A diagnostic model for predicting type 2 nasal polyps using biomarkers in nasal secretion
BackgroundPredicting type 2 chronic rhinosinusitis with nasal polyps (CRSwNP) may help for selection of appropriate surgical procedures or pharmacotherapies in advance. However, an accurate non-invasive method for diagnosis of type 2 CRSwNP is presently unavailable.MethodsTo optimize the technique f...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.1054201/full |
| _version_ | 1811195463256244224 |
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| author | Zaichuan Wang Zaichuan Wang Qiqi Wang Su Duan Su Duan Yuling Zhang Yuling Zhang Limin Zhao Limin Zhao Shujian Zhang Shujian Zhang Liusiqi Hao Liusiqi Hao Yan Li Xiangdong Wang Xiangdong Wang Chenshuo Wang Chenshuo Wang Nan Zhang Claus Bachert Luo Zhang Luo Zhang Luo Zhang Feng Lan |
| author_facet | Zaichuan Wang Zaichuan Wang Qiqi Wang Su Duan Su Duan Yuling Zhang Yuling Zhang Limin Zhao Limin Zhao Shujian Zhang Shujian Zhang Liusiqi Hao Liusiqi Hao Yan Li Xiangdong Wang Xiangdong Wang Chenshuo Wang Chenshuo Wang Nan Zhang Claus Bachert Luo Zhang Luo Zhang Luo Zhang Feng Lan |
| author_sort | Zaichuan Wang |
| collection | DOAJ |
| description | BackgroundPredicting type 2 chronic rhinosinusitis with nasal polyps (CRSwNP) may help for selection of appropriate surgical procedures or pharmacotherapies in advance. However, an accurate non-invasive method for diagnosis of type 2 CRSwNP is presently unavailable.MethodsTo optimize the technique for collecting nasal secretion (NasSec), 89 CRSwNP patients were tested using nasal packs made with four types of materials. Further, Th2low and Th2highCRSwNP defined by clustering analysis in another 142 CRSwNP patients using tissue biomarkers, in the meanwhile, inflammatory biomarkers were detected in NasSec of the same patients collected by the selected nasal pack. A diagnostic model was established by machine learning algorithms to predict Th2highCRSwNP using NasSecs biomarkers.ResultsConsidering the area under receiver operating characteristic curve (AUC) for IL-5 in NasSec, nasal pack in polyvinyl alcohol (PVA) was superior to other materials for NasSec collection. When Th2low and Th2highCRSwNP clusters were defined, logistic regression and decision tree model for prediction of Th2highCRSwNP demonstrated high AUCs values of 0.92 and 0.90 respectively using biomarkers of NasSecs. Consequently, the pre-pruned decision tree model; based on the levels of IL-5 in NasSec (≤ 15.04 pg/mL), blood eosinophil count (≤ 0.475*109/L) and absence of comorbid asthma; was chosen to define Th2lowCRSwNP from Th2highCRSwNP for routine clinical use.ConclusionsTaken together, a decision tree model based on a combination of NasSec biomarkers and clinical features can accurately define type 2 CRSwNP patients and therefore may be of benefit to patients in receiving appropriate therapies in daily clinical practice. |
| first_indexed | 2024-04-12T00:43:45Z |
| format | Article |
| id | doaj.art-6c374fe5a8e5442389dbe7861c0065bb |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-12T00:43:45Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj.art-6c374fe5a8e5442389dbe7861c0065bb2022-12-22T03:54:56ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-12-011310.3389/fimmu.2022.10542011054201A diagnostic model for predicting type 2 nasal polyps using biomarkers in nasal secretionZaichuan Wang0Zaichuan Wang1Qiqi Wang2Su Duan3Su Duan4Yuling Zhang5Yuling Zhang6Limin Zhao7Limin Zhao8Shujian Zhang9Shujian Zhang10Liusiqi Hao11Liusiqi Hao12Yan Li13Xiangdong Wang14Xiangdong Wang15Chenshuo Wang16Chenshuo Wang17Nan Zhang18Claus Bachert19Luo Zhang20Luo Zhang21Luo Zhang22Feng Lan23Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, ChinaDepartment of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, ChinaBeijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, ChinaDepartment of Allergy, Beijing TongRen Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, ChinaDepartment of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, ChinaDepartment of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, ChinaDepartment of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, ChinaDepartment of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, ChinaBeijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, ChinaDepartment of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, ChinaDepartment of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, ChinaUpper Airways Research Laboratory, Department of Otorhinolaryngology, Ghent University, Ghent, BelgiumUpper Airways Research Laboratory, Department of Otorhinolaryngology, Ghent University, Ghent, BelgiumBeijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, ChinaDepartment of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, ChinaDepartment of Allergy, Beijing TongRen Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, ChinaBackgroundPredicting type 2 chronic rhinosinusitis with nasal polyps (CRSwNP) may help for selection of appropriate surgical procedures or pharmacotherapies in advance. However, an accurate non-invasive method for diagnosis of type 2 CRSwNP is presently unavailable.MethodsTo optimize the technique for collecting nasal secretion (NasSec), 89 CRSwNP patients were tested using nasal packs made with four types of materials. Further, Th2low and Th2highCRSwNP defined by clustering analysis in another 142 CRSwNP patients using tissue biomarkers, in the meanwhile, inflammatory biomarkers were detected in NasSec of the same patients collected by the selected nasal pack. A diagnostic model was established by machine learning algorithms to predict Th2highCRSwNP using NasSecs biomarkers.ResultsConsidering the area under receiver operating characteristic curve (AUC) for IL-5 in NasSec, nasal pack in polyvinyl alcohol (PVA) was superior to other materials for NasSec collection. When Th2low and Th2highCRSwNP clusters were defined, logistic regression and decision tree model for prediction of Th2highCRSwNP demonstrated high AUCs values of 0.92 and 0.90 respectively using biomarkers of NasSecs. Consequently, the pre-pruned decision tree model; based on the levels of IL-5 in NasSec (≤ 15.04 pg/mL), blood eosinophil count (≤ 0.475*109/L) and absence of comorbid asthma; was chosen to define Th2lowCRSwNP from Th2highCRSwNP for routine clinical use.ConclusionsTaken together, a decision tree model based on a combination of NasSec biomarkers and clinical features can accurately define type 2 CRSwNP patients and therefore may be of benefit to patients in receiving appropriate therapies in daily clinical practice.https://www.frontiersin.org/articles/10.3389/fimmu.2022.1054201/fullchronic rhinosinusitis with nasal polypsdiagnostic modelnasal secretionIL-5blood eosinophils |
| spellingShingle | Zaichuan Wang Zaichuan Wang Qiqi Wang Su Duan Su Duan Yuling Zhang Yuling Zhang Limin Zhao Limin Zhao Shujian Zhang Shujian Zhang Liusiqi Hao Liusiqi Hao Yan Li Xiangdong Wang Xiangdong Wang Chenshuo Wang Chenshuo Wang Nan Zhang Claus Bachert Luo Zhang Luo Zhang Luo Zhang Feng Lan A diagnostic model for predicting type 2 nasal polyps using biomarkers in nasal secretion Frontiers in Immunology chronic rhinosinusitis with nasal polyps diagnostic model nasal secretion IL-5 blood eosinophils |
| title | A diagnostic model for predicting type 2 nasal polyps using biomarkers in nasal secretion |
| title_full | A diagnostic model for predicting type 2 nasal polyps using biomarkers in nasal secretion |
| title_fullStr | A diagnostic model for predicting type 2 nasal polyps using biomarkers in nasal secretion |
| title_full_unstemmed | A diagnostic model for predicting type 2 nasal polyps using biomarkers in nasal secretion |
| title_short | A diagnostic model for predicting type 2 nasal polyps using biomarkers in nasal secretion |
| title_sort | diagnostic model for predicting type 2 nasal polyps using biomarkers in nasal secretion |
| topic | chronic rhinosinusitis with nasal polyps diagnostic model nasal secretion IL-5 blood eosinophils |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.1054201/full |
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