Exploring the utility of bedside tests for predicting cardiorespiratory fitness in older adults
Exploring the utility of bedside tests for predicting cardiorespiratory fitness in older adults
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探索床边测试在预测老年人心肺健康方面的效用
DOI：
10.1002/agm2.12280
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Carrick L
中科院分区：
文献类型：
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作者：
Carrick L
慢性呼吸适应性（CRF）随着年龄的增长而下降，并且与年龄无关，已被证明可预测全因死亡率，发病率和不良临床结局。关于老年患者，有大量证据强调低CRF和手术结局差之间的关系。心肺功能运动试验（CPET）被认为是CRF的金标准测量。然而，这种形式的评估对人员和设备的需求很大，对于具有某些年龄相关的身体限制（包括关节和心血管合并症）的患者不可行。因此，替代方法来评估老年患者的CRF是非常需要的。MethodsSixty-four参与者（45%女性），中位年龄为74（65-90）岁，通过社区为基础的广告招募到这项研究。所有参与者完成了三项身体功能测试：（1）步箱测试;（2）握力测力计;（3）自行车测力计上的CPET;还通过B模式超声检查评估了他们的肌肉结构（股外侧肌），以提供肌肉厚度、羽状角和肌束长度的测量值。然后使用多元线性回归确定床旁预测CPET参数的替代措施的身体功能和人口统计学（年龄，性别，体重指数（BMI））data.ResultsThere是没有显着关联之间的超声评估参数的肌肉结构和措施的CRF。VO 2峰值在一定程度上是由步进箱测试期间的快步时间、性别和BMI预测的，导致模型达到0.40的R2（p< 0.001）。此外，为了开发具有最小评估需求的模型（即，使用手柄测力法而不是步箱测试），用非显性HGS代替快速步时间导致模型达到0.36的R2（p< 0.001）。非优势握力结合快步时间和BMI的步箱测试参数提供了最具预测性的VO 2峰值模型，R2为0.45（p< 0.001）。结论我们的研究结果表明，简单确定的患者特征和床旁身体功能评估能够预测CPET衍生的CRF。结合性别和BMI，步箱测试期间的握力和快步时间均可预测VO 2峰值。未来的工作应该将该模型应用于临床人群，以确定其在这种情况下的效用，并探索简单的床边试验是否能预测老年人的重要临床结局（即，术后并发症）。
ObjectivesCardiorespiratory fitness (CRF) declines with advancing and has also, independent of age, been shown to be predictive of all‐cause mortality, morbidity, and poor clinical outcomes. In relation to the older patient, there is a particular wealth of evidence highlighting the relationship between low CRF and poor surgical outcomes. Cardiopulmonary exercise testing (CPET) is accepted as the gold‐standard measure of CRF. However, this form of assessment has significant personnel and equipment demands and is not feasible for those with certain age‐associated physical limitations, including joint and cardiovascular comorbidities. As such, alternative ways to assess the CRF of older patients are very much needed.MethodsSixty‐four participants (45% female) with a median age of 74 (65–90) years were recruited to this study via community‐based advertisements. All participants completed three tests of physical function: (1) a step‐box test; (2) handgrip strength dynamometry; and (3) a CPET on a cycle ergometer; and also had their muscle architecture (vastus lateralis) assessed by B‐mode ultrasonography to provide measures of muscle thickness, pennation angle, and fascicle length. Multivariate linear regression was then used to ascertain bedside predictors of CPET parameters from the alternative measures of physical function and demographic (age, gender, body mass index (BMI)) data.ResultsThere was no significant association between ultrasound‐assessed parameters of muscle architecture and measures of CRF. VO2peakwas predicted to some extent from fast step time during the step‐box test, gender, and BMI, leading to a model that achieved anR2of 0.40 (p< 0.001). Further, in aiming to develop a model with minimal assessment demands (i.e., using handgrip dynamometry rather than the step‐box test), replacing fast step time with non‐dominant HGS led to a model which achieved anR2of 0.36 (p< 0.001). Non‐dominant handgrip strength combined with the step‐box test parameter of fast step time and BMI delivered the most predictive model for VO2peakwith anR2of 0.45 (p< 0.001).ConclusionsOur findings show that simple‐to‐ascertain patient characteristics and bedside assessments of physical function are able to predict CPET‐derived CRF. Combined with gender and BMI, both handgrip strength and fast step time during a step‐box test were predictive for VO2peak. Future work should apply this model to a clinical population to determine its utility in this setting and to explore if simple bedside tests are predictive of important clinical outcomes in older adults (i.e., post‐surgical complications).