Guiding principles for the management of ITP, based on the bleeding risk are: (1) Decide when treatment is needed and at the same platelet count level, patients may have bleeding Association between thrombocytopenia and bleeding. Thrombocytopenia — Comprehensive overview covers symptoms, causes, treatment of a low platelet count. Sometimes it can occur in association with a bacterial Escherichia coli (E. coli) infection, such as may be acquired Patient Care & Health Information · Diseases & Conditions; Thrombocytopenia. This bleeding is observed mostly in acute myeloid leukemia patients, 6%–7% of whom Several risk factors for bleeding and their relation to platelet counts were .. Relationship between platelet count and bleeding risk in.
Dashed lines represent bleeding probability scale on the right y-axis over time. A patient 1 and B patient 2. Patient 1 has a low bleeding risk with low susceptibility to low platelet counts eg, autologous transplant patientbut 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. The exact magnitude and even direction of the effects of exposure time and lag time on these associations are extremely difficult to understand intuitively.
Simulation studies can be a powerful tool to explore the possible impact of difficult to intuit effects by artificially varying the responsible parameters.
Relationship between platelet count and bleeding risk in thrombocytopenic patients.
For this purpose, the exact values of these parameters do not even have to be biologically accurate, as long as their interrelatedness is reasonably correctly specified. We therefore performed simulation studies, based on realistic assumptions about the relation between platelet counts and bleeding risk, to create examples that illustrate the possible implications of these often counterintuitive and contradictory effects on data from both randomized trials and observational studies.
Methods Simulation All simulations were performed in the statistical package R. This study was approved by the medical ethics committee of the Leiden University Medical Center, which waived the need for informed consent.
Since data would not be linked to identifying patient characteristics and data could be gathered without inconveniencing ie, contacting the patient, obtaining informed consent was considered a greater breach of privacy.
Data from the literature were used to estimate the probability of refractoriness and bleeding. Platelet counts and platelet transfusions Platelet count values were simulated by drawing from a normal distribution, as were daily decreases in platelet counts and the hour count increments observed after platelet transfusions. Platelet counts were set to decrease on average by a fraction of 0.
The variation in decrease of the platelet count was simulated with the standard deviation for the decrease set at 4. Platelet transfusions were simulated to occur on the day the platelet count dropped below the specified transfusion trigger.
For simplicity, refractoriness of any etiology was set to be irreversible over the relatively short period simulated.
Bleeding Bleeding events were simulated by comparing the simulated platelet-dependent bleeding probability to a random number between 0 and 1. A new random number was drawn from a uniform distribution ie, equal probabilities assigned to all possible values for each patient-day.
Relationship between platelet count and bleeding risk in thrombocytopenic patients.
Bleeding was set to have occurred if the platelet-dependent bleeding probability was higher than the random number. The random number can therefore be thought of as the inverse of all patient specific bleeding risk ie, if the random number was high, the patient had a low intrinsic bleeding risk and bleeding was unlikely to occur, even if the platelet-dependent bleeding probability was high.
This platelet-dependent bleeding probability was simulated to vary from day to day by applying odds ratios for bleeding based on the simulated platelet count for that and the two previous days and bleeding events simulated to have occurred on the previous 2 days. Odds ratios for bleeding based on platelet counts were applied at three different cut-off levels: The current and previous 2 days together make up the combined exposure and lag times.
The time window of 3 days, the cut-offs, and the odds ratios were all chosen for practical reasons, general biological plausibility, previous observations of time-varying associations of bleeding risk and platelet count unpublished resultsand the ability of the simulation to reproduce results from various published clinical trials on bleeding risk and platelet counts.
All odds ratios were considered to be multiplicative, for example, odds ratios of bleeding were 5 after bleeding on the previous day and 2 after bleeding 2 days previously. Different definitions would influence the bleeding incidence, but not the relation with other parameters. As further described, we also performed sensitivity analyses to explore the effect of differences in bleeding incidence. Sensitivity analyses Some sensitivity analyses were performed to explore the sensitivity of our conclusions to deviations from the assumptions used for the simulations.
We incrementally reduced the probability of refractoriness 0. Statistical analyses No statistical tests were applied. In this simulation study, the sample size was arbitrarily set at 10, patients. Presence or absence of statistical significance is wholly uninformative in this setting, since the sample size could easily be reset at, for example, a hundred-fold higher or lower number.
Whenever their platelet count dropped below their prespecified transfusion trigger a simulated platelet concentrate transfusion was followed by an increase in platelet count. Exposure time and the frequency of platelet count measurements Figure 1 shows platelet counts for a single simulated patient. Figure 1A shows the course of events if platelet counts are measured daily.
Figure 1B shows the course of events if platelet counts are measured every other day. This could, for example, happen if we applied the results from the trial depicted in Figure 1A to daily clinical practice. The clinical course remains largely the same. However, at day 16, no platelet count will be performed, and therefore no transfusion will be given. This causes the minimum exposure time to be exceeded and bleeding to occur on day The transfusion given on this day will be too late to prevent this bleeding.
Also note, due to underappreciation of the role of a minimum exposure time, we are likely to make a false assumption. If these triggers are close together and platelet counts are measured frequently, patients are likely to receive a transfusion before their counts have remained under the bleeding trigger long enough to cause bleeding.
Although bleeding is then indeed prevented, the causal mechanism behind it remains obscured. As a result, a seemingly harmless reduction in the frequency of count measurements can result in increased bleeding risk despite an equal transfusion strategy. Lag time and platelet counts at time of bleeding Another potential challenge in the interpretation of the observed association between platelet counts and bleeding risk originates from the presence of lag time ie, the time between completing the necessary exposure time and the actual occurrence of identifiable symptoms.
Figure 2 shows platelet counts and bleeding risk for two very different, both commonly encountered, patients. Both patient profiles would have a highly distortive influence on the observed association between morning platelet counts and bleeding. One patient is platelet transfusion refractory, but has a bleeding risk which is very insensitive to low platelet counts.
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The results from this patient yield a poor correlation between platelet counts and bleeding, because both are always low. The other patient has a bleeding risk which is very sensitive to platelet counts, but the risk is always lagging behind the platelet counts, due to the effect of the lag time. Therefore, as also schematically represented in Figure 3bleeding risk is low when counts are low and bleeding risk is high when counts are high. This, however, was beyond the scope of the current simulation, which, like many clinical studies, primarily considered start of first bleeding.
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. Arrows represent causal relationships between two variables. 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.
Morning platelet counts and bleeding The observed association between morning platelet counts and bleeding risks is depicted in Figure 4. This association is strongly affected by transfusion trigger policy. Figure 4 Days with bleeding events according to the morning platelet count, for different platelet count triggers.
Markers represent fraction of days, with the indicated morning platelet count, at which patients experience bleeding. This lower number of patient-days also explains the much wider confidence intervals observed at these low platelet counts. Figure 5 Patients with bleeding events, according to the platelet count used as a trigger for platelet transfusions.Low Platelets: Causes, conditions and treatment
Markers represent fraction of patients who experience bleeding at any time during the 30 day simulation period. Sensitivity analyses Results of our sensitivity analyses are shown in Figures S2 and S3corresponding to Figures 4 and 5 from the main text.
Factors that can decrease platelet production include: Leukemia Viral infections, such as hepatitis C or HIV Chemotherapy drugs Heavy alcohol consumption Increased breakdown of platelets Some conditions can cause your body to use up or destroy platelets more rapidly than they're produced. This leads to a shortage of platelets in your bloodstream.
Examples of such conditions include: Thrombocytopenia caused by pregnancy is usually mild and improves soon after childbirth. This type is caused by autoimmune diseases, such as lupus and rheumatoid arthritis. The body's immune system mistakenly attacks and destroys platelets. If the exact cause of this condition isn't known, it's called idiopathic thrombocytopenic purpura.
[Full text] Thrombocytopenia and bleeding in myelosuppressed transfusion-dependent | CLEP
This type more often affects children. Bacteria in the blood. Severe bacterial infections involving the blood bacteremia may lead to destruction of platelets.
This is a rare condition that occurs when small blood clots suddenly form throughout your body, using up large numbers of platelets. This rare disorder causes a sharp drop in platelets, destruction of red blood cells and impairment of kidney function.
Sometimes it can occur in association with a bacterial Escherichia coli E. Certain medications can reduce the number of platelets in your blood.