Toward a personalized therapy for panic disorder: Preliminary considerations from a work in progress

Abstract

Although several treatment options for panic disorder (PD) are available, the best intervention for each individual patient remains uncertain and the use of a more personalized therapeutic approach in PD is required. In clinical practice, clinicians combine general scientific information and personal experience in the decision-making process to choose a tailored treatment for each patient. In this sense, clinicians already use a somehow personalized medicine strategy. However, the influence of their interpretative personal models may lead to bias related to personal convictions, not sufficiently grounded on scientific evidence. Hence, an effort to give some advice based on the science of personalized medicine could have positive effects on clinicians’ decisions. Based on a narrative review of meta-analyses, systematic reviews, and experimental studies, we proposed a first-step attempt of evidence-based personalized therapy for PD. We focused on some phenomenological profiles, encompassing symptoms during/outside panic attacks, related patterns of physiological functions, and some aspects of physical health, which might be worth considering when developing treatment plans for patients with PD. We considered respiratory, cardiac, vestibular, and derealization/depersonalization profiles, with related implications for treatment. Given the extensiveness of the topic, we considered only medications and some somatic interventions. Our proposal should be considered neither exhaustive nor conclusive, as it is meant as a very preliminary step toward a future, robust evidence-based personalized therapy for PD. Clearly much more work is needed to achieve this goal, and recent technological advances, such as wearable devices, big data platforms, and the application of machine learning techniques, may help obtain reliable findings. We believe that combining the efforts of different research groups in this work in progress can lead to largely shared conclusions in the near future.