Results
We included a total of 325 patients
with Chronic rhinosinusitis with nasal polyps (CRSwNP) from June 2018 to
July 2020 at the A, B, C hospital. Included patients were following the
doctor’s instructions and had a follow up till 1 year. The enrolled
patients were randomly assigned to a training (n=195) and validation
cohort (n=130). The nomogram was based on the training cohort and its
accuracy was internally validated through the validation cohort. The
baseline characteristics of the CRS patients between the training cohort
and validation cohort are shown in table 1. No significant differences
were observed for these characteristics between the training and
validation cohort. Univariate analyses were done with the primary
objective to confirm the statistical effect between each covariate and
the endpoints. Results showed that most covariates had statistically
significant associations with the endpoints, except for age, gender, and
smoking (Table 2).
Nomogram development
After the initial univariate analyses with extensive review of the
medical literature, we included all the covariates in the subsequent
multivariate logistic regression models, except for age, gender,
smoking, tissue eosinophil counts, preoperative Lund Kennedy score, and
Lund Mackay Score. Based on these factors, the nomogram was constructed
for calculating the risk of recurrence of the CRS after operation 1year
(Figure1A).
A case demonstrating our nomogram usage is shown in Figure1B. For
example, if the patient had tissue eosinophil ratio >=10%,
low blood eosinophilia, no AR, and asthma, then the total points would
be 196 with the corresponding risk of recurrence at 46.11%.
Nomogram validation
Both internal and external validation of the nomogram was performed in
this study. The plotted calibration curves correspond to the ideal plot
(45°line), which reveals a favorable agreement on the nomogram
estimation and the actual observation regarding the probability of
uncontrolled disease after the 1 year of post endoscopic sinus surgery.
In the training cohort, the nomogram showed the highest accuracy with an
AUC of 0.760 (95% CI, 0.688-0.830) (Figure 2.A). The corresponding
calibration plot indicates the similarity in the estimation made by the
nomogram and clinical findings made during the follow-up period for the
recurrence of CRSwNP (Figure 2B). In the validation cohort, the nomogram
prediction was 0.635 (95% CI, 0.537-0.733) (Figure 3A). The calibration
curve showed a concurrence of predicted probability with the actual
probability (Figure 3B).
To
assess the clinical applicability of our risk prediction nomogram,
clinical impact curve analysis (CICA) and decision curve analysis (DCA)
was also performed. The CICA and DCA visually exhibited that the
nomogram had superior practical ranges of threshold probabilities and an
overall net benefit in terms of outcome for the impacted patient (Figure
4A and 4B).