Assessing Cut-off Points of Eosinophils, Nasal Polyp, and Lund-Mackay Scores to Predict Surgery in Nasal Polyposis: A Real-World Study

Virkkula, Paula; Penttilä, Elina; Vento, Seija; Myller, Jyri; Koskinen, Anni; Hammarén‐Malmi, Sari; Laulajainen‐Hongisto, Anu; Hytönen, Maija; Lilja, Markus; Numminen, Jura; Sillanpää, Saara; Sahlman, Johanna; Toppila‐Salmi, Sanna

doi:10.1177/2152656720956596

Cited by 7 publications

(9 citation statements)

References 48 publications

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“…Additionally, prior study showed that the cut-off value of blood eosinophil count (>0.26 Â 10 9 /L) might have relatively good, predictive potential in patients with nasal polyps who require surgical treatment. 28 In the present study, we further found that there was no significant difference in the rate of uncontrolled subjects between IH-tissue-eos and H-tissue-eos subgroups, whereas the IHblood-eos patients had higher rates of uncontrolled subjects and asthma and CT scores than the H-blood-eos patients (p = .09, Table 2). These results imply that blood eosinophils might possibly have a larger influence on treatment outcomes and disease severity than tissue eosinophils.…”

Section: Discussionsupporting

confidence: 56%

“…Recently, our studies revealed that the blood eosinophil count and the tissue eosinophil percentage are independently associated with an increased risk for poor disease control in CRSwNP patients receiving current standard‐of‐care therapy after adjustments for covariates previously shown to relate to poor treatment outcome. Additionally, prior study showed that the cut‐off value of blood eosinophil count (>0.26 × 10 9 /L) might have relatively good, predictive potential in patients with nasal polyps who require surgical treatment 28 . In the present study, we further found that there was no significant difference in the rate of uncontrolled subjects between IH‐tissue‐eos and H‐tissue‐eos subgroups, whereas the IH‐blood‐eos patients had higher rates of uncontrolled subjects and asthma and CT scores than the H‐blood‐eos patients ( p = .09, Table 2).…”

Section: Discussionmentioning

confidence: 99%

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Clinical characteristics of patients with CRSwNP with intensely high eosinophil level

Shi

Wang

et al. 2022

Laryngoscope Investig Oto

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Background High tissue and blood eosinophils are linked to poor treatment outcome in patients with chronic rhinosinusitis with nasal polyps (CRSwNP). However, the difference between the patients with different level of blood or tissue eosinophilia in relation to disease control is still lacking. Objectives This study aimed to characterize patients with CRSwNP with intensely high eosinophil level. Methods A retrospective study of CRSwNP patients underwent endoscopic sinus surgery was conducted. The patients were subdivided with various cut‐off values of blood and tissue eosinophils. Tissue eosinophils of ≥50% and 10%–20% were defined as intensely high tissue eosinophils (IH‐tissue‐eos) and high tissue eosinophils (H‐tissue‐eos), respectively. Blood eosinophils of ≥0.6 × 109/L and 0.2 × 109/L–0.3 × 109/L were defined as intensely high blood eosinophils (IH‐blood‐eos) and high blood eosinophils (H‐blood‐eos). Clinical characteristics, Lund‐Mackay score (LMS), and disease control status were compared between subgroups. Results A total of 302 patients were enrolled. There was a trend toward a higher rate of uncontrolled disease when the patients subdivided by blood eosinophil cut‐off values ranged from 0.1 × 109/L to 0.6 × 109/L. The controlled patients accounted for 27%, 36%, 22%, and 31%, and the uncontrolled patients 46%, 32%, 52%, and 31% of patients in IH‐tissue‐eos, H‐tissue‐eos, IH‐blood‐eos, and H‐blood‐eos subgroup, respectively. The percentages of patients in the three categories of control were not significantly different between IH‐tissue‐eos and H‐tissue‐eos subgroups. By contrast, the IH‐blood‐eos subgroup had higher percentages of current smoker and asthma, preoperative LMS and tissue eosinophil percentage, and showed a trend toward a higher rate of uncontrolled subjects compare with the H‐blood‐eos subgroup. Conclusion CRSwNP patients with IH‐blood‐eos (≥0.6 × 109/L) may be possibly associated with a poorer disease control than those with levels near the threshold of blood eosinophilia (0.2 × 109/L–0.3 × 109/L). Further larger, preferably prospective studies are needed to confirm this relationship. Level of Evidence: 4.

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Section: Discussionsupporting

confidence: 56%

Section: Discussionmentioning

confidence: 99%

Clinical characteristics of patients with CRSwNP with intensely high eosinophil level

Shi

Wang

et al. 2022

Laryngoscope Investig Oto

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“…The current study showed that CRSwNP, asthma, and NERD are important predictors of revision ESS also at the individual level. In accordance to this, previous studies have demonstrated on hospital population level that several factors are associated with the CRS recurrence and/or revision ESS, such as CRSwNP, asthma, AR, NERD, eosinophilia and smoking 1,7,23,24 . CRSwNP patients with co-morbid asthma and/or NERD have an increased risk for recurrence and revision ESS, although these patients seem benefit from initial ESS 13,19,[25][26][27] .…”

Section: Discussionsupporting

confidence: 77%

Using machine learning for personalized prediction of revision paranasal sinus surgery

Nuutinen

Haukka

Virkkula

et al. 2021

Preprint

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Background: Uncontrolled chronic rhinosinusitis (CRS) needing consideration of surgery is a growing health problem yet its risk factors at individual level are not known. Our aim was to examine risk factors of revision endoscopic sinus surgery (ESS) at the individual level by using artificial intelligence. Methods: Demographic and visit variables were collected from electronic health records (EHR) of 790 operated CRS patients. The effect of variables on the prediction accuracy of revision ESS was examined at the individual level via machine learning models. Results: Revision ESS was performed to 114 (14.7%) CRS patients. The logistic regression, gradient boosting and random forest classifiers had similar performance (AUC values .746, .745 and .747, respectively) for predicting revision ESS. The best performance was yielded by using logistic regression and long predictor data retrieval time (AUC .809, precision 36%, sensitivity 70%) as compared with data collection time from baseline visit until 0, 3 and 6 months after the baseline ESS (AUC values .668, .717 and .746, respectively). The number of visits, number of days from the baseline visit to the baseline ESS, age, CRS with nasal polyps (CRSwNP), asthma, NERD and immunodeficiency or its suspicion were associated with revision ESS. Age and the number of visits before baseline ESS had non-linear effects for the predictions. Conclusions: Intelligent data analysis found important predictors of revision ESS at the individual level, such as visit frequency, age, Type 2 high diseases and immunodeficiency or its suspicion.

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“…Our study also showed that CRSwNP, asthma and NERD represent important predictors of revision ESS at the individual level. In accordance with this, previous studies demonstrated at the hospital population level that several factors associate with CRS recurrence and/or revision ESS, including CRSwNP, asthma, allergic rhinitis, NERD, eosinophilia and smoking [1,13,57,58]. CRSwNP patients with a comorbidity of asthma and/or NERD carry an increased risk for recurrence and revision ESS, although these patients appear to benefit from an initial ESS [19,41,[59][60][61].…”

Section: Plos Onesupporting

confidence: 70%

Using machine learning for the personalised prediction of revision endoscopic sinus surgery

et al. 2022

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Background Revision endoscopic sinus surgery (ESS) is often considered for chronic rhinosinusitis (CRS) if maximal conservative treatment and baseline ESS prove insufficient. Emerging research outlines the risk factors of revision ESS. However, accurately predicting revision ESS at the individual level remains uncertain. This study aims to examine the prediction accuracy of revision ESS and to identify the effects of risk factors at the individual level. Methods We collected demographic and clinical variables from the electronic health records of 767 surgical CRS patients ≥16 years of age. Revision ESS was performed on 111 (14.5%) patients. The prediction accuracy of revision ESS was examined by training and validating different machine learning models, while the effects of variables were analysed using the Shapley values and partial dependence plots. Results The logistic regression, gradient boosting and random forest classifiers performed similarly in predicting revision ESS. Area under the receiving operating characteristic curve (AUROC) values were 0.744, 0.741 and 0.730, respectively, using data collected from the baseline visit until six months after baseline ESS. The length of time during which data were collected improved the prediction performance. For data collection times of 0, 3, 6 and 12 months after baseline ESS, AUROC values for the logistic regression were 0.682, 0.715, 0.744 and 0.784, respectively. The number of visits before or after baseline ESS, the number of days from the baseline visit to the baseline ESS, patient age, CRS with nasal polyps (CRSwNP), asthma, non-steroidal anti-inflammatory drug exacerbated respiratory disease and immunodeficiency or suspicion of it all associated with revision ESS. Patient age and number of visits before baseline ESS carried non-linear effects for predictions. Conclusions Intelligent data analysis identified important predictors of revision ESS at the individual level, such as the frequency of clinical visits, patient age, Type 2 high diseases and immunodeficiency or a suspicion of it.

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Assessing Cut-off Points of Eosinophils, Nasal Polyp, and Lund-Mackay Scores to Predict Surgery in Nasal Polyposis: A Real-World Study

Cited by 7 publications

References 48 publications

Clinical characteristics of patients with CRSwNP with intensely high eosinophil level

Clinical characteristics of patients with CRSwNP with intensely high eosinophil level

Using machine learning for personalized prediction of revision paranasal sinus surgery

Using machine learning for the personalised prediction of revision endoscopic sinus surgery

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