Abstract

Medicine has moved from phenomenological diagnosis based on syndromes (i.e. a pattern of symptoms and clinical findings) to diagnosis based on imaging and on organ-specific biomarkers, typically assuming a homogenous population. This approach, which often identifies a single disease entity by a single biomarker (like troponin for heart attack, procalcitonin for bacterial infection in respiratory disease, and rapid diagnostic testing for malaria) has been highly successful in identifying frequent diseases and has paved the way to more effective therapy. However, predicting the course of disease in individuals is still a major challenge for clinical reality and experienced physicians refrain from predicting the future in a too assertive manner when consulting their patients. This is due to the fact that a seemingly clear disease entity like myocardial infarction is a continuum in space (location of infarct related artery), time (critical relevance of timing of reopening of occluded artery), severity, and individual factors (degree of subclinical atherosclerosis not related to the event, variability of coagulation system, response to drugs). This inability to predict leads to treatment of individuals who will not benefit from therapy (like platelet inhibition by clopidogrel in clopidogrel nonresponders), while important side effects (e.g., allergic reaction) of drugs can occur even in the absence of patient-specific benefits due to the taking of a particular drug.

Time has come to assess disease not only in a “monomarker” fashion, but to open our eyes to the individuality of each patient and his disease by identifying of the individual biological context (e.g., genetic variabilities in metabolism, immune system state, coagulation system), in its temporal dimension.