Description Usage Arguments Value References Examples
estimate blood volume according to the classic 1960s paper by Nadler. Surgery. 1962 Feb;51(2):224-32. Prediction of blood volume in normal human adults. Nadler SB, Hidalgo JH, Bloch T.
This effectively reverses engineers an ideal weight from BMI of
22, then use the square root of its ratio to actual body weight to adjust
the 70ml per kg of an ideal weight person. Age-dependent regression
equations for indexed blood volume InBV
at ideal body weight. (No
adjustment made in obesity by Lemmens.) InBV = 90-0.4 X age
(males) InBV = 85-0.4 X age
(females).
applies to slim adults, but note that the age-related decline is not seen if high degree of physical activity is maintained. TODO: check BMI not elevated
1 2 3 4 5 6 7 | blood_vol_Nadler(height_m, weight_kg, male, ...)
blood_vol_Lemmens_sedentary(height_m, weight_kg, ...)
blood_vol_Lemmens_indexed(height_m, weight_kg, ...)
blood_vol_Lemmens_non_obese(weight_kg, age_y, male, ...)
|
height_m |
single numeric, height in meters |
weight_kg |
numeric vector of weight(s) in kg |
male |
logical |
... |
arguments passed to downstream functions, e.g. |
age_y |
numeric vector, age(s) in years. Extremely exact age is not required, so for age in days or months, simplest just to divide. This is not used in the calculation itself, so may be missing. |
numeric vector
'Davy KP, Seals DR. Total blood volume in healthy young and older men. J Appl Physiol 1994; 76: 2059-62'
'Parker-Jones P, Davy KP, DeSouza CA et al. Absence of age-related decline in total blood volume in physically active females. Am J Physiol 1997; 272: H2534-40'
1 2 3 4 5 6 7 8 9 | blood_vol_Nadler(1.8, 80, male = TRUE)
blood_vol_Nadler(1.8, 160, male = TRUE)
blood_vol_Nadler(1.8, 80, male = FALSE)
blood_vol_Lemmens_sedentary(1.8, 80)
blood_vol_Lemmens_sedentary(1.8, 160)
blood_vol_Lemmens_indexed(1.8, 80)
blood_vol_Lemmens_indexed(1.8, 160)
blood_vol_Lemmens_non_obese(80, age_y = 25, male = TRUE)
blood_vol_Lemmens_non_obese(80, age_y = 75, male = TRUE)
|
[1] 5.319061
[1] 7.894261
[1] 4.906475
[1] 5285.997
[1] 7475.528
[1] 66.07496
[1] 46.72205
[1] 6400
[1] 4800
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