Li Lang, Desai Mehul, Desta Zeruesenay, Flockhart David
Division of Biostatistics, Department of Medicine, Indiana University 46202-2678, USA.
Stat Med. 2004 Sep 15;23(17):2625-43. doi: 10.1002/sim.1863.
Prolongation of the QT interval on a surface electrocardiogram is a biomarker for a potentially life-threatening arrhythmia. It is used by drug developers and regulatory agencies as a measure of drug safety. Heart rate or RR interval (the inverse of heart rate) correction of the QT interval is necessary because of the QT interval shortening that accompanies physiologic decreases in the RR interval. When a drug alters the RR interval, it is important to distinguish a QT change that is due to a drug effect versus an artefact of a heart rate change. A two-step off-drug subject-specific QT correction analysis is discussed. At the first step, a linear mixed model based only on the placebo (off-drug) RR/QT data produces a correction coefficient that can be applied to a generic formula, and QT intervals are corrected for heart rate on both placebo and treatment period data using that formula. At step two, the heart rate corrected QT interval (QTc) is then compared between placebo and treatment groups at a pre-specified heart rate (usually 60 bpm) based on another linear mixed model. This two-step QT analysis implicitly assumes the slope of log(QT) versus log(RR) is unchanged by drug. Practically, it is important to understand how much this assumption can bias the QT prolongation estimates if it is not valid. We propose a one-step off-on-drug subject-specific QT correction analysis that would pool placebo and treatment period RR/QT data and derive different subject specific coefficients for the treatment and placebo data based on a linear mixed model, which can avoid the unchanged slope assumption. It is also a known unbiased and the most efficient method. The applications of both methods are demonstrated through the QT analysis of haloperidol, a neuroleptic known to prolong QTc. Both theoretical and empirical results show that, although the two-step off-drug QT correction analysis is biased, the bias is small in the case of haloperidol (0.1-0.2 ms). The two-step off-drug QT correction analysis is shown to be almost as efficient as our one-step off- and on-drug QT analysis.
体表心电图QT间期延长是一种潜在危及生命的心律失常的生物标志物。药物研发人员和监管机构将其用作衡量药物安全性的指标。由于RR间期生理性缩短会伴随QT间期缩短，因此有必要对QT间期进行心率或RR间期（心率的倒数）校正。当药物改变RR间期时，区分由药物效应引起的QT变化与心率变化的伪差非常重要。本文讨论了一种两步法的停药后个体特异性QT校正分析。第一步，仅基于安慰剂（停药）RR/QT数据的线性混合模型产生一个校正系数，该系数可应用于通用公式，并使用该公式对安慰剂期和治疗期数据的心率进行QT间期校正。第二步，然后基于另一个线性混合模型，在预先指定的心率（通常为60次/分钟）下比较安慰剂组和治疗组的心率校正QT间期（QTc）。这种两步法QT分析隐含地假设药物不会改变log(QT)与log(RR)的斜率。实际上，如果该假设不成立，了解其对QT延长估计值的偏倚程度非常重要。我们提出了一种一步法的服药-停药个体特异性QT校正分析，该方法将安慰剂期和治疗期RR/QT数据合并，并基于线性混合模型为治疗数据和安慰剂数据推导不同的个体特异性系数，这可以避免斜率不变的假设。它也是一种已知的无偏且最有效的方法。通过对已知可延长QTc的抗精神病药物氟哌啶醇的QT分析，展示了这两种方法的应用。理论和实证结果均表明，虽然两步法停药后QT校正分析存在偏倚，但在氟哌啶醇的情况下偏倚较小（0.1 - 0.2毫秒）。两步法停药后QT校正分析几乎与我们的一步法服药-停药QT分析一样有效。
