Proton pump inhibitors and risk of severe COVID-19 in older people.

INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 is a viral respiratory infection that can cause systemic disorders and lead to death, particularly in older people. Proton pump inhibitors (PPIs) increase the risk of enteric and lung infections. Considering the broad use of PPIs in older people, the potential role of PPIs in COVID-19 could be of dramatic significance. The objective of our study was to evaluate the link between PPIs and severe COVID-19 in older people. METHOD: We performed a retrospective cohort study, including all patients aged ≥65, hospitalised for a diagnosis of COVID-19. Epidemiological, clinical and biological data were extracted and we performed an Inverse Probability of Treatment Weighing method based on a propensity score. RESULTS: From March 2020 to February 2021, a total of 834 patients were included, with a median age of 83 and 52.8% were male. A total of 410 patients had a PPIs prescription, 358 (87.3%) were long-term PPIs-users and 52 (12.7%) were recent PPIs-users. Among PPIs-users, 163 (39.8%) patients developed severe COVID-19 versus 113 (26.7%) in PPIs-non users (odds ratio (OR) = 1.59 [1.18-2.14]; P < 0.05). Moreover, the double dose PPI-users had a higher risk of developing severe COVID-19 (OR = 3.36 [1.17-9.66]; P < 0.05) than the full dose PPI-users (OR = 2.15 [1.22-3.76]; P < 0.05) and the half dose PPI-users (OR = 1.64 [1.13-2.37]; P < 0.05). CONCLUSION: Our study reports evidence that the use of PPIs was associated with an increased risk of severe COVID-19 in older people.

Ethical Issues in the Utilization of Black Boxes for Artificial Intelligence in Medicine.

Artificial Intelligence (AI) has made major progress in recent years in many fields. With regard of medicine however, the utilization of AI raises numerous ethical questions, especially since newer and much more accurate algorithms function as black boxes. A trade-off must then be made between having algorithms being very accurate and effective, and algorithms that are explainable but less proficient. In this paper we address the ethical implications of utilizing black box algorithms in medicine.

Machine Learning in Medicine: To Explain, or Not to Explain, That Is the Question.

In 2022, the Medical Informatics Europe conference created a special topic called "Challenges of trustable AI and added-value on health" which was centered around the theme of eXplainable Artificial Intelligence. Unfortunately, two opposite views remain for biomedical applications of machine learning: accepting to use reliable but opaque models, vs. enforce models to be explainable. In this contribution we discuss these two opposite approaches and illustrate with examples the differences between them.