Risk stratification schemes

A prognostic model is the mathematical combination of two or more patient or disease characteristics to predict outcome. Confusingly, prognostic models are also termed prognostic indexes, risk scores, probability models, risk stratification schemes or clinical prediction rules (Reilly and Evans 2006). To be useful, they must be shown to predict clinically relevant outcomes reliably. They must, therefore, be derived from a representative cohort in which outcome has been measured accurately. Next, they must be validated, not just in the data from which they were derived (internal validation) but also on data from independent cohorts (external validation) (Wyatt and Altman 1995 Justice et al. 1999 Altman and Royston 2000). Lastly, a model must be simple to use and have clinical credibility, otherwise it is unlikely to be taken up in routine clinical practice (Table 14.1). 

Persistent and paroxysmal atrial fibrillation (AF) are potent risk factors for first and recurrent stroke. It has been estimated that AF affects more than 2,000,000 Americans and becomes more frequent with age, being the most frequent cardiac arrhythmia in the elderly [6,38], The prevalence of AF peaks at 8.8% among people over the age of 80 years, hi the Framingham Stroke Study, 14% of strokes occurred because of AF. The absolute risk of stroke in patients with AF varies 20-fold, according to age and the presence of vascular risk factors [6,7]. Several stroke risk stratification schemes have been developed and validated. Overall, patients with prior stroke or transient ischanic attack carry the highest stroke risk [6,39]. 

Troponin has emerged as the biomarker of choice for diagnosis and risk stratification of patients with ACS. Optimum use of this biomarker requires improvements in the performance of analytical measurements made from the central laboratory, especially from POCT platforms. To meet the current and future chnical needs, troponin assays must be faster, more analytically sensitive, and more convenient from a standpoint of information transfer. This requires development of novel delivery and detection schemes, and approaches towards data connectivity. This real-world problem illustrates the need for cooperation between clinical laboratory science, biomedical engineering, and informational science. 

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