Methods
The study prospectively evaluates
consecutive patients with AS referred to the HT meeting between June
2014 and February 2017 collated in a dedicated database. Patients were
referred from the catchment area of our main hospital and from two other
satellite hospitals with an established alliance. Our study was approved
by the Institutional Review Board of our institution and all patients
provided written informed consent for the procedures. Heart team
meetings were weekly scheduled in the main hospital with the attendance
of at least two cardiac surgeons, two interventional cardiologists, one
imaging cardiologist and one clinical cardiologist. Physicians from
other specialities, such as internal medicine, oncology or geriatric
medicine, were invited to participate in the discussion when necessary
and physicians from the satellite hospitals participated fully via
video-link. By closely following previous Clinical Practice Guidelines
(2,3), a local consensus document (Annex I in Supplementary
data ) was developed and signed jointly by cardiac surgeons and
cardiologists, with the aim of identifying potential candidates with AS
for HT discussion. This document was distributed to all potential
referral physicians within the three hospitals. All patients with AS
originally referred for TAVR and those in whom the management was
undecided were discussed by the HT and were included in this analysis.
However, patients directly referred to SAVR who did not meet any of the
criteria in the consensus document, were excluded from this analysis
unless the cardiac surgeon deemed it necessary to discuss the case in
the HT.
During the HT meeting, clinical data from each patient was summarized in
a formal presentation, which also included a prospective evaluation of
surgical risk using the logistic European System for Cardiac Operative
Risk Evaluation logistic EuroSCORE and the Society of Thoracic Surgeons
(STS) score. Each case presentation was followed by a discussion and
assessment of the overall risk profile. The HT then decided to refer the
patient to either medical treatment (MT), TAVR or SAVR. A prospective
clinical follow-up at 1- 6- 12- and 24-month was carried out through
clinical visits for all patients in the SAVR and TAVR groups, whereas
clinical outcomes were analysed retrospectively in the MT group. The
median follow-up time was 18 months [11-26] and only one patient was
lost to follow-up. In-hospital and long-term outcomes were defined
according to the Valve Academic Research Consortium 2 (VARC-2) criteria
(8).
Quantitative continuous variables are expressed as mean (standard
deviation) or median (interquartile range [IQR]) according to their
distribution. Assessment of normality was performed using the
Shapiro-Wilk test. Differences between treatment groups were evaluated
using the non-parametric Kruskal-Wallis rank test and Wilcoxon rank test
for continuous variables without normal distribution. Categorical
variables were summarized as number (percentage) and comparisons were
analysed by the chi-square or the Fisher´s exact test. Patient baseline
characteristics were identified that significantly influenced decision
making within the HT. Considering these factors, a decision tree to
guide the decision-making process was built using CART (classification
and regression tree) methodology (9). The CART method is used for
constructing prediction models from data. The models are obtained by
dividing the data and adjusting a simple prediction model within each
partition. The programme determines cut-off points which best explain
the categorical endpoint of the analysis (MT or TAVR or SAVR) and
selects the predictor with the lowest p-value of a logistic regression
to make a first division. The result is a decision tree. We used the
registered baseline characteristics to reproduce the decision process by
using the non-parametric CART methodology. Survival curves were
calculated using the Kaplan-Meier method, and comparison was obtained
with the log-rank test. All analyses were performed using Stata 14
(StataCorp, College Station, TX, USA) and RStudio Team (2018). RStudio:
Integrated Development for R. RStudio, Inc., Boston, MA.