A computerized prediction model of hazardous inflammatory platelet transfusion outcomes.
BACKGROUND
Platelet component (PC) transfusion leads occasionally to inflammatory hazards. Certain BRMs that are secreted by the platelets themselves during storage may have some responsibility.


RESULTS
First, we identified non-stochastic arrangements of platelet-secreted BRMs in platelet components that led to acute transfusion reactions (ATRs). These data provide formal clinical evidence that platelets generate secretion profiles under both sterile activation and pathological conditions. We next aimed to predict the risk of hazardous outcomes by establishing statistical models based on the associations of BRMs within the incriminated platelet components and using decision trees. We investigated a large (n = 65) series of ATRs after platelet component transfusions reported through a very homogenous system at one university hospital. Herein, we used a combination of clinical observations, ex vivo and in vitro investigations, and mathematical modeling systems. We calculated the statistical association of a large variety (n = 17) of cytokines, chemokines, and physiologically likely factors with acute inflammatory potential in patients presenting with severe hazards. We then generated an accident prediction model that proved to be dependent on the level (amount) of a given cytokine-like platelet product within the indicated component, e.g., soluble CD40-ligand (>289.5 pg/109 platelets), or the presence of another secreted factor (IL-13, >0). We further modeled the risk of the patient presenting either a febrile non-hemolytic transfusion reaction or an atypical allergic transfusion reaction, depending on the amount of the chemokine MIP-1α (<20.4 or >20.4 pg/109 platelets, respectively).


CONCLUSIONS
This allows the modeling of a policy of risk prevention for severe inflammatory outcomes in PC transfusion.
GIMAP-EA3064, Université de Lyon, Saint-Étienne, France.
Kim Anh
KA
Nguyen
GIMAP-EA3064, Université de Lyon, Saint-Étienne, France.
Hind
H
Hamzeh-Cognasse
Laboratoire Hubert Curien - UMR CNRS 5516, Saint-Etienne, France.
Marc
M
Sebban
Laboratoire Hubert Curien - UMR CNRS 5516, Saint-Etienne, France.
Elisa
E
Fromont
EFS Auvergne-Loire, Saint-Etienne, France.
Patricia
P
Chavarin
EFS Auvergne-Loire, Saint-Etienne, France.
Lena
L
Absi
GIMAP-EA3064, Université de Lyon, Saint-Étienne, France.
Bruno
B
Pozzetto
GIMAP-EA3064, Université de Lyon, Saint-Étienne, France; EFS Auvergne-Loire, Saint-Etienne, France.
Fabrice
F
Cognasse
GIMAP-EA3064, Université de Lyon, Saint-Étienne, France; EFS Auvergne-Loire, Saint-Etienne, France.
Olivier
O
Garraud
D000328:Adult; D000368:Aged; D001792:Blood Platelets / Q000276:immunology*; D023201:CD40 Ligand / Q000097:blood; D054405:Chemokine CCL3 / Q000097:blood; D003198:Computer Simulation; D016207:Cytokines / Q000378:metabolism; D003663:Decision Trees; D005260:Female; D006801:Humans; D007249:Inflammation; D018793:Interleukin-13 / Q000097:blood; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D017713:Platelet Transfusion / Q000009:adverse effects*; D012306:Risk; D055815:Young Adult
C515429:CCL3 protein, human; D054405:Chemokine CCL3; D016207:Cytokines; C578095:IL13 protein, human; D018793:Interleukin-13; D023201:CD40 Ligand