Press Release

Results presented at ASCO 2022: patient-reported symptom data can predict antibody levels against SARS-COV-2 on vaccinated cancer patients

Research conducted at the University Hospital Geneva aimed to investigate the potential correlation between symptom burden reported through electronic patient-reported outcomes (ePRO) and the serological response to SARS-CoV-2 vaccines on cancer patients. The promising results are presented at ASCO 2022 this week.

The results show that a machine learning model based on patient-reported symptom data coupled with clinical data could accurately predict the serology response to vaccines, hence foresee the level of protection for patients with cancer against COVID-19. These findings are the first ones to show that symptoms assessed through digital patient monitoring can be used to predict response to vaccines in cancer patients. 

The patients used Kaiku Health digital patient monitoring and management tool to report their symptoms after receiving their vaccinations. The same prediction performance could not be achieved using only clinical data without the patients’ symptom data, making patient-reported outcomes a significant source of data for predicting antibody levels. On a wider scope, this implies that ePRO data could bring an important addition to clinical data when investigating clinical parameters in trials.

The results are also important for decreasing anxiety in patients. 

“The same toxicity-based prediction of efficacy has been identified with immunotherapy in cancer and is now a routine part of clinical discussions with patients. In the same way, these findings could assuage patients’ fears about vaccination related adverse effects, through the knowledge that toxicity could predict a potential better protection against SARS-COV-2.”, says Dr. Alfredo Addeo, Senior Oncologist and an author of the study, from the University Hospital Geneva.

Kaiku Health also collaborated in two other studies accepted to ASCO 2022. A study by Faron Pharmaceuticals investigated the use of machine learning models in predicting treatment benefit for novel cancer drugs, with results implying that such models can predict treatment responses. In a study by Roche, Kaiku Health digital patient monitoring and management platform was used to investigate the real-world implementation and user experience of tailored digital patient monitoring modules for patients with locally advanced or metastatic cancer.


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