Development and validation of a predictive 6-min walk score in patients with idiopathic pulmonary fibrosis.

The ISABELA studies were two replicate randomised controlled trials of idiopathic pulmonary fibrosis (IPF) that included a regimented 6MWT protocol.

ISABELA研究是两项复制的特发性肺纤维化（IPF）随机对照试验，其中包括了规范化的6MWT方案。

The goal of this study was to combine 6MWT components into a pragmatic, easy to apply, composite clinical prediction score.

本研究的目标是将6分钟步行试验（6MWT）的组成部分结合成一个实用、易于应用的综合临床预测评分。

Methods

6MWT parameters associated with time to death or respiratory hospitalisation in the ISABELA studies were integrated into a single composite score.

在ISABELA研究中，与死亡或呼吸系统住院时间相关的6MWT参数被整合成一个单一的综合评分。

This score was then validated in an external cohort.

然后在外部队列中对该评分进行了验证。

Results

There were 1251 patients in the derivation set with 83 respiratory-related hospitalisations and 21 deaths observed after 48 weeks.

衍生集中有1251名患者，在48周后观察到83次与呼吸系统相关的住院和21例死亡。

After multivariable analysis, four parameters were independently predictive of outcomes: Borg dyspnoea score, oxygen flow rate, oxygen saturation nadir and the 6-min walk distance.

在多变量分析后，四个参数独立预测结果：Borg呼吸困难评分、氧流量、氧饱和度最低值和6分钟步行距离。

A pragmatic model, termed the ODDS (oxygen, distance, dyspnoea, saturation) was then developed.

然后开发了一个实用模型，称为ODDS（氧气、距离、呼吸困难、饱和度）。

This performed better than the individual parameters alone with an area under the curve (AUC) of 0.797, 0.781 and 0.766 for events at 12, 24 and 48 weeks, respectively.

与单独使用各个参数相比，该方法在预测12周、24周和48周事件时表现更佳，其曲线下面积（AUC）分别为0.797、0.781和0.766。

The ODDS model was similarly accurate when applied to the external validation set (n=295) at 48 weeks (AUC 0.758, 95% CI 0.688-0.825).

将ODDS模型应用于外部验证集（n=295）时，在48周时同样显示出较高的准确性（AUC为0.758，95%置信区间为0.688-0.825）。

Conclusions

The 6MWT imparts important prognostic information which is best captured by combining constituent variables in a composite score system.

6分钟步行试验（6MWT）提供了重要的预后信息，通过结合构成变量在复合评分系统中可以最好地捕捉到这些信息。

The ODDS model might find utility in the clinical setting as well as in IPF studies where it can be used to risk-stratify patients for outcomes.

ODDS模型可能在临床环境以及特发性肺纤维化（IPF）研究中也有用，它可以用来对患者进行风险分层以预测结果。