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RESEARCH PRODUCT

How to assess the risks associated with the usage of a medical device based on predictive modeling: the case of an anemia control model certified as medical device.

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description

Background The successful application of Machine Learning (ML) to many clinical problems can lead to its implementation as medical devices (MD), being important to assess the associated risks. Methods An anemia control model (ACM), certified as MD may face adverse events as the result of wrong predictions that are translated into suggestions of doses of erythropoietic stimulating agents to dialysis patients. Risks are assessed as the combination of severity and probability of a given hazard. While severities are typically assessed by clinicians, probabilities are tightly related to the performance of the predictive model. Results A post-marketing dataset formed by all adult patients registered in French, Portuguese and Spanish clinics, belonging to an international network was considered; 3876 patients and 11508 suggestions were eventually included. The achieved results show that there are no statistical differences between the probabilities of adverse events that are estimated in the ACM test set (using only Spanish clinics) and those actually observed in the post-marketing cohort. Conclusions The risks of an ACM-MD can be accurately and robustly estimated, thus enhancing patients' safety. The proposed methodology is applicable to other clinical decisions based on predictive models since our proposal does not depend on the particular predictive model.