Predicting patient-reported symptom clusters in lung cancer patients: a machine learning approach
Rammant, E. Deman, E. Poppe, L. Billiet, C. Lambrecht, M. Bultijnck, R. Van Hecke, A. Azria, D. Chang-Claude, J. Choudhury, Ananya
Rammant, E.
Deman, E.
Poppe, L.
Billiet, C.
Lambrecht, M.
Bultijnck, R.
Van Hecke, A.
Azria, D.
Chang-Claude, J.
Choudhury, Ananya
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Abstract
Purpose or Objective
Lung cancer is one of the most common cancer types in the world, with patients suffering from multiple co-occurring
symptoms: i.e. ‘symptom clusters (SC)’. Identifying SC is important to anticipate on other symptoms within a cluster and
to uncover possibly overlooked symptoms. Also, supportive care interventions should aim to target multiple symptoms within a SC by addressing 1 or 2 symptoms and therefore alleviating the severity of other symptoms within that SC. This
way, greater gains in a patients’ health-related quality of life (HRQoL) can be achieved and patient care can be simplified.
The aims of this study are to identify (1) SC and their change over time in lung cancer patients undergoing radiotherapy
(RT), (2) SC with the greatest impact on HRQoL, and (3) demographical, clinical and/or treatment-related predictors of SC.
Materials and Methods
Data were used from the REQUITE study: an international prospective cohort study including lung cancer patients receiving
RT from 26 different hospitals and 8 countries. SC were identified based on patient-reported outcomes collected before
RT(T1), at month 3(T2), and month 6(T3) after RT with the EORTC QLQ-C3O and the lung symptom questionnaire. A
combination of the following machine learning techniques were used to identify symptom clusters at different time-points,
to investigate the impact of the SC on HRQoL and to predict the SC, respectively: hierarchical agglomerative clustering,
linear regression and random forest regression. To guarantee external validity of the prediction model, a first part of the
data set was used to develop the prediction model, and a second part to validate the prediction model for unseen data.
Results
Data from 418, 341, and 299 lung cancer patients were analysed at T1, T2, and T3, respectively. Three SC were identified
and remained stable over time: cluster 1 (fatigue, dyspnoea, physical and role functioning), cluster 2 (coughing blood,
swallowing problems, nausea and diarrhoea), and cluster 3 (social, emotional and cognitive functioning). On T1 and T2, a
4th cluster was identified (general pain, chest pain and coughing). Cluster 1 was most common across all time points,
followed by clusters 3, 4 and 2. At T1, cluster 3 had the greatest impact on overall HRQoL (34% explained variance) while
cluster 1 had the greatest impact at T2 (39%) and T3 (50%). Two symptoms within cluster 1 (dyspnoea and physical
functioning) could be moderately predicted at T2 with age and RT parameters (i.e. planned target volume, max. dose
oesophagus and dose per fraction) being the greatest predictors.
Conclusion
Supportive care interventions for lung cancer patients undergoing RT must tackle 1 or 2 symptoms of the ‘fatigue, dyspnoea,
physical and role functioning’ cluster because this SC is most common across time-points and has the greatest impact on
the patients’ HRQoL. Furthermore, age and RT parameters should be taken into account to further tailor future
interventions in lung cancer patients.
Description
Date
2022
Publisher
Collections
Keywords
Type
Meetings and Proceedings
Citation
Rammant E, Deman E, Poppe L, Billiet C, Lambrecht M, Bultijnck R, et al. Predicting patient-reported symptom clusters in lung cancer patients: a machine learning approach. Radiotherapy and Oncology. 2022 May;170:S111-S2. PubMed PMID: WOS:000806759200110.