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BACKGROUND: Trastuzumab has had a major impact on the treatment of human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). Anti-HER2 biosimilars such as Ogivri have demonstrated safety and clinical equivalence to trastuzumab (using Herceptin as the reference product) in clinical trials. To our knowledge, there has been no real-world report of the side effects and quality of life (QoL) in patients treated with biosimilars using electronic patient-reported outcomes (ePROs). OBJECTIVE: The primary objective of this prospective observational study (OGIPRO study) was to compare the ePRO data related to treatment side effects collected with the medidux app in patients with HER2-positive BC treated with the trastuzumab biosimilar Ogivri (prospective cohort) to those obtained from historical cohorts treated with Herceptin alone or combined with pertuzumab and/or chemotherapy (ClinicalTrials.gov NCT02004496 and NCT03578731). METHODS: Patients were treated with Ogivri alone or combined with pertuzumab and/or chemotherapy and hormone therapy in (neo)adjuvant and palliative settings. Patients used the medidux app to dynamically record symptoms (according to the Common Terminology Criteria for Adverse Events [CTCAE]), well-being (according to the Eastern Cooperative Oncology Group Performance Status scale), QoL (using the EQ-5D-5L questionnaire), cognitive capabilities, and vital parameters over 6 weeks. The primary endpoint was the mean CTCAE score. Key secondary endpoints included the mean well-being score. Data of this prospective cohort were compared with those of the historical cohorts (n=38 patients; median age 51, range 31-78 years). RESULTS: Overall, 53 female patients with a median age of 54 years (range 31-87 years) were enrolled in the OGIPRO study. The mean CTCAE score was analyzed in 50 patients with available data on symptoms, while the mean well-being score was evaluated in 52 patients with available data. The most common symptoms reported in both cohorts included fatigue, taste disorder, nausea, diarrhea, dry mucosa, joint discomfort, tingling, sleep disorder, headache, and appetite loss. Most patients experienced minimal (grade 0) or mild (grade 1) toxicities in both cohorts. The mean CTCAE score was comparable between the prospective and historical cohorts (29.0 and 30.3, respectively; mean difference -1.27, 95% CI -7.24 to 4.70; P=.68). Similarly, no significant difference was found for the mean well-being score between the groups treated with the trastuzumab biosimilar Ogivri and Herceptin (74.3 and 69.8, respectively; mean difference 4.45, 95% CI -3.53 to 12.44; P=.28). CONCLUSIONS: Treatment of patients with HER2-positive BC with the trastuzumab biosimilar Ogivri resulted in equivalent symptoms, adverse events, and well-being as found for patients treated with Herceptin as determined by ePRO data. Hence, integration of an ePRO system into research and clinical practice can provide reliable information when investigating the real-world tolerability and outcomes of similar therapeutic compounds. TRIAL REGISTRATION: ClinicalTrials.gov NCT05234021; https://clinicaltrials.gov/study/NCT05234021.
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Background: The use of smartphone apps in cancer patients undergoing systemic treatment can promote the early detection of symptoms and therapy side effects and may be supported by machine learning (ML) for timely adaptation of therapies and reduction of adverse events and unplanned admissions. Objective: We aimed to create an Early Warning System (EWS) to predict situations where supportive interventions become necessary to prevent unplanned visits. For this, dynamically collected standardized electronic patient reported outcome (ePRO) data were analyzed in context with the patient's individual journey. Information on well-being, vital parameters, medication, and free text were also considered for establishing a hybrid ML model. The goal was to integrate both the strengths of ML in sifting through large amounts of data and the long-standing experience of human experts. Given the limitations of highly imbalanced datasets (where only very few adverse events are present) and the limitations of humans in overseeing all possible cause of such events, we hypothesize that it should be possible to combine both in order to partially overcome these limitations. Methods: The prediction of unplanned visits was achieved by employing a white-box ML algorithm (i.e., rule learner), which learned rules from patient data (i.e., ePROs, vital parameters, free text) that were captured via a medical device smartphone app. Those rules indicated situations where patients experienced unplanned visits and, hence, were captured as alert triggers in the EWS. Each rule was evaluated based on a cost matrix, where false negatives (FNs) have higher costs than false positives (FPs, i.e., false alarms). Rules were then ranked according to the costs and priority was given to the least expensive ones. Finally, the rules with higher priority were reviewed by two oncological experts for plausibility check and for extending them with additional conditions. This hybrid approach comprised the application of a sensitive ML algorithm producing several potentially unreliable, but fully human-interpretable and -modifiable rules, which could then be adjusted by human experts. Results: From a cohort of 214 patients and more than 16'000 available data entries, the machine-learned rule set achieved a recall of 19% on the entire dataset and a precision of 5%. We compared this performance to a set of conditions that a human expert had defined to predict adverse events. This "human baseline" did not discover any of the adverse events recorded in our dataset, i.e., it came with a recall and precision of 0%. Despite more plentiful results were expected by our machine learning approach, the involved medical experts a) had understood and were able to make sense of the rules and b) felt capable to suggest modification to the rules, some of which could potentially increase their precision. Suggested modifications of rules included e.g., adding or tightening certain conditions to make them less sensitive or changing the rule consequences: sometimes further monitoring the situation, applying certain test (such as a CRP test) or applying some simple pain-relieving measures was deemed sufficient, making a costly consultation with the physician unnecessary. We can thus conclude that it is possible to apply machine learning as an inspirational tool that can help human experts to formulate rules for an EWS. While humans seem to lack the ability to define such rules without such support, they are capable of modifying the rules to increase their precision and generalizability. Conclusions: Learning rules from dynamic ePRO datasets may be used to assist human experts in establishing an early warning system for cancer patients in outpatient settings.
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A simple and rapid method was developed for the determination of 20 antibiotics (sulfonamides, tetacyclines, and flumequine) in honey by liquid chromatography tandem mass spectrometry. The proposed method is sensitive (limit of detection 0.5 to 10 ppb for the various antibiotics) and selective. A hydrolysis step ensures the liberation of sugar-bound sulfonamides. The approach has been used to analyze some 300 honey samples. A number of them were found to have exceeded the Swiss limit of 50 ppb.