Predicting acute odynophagia during lung cancer radiotherapy using observations derived from patient-centred nursing care
Open accessAbstract
During radiotherapy, lung cancer patients commonly experience pain while swallowing (odynophagia) of food and drink. Observations from patient-centred nursing practice have been used to generate predictive models for odynophagia needing prescription pain medication during external beam lung radiotherapy for non-small cell and small-cell lung cancer. Three multivariate logistic models were evaluated in repeat cross-validation: a manual-stepwise model and two supervised machine learning models. Overall predictive performance was good. Correct classification rates ranged from 0.82 to 0.84, and areas under the receiver operator curve ranged from 0.83 to 0.85. Model sensitivity (range: 0.92-0.97) was higher than model specificity (range: 0.58-0.63). Further validation of the models in clinical context is required. A predictive model for pain medication for odynophagia prior to commencement of radiotherapy would support Radiotherapy Technologists Nurses (RTNs) in directing nursing interventions towards patients at risk.
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One study noted a concern about the impact of AI-based digital tools on the workflow of clinicians.
“One study also noted a concern about the impact of AI-based digital tools on the workflow of clinicians (Olling et al., 2018).”
From The application and use of artificial intelligence in cancer nursing: A systematic review (2024) · cites this paper as Olling et al., 2018
In studies where nurses did not lead, nurses were actively involved by identifying eligible cancer patients, undertaking clinical assessments or interviews, or analysing oncological patient data to identify clinical issues to compare to the outputs of a predictive model.
“In these studies, nurses were actively involved by identifying eligible cancer patients for a study, undertaking clinical assessments or interviews as part of the data collection process, or analysing oncological patient data to identify clinical issues to compare to the outputs of a predictive model (Olling et al., 2018;Chiesa et al., 2021;Günther et al., 2022).”
From The application and use of artificial intelligence in cancer nursing: A systematic review (2024) · cites this paper as Olling et al., 2018
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- Authors
- Olling, K., Nyeng, D. W., & Wee, L.
- Affiliations
- Vejle Sygehus (DK)
- Corresponding author
- Olling, Karina
- Venue
- Technical Innovations & Patient Support in Radiation Oncology
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 5
- Pages
- 16-20
- ISSN
- 2405-6324 (ISSN-L)
- PMC
- PMC7033763
- OpenAlex
- W2790631721
- Cited by
- 24
- References
- 17
- Citations by year
- 2025: 3 · 2024: 9 · 2023: 2 · 2022: 2 · 2021: 3 · 2020: 2 · 2019: 2 · 2018: 1
- FWCI
- 3.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Technical Innovations & Patient Support in Radiation Oncology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Radiotherapy Research Team (Vejle Hospital, Denmark)
- Topics
- Health Sciences › Health Professions › Speech and Hearing › Dysphagia Assessment and Management
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Health Sciences › Medicine › Otorhinolaryngology › Head and Neck Cancer Studies
- Keywords
- odynophagia, dysphagia, lung cancer, radiotherapy complications, analgesics, prediction model, non-small cell lung cancer, small cell lung cancer, external beam radiotherapy, nursing interventions
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