| Title: | The Abnormal Blood Profile Score to Detect Blood Doping |
|---|---|
| Description: | An implementation of the Abnormal Blood Profile Score (ABPS, part of the Athlete Biological Passport program of the World Anti-Doping Agency), which combines several blood parameters into a single score in order to detect blood doping (Sottas et al. (2006) <doi:10.2202/1557-4679.1011>). The package also contains functions to calculate other scores used in anti-doping programs, such as the OFF-score (Gore et al. (2003) <http://www.haematologica.org/content/88/3/333>), as well as example data. |
| Authors: | Frédéric Schütz [aut, cre], Alix Zollinger [aut] |
| Maintainer: | Frédéric Schütz <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.3 |
| Built: | 2024-11-10 05:14:58 UTC |
| Source: | https://github.com/cran/ABPS |
The ABPS function computes the Abnormal Blood Profile Score
from seven haematological markers. Higher values of this composite
score are associated with a higher likelihood of blood doping.
ABPS(haemdata = NULL, HCT = NULL, HGB = NULL, MCH = NULL, MCHC = NULL, MCV = NULL, RBC = NULL, RETP = NULL)ABPS(haemdata = NULL, HCT = NULL, HGB = NULL, MCH = NULL, MCHC = NULL, MCV = NULL, RBC = NULL, RETP = NULL)
haemdata |
a vector or data frame containing (at least) the 7 haematological variables, either with the same names as the parameters below, or (not recommended) without names but in the same order as the parameters. |
HCT |
haematocrit level [%] |
HGB |
haemoglobin level [g/dL] |
MCH |
mean corpuscular haemoglobin [pg] |
MCHC |
mean corpuscular haemoglobin concentration [g/dL] |
MCV |
mean corpuscular volume [fL] |
RBC |
red blood cell count [10^6/uL] |
RETP |
reticulocytes percent [%] |
The ABPS uses the seven haematological variables (HCT, HGB, MCH, MCHC, MCV, RBC, RETP) in order to obtain a combined score. This score is more sensitive to doping than the individual markers, and allows the detection of several types of blood doping using a single score.
The combined score is based on two classification techniques, a naive Bayesian classifier and an SVM (Support Vector Machine). The two models were trained using a database of 591 blood profiles (including 402 control samples from clean athletes and 189 samples of athletes who abused of an illegal substance); the two scores were then combined using ensemble averaging to obtain the final ABPS score.
The ABPS is part of the Athlete Biological Passport program managed by the World Anti-Doping Agency. While it is not a primary marker of doping, it has been used as corroborative evidence (see e.g. https://jurisprudence.tas-cas.org/Shared%20Documents/2773.pdf)
a vector containing the ABPS score(s). Scores between 0 and 1 indicate a possible suspicion of doping; a score above 1 should only be found in 1 in 1000 male athletes.
The values for the markers can be specified using either a data frame containing (at least) the 7 haematological variables, or using seven named parameters, but not both at the same time.
The calculation of the ABPS depends on two sets of parameters, for the two machine learning techniques (naive Bayesian classifier and Support Vector Machine), which are provided in the package.
Each parameter must be in a prespecified range; parameters outside this range are constrained to the min (respectively max) values.
Note that several versions of the ABPS were developed (including
several different combinations of parameters). The version provided
in this package provides the same results as the WADA version
included in their ADAMS database. However, some values calculated
with other versions of the software have also been distributed (see
the help page for the blooddoping dataset for an example).
Sottas, P.E., N. Robinson, S. Giraud, et al., Statistical classification of abnormal blood profiles in athletes. Int J Biostat, 2006. 2(1): p. 1557-4679.
https://jurisprudence.tas-cas.org/Shared%20Documents/2773.pdf
ABPS(HCT=43.2, HGB=14.6, MCH=31.1, MCHC=33.8, MCV=92.1, RBC=4.69, RETP=0.48) ABPS(data.frame(HCT=43.2, HGB=14.6, MCH=31.1, MCHC=33.8, MCV=92.1, RBC=4.69, RETP=0.48)) ABPS(c(43.2, 14.6, 31.1, 33.8, 92.1, 4.69, 0.48)) data(blooddoping); ABPS(blooddoping) data(bloodcontrol); ABPS(bloodcontrol)ABPS(HCT=43.2, HGB=14.6, MCH=31.1, MCHC=33.8, MCV=92.1, RBC=4.69, RETP=0.48) ABPS(data.frame(HCT=43.2, HGB=14.6, MCH=31.1, MCHC=33.8, MCV=92.1, RBC=4.69, RETP=0.48)) ABPS(c(43.2, 14.6, 31.1, 33.8, 92.1, 4.69, 0.48)) data(blooddoping); ABPS(blooddoping) data(bloodcontrol); ABPS(bloodcontrol)
A dataset containing the result of the analysis of 13 blood samples from different individuals.
bloodcontrolbloodcontrol
A data frame with 13 rows and 12 haematological variables:
haematocrit [%]
haemoglobin [g/dL]
immature reticulocyte fraction [%]
mean corpuscular haemoglobin [pg]
mean corpuscular haemoglobin concentration [g/dL]
mean corpuscular volume [fL]
red blood cell count [10^6/uL]
red blood cell distribution width [fL]
reticulocyte count [10^6/uL]
reticulocyte percentage [%]
OFF-score
Abnormal Blood Profile Score
These samples are assumed to represent normal population.
One of the rows actually belongs to one of the authors of this package, who promises that he was not doped.
Swiss Laboratory for Doping Analyses (LAD), with some calculations performed by the World Anti-Doping Agency (WADA).
A dataset containing the result of the analysis of 13 blood samples, taken over a period of 5 years, of a female athlete who was convicted of doping on the basis of the Athlete Biological Passport.
blooddopingblooddoping
A data frame with 13 rows and 11 variables (including 10 haematological variables):
the date of test
haematocrit [%]
haemoglobin [g/dL]
mean corpuscular haemoglobin [pg]
mean corpuscular haemoglobin concentration [g/dL]
mean corpuscular volume [fL]
red blood cell count [10^6/uL]
reticulocyte count [10^6/uL]
reticulocyte percentage [%]
OFF-score
Abnormal Blood Profile Score
In November 2012, the athlete was convicted of doping by the Court of arbitration for Sport, the evidence showing at least two occurences of doping, in summer 2009 (shortly before the test of 2 July 2009) and in June 2011, likely using an agent such as recombinant erythropoietin (rhEPO).
Doping is indicated in particular by high haemoglobin values associated with very low reticulocyte % and high OFF scores (a combination of these two variables).
The data tables published in the original source contain several typos, as confirmed by the World Anti-Doping Agency (WADA). In particular, values for RETP in rows 8 and 10 were swapped and the MCHC value for row 7 was incorrect. This package's source code contains both the original data and the details of the corrections that were applied.
The ABPS values provided are very close (within <2%) to those
obtained using the ABPS function, but some of them were likely
calculated using different (older) versions of the ABPS code, which
may explain some of these differences.
https://jurisprudence.tas-cas.org/Shared%20Documents/2773.pdf
The OFFscore function computes the value of the OFF-hr
score (or OFF score), a combination of the haemoglobin level and
the percentage of reticulocytes, used for detecting blood doping.
OFFscore(haemdata = NULL, HGB = NULL, RETP = NULL)OFFscore(haemdata = NULL, HGB = NULL, RETP = NULL)
haemdata |
a vector or data frame containing (at least) the 2 haematological variables, either with the same names as the parameters below, or (not recommended) without names but in the same order as the parameters. |
HGB |
level of haemoglobin HGB [g/dL] |
RETP |
percentage of reticulocytes [%] |
The OFF-hr score is defined as 10*HGB [g/dL] - 60*sqrt(RETP [%]).
It is one of the parameters of the Athlete Biological Passport (ABP) program managed by the World Anti-Doping Agency (WADA), and is routinely used to identify athletes who use a substance prohibited by anti-doping rules (see e.g. https://jurisprudence.tas-cas.org/Shared%20Documents/4006.pdf).
The rationale for using this score for detecting blood doping is the following: if a manipulation (use of transfusion or of erythropoietic stimulating agents (ESA) such as recombinant human erythropoietin (rhEPO)) increases the number of circulating red cells (and thus increases the level of haemoglobin), the organism will react by stopping its own production of red blood cells. This negative feedback will be observed in the reduced percentage of reticulocytes (immature red blood cells). Such a combination of elevated HGB and reduced RETP, which will produce a high OFF score, is found neither naturally, nor as the consequence of a medical condition, and is thus indicative of doping.
The OFF score will pick up both withdrawal of blood (which induces a reduction in haemoglobin, a rise in reticulocytes, and thus a rise of RETP and a reduction of OFF score), and its re-infusion (HGB concentration increases, number of reticulocytes and RETP decrease, and OFF score increases).
value of the OFF score.
The values for HGB and RETP can be specified using either a data frame containing (at least) the 2 haematological variables, or using two named parameters, but not both at the same time.
The original OFF-hr score, as described by Gore et al., expects the haemoglobin level HGB to be specified in g/L. In order to be coherent with other functions in the package, this function assumes that HGB is specified in g/dL, and will multiply the value by 10. A warning will be emitted if units used seem wrong.
Gore, C.J., R. Parisotto, M.J. Ashenden, et al., Second-generation blood tests to detect erythropoietin abuse by athletes. Haematologica, 2003. 88(3): p. 333-44.
OFFscore(HGB=14.6, RETP=0.48) data(blooddoping) OFFscore(HGB=blooddoping$HGB, RETP=blooddoping$RETP)OFFscore(HGB=14.6, RETP=0.48) data(blooddoping) OFFscore(HGB=blooddoping$HGB, RETP=blooddoping$RETP)