Monoclonal Antibodies and Airway Diseases

Lyly, Annina; Laulajainen‐Hongisto, Anu; Gevaert, Philippe; Kauppi, Paula; Toppila‐Salmi, Sanna

doi:10.3390/ijms21249477

Cited by 14 publications

(12 citation statements)

References 164 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Only two were undergoing biological anti-immunoglobulin-E (IgE) treatment for asthma, and none were undergoing biological treatment for nasal polyposis, despite recurrent courses of corticosteroids and numerous surgical procedures for nasal polyp control. Biological monoclonal antibody medication for severe nasal polyposis is a relatively recent therapeutic option ( 23 ), and the timing of the data collection is likely to have played a part in the scarce usage of biological medication in our cohort.…”

Section: Discussionmentioning

confidence: 99%

Patient-Reported Control of Asthma, Nasal Polyposis, and Middle-Ear Symptoms in NSAID-Exacerbated Respiratory Disease

et al. 2021

Self Cite

View full text Add to dashboard Cite

Non-steroidal anti-inflammatory drug (NSAID)—exacerbated respiratory disease (NERD) is an adult-onset inflammatory condition of the upper and lower airways. It is characterized by the co-existence of asthma, nasal polyposis, and hypersensitivity to NSAIDs. Over one-fourth of patients also have symptoms of chronic middle-ear infection. The clinical course of NERD is often severe and generally requires multimodal treatment with recurrent surgical measures. Studies presenting the disease burden and subjective symptom control of NERD are limited. In this qualitative questionnaire study, we present the clinical characteristics of asthma, nasal polyposis, NSAID intolerance and possible recurrent or chronic middle-ear infection of 66 confirmed NERD patients treated at our tertiary referral center between January 2016 and May 2017. Additionally, we present the patient-reported disease control of asthma, nasal polyposis, and middle-ear symptoms on a four-category Likert scale. The proportion of NERD patients with recurrent or chronic middle-ear infection was 18%. The proportion of good or very good subjective disease control was 83% for asthma, 58% for nasal polyposis, and 33% for chronic middle-ear infection, if present. Chronic middle-ear infection is common among NERD patients and should more often be recognized as part of the entity. Together with nasal polyposis, chronic middle-ear infection seems to affect patients more than asthma. The patient's perspective of disease control should be considered when planning the interdisciplinary follow-up and treatment of NERD.

show abstract

Section: Discussionmentioning

confidence: 99%

Patient-Reported Control of Asthma, Nasal Polyposis, and Middle-Ear Symptoms in NSAID-Exacerbated Respiratory Disease

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In terms of clinical implications, our findings may prove relevant to patient counselling, following up on and planning treatment, such as that of biological therapy [12]. However, validation studies for these results remain necessary.…”

Section: Plos Onementioning

confidence: 74%

“…The success rates for initial ESS range from 76% to 98% [8,9]. The early identification of CRS recurrence risk following ESS is cost-effective [10,11], helping to correctly target treatment [12] and prevent permanent tissue changes [1].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Public medical care covers over 90% of our operations 56 thus minimizing possibility of bias due to loss of follow up, yet we acknowledge that some individual patients with recurrence may have sought treatment elsewhere. Despite these limitations, we found that intelligent data analysis is feasible to obtain individual probability of revision ESS, and thus could help in informing discussions and decision making of advanced therapy, such as biologicals 57 .…”

Section: Discussionmentioning

confidence: 99%

Using machine learning for personalized prediction of revision paranasal sinus surgery

Nuutinen

Haukka

Virkkula

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Monoclonal Antibodies and Airway Diseases

Cited by 14 publications

References 164 publications

Patient-Reported Control of Asthma, Nasal Polyposis, and Middle-Ear Symptoms in NSAID-Exacerbated Respiratory Disease

Patient-Reported Control of Asthma, Nasal Polyposis, and Middle-Ear Symptoms in NSAID-Exacerbated Respiratory Disease

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

Using machine learning for personalized prediction of revision paranasal sinus surgery

Contact Info

Product

Resources

About