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呼吸检测算法会影响学龄前和学龄儿童的多次呼吸冲洗结果。

Breath detection algorithms affect multiple-breath washout outcomes in pre-school and school age children.

机构信息

Division of Paediatric Respiratory Medicine and Allergology, Department of Paediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

Graduate School for Health Sciences, University of Bern, Bern, Switzerland.

出版信息

PLoS One. 2022 Oct 14;17(10):e0275866. doi: 10.1371/journal.pone.0275866. eCollection 2022.

Abstract

BACKGROUND

Accurate breath detection is essential for the computation of outcomes in the multiple-breath washout (MBW) technique. This is particularly important in young children, where irregular breathing is common, and the designation of inspirations and expirations can be challenging.

AIM

To investigate differences between a commercial and a novel breath-detection algorithm and to characterize effects on MBW outcomes in children.

METHODS

We replicated the signal processing and algorithms used in Spiroware software (v3.3.1, Eco Medics AG). We developed a novel breath detection algorithm (custom) and compared it to Spiroware using 2,455 nitrogen (N2) and 325 sulfur hexafluoride (SF6) trials collected in infants, children, and adolescents.

RESULTS

In 83% of N2 and 32% of SF6 trials, the Spiroware breath detection algorithm rejected breaths and did not use them for the calculation of MBW outcomes. Our custom breath detection algorithm determines inspirations and expirations based on flow reversal and corresponding CO2 elevations, and uses all breaths for data analysis. In trials with regular tidal breathing, there were no differences in outcomes between algorithms. However, in 10% of pre-school children tests the number of breaths detected differed by more than 10% and the commercial algorithm underestimated the lung clearance index by up to 21%.

CONCLUSION

Accurate breath detection is challenging in young children. As the MBW technique relies on the cumulative analysis of all washout breaths, the rejection of breaths should be limited. We provide an improved algorithm that accurately detects breaths based on both flow reversal and CO2 concentration.

摘要

背景

准确的呼吸检测对于多呼吸冲洗(MBW)技术的结果计算至关重要。这在年幼的儿童中尤为重要,因为不规则的呼吸很常见,并且呼气和吸气的指定可能具有挑战性。

目的

研究商业和新型呼吸检测算法之间的差异,并分析其对儿童 MBW 结果的影响。

方法

我们复制了 Spiroware 软件(v3.3.1,Eco Medics AG)中使用的信号处理和算法。我们开发了一种新型呼吸检测算法(定制),并使用 2455 次氮气(N2)和 325 次六氟化硫(SF6)试验对 Spiroware 进行了比较,这些试验是在婴儿、儿童和青少年中收集的。

结果

在 83%的 N2 和 32%的 SF6 试验中,Spiroware 呼吸检测算法拒绝了呼吸,并未将其用于 MBW 结果的计算。我们的定制呼吸检测算法基于流量反转和相应的 CO2 升高来确定呼气和吸气,并将所有呼吸用于数据分析。在有规律的潮式呼吸试验中,两种算法的结果没有差异。然而,在 10%的学龄前儿童测试中,检测到的呼吸次数差异超过 10%,商业算法低估了肺清除指数,最高可达 21%。

结论

准确的呼吸检测在年幼的儿童中具有挑战性。由于 MBW 技术依赖于所有冲洗呼吸的累积分析,因此应限制呼吸的拒绝。我们提供了一种改进的算法,该算法基于流量反转和 CO2 浓度准确地检测呼吸。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/256d/9565421/69ea78d9305f/pone.0275866.g001.jpg

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