A computerized prediction model of hazardous inflammatory platelet transfusion outcomes

Abstract

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.

Methodology/principal findings: 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/significance: This allows the modeling of a policy of risk prevention for severe inflammatory outcomes in PC transfusion.

Figure 1. Distribution of AE clinical observations resulting from a platelet transfusion.

A. All PCs. B. PCs delivered before 3 days. C. PCs delivered from 3 to 5 days. The data are shown as percentages. FNHTR, febrile non-hemolytic transfusion reaction (fever or chill); AATRs, atypical allergic transfusion reactions (erythematous rash, urticaria, and/or pruritus or more severe reactions with angioedema); hemodynamic trouble (HT), excluding ALI (and TRALI), TACO, myocardial infarctions, and pulmonary embolism; combined ATRs, ATRs with two or more associated manifestations. We did not analyze any case with bronchospasm or anaphylaxis.

Figure 2. Concentrations of 17 soluble factors in the supernatants from 65 ATRs PCs and 59 control PCs.

The data are adjusted to pg/10⁹ platelets and expressed as the mean ± SEM. A. Factors that did not display any difference between the control and AE samples. B. Factors that had a concentration in the AE samples that was significantly higher than in the control samples or that were detected only in the ATR samples. C. Factors that were not detected in the controls, regardless of the amounts in the ATR samples (concentrations in the control and ATR samples were compared using two-tailed Student's t test, *p<0.05).

Figure 3. Release of soluble factors during platelet storage for 5 days.

The data are adjusted to pg/10⁹ platelets and expressed as the mean ± SEM. A. Factors that increased over 5 days of storage in only the control samples. B. Factors that were constantly secreted with almost equivalent amounts between days 1 and 5 in the supernatants from “pathogenic” PCs but that were not detectable at any time between days 1 and 5 in the control samples. C. Factors with invariable trace amounts—between days 1 and 5 in the control PC supernatants and with elevated concentrations, but only on days 4 and 5, in the “pathogenic” PC supernatants. D. Factors that were elevated in both the control and pathogenic supernatants, although with significant variations between the control and ATR samples (concentrations of the soluble factors on days 2–5 vs. day 1 in the same group were compared using ANOVA, *p<0.05).

Figure 4. Factors that displayed no significant modulation in both the control and ATR samples during storage.

Figure 5. Examples of areas under the ROC curves.

A. sCD40L; B. MIP-1α; C. IL13; D. BCA-1.

Figure 6. Decision tree.

A. Assays without IL13 (among 16 assays, the success rate of the sCD40L model was the highest, 78%); B. Assays with IL13 (among 17 assays, the success rate of the IL13 model was the highest, 82%).