Background Identifying hospitalized patients at risk for QT interval prolongation could lead to interventions to reduce the risk of torsades de pointes (TdP). Our objective was to develop and validate a risk score for QT prolongation in hospitalized patients. Methods and Results In this study in a single tertiary care institution, consecutive patients (n=900) admitted to cardiac care units comprised the risk score development group (DG). The score was then applied to 300 additional patients in a validation group (VG). Corrected QT (QTc) interval prolongation (defined as QTc > 500 ms and/or an increase of > 60 ms from baseline) occurred in 274 (30.4%) and 90 (30.0%) patients in the DG and VG, respectively. Independent predictors of QTc prolongation included: female (odds ratio [OR], 1.5; 95% confidence interval [CI], 1.1–2.0), diagnosis of myocardial infarction [2.5 (1.6–3.9)], sepsis [2.7 (1.5–4.8)], left ventricular dysfunction [2.7 (1.6–5.0)], administration of a QT-prolonging drug [2.8 (2.0–4.0)], ≥ 2 QT- prolonging drugs [2.6 (1.9–5.6)], or loop diuretic [1.4 (1.0–2.0)], age > 68 years [1.3 (1.0–1.8)], serum K+ < 3.5 mEq/L [2.1 (1.5–2.9)], and admitting QTc > 450 ms [2.3; CI (1.6–3.2)]. Risk scores were developed by assigning points based on Log ORs. Low, moderate and high risk ranges of 0–6, 7–10 and 11–21 points, respectively, best predicted QTc prolongation (C statistic = 0.823). A high risk score > 11 was associated with sensitivity = 0.74, specificity = 0.77, positive predictive value = 0.79 and negative predictive value = 0.76. In the VG, the incidences of QTc prolongation were 15% (low risk); 37% (moderate risk); 73% (high risk). Conclusions A risk score using easily obtainable clinical variables predicts patients at highest risk for QTc prolongation and may be useful in guiding monitoring and treatment decisions.

Biostatistics is the application of statistics to biologic data. This article is the second part of a 2-part series on the application of statistics in nutrition science. The first article, published in the December 2007 issue, reviewed descriptive statistics. Inferential statistics, to be discussed in this article, can be used to make predictions based on a sample obtained from a population or some large body of information. It is these inferences that are used to test specific research hypotheses. This article focuses on inferential statistics and their application in the nutrition and biomedical literature. Additionally, this review will outline some of the most commonly used statistical tests found in the biomedical literature.

QT(c) interval prolongation is common among patients admitted to cardiac units. QT interval-prolonging drugs are commonly prescribed to patients presenting with QT(c) interval prolongation.