Discussion
Atrial fibrillation is the most common sustained arrhythmia that
increases with age and presents with a wide spectrum of symptoms and
severity, including reentry theory and focal agitation[17,18].
Despite shorter ablation times, faster balloon cooling, and longer
thawing times, ablation of atrial fibrillation with a second-generation
cryoballoon is associated with higher success rates of pulmonary venous
dissociation, acute and long-term PV isolation rates are high, AE rates
are similar, and atrial fibrillation is absent[19]. Therefore, the
data we collected and collated were all studies and analyses of AF
patients after cryoablation.
In this study, prognostic variables were not simply analyzed by clinical
index (CLI); imaging data was used to construct radioactivity index
(RSI). With the joint action of clinical and radioactivity indicators,
the C-index and ROC curve results of the formed risk score model showed
a significant improvement in the prediction effect on the whole set,
training set, validation set 1, or
validation set 2. Meyre et al. (2019)[20] performed Cox regression
analysis adjusted for risk factors for routine admission using clinical
data to calculate the risk ratio (HR) and obtained a C-statistic of 0.64
(95% CI: 0.61-0.66). Similarly, Peng et al. (2019)[21] constructed
radiomic features based on features extracted from PET and CT images in
the training set to predict disease-free survival (DFS). To this end,
the radioactivity indexes (RSI) of AF patients after cryoablation were
integrated into this paper, and the original clinical indicators (CLI)
were combined to form the comprehensive risk score (TCRS). From an
effects standpoint, the combination of RSI and CLI indicators showed a
significant increase compared to the AUC and C-index of a single
indicator. In the ROC curve of the training set, the AUC of RSI was
0.942 and the AUC of CLI was 0.862, but the AUC of TCRS obtained by
linearly weighting the two indicators was 0.955. Similarly, in the
validation set 1, the TCRS index formed by TCRS showed superior
prognostic performance. In order to improve the accuracy of the model, a
second validation set was set up to witness the TCRS constructed from
the nonlinear survival model. The results of the C-index also confirm
the accuracy of the model, such as RSI (C-index: 0.8894; 95%CI:
0.8166-0.9621), CLI (C-index: 0.8431; 95%CI: 0.7466-0.9395), and TCRS
(C-index: 0.9072; 95%CI: 0.8281-0.9864) in validation set 2.
Researchers have traditionally used Lasso Cox analysis to perform
survival analyses for various diseases. Bieging et al. (2018)[22]
selected only shape parameters using the Lasso method and factor
analysis, adding them to a Cox regression model that included multiple
clinical parameters and LA fibrosis (C-index: 0.68-0.72). Other scholars
have used the nomogram method to analyze the postoperative prognosis of
patients with AF. Zhou et al.
(2021)[23] explored the risk
factors for recurrence of atrial fibrillation (AF) in patients after
radio frequency ablation and constructed a targeted nomogram prediction
model (AUC=0.852). Dong et al. (2022)[24] used the least absolute
shrinkage and selection operator regression for variable screening and a
multi-variable Cox survival model for nomogram development, obtaining an
AUC of between 0.855 and 0.863 in the development and validation
cohorts. Our study combines the nonlinear part of machine learning and
the linear part of the Cox model to obtain a nonlinear proportional risk
survival model, enabling the construction of the risk score of AF
patients to divide them into high- and low-risk groups. First, from the
perspective of ROC curve, TCRS constructed from non-linear survival
models had better prognosis whether in the training set (AUC: 0.955 vs
0.664), validation set 1 (AUC: 0.920
vs 0.548) or validation set 2 (AUC: 0.945 vs 0.591). The closer the AUC
is to 1, the better the prediction. Second, the results were more
credible through the auxiliary verification of C-index, such as RSI
(C-index: 0.9125; 95%CI: 0.8596-0.9653), CLI (C-index: 0.8479; 95%CI:
0.7656-0.9303) and TCRS (C-index: 0.9454; 95%CI: 0.9219-0.9689).
The innovation of this paper lies in the construction of an optimal
non-linear survival model using a variety of machine learning models,
and the demonstration of its superior prognostic performance. While
Katzman et al. (2016)[15] proposed a combination of nonlinear and
linear models in theory, their prognostic effect lacked empirical
research. In contrast, the prognostic performance of the nonlinear
proportional hazard survival model developed in this study was
significantly improved. From the model correction diagram, the predicted
value and the real value are basically in the same straight line, and
there is only a small error (Fig. 5). It can also be seen from the
forest plot that the higher the TCRS value of the indicator we
constructed, the higher the risk of recurrence, and the P value is less
than 0.001, indicating that the result is
significant (Fig. 7). The
constructed risk score index was then used to divide patients into high
and low risk groups using X-tile software. The Kaplan-Meier curve showed
a significant difference between the two groups, and the log-rank test
was less than 0.001, indicating clear differences in survival time and
survival rate between the high and low risk groups. Both the training
set and the validation set showed that there were significant
differences in the high and low analysis groups divided by
TCRS (Fig. 6). This predicted
non-linear proportional hazard survival model can serve as a
quantitative means to assess the high and low risk of atrial
fibrillation recurrence in cryoablation patients.