Therapeutic Decision-Making and Outcomes in Elderly Patients With Severe Symptomatic Aortic Stenosis: Prognostic Implications of Elderly Patients' Initial Decisions

Abstract

Background: Despite clear indications for intervention, therapeutic decision-making for elderly patients with severe symptomatic aortic stenosis (AS) remains a complex issue due to the wide variation in individual risk profiles and the involvement of patients' subjective preferences. We aimed to investigate the reasons leading to the decisions against intervention and the consequences thereof on survival. Methods: Data were derived from the China Elderly Valve Disease (China-DVD) Cohort Study on patients aged ≥60-year-old with severe symptomatic AS consecutively enrolled between September to December 2016. Patients were analyzed according to the initial therapeutic decisions made by consensus between patients and physicians at the time of the index evaluation: intervention group (patients who were evaluated as suitable for intervention and accepted the treatment proposal); patient-refusal group (patients who were evaluated as suitable for intervention but refused due to subjective preferences); physician-deny group (patients who were denied intervention by physicians after evaluation). The least absolute shrinkage and selection operator (LASSO)-penalized logistic regression model was used to identify the factors associated with physicians' decisions against intervention. Twelve-month survival was analyzed using Cox proportional hazards models, with multivariate adjustment using inverse probability weighting (IPW). Results: Among the enrolled 456 elderly patients with severe symptomatic AS, 52 (11.4%) patients refused intervention and 49 (10.7%) patients were denied intervention by their physicians. LASSO-penalized logistic regression model identified that reduced left ventricular ejection fraction and increased EuroSCORE-II were strongly associated with physicians' decisions against intervention. At 12-month follow-up, only 8 (15.4%) patients who initially refused the intervention proposal underwent the subsequent intervention, with an average delay of 195 days. Patients' initial decisions against intervention were significantly associated with 12-month mortality, even after IPW adjustment (Hazard ratio: 2.61; 95% confidence interval: 1.09-6.20; P = 0.031). Conclusions: The decision against intervention was taken in about one-fifth of elderly patients with symptomatic severe AS, half of which were due to patients' subjective preferences. Surgical risk remains the primary concern for physicians when making therapeutic decisions. Elderly patients' initial decisions against intervention have a profound impact on subsequent intervention rates and prognosis, and therefore should be treated as a "risk factor" at the subjective level. Clinical Trial Registration: clinicaltrials.gov/ct2/show/NCT02865798, China elDerly Valve Disease (China-DVD) cohort study (NCT02865798).

Keywords: aortic stenosis; aortic valve replacement; elderly; outcomes; therapeutic decision making.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Zhao, Xu, Liu, Ye, Zhang, Li, Gao and Wu.

Figures

Figure 1

Study cohort flow diagram.

Figure 2

Analysis of the factors associated with physicians' decisions against intervention using LASSO-penalized logistic regression model. (A) The plot showing the deviance values of the LASSO model as a function of the tuning parameter λ. The optimal λ is the value that minimizes the deviance curve (dashed line). (B) Trace plot showing non-zero model coefficients as a function of the tuning parameter λ. As the λ increases, LASSO sets various coefficients to zero, thus removing them from the model. When λ corresponds to the minimum-deviance (dashed line), two variables are selected (C).

Figure 3

Analysis of the predictors of 12-month mortality using LASSO-penalized Cox regression model. (A) The plot showing the deviance values of the LASSO model as a function of the tuning parameter λ. The optimal λ is the value that minimizes the deviance curve (dashed line). (B) Trace plot showing non-zero model coefficients as a function of the tuning parameter λ. As the λ increases, LASSO sets various coefficients to zero, thus removing them from the model. When λ corresponds to the minimum-deviance (dashed line), five variables are selected (C).

Figure 4

Kaplan-Meier survival curves for patients with different therapeutic decisions. The plot showing the survival distributions of patients who accepted the intervention proposal (Blue); refused the intervention proposal (Yellow); were denied intervention by physicians (Red), as well as the expected survival of the age- and sex-specific general population (Green). The dash lines represent the crude survival rates and the solid lines indicate the adjusted survival rates using IPW.