February 18, 2019Open Access
Noninvasive prediction of Blood Lactate through a machine learning-based approach
Key Result
A machine learning-based mathematical model using noninvasive cardio-respiratory parameters accurately estimated blood lactate concentration during low and moderate intensity exercise with a fitting error standard deviation of 0.52 mmol/liter.
Study Design
Type
Observational (n=31)
Multicenter
No
Structured PICO
Does a mathematical model using noninvasive cardio-respiratory parameters accurately estimate blood lactate concentration during constant work rate exercise in healthy adults?
P
Population
31 healthy male and female participants between 20 and 50 years-old without cardiovascular illness
I
Intervention
Noninvasive estimation of blood lactate concentration using a mathematical model based on cardiopulmonary variables (VT, BF, ReHR, ExHR) and anthropometric elements
C
Comparator
Invasive venous blood lactate measurement
O
Outcome
Standard deviation of fitting error between estimated and measured blood lactate concentrationsurrogate
A mathematical model using noninvasive cardio-respiratory parameters can accurately estimate blood lactate concentration during constant work rate exercise, with potential applications in endurance training.
Limitations
- Sample size is relatively small
- Equation obtained may only be relevant to constant work rate cycle ergometer exercise
- Prospective validation procedures were not performed
Abstract
Abstract We hypothesized that blood lactate concentration(Lac blood ) is a function of cardiopulmonary variables, exercise intensity and some anthropometric elements during aerobic exercise. This investigation aimed to establish a mathematical model to estimate Lac blood noninvasively during constant work rate (CWR) exercise of various intensities. 31 healthy participants were recruited and each underwent 4 cardiopulmonary exercise tests: one incremental and three CWR tests (low: 35% of peak work rate for 15 min, moderate: 60% 10 min and high: 90% 4 min). At the end of each CWR test, venous blood was sampled to determine Lac blood . 31 trios of CWR tests were employed to construct the mathematical model, which utilized exponential regression combined with Taylor expansion. Good fitting was achieved when the conditions of low and moderate intensity were put in one model; high-intensity in another. Standard deviation of fitting error in the former condition is 0.52; in the latter is 1.82 mmol/liter. Weighting analysis demonstrated that, besides heart rate, respiratory variables are required in the estimation of Lac blood in the model of low/moderate intensity. In conclusion, by measuring noninvasive cardio-respiratory parameters, Lac blood during CWR exercise can be determined with good accuracy. This should have application in endurance training and future exercise industry.
Cite This Study
Huang et al. (Mon,) conducted a observational in Healthy participants (n=31). Machine learning-based mathematical model using noninvasive cardio-respiratory parameters vs. Measured venous blood lactate concentration was evaluated on Standard deviation of fitting error for estimated blood lactate concentration (low/moderate intensity). A machine learning-based mathematical model using noninvasive cardio-respiratory parameters accurately estimated blood lactate concentration during low and moderate intensity exercise with a fitting error standard deviation of 0.52 mmol/liter.