3. Establishment and verification of the prediction model for
post-CPVA recurrence in patients with PAF
In the modeling group, by using AF recurrence after radiofrequency
ablation as the dependent variable, and using the patient’s gender, age,
body mass index (BMI), LAD, LVEF, left ventricular end-diastolic
dimension (LVEDD), LAAV, degree of MR, B-type brain natriuretic peptide
(BNP), CHA2DS2-Vasc as independent
variables, we performed multivariate logistic regression analysis. The
results showed that the age of PAF patients, the degree of MR, and LAAV
were all independent risk factors for recurrence after CPVA, Table 4.
The equation for predicting AF recurrence after radiofrequency was:
Logit(P)= -3.253 +0.092×age+1.263×mild MR +2.325×moderate MR
+5.111×severe MR -0.113×LAAV.
The ROC curve of the prediction model in the modeling group was plotted.
The AUC was 0.889 (95%CI: 0.793-0.986), the sensitivity was 76.5%, the
specificity was 94.6%, the positive likelihood ratio was 14.224, and
the negative likelihood ratio was 0.249. The AUC of the prediction model
was better than the single-factor parameters LAD, LVEF, LVEDD, LAAV,
BNP, CHA2DS2-Vasc (allP <0.05) (Figure 1A).
The ROC curve of the prediction model in the verification group was
plotted. The AUC was 0.866 (95%CI: 0.711-1.000), the sensitivity was
71.4%, the specificity was 97.0%, the positive likelihood ratio was
23.571, and the negative likelihood ratio was 0.295. There was no
significant difference in AUC between the modeling group and
verification group (0.889 vs 0.866, P >0.05) (Figure
1B).
The nomogram (Figure 2) and calibration curve (Figure 3) of the
prediction model were plotted. The calibration curve showed that the
prediction model had good consistency between the predicted value and
observed value in both modeling group and verification group.