Baseline characteristics of participants
Table 1 summarizes the clinical characteristics of the derivation cohort (n=216) and validation cohort (n=86). The mean age was 57.8±10.2 and 58.6±10.0 years in these groups, respectively. The incidences of hyperlipidemia, diabetes, stroke, history of PCI, and smoking were not significantly differently between the groups (all p>0.05). Similarly, there were no statistical significance in the body mass index (BMI), left ventricular ejection fraction, Gensini score, SYNTAX score, J-CTO score, and Progress Score (all p>0.05). The main angiographic characteristics of the two groups are summarized in Table 1.
In the derivation set, the incidence of diabetes was significantly higher in the failed group than that in the successful group (p<0.05). In terms of angiographic characteristics, the failed group demonstrated significantly higher proportion of small size, corkscrew, and side branch at tortuosity than those in the successful group (all p<0.05, Table 2).
Univariate and multivariate analysis
Univariate and multivariate logistic regression analyses were used to explore the association of risk factors with success rate of GW crossing. The univariate analysis demonstrated that diabetes (odds ratio, OR: 2.305), small size (OR: 2.778), corkscrew (OR: 4.028), and side branch at tortuosity (OR: 2.190) were associated with success in GW crossing (all P<0.05). According to multivariate logistic regression analyses, diabetes, small size, corkscrew, and side branch at tortuosity were identified as independent factors in the training cohort (Table 3, Figure 4).
Novel nomogram score system
Corkscrew was the biggest influencing factor on the prognosis, whereas diabetes had the least effect. Validation of the nomogram was performed using bootstrap analyses with 1,000 resamples; the internal validation cohorts revealed favorable discrimination of the nomogram, demonstrating that the nomogram could be clinically implemented (Figure 5).
Validation of the predictive accuracy of the nomogram
Overall, 86 patients were included in the validation cohort between February 2019 and October 2020. The calibration was drawn to evaluate the calibration of the model in the validation set (Figure 6). ROC analyses were used to evaluate the discrimination of the model; the area under the curve (AUC) was 0.870 (95% confidence interval, CI: 0.792–0.948, Table 5) and indicated better predictive accuracy. Additionally, the decision curve analysis (DCA) curve demonstrated that the novel nomogram also included a higher clinical net (Figure 7).
Procedure data and clinical outcome
Table 4 summarizes the procedures and clinical outcome data. Femoral and radial access were used in the majority of patients (91.0%). The high prevalence of GWs used was Sion (Asahi Intecc, Nagoya, Japan), which was 93.3%. The common final crossing technique included the Reverse CART (49.1%), RWE (19.7%), AWE (8.3%), and parallel wires (5.3%). Other techniques included kissing wires (10.1%) and ADR (3.1%). Furthermore, the technical success rate was 79.1% and the procedural success rate was 75.5%. For procedural complications and adverse events, the incidence of CC perforation was 4.6%. One patient developed periprocedural myocardial infarction and six patients developed no-flow or slow-flow.
Discriminatory power of different scores
We also compared the predictive ability of the new model and conventional staging systems by comparing AUC of ROC curves (Table 5; Figure 8). The AUC of Retro-CTO score was 0.698 (95% CI: 0.563–0.834, P<0.05) and Figure 8 illustrates Retro-CTO score’s probability of success in different groups. The J-Channel score demonstrated intermediate predictive value (AUC: 0.776; 95% CI: 0.644–0.909; P<0.01). The predictive value of EPI-score included AUC of 0.702 (95% CI: 0.558–0.846; P<0.05). The HLK score demonstrated little predictive value on the septal channel retrograde revascularization (AUC: 0.611; 95% CI: 0.4614–0.761, P=0.19). Compared to those systems, the Sep-CTO score had the strongest predictive power with success rates of 100%, 75%, and 50% for easy, intermediate, and difficult groups, respectively (Figure 9). Additionally, the Sep-CTO score may predict the GW crossing time as well (Figure 10).