A prediction modeling based on SNOT-22 score for endoscopic nasal septoplasty: a retrospective study

Introduction

Nasal septum deviation (NSD) is one of the most frequently encountered diseases in the rhinology clinic in which the nasal septum deviates from the midline, causing the nasal cavity to shrink in size (Cho et al., 2010; Mattos, Woodard & Payne, 2011). It occurs in 77–90% of the general population (Gray, 1978; Mladina et al., 2008). Common symptoms are stuffy nose, headache, nosebleed, etc. NSD is an expensive medical condition, and also acts as a predisposing factor for other diseases (Bousquet et al., 2008; Fokkens et al., 2012). For instance, long-term NSD may indirectly increase the risk of cardiovascular disease (Ozkececi et al., 2016; Uluyol et al., 2016). Conservative treatments for NSD have a low benefit likelihood (Lindsay, 2012; Rhee et al., 2014; Rudy & Most, 2017), and according to studies published in The Lancet in 2019, nasal septoplasty is more effective than non-surgical treatment in adults with NSD (Van Egmond et al., 2019). Therefore, in NSD treatment, surgical method should be considered over conservative non-surgical method (Aydogdu et al., 2019). The use of endoscopy provides better illumination for nasal septum correction, making it easier to manage spur or crest. Compared with traditional surgical methods, endoscopic nasal septoplasty surgery has fewer complications and better results. Since it was first described by Stammberger in 1991, it has been increasingly adopted by clinicians and patients (Dell’Aversana Orabona et al., 2018). However, the surgical outcome of NSD correction is affected by many factors including: disease-related factors (such as the severity of the disease, combined with sinusitis and middle turbinate plasty); treatment-related factors (such as surgery and rehabilitation protocol, follow-up time, and other medical-related medications); and patient-related factors (such as gender, BMI, age, smoking, drinking history, and seasonal allergies) (Ahn et al., 2016; Alakarppa et al., 2018; Becker et al., 2008; Hong et al., 2015). In addition, studies have shown that nasal valve collapse and sinusitis, caused by a history of nasal surgery, could make a profound impact on the efficacy of NSD surgery (Becker et al., 2008; Bhattacharyya, 2005).

Despite a number of variables that influence the efficacy of NSD surgery being identified in previous studies, no systematic assessment aimed at predicting the surgery’s efficacy exists. Therefore, accurate predictive tools and early individualized interventions could be effective in improving the surgical outcomes in patients (Van Egmond et al., 2015). SNOT-22 is deemed a credible, valid and responsive disease-specific instrument (Dietz de Loos et al., 2013; Hopkins et al., 2009; Morley & Sharp, 2006). In addition, previous researches support that SNOT-22 is an effective and reliable tool in assessing the results of nasal septal surgery or septorhinoplasty (Aydogdu et al., 2019; Buckland, Thomas & Harries, 2003; Poirrier et al., 2013). Creating a prediction model based on SNOT-22 score for endoscopic nasal septoplasty was the main objective of this study (Alakarppa et al., 2017; Alakarppa et al., 2018; Van Egmond et al., 2019). We hypothesized that based on the degree of improvement in SNOT-22 scores after nasal septoplasty, an effective nomogram model can be developed and used in predicting the likelihood of a benefit outcome from the surgery.

This study sought to develop a simple and effective predictive tool that can be used by clinicians for the efficacy prediction of nasal septoplasty.

Materials & Methods

Results

Building a personalized predictive model

After logistic model analysis of the nine predictors (Table 3), we used R software nomogramEx package to construct the nomogram, as shown in Fig. 2. Among them, age, SNOT-22 score, history of sinusitis and nasal surgery were revealed as significant factors affecting the efficacy of nasal septum surgery (P < 0.05).

Table 2:

Rank the order of features in SVM-RFE method.

Features	Coefficients	Rank the order
Middle turbinate plasty	−4.75266432	1
Nasosinusitis	−2.21413383	2
Nasal surgery history	−2.3517654	3
Seansonal allergy history	3.03876115	4
Age	−0.06223234	5
SNOT22 Score	0.03536382	6
BMI	−0.17801399	7
Smoke	−1.088097	8
Follow up time	−0.35657536	9

DOI: 10.7717/peerj.9890/table-2

Table 3:

Chart of prediction factors.

Variable	Prediction model
	β	Odds ratio (95% CI)		P-value
SNOT22 score	0.034	1.035(1.004–1.070)		0.028
Age	−0.059	0.943(0.895–0.988)		0.019
Smoke	−0.678	0.508(0.154–1.680)		0.259
Seansonal allergy history	2.189	8.930(1.817–74.700)		0.017
Nasosinusitis	−2.003	0.135(0.034–0.460)		0.002
Follow up time	−0.001	0.999(0.998–1.000)		0.043
BMI	−0.065	0.937(0.780–1.110)		0.471
Nasal surgery history	−2.823	0.059(0.013–0.227)		0.0000975
Middle turbinate plasty	−1.875	0.153(0.017–1.800)		0.104

DOI: 10.7717/peerj.9890/table-3

Notes:

β is the regression coefficient.

Inspection of the nomogram prediction model

The calibration curve of nomogram was consistent (Fig. 3A), indicating that the model was competent to predict the surgical outcome of NSD. The model had a good predictive power, with a C-index of 0.920 (95% CI [0.854–0.986]), 0.920 (95% CI [0.854–0.986]), 0.834 (95% CI [0.655–1.000]) and 0.765 (95% CI [0.555–0.974]) in the training queue, the validation queue, the overall sample, and the external dataset, respectively. In addition, the AUC of the prediction model were 0.920 in training set (the AUC curve is shown in Fig. 3B). For our research, we believe that increasing the sample size will be the main solution to overfitting. We have tested the out-of-group samples according to your suggestions and still obtain a high accuracy (Fig. 3B). Therefore, we believe that using larger sample size in future research will greatly decrease the risk of overfitting.

Discussion

In this retrospective study, we established a continuum prediction model for surgical outcomes based on nine factors. The nomogram prediction model has good quantitative indicators and is suitable for prognosis and evaluation of clinical outcomes (Wei et al., 2017). To the best of our knowledge, this study is the first to apply a nomogram model to the evaluation of the efficacy of endoscopic NSD correction. A previous study validated the usefulness of the SNOT-22 score in patients with nasal septum deviation, but did not construct a predictive model based on the SNOT-22 score (Pannu, Chadha & Kaur, 2009). Early identification of patients with nasal septum deviation who may benefit from surgery will improve the management and optimal use of medical resources, and provide reasonable prediction of the surgical effects by doctors and patients. This will improve the doctor-patient relationship.

We created and validated a new tool for predicting the efficacy of endoscopic NSD correction. The tool uses nine readily available variables and has a high predictive accuracy. The incorporation of various characteristic factors into an easy-to-use nomogram facilitates individualized evaluation of the efficacy of endoscopic NSD correction. Our internal sampling showed the model’s strong predictive ability. In addition, the high c-index and AUC index indicate that this predictive model can be widely and accurately used for the evaluation of therapeutic effect of nasal endoscopic deviation (Wei et al., 2017).

Studies that compare nasal patency before and after surgery (Cantone et al., 2018; Hsu et al., 2017) have been reported to have a higher risk of bias since treatment may improve the quality of life of patients, and be reflected in the SNOT-22 score. Therefore, in this study we selected SNOT-22 score change as the primary outcome since it included quality of life for both general and specific diseases (Van Egmond et al., 2019). Moreover, assessment of postoperative quality of life in patients facilitates interdisciplinary research, and makes it possible to compare the depth of nasal septum to other interventions.

In this study, 81.9% of the patients attained satisfactory results after NSD surgery. In the risk factor analysis, history of nasal surgery, lower preoperative SNOT-22 score, combined sinusitis, combined with middle turbinate plasty, high BMI, smoking, longer follow-up, and advanced age were revealed to be the poor risk factors for NSD outcomes. Among these, SNOT-22 score, history of nasal surgery, sinusitis and age were the main factors affecting the efficacy of nasal septum surgery.

Our results suggest that preoperative SNOT-22 score is a good predictor of surgical outcomes. By definition, a SNOT-22 score change of 9 points after surgery is considered a good surgical outcome, a change that cannot be attained by patients with a lower preoperative SNOT-22 score. Therefore, the severity of a patient’s condition has a bearing on the SNOT-22 score change, with more severe patients likely to record improvement in the quality of life after surgery. In addition, studies have shown that a preoperative SNOT-22 score of ≥ 20 can significantly improve the likelihood of surgical outcomes (Hopkins et al., 2009). The history of nasal surgery is a risk factor for the prognosis of surgical outcomes, since repeated nasal surgery often results into more complex lesions of local tissue, other than just a scar formation. We found that the probability of a nasal valve collapse during revision surgery was higher, and could be a likely cause of refractory nasal congestion (Becker et al., 2008), making efficacy of surgery in such patients worse.

Similar to most studies, we identified sinusitis as one of the risk factors for poor outcome in NSD surgery. NSD patients with CRS have been reported to have poor prognosis, longer treatment cycles, and higher consumption of medical resources (Ahn et al., 2016; Bhattacharyya, 2005). In addition, NSD also acts as a susceptibility factor for sinusitis by reducing nasal mucociliary activity, and inducing chronic inflammation and squamous metaplasia (Ahn et al., 2016; Ji, Fu & Song, 2015; Kamani et al., 2014; Karatas et al., 2015). Therefore, sinusitis treatment is a serious problem in the clinical treatment of NSD. Moreover, studies have shown that in patients with refractory sinusitis, no significant difference is observed between the efficacy of nasal septoplasty and conservative treatment methods (Rudmik et al., 2011). Our predictive model revealed that the middle turbinate plasty could affect the outcome of surgical treatment, making it a potential independent risk factor for sinusitis (Javadrashid et al., 2014). Concha bullosa, which is the aberrant pneumatization of the middle turbinate, affects the shape of the nasal septum leading to the occurrence of NSD (Lee et al., 2008). Incidence of Concha bullosa in patients with NSD has been shown to be higher in previous studies (Yazici, 2019). Patients with both nasal septum and Concha bullosa often display more severe histomorphological variation (Tomblinson et al., 2016). Subjective scoring for improvement in nasal symptoms and changes in short-term quality of life are often influenced by surgical comfort effects, hence should be performed early in the postoperative period. In our findings, we observed that patients with nasal septum surgery became less aggressive with prolonged postoperative time (Bitzer, 2004; Bitzer, Dorning & Schwartz, 1996; Jessen, Ivarsson & Malm, 1989; Konstantinidis et al., 2005).

In addition, over time, uncontrolled chronic sinusitis or other nasal structural lesions could affect the patient’s subjective perception of nasal septoplasty, leading to a bias surgical evaluation (Jessen, Ivarsson & Malm, 1989; Stewart, Robinson & Wilson, 1996). Notably, the prognosis of smokers deteriorated faster than non-smokers. Studies have shown that after nasal surgery, smokers record longer recovery time due to slow wound healing process (Cetiner, Cavusoglu & Duzer, 2017). In addition, smoking also leads to a decrease in nasal mucociliary clearance (MCC) time (Karaman & Tek, 2009). This indicates that smoking could be a potential risk factor for poor prognosis in NSD surgery.

Recent studies have reported that among children and adolescents with severe symptoms, nasal septoplasty is a safe and effective procedure (Fuller, Levesque & Lindsay, 2018). However, patient satisfaction significantly declined with increase in age (Habesoglu et al., 2015). In addition, our multivariate analysis model identified advanced age as a risk factor for poor outcome after NSD (Ahn et al., 2016). Notably, the improvement of airway ventilation after NSD was more significant in young people, with the older patients not benefiting significantly after surgery (Ratajczak et al., 2009). Patients with NSD have poor nasal ventilation (Glotov et al., 2017), and obesity can aggravate the condition (Ertugay et al., 2015). Reports have shown that patients with a history of seasonal allergies in the nasal cavity have a better prognosis. This could be attributed to the poor adaptation of the nasal microenvironment to environmental changes in patients with severe NSD. However, the situation has been shown to be reversible though NSD surgery.

When considering surgery, clinical benefits, morbidity and associated complications, should be taken into consideration by patients and physicians. Therefore, an accurate prognosis would enhance doctors’ assessment for the likelihood of patient’s benefit and pre-operative communication with patients. In addition, this minimizes wastage of the costly medical resources. Therefore, in this study, we have developed an effective predictive model for the efficacy of NSD, which provides further theoretical guidance for clinical surgical treatment, and research of NSD. Despite the efficiency of our model, accurate determination of patient’s benefit after surgery requires reasonable evaluation, and targeted interventions aimed at improving the postoperative efficacy of patients with NSD. To eliminate bias that could arise from differences in surgical proficiency between surgeons, all the operations were performed by an experienced surgeon. However, our study had the following limitations. First, the proportion of females in our sample was lower than males due to the higher risk of NSD in the latter. In addition, this study only focused on patients from China therefore does not represent patients with NSD in other countries and regions. Second, not all factors affecting the efficacy of endoscopic nasal septoplasty were included in the risk factor analysis. It is challenging to incorporate all objective factors in the statistical analysis. This is one of the dilemmas inherent in machine learning. We have attempted to obtain all the characteristic factors that we could reasonably access. Our results show that the factors obtained are representative, and they can effectively describe the prognosis of patients. Third, although our nomogram prediction model exhibited good prediction accuracy, its further optimization is required using more data. And SNOT-22 version is not yet fully content valid for NSD patients because other nasal symptoms common to NSD patients, such as epistaxis, which we added into our questionnaire, are not included in the current SNOT-22 architecture. Therefore, previous study suggests that further studies be performed to improve the SNOT score (Leong & Webb, 2018).

Conclusions

In this study, we developed a robust predictive model that can be used by clinicians for the efficacy prediction of endoscopic NSD correction. Our model incorporates the history of nasal surgery, lower preoperative SNOT-22 score, combined sinusitis, combined with middle turbinate plasty, high BMI, smoking, longer follow-up, and advanced risk factors. We demonstrate that the use of this model for the prediction of the therapeutic effect of nasal endoscopic deviation, is effective and economical. In addition, ability of the clinicians to estimate individual risk ensures better communication with patients regarding the best treatment option. However, further research should be conducted to confirm the efficiency of this nomogram prediction model in predicting the efficacy and clinical utility of NSD correction.

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