Purpose: Electronic (e) patient reported outcomes (PROs) have been shown to improve the quality of life and survival in chemotherapy treated advanced cancer patients. We hypothesized that multidimensional ePRO centered approach could improve symptom management, streamline patient flow, and optimize the use of healthcare resources. Methods: In this multicenter trial (NCT04081558), colorectal cancer (CRC) patients receiving oxaliplatin-based chemotherapy as adjuvant or in the first or second line setting in advanced disease were included in the prospective ePRO cohort while a comparative retrospective cohort was collected from the same institutes. The investigated tool consisted of a weekly e-symptom questionnaire integrated to an urgency algorithm and laboratory value interface, which generated semi-automated decision support for chemotherapy cycle prescription, and individualized symptom management. Results: Recruitment to the ePRO cohort occurred 1/2019-1/2021 (n=43). The comparator group (n=194) consisted of patients treated in the same institutes 1-7/2017. The analysis was limited to adjuvant treated (n=36 and n=35). The feasibility of the ePRO follow-up was good with 98% reporting easy usage and 86% improved care, while health care personnel valued the easy use and logical workflow. In the ePRO cohort, 42% needed a phone call before planned chemotherapy cycles while this was 100% in the retrospective cohort (p=1.4e-8). Peripheral sensory neuropathy was detected significantly earlier with ePRO followed (p=1e-5) but did not translate to earlier dose reduction, delays, or unplanned therapy termination compared to the retrospective cohort. Conclusion: The results suggest that the investigated approach is feasible and streamlines workflow. Earlier symptom detection may improve the quality in cancer care.

Background Chemotherapy cycle prescription is generally carried out through a manual process. ICT tools with data analytics could streamline this process and limit human errors. Methods A one-arm multicenter prospective clinical trial ECHO 7/2019-1/2021 (NCT04081558) investigated the use of a novel Kaiku Health ePRO tool in cancer care. The most important patient inclusion criteria were colorectal cancer (CRC) planned to be treated with oxaliplatin-based chemotherapy as an adjuvant therapy or in the first or second line setting of advanced disease, age ≥18 years, ECOG performance score of 0-2, and internet access. A decision support tool consisting of a digital symptom monitoring, laboratory value interface, and treatment schedule integration for a semi-automatized chemotherapy cycle prescribing was created for the trial. Results The dataset included CRC patients (n=43) treated with doublet or triplet chemotherapy in adjuvant or metastatic setting, and 339 prescribed chemotherapy cycles. For the 77% of the new chemotherapy cycles, ePRO questionnaire data was available. 65% of cycles had symptom questionnaires grading at ≤ 1 while 67% of the cycles had laboratory values at pre-set range. The recommendation by the tool for a new chemotherapy cycle was (green/go) in 42.8%, two (yellow/evaluate) in 24.5%, and three (red/hold) 32.7% of the cycles. HCPs valued the improved workflow, faster patient evaluation, and direct messaging option the most. Conclusions In this study, we investigated the feasibility of a decision support system in chemotherapy cycle pre-evaluation and prescription. The study shows that the functionalities of the investigated tool were feasible, and an automated approach to chemotherapy cycle prescription was possible for nearly half of the cycles. Trial registration: NCT04081558 9th Sep 2019

Purpose Electronic (e) patient-reported outcomes (PROs) have been shown to improve the quality of life and survival in chemotherapy treated advanced cancer patients. We hypothesized that multidimensional ePRO centered approach could improve symptom management, streamline patient flow, and optimize the use of healthcare resources. Methods In this multicenter trial (NCT04081558), colorectal cancer (CRC) patients receiving oxaliplatin-based chemotherapy as adjuvant or in the first- or second-line setting in advanced disease were included in the prospective ePRO cohort, while a comparative retrospective cohort was collected from the same institutes. The investigated tool consisted of a weekly e-symptom questionnaire integrated to an urgency algorithm and laboratory value interface, which generated semi-automated decision support for chemotherapy cycle prescription and individualized symptom management. Results Recruitment to the ePRO cohort occurred 1/2019–1/2021 (n = 43). The comparator group (n = 194) consisted of patients treated in the same institutes 1–7/2017. The analysis was limited to adjuvant treated (n = 36 and n = 35). The feasibility of the ePRO follow-up was good with 98% reporting easy usage and 86% improved care, while health care personnel valued the easy use and logical workflow. In the ePRO cohort, 42% needed a phone call before planned chemotherapy cycles, while this was 100% in the retrospective cohort (p = 1.4e−8). Peripheral sensory neuropathy was detected significantly earlier with ePRO followed (p = 1e−5) but did not translate to earlier dose reduction, delays, or unplanned therapy termination compared to the retrospective cohort. Conclusion The results suggest that the investigated approach is feasible and streamlines workflow. Earlier symptom detection may improve the quality in cancer care.

e13606 Background: SARS-CoV-2 vaccines have changed the course of the current global pandemic. Cancer patients were identified as highrisk of adverse infection outcomes. We have previously characterised the serological response to SARS-CoV-2 vaccines in 220 cancer patients treated at our institution. In addition these patients were given the possibility to report their symptoms (patient-reported outcomes, PROs) weekly using a digital platform (ePROs). We sought to determine if, in cancer patients, the prospectively recorded post-vaccination ePROs could predict the serological response to SARS-CoV-2 vaccines. Methods: We used a pre-existing digital platform that allows monitoring of PROs using weekly questionnaires sent to patients and available on their desktop computers, tablets or smartphones. Serial serologies were performed at 28, 50 and 115 days after vaccination. Results: We observed that at day 50 after the first vaccination dose, coinciding with three weeks after the second dose, patients could be divided into two groups according to their serological response (low – below 1500 U/ml and high – above or equal 1500 U/ml). A peak in symptom burden could be observed after the second dose, as previously described. Omitting ePRO features decreased prediction performance of all models, whereas omitting baseline symptom scores had inconsistent effects. Among all models and feature constructions, the top performance metrics were given by the nearest centroid model7 with baseline symptoms omitted and 20 features chosen with the aforementioned procedure. The model achieved an accuracy of 0.704, an F1-score of 0.759 and an MCC of 0.398. Conclusions: we were able to identify the patients who achieved higher antibody levels against SARS-COV-2 based on the symptom burden reported through ePROs. This represents the first model showing that symptoms, assessed through ePRO can be predictive of response to vaccines. Our results could also be useful information for patients, as they could assuage their fears about adverse -effects, through the knowledge that toxicity could predict better protection against SARS-COV-2. 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.