patients were then stratified into 3 groups according to their predicted risk of poor outcome at 6 months: low risk (< 25%; n = 612), intermediate risk (25% to < 50%; n = 1,314), and high risk (> 50%; n = 211). Compared with patients at low risk, high-risk patients had less diabetes, lower body mass indices, worse kidney function, more frequent oxygen-dependent lung disease, lower mean aortic valve gradients, worse cognitive function, worse functional status, and worse QoL at baseline. Mortality was highest in the high-risk group at 31%. In this group an additional 24% had very poor QoL or a decline in QoL.

Similarly, patients were stratified into 4 groups based on their predicted risk of poor outcome at 1 year: low risk (< 25%; n = 65), intermediate risk (25% to <50%; n = 963), high risk (50% to <70%; n = 924), and very high risk (>70%, n = 178). Mortality was 50% in the very high-risk group. In this group an additional 23% had poor QoL or a decline in QoL. In contrast, among low-risk patients, only 17% had died and 12% had a poor QoL or a decline in QoL (P for difference < .001).

Predictors of What ‘Patients Actually Care About’

In a telephone interview, Dr. Arnold told TCTMD that the method for defining poor outcome in this study differentiates it from previous attempts to examine factors associated with how patients fare after TAVR.

“A lot of the analyses have looked at mostly mortality,” she said. “Being able to integrate mortality and quality of life into [an endpoint] we think patients actually care about was our goal, especially in this population where quality of life benefits are as important as, if not more important than, the survival benefit.”

Dr. Arnold said she believes eventual use of this or a similar model in clinical practice “will help ground the [treatment] decision in something that is at least data-driven so that we’re not just guessing.” Also important, she added, is how best to present such information to patients in a way that is informative and helpful.

A Step Toward Patient-Oriented Risk Models

In an editorial accompanying the study, Larry A. Allen, MD, MHS, and John S. Rumsfeld, MD, PhD, both of the University of Colorado School of Medicine (Aurora, CO), say the novelty of the risk model is noteworthy.

“It is a true patient outcome model, predicting both survival and patient health status. Despite the obvious relevance to patients, few models of this type exist in the medical literature,” they write.

“A key to the creation of such models is the increasing availability of standardized tools to measure patient health status,” they continue, “These instruments, such as the [KCCQ] used in this study, are reproducible, valid, and clinically interpretable. The era of measuring and predicting patient-reported outcomes as part of clinical practice is just dawning, and is bolstered by [this study].”

However, Drs. Allen and Rumsfeld add that most risk models “remain more academic than practical.” To be helpful in clinical practice, they say, models must:

• Become integrated with routine workflow and not inhibit or add significant time to patient care
• Give easily interpretable results akin to clinical test results that inform treatment recommendations
• Be implemented as part of a meaningful shared decision-making process between clinicians, patients, and families

“For TAVR and other medical treatment decisions, shared decision making that is supported by risk-model estimates for individual patients is a path to higher quality of care through better decision quality,” the editorial notes. “This is the promise of personalized medicine—a promise that remains unfulfilled in contemporary medical practice.”

Note: Several coauthors are faculty members of the Cardiovascular Research Foundation, which owns and operates TCTMD.

 

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Sources:
1. Arnold SV, Reynolds MR, Lei Y, et al. Predictors of poor outcomes after transcatheter aortic valve replacement: results from the PARTNER trial. Circulation. 2014;Epub ahead of print.

2. Allen L, Rumsfeld JS. Can we predict who will be alive and well after TAVR? Is that useful to individual patients? Circulation. 2014;Epub ahead of print.

 

 

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