Thrombocytopenia and bleeding in myelosuppressed transfusion-dependent patients: a simulation study exploring underlying mechanisms

Figures & data

Figure 1 Platelet counts, platelet transfusions, and exposure time intervals in a single simulated patient with platelet counts performed every day (A) or every other day (B).

Notes: Lines represent platelet counts over time. (A) a scenario using a transfusion trigger of 10×10⁹ platelets/L and daily platelet count measurements. This scenario will result in transfusions on days 11, 13, 16, and 18. Assuming bleeding will occur only after platelets counts have remained below the bleeding trigger (ie, 25×10⁹ platelets/L) for ≥48 hours (ie, minimum exposure time is 48 hours), there will be no bleeding in this scenario. (B) a scenario under the exact same assumptions, but with platelet counts performed every other day, instead of daily. The transfusion normally given at day 16 will be postponed till day 17, since no platelet count measurement will be performed on day 16. This delay will cause the third exposure interval (ie, interval below the bleeding trigger) to exceed 48 hours, causing bleeding in a susceptible patient.

Figure 2 Platelet counts, platelet transfusions, and bleeding probability in two simulated patients.

Notes: Solid lines represent platelet counts (scale on the left y-axis) over time. Dashed lines represent bleeding probability (scale on the right y-axis) over time. (A) patient 1 and (B) patient 2. Both patients have daily platelet count measurements performed and receive transfusions if counts drop below the trigger of 10×10⁹ platelets/L. Patient 1 has a low bleeding risk with low susceptibility to low platelet counts (eg, autologous transplant patient), but is platelet transfusion refractory. Patient 2 has normal increments, but has high susceptibility to low platelet counts (eg, acute myeloid leukemia patient receiving remission induction treatment). Note that bleeding probability in patient 2 is lagging behind low platelet counts. This patient also has good increments and subsequent normal decrease in counts. Therefore, bleeding risk is high when platelet counts are high and low when platelet counts are low.

Figure 3 Association between high bleeding risk and high platelet count on day 2 is explained by confounding, caused by low platelet count on day 1, with platelet transfusion on day 1 as an intermediate.

Notes: Arrows represent causal relationships between two variables. Boxes indicate days. Low platelet counts on day 1 cause both a transfusion on day 1 and high bleeding risk on day 2 (ie, due to lag time). The transfusion on day 1 acts as a mediator in the causal chain from low platelet counts on day 1 to high platelet counts on day 2. The association we observe on day 2, between high platelet counts and high bleeding risk, is caused by confounding by low platelet counts on day 1.

Figure 4 Days with bleeding events according to the morning platelet count, for different platelet count triggers.

Notes: Markers represent fraction of days, with the indicated morning platelet count, at which patients experience bleeding. Error bars represent 95% confidence intervals. (A) bleeding frequency at different morning platelet counts without transfusions (squares) and with a platelet count trigger of 10×10⁹ platelets/L (diamonds). (B) bleeding frequency at different morning platelet counts without transfusions (squares) and with a platelet count trigger of 50×10⁹ platelets/L (diamonds).

Figure 5 Patients with bleeding events, according to the platelet count used as a trigger for platelet transfusions.

Notes: Markers represent fraction of patients who experience bleeding at any time during the 30 day simulation period. Error bars represent 95% confidence intervals. The lowest platelet count trigger (ie, 0×10⁹ platelets/L) indicates no platelet transfusions were given, since the minimum simulated platelet count was 10⁹ platelets/L.

Figure S1 Platelet counts and bleeding events in 25 simulated patients without platelet transfusions (A) or with a transfusion trigger of 50×10⁹ platelets/L (B).

Notes: (A) First 25 patients from the simulation without platelet transfusions. (B) First 25 patients from the simulation with a platelet count trigger of 50×10⁹ platelets/L. Each line represents the consecutive morning platelet counts of a single patient. Markers at the upper right represent bleeding events. Each line of markers represents a single patient’s days with bleeding events. Lines dropping to the bottom of the graph (B) indicate patients becoming refractory to platelet transfusions and therefore remaining at 1×10⁹ platelets/L for the remainder of the simulated period.

Figure S2 Days with bleeding events according to the morning platelet count, for different platelet count triggers and different assumption violations.

Notes: Markers represent fraction of days, with the indicated morning platelet count, at which patients experience bleeding. (A, B, and C) Results for simulations with different transfusion triggers. Different lines show different assumptions from the sensitivity analyses. Assumptions were adapted incrementally (ie, previous adaptations were maintained, rather than reset, when adding a new adaptation). Circles represent results from the original simulations, squares from simulations with a probability of refractoriness of 0.5% (instead of 5%), diamonds of simulations with a bleeding probability of 0.1% (instead of 0.5%), and triangles of simulations in which bleeding risk did not increase until platelet counts dropped below 5×10⁹ platelets/L. Lines for original simulations and adapted probability of refractoriness overlap.

Figure S3 Patients with bleeding events, according to the platelet count used as a trigger for platelet transfusions and different assumption violations.

Notes: Markers represent fraction of patients who experience bleeding at any time during the 30 day simulation period. Error bars represent 95% confidence intervals. Different markers show different assumptions from the sensitivity analyses. Assumptions were adapted incrementally (i.e. previous adaptations were maintained, rather than reset, when adding a new adaptation). Circles (A) represent results from the original simulations, squares (A) from simulations with a probability of refractoriness of 0.5% (instead of 5%), circles (B) of simulations with a bleeding probability of 0.1% (instead of 0.5%), and squares (B) of simulations in which bleeding risk didn’t increase until platelet counts dropped below 5×10⁹ platelets/L.

Note: The lowest platelet count trigger (i.e. 0×10⁹ platelets/L) indicates no platelet transfusions were given, since the minimum simulated platelet count was 10⁹ platelets/L.