Blood eosinophil count combined with asthma history could predict chronic rhinosinusitis with nasal polyp recurrence

Abstract

Background

The use of non-invasive clinical markers for predicting CRS recurrence is still not well investigated.

Objective

The aim of this study was to investigate the comprehensive effects of non-invasive clinical markers on the recurrence of CRS with nasal polyps (CRSwNP).

Materials and methods

A total of 346 consecutive CRSwNP patients undergoing endoscopic functional sinus surgery were recruited. The demographic characteristics and clinical parameters were recorded. Machine learning algorithm were used for evaluating the predictive value of asthma history and blood eosinophils percentage.

Results

Finally, 313/346 patients completed the study. The average follow-up time was 24 months after the first surgery. For the CRSwNP with asthma patients, the blood eosinophils percentage cut-off value was 3.7%. However, for the CRSwNP without asthma patients, the blood eosinophils percentage cut-off value was high, at 6.9%.

Conclusion

Combined asthma history and blood eosinophils percentage can predict CRSwNP recurrence, while asthma history can reduce the threshold of blood eosinophils percentage to predict CRSwNP recurrence.

Significance

For the CRS patients, combined asthma history and blood eosinophils percentage can predict recurrence, while asthma history can reduce the threshold of blood eosinophils percentage to predict recurrence.

Chinese abstract

背景：关于使用非侵入性临床标志物预测CRS复发的研究仍不太理想。

目的：研究鼻息肉CRS（CRSwNP）复发的非侵入性临床标志物的综合效果。

材料和方法：总共招募了346例接受内镜功能性鼻窦手术的连续CRSwNP患者。记录了人口统计学特征和临床参数。使用机器学习算法评估哮喘病史和血液嗜酸性粒细胞百分比的预测价值。

结果：最终, 有313位患者完成了研究。第一次手术后平均随访时间为24个月。对于患有哮喘的CRSwNP患者, 血液嗜酸性粒细胞百分率临界值是3.7％。但是, 对于没有哮喘的CRSwNP患者, 血液嗜酸性粒细胞百分比临界值很高, 为6.9％。

结论：结合哮喘病史和血液嗜酸性粒细胞百分比可以预测CRSwNP的复发, 而哮喘病史可以降低血液嗜酸性粒细胞百分比阈值来预测CRSwNP复发。

意义：对于CRS患者, 哮喘病史和血液嗜酸性粒细胞百分比的综合可以预测复发, 而哮喘病史可将血嗜酸性粒细胞百分比阈值降低来预测复发。

Keywords:

• Asthma
• blood eosinophils percentage
• chronic rhinosinusitis with nasal polyps
• endoscopic sinus surgery
• machine learning algorithm
• prediction
• recurrence

Acknowledgments

The authors thank Dr. Hui Chen for the help of data statistic.

Disclosure statement

The authors declare that they have no financial and personal relationships with other people or organizations that can inappropriately influence their work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of the manuscript entitled.

Additional information

Funding

This work was supported by grants from the National Natural Science Foundation of China (81900916), Beijing Nova Program (Z201100006820043), Beijing Municipal Administration of Hospitals’ Youth Programme (QML20190208), the Priming Scientific Research Foundation for the Senior Researcher in Beijing TongRen Hospital, Capital Medical University (2017-YJJ-GGL-005), National Key R&D Program of China (2018YFC0116800), the program for Changjiang Scholars and Innovative Research Team (IRT13082), Beijing municipal administration of hospitals’ mission plan (SML20150203), Beijing Municipal Administration of hospitals’ Dengfeng plan (DFL20190202), Beijing Municipal Administration of hospitals clinical medicine development of special funding support (XMLX201816), CAMS Innovation Fund for Medical Sciences (2019-I2M-5-022).