#VARIABLES OF TIMI RISK SCORE SOFTWARE#

The test is defined as the ratio of the difference in the log estimates to the standard error of this difference. 13 Estimates from each subgroup are analyzed on a log scale. To test this hypothesis, we arbitrarily defined low prevalence as less than 10% and conducted an a priori subgroup analysis of studies with low (< 10%) versus high (≥ 10%) prevalence using a test of statistical interaction as defined by Altman. We hypothesized that statistical heterogeneity identified by the I 2 statistic could be explained by differences in the prevalence of cardiac events between studies. We used the I 2 statistic to quantify statistical heterogeneity between studies.

#VARIABLES OF TIMI RISK SCORE SOFTWARE#

We assessed the prognostic performance of the TIMI risk score at potential decision thresholds using Meta-DiSc software (Unit of Clinical Biostatistics and the Ramon y Cajal Hospital). We performed meta-regression to determine whether a linear relation exists between TIMI risk score (independent variable) and the logit event rate (dependent variable) using Comprehensive Meta-Analysis version 2 (Biostat Inc., Englewood, NJ). Categorical data are presented as per cent frequency of occurrence. We present all continuous data as either means with standard deviations (SD) or medians with interquartile ranges as reported in the primary study. Any disagreements were resolved by consensus. We assessed the degree of interobserver agreement for quality assessment using the Cohen κ. We selected the following seven criteria: patients selected in an unbiased fashion (consecutive or random sample) study sample representative of a wide spectrum of the severity of disease predictor variables assessed without knowledge of the outcome outcome assessed without knowledge of the predictor variables outcomes accurately defined (particularly, acute myocardial infarction) explicit interpretation of the risk score by clinicians in practice without knowledge of the outcome and adequate follow-up (arbitrarily defined as a lost-to-follow-up rate of < 10%). Because there is no single quality assessment tool that has been designed to assess the methodological quality of studies of risk scores, we selected criteria from assessment tools 10 – 12 that, based on a consensus among investigators, most closely indicated study quality. Two reviewers (D.A., S.C.) independently assessed the methodological quality of each eligible article. We conducted a comprehensive systematic review and meta-analysis to assess the methodological quality and prognostic performance of studies that had prospectively validated the TIMI risk score in patients in the emergency department. This estimate may serve as a useful baseline for comparison as emerging clinical prediction rules and imaging modalities continue to refine our approach to diagnosis and risk stratification in patients in the emergency department with potential acute coronary syndromes. 7 Researchers would also have an estimate of the prognostic accuracy of the TIMI risk score derived from different practice settings and patient populations that represent a wide variety of ethnic backgrounds. This could be used as an adjunct to clinical acumen and as a tool to communicate risk to patients in a shared decision-making model of care. Clinicians would have a reliable quantitative estimate of a patient’s short-term risk of a cardiac event.

A robust estimate of the performance of the TIMI risk score obtained from a systematic review may prove useful to both clinicians and researchers.

Variables of timi risk score