Personalized risk stratification through attribute matching for clinical decision making in clinical conditions with aspecific symptoms: The example of syncope

Abstract

Background Risk stratification is challenging in conditions, such as chest pain, shortness of breath and syncope, which can be the manifestation of many possible underlying diseases. In these cases, decision tools are unlikely to accurately identify all the different adverse events related to the possible etiologies. Attribute matching is a prediction method that matches an individual patient to a group of previously observed patients with identical characteristics and known outcome. We used syncope as a paradigm of clinical conditions presenting with aspecific symptoms to test the attribute matching method for the prediction of the personalized risk of adverse events. Methods We selected the 8 predictor variables common to the individual-patient dataset of 5 prospective emergency department studies enrolling 3388 syncope patients. We calculated all possible combinations and the number of patients in each combination. We compared the predictive accuracy of attribute matching and logistic regression. We then classified ten random patients according to clinical judgment and attribute matching.

Personalized Critical Care Medicine (Review)

“…Объединение этих данных с индивидуальной геномикой пациента позволяет определить генетические основы клинических проявлений и вариабельности ответа на лечение. Авторы считают, что биобанки будут важным ресурсом в неотложной медицине [26].…”

A Rational Evaluation of the Syncope Patient: Optimizing the Emergency Department Visit

“…A meta-analysis utilizing individual patient data failed to find additive value of the OESIL, EGSYS, and SFSR beyond clinical judgement to predict serious adverse outcomes in the ED or at 10 and 30 days [ 51 ]. Solbiati et al used attribute matching—a tool that allows for personalized risk prediction by computer generated modeling—in an effort to refine 10-day risk prediction of serious adverse events, as compared to clinical judgment and a regression model [ 52 ]. The matching cohort included 3388 patients from five previous prospective trials.…”