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加速度计衍生的运动特征作为神经危重症肌肉萎缩的预测生物标志物:一项前瞻性队列研究。

Accelerometer-derived movement features as predictive biomarkers for muscle atrophy in neurocritical care: a prospective cohort study.

机构信息

Department of Neurology, LMU University Hospital, LMU Munich, Munich, Germany.

Department of Anaesthesiology, LMU University Hospital, LMU Munich, Munich, Germany.

出版信息

Crit Care. 2024 Aug 31;28(1):288. doi: 10.1186/s13054-024-05067-y.

Abstract

BACKGROUND

Physical inactivity and subsequent muscle atrophy are highly prevalent in neurocritical care and are recognized as key mechanisms underlying intensive care unit acquired weakness (ICUAW). The lack of quantifiable biomarkers for inactivity complicates the assessment of its relative importance compared to other conditions under the syndromic diagnosis of ICUAW. We hypothesize that active movement, as opposed to passive movement without active patient participation, can serve as a valid proxy for activity and may help predict muscle atrophy. To test this hypothesis, we utilized non-invasive, body-fixed accelerometers to compute measures of active movement and subsequently developed a machine learning model to predict muscle atrophy.

METHODS

This study was conducted as a single-center, prospective, observational cohort study as part of the MINCE registry (metabolism and nutrition in neurointensive care, DRKS-ID: DRKS00031472). Atrophy of rectus femoris muscle (RFM) relative to baseline (day 0) was evaluated at days 3, 7 and 10 after intensive care unit (ICU) admission and served as the dependent variable in a generalized linear mixed model with Least Absolute Shrinkage and Selection Operator regularization and nested-cross validation.

RESULTS

Out of 407 patients screened, 53 patients (age: 59.2 years (SD 15.9), 31 (58.5%) male) with a total of 91 available accelerometer datasets were enrolled. RFM thickness changed - 19.5% (SD 12.0) by day 10. Out of 12 demographic, clinical, nutritional and accelerometer-derived variables, baseline RFM muscle mass (beta - 5.1, 95% CI - 7.9 to - 3.8) and proportion of active movement (% activity) (beta 1.6, 95% CI 0.1 to 4.9) were selected as significant predictors of muscle atrophy. Including movement features into the prediction model substantially improved performance on an unseen test data set (including movement features: R = 79%; excluding movement features: R = 55%).

CONCLUSION

Active movement, as measured with thigh-fixed accelerometers, is a key risk factor for muscle atrophy in neurocritical care patients. Quantifiable biomarkers reflecting the level of activity can support more precise phenotyping of ICUAW and may direct tailored interventions to support activity in the ICU. Studies addressing the external validity of these findings beyond the neurointensive care unit are warranted.

TRIAL REGISTRATION

DRKS00031472, retrospectively registered on 13.03.2023.

摘要

背景

在神经重症监护中,身体活动不足和随后的肌肉萎缩非常普遍,被认为是重症监护病房获得性肌无力(ICUAW)的关键机制。由于缺乏可量化的活动生物标志物,因此难以评估其相对于 ICUAW 综合征诊断下的其他情况的相对重要性。我们假设与没有患者主动参与的被动运动相比,主动运动可以作为活动的有效替代指标,并可能有助于预测肌肉萎缩。为了验证这一假设,我们使用非侵入性的身体固定加速度计来计算主动运动的指标,随后开发了一种机器学习模型来预测肌肉萎缩。

方法

这项研究是作为 MINCE 登记研究(神经重症监护中的代谢和营养,DRKS-ID:DRKS00031472)的一部分,在一家中心进行的前瞻性观察性队列研究。在入住重症监护病房(ICU)后第 3、7 和 10 天,评估股直肌(RFM)相对于基线(第 0 天)的萎缩情况,该指标作为广义线性混合模型的因变量,该模型采用最小绝对值收缩和选择算子正则化和嵌套交叉验证。

结果

在筛选出的 407 名患者中,有 53 名患者(年龄:59.2 岁(SD 15.9),31 名(58.5%)男性)共获得 91 个可用的加速度计数据集。到第 10 天,RFM 厚度减少了 -19.5%(SD 12.0)。在 12 个人口统计学、临床、营养和加速度计衍生变量中,基线 RFM 肌肉质量(β-5.1,95%CI-7.9 至-3.8)和主动运动比例(%活动)(β1.6,95%CI 0.1 至 4.9)被选为肌肉萎缩的显著预测因子。将运动特征纳入预测模型可显著提高对未见测试数据集的性能(包括运动特征:R=79%;不包括运动特征:R=55%)。

结论

使用大腿固定加速度计测量的主动运动是神经重症监护患者肌肉萎缩的一个关键危险因素。反映活动水平的可量化生物标志物可以支持对 ICUAW 更精确的表型分析,并可能指导针对 ICU 中活动的针对性干预。需要进行研究以确定这些发现在神经重症监护病房以外的外部有效性。

试验注册

DRKS00031472,于 2023 年 3 月 13 日回顾性注册。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/479f/11366141/6efb19863892/13054_2024_5067_Fig1_HTML.jpg

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