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The prediction of cardiovascular disease (CVD) events is of strategic importance for the primary prevention of one of the big killers in the world. Predictive models have a history of decades, but still the desired accuracy is not reached by any of the existing models. The inclusion of inflammatory factors in the models did not increase their accuracy. In this review, we discuss the possible reasons for that failure and we propose a paradigm shift.Systemic inflammation is a very volatile phenomenon. The blood concentration of inflammatory biomarkers may change considerably in one individual with a timescale of seconds. Sudden changes in environmental conditions can trigger rapid modifications in the inflammatory profile of an individual. In routine clinical practice, the blood tests for inflammation are carried out at one point in time, not in standard environmental conditions, and are therefore inadequate.We have to direct CVD research toward the understanding of the synchronic relationship between external environmental conditions and internal physiological reactions. CVD risk assessment must be carried out by using continuous real-time monitoring of external and internal parameters together, something that may become possible with the advent of new technological devices.