scholarly journals Patient-reported outcomes for monitoring symptomatic toxicities in cancer patients treated with immune-checkpoint inhibitors: A Delphi study

2021 ◽  
Vol 157 ◽  
pp. 225-237
Author(s):  
André Manuel Da Silva Lopes ◽  
Sara Colomer-Lahiguera ◽  
Nuria Mederos Alfonso ◽  
Veronica Aedo-Lopez ◽  
Gilliosa Spurrier-Bernard ◽  
...  
2021 ◽  
Vol 32 ◽  
pp. S1180
Author(s):  
A.M.D.S. Lopes ◽  
S. Colomer-Lahiguera ◽  
N-N. Mederos-Alfonso ◽  
V. Aedo Lopez ◽  
G. Spurrier-Bernard ◽  
...  

2021 ◽  
Vol 4 (8) ◽  
pp. e2122998
Author(s):  
Pavlos Msaouel ◽  
Clara Oromendia ◽  
Arlene O. Siefker-Radtke ◽  
Nizar M. Tannir ◽  
Sumit K. Subudhi ◽  
...  

Author(s):  
Brian D Gonzalez ◽  
Sarah L Eisel ◽  
Kristina E Bowles ◽  
Aasha I Hoogland ◽  
Brian W James ◽  
...  

Abstract Background Trials of immune checkpoint inhibitors (ICIs) have published patient-reported quality of life (QOL), but the size and heterogeneity of this literature can make patient education difficult. This meta-analysis aimed to describe change in QOL and symptomatology in patients receiving ICIs for cancer. Methods Following PRISMA guidelines, databases were searched through November 2019 for articles or abstracts of prospective, original studies reporting longitudinal QOL in adult cancer patients treated with ICIs. The prespecified primary outcomes were change in global QOL among patients treated with ICIs and difference in change since baseline in global QOL between patients treated with ICI vs. non-ICI active treatment. Secondary outcomes included physical functioning and symptomatology. All statistical tests were 2-sided. Results Twenty-six of 20,323 publications met inclusion criteria. Global QOL did not change over time in patients treated with ICIs (k = 26, n = 6,974, P = .19). Larger improvements in global QOL was observed in patients receiving ICI vs. non-ICI regimens (k = 16, ICI n = 3,588, non-ICI n = 2,948, P < .001). Physical functioning did not change in patients treated with ICIs (k = 14, n = 3,169, P=.47); there were no differences in mean change between ICI vs. non-ICI regimens (k = 11, n = 4,630, P=.94. Regarding symptoms, appetite loss, insomnia, and pain severity decreased but dyspnea severity increased in patients treated with ICIs (k = 14, n = 3,243–3,499) (Ps < 0.001). Insomnia severity was higher in patients treated with ICIs than non-ICI regimens (k = 11, n = 4,791) (P < .001). Conclusions This study is among the first to quantitatively summarize QOL in patients treated with ICIs. Findings suggest ICI recipients report no change in global QOL and higher QOL than patients treated with non-ICI regimens.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sanna Iivanainen ◽  
Jussi Ekstrom ◽  
Henri Virtanen ◽  
Vesa V. Kataja ◽  
Jussi P. Koivunen

Abstract Background Immune-checkpoint inhibitors (ICIs) have introduced novel immune-related adverse events (irAEs), arising from various organ systems without strong timely dependency on therapy dosing. Early detection of irAEs could result in improved toxicity profile and quality of life. Symptom data collected by electronic (e) patient-reported outcomes (PRO) could be used as an input for machine learning (ML) based prediction models for the early detection of irAEs. Methods The utilized dataset consisted of two data sources. The first dataset consisted of 820 completed symptom questionnaires from 34 ICI treated advanced cancer patients, including 18 monitored symptoms collected using the Kaiku Health digital platform. The second dataset included prospectively collected irAE data, Common Terminology Criteria for Adverse Events (CTCAE) class, and the severity of 26 irAEs. The ML models were built using extreme gradient boosting algorithms. The first model was trained to detect the presence and the second the onset of irAEs. Results The model trained to predict the presence of irAEs had an excellent performance based on four metrics: accuracy score 0.97, Area Under the Curve (AUC) value 0.99, F1-score 0.94 and Matthew’s correlation coefficient (MCC) 0.92. The prediction of the irAE onset was more difficult with accuracy score 0.96, AUC value 0.93, F1-score 0.66 and MCC 0.64 but the model performance was still at a good level. Conclusion The current study suggests that ML based prediction models, using ePRO data as an input, can predict the presence and onset of irAEs with a high accuracy, indicating that ePRO follow-up with ML algorithms could facilitate the detection of irAEs in ICI-treated cancer patients. The results should be validated with a larger dataset. Trial registration Clinical Trials Register (NCT3928938), registration date the 26th of April, 2019


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