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Acute Leukemias

Risk score model for fatal intracranial hemorrhage in acute leukemia

Abstract

To build a risk score (RS) model of fatal intracranial hemorrhage (FICH) in patients with acute leukemia, we retrospectively assessed risk factors in 792 patients newly diagnosed with acute leukemia, 41 of whom had analyzable FICH. We found that female gender (relative risk (RR)=5.234, P<0.001), acute promyelocytic leukemia (RR=4.057, P=0.003), leukocytosis (RR=3.301, P=0.004), thrombocytopenia (RR=3.283, P=0.005) and prolonged prothrombin time (RR=3.291, P=0.016) were significantly associated with occurrence of FICH in multivariate analysis. To calculate RS for FICH, one point was assigned for each risk factor, making the RS between 0 and 5. The RS model segregated patients into three prognostic groups: a low-risk group (LRG) for RS of 0 or 1; an intermediate-risk group (IRG) for RS of 2 or 3; and a high-risk group (HRG) for RS of 4 or 5. Expectation of FICH was well correlated with risk groups (all P-values <0.001). Overall survival was significantly shorter in the HRG compared with the LRG. The RS model we constructed to predict the occurrence of FICH will be verified through prospective studies.

Introduction

In patients with acute leukemia, hemorrhage is an important cause of failure of remission induction and early death.1, 2, 3 Hemorrhage in acute leukemia is caused by hemorrhagic diathesis, including disseminated intravascular coagulation (DIC), thrombocytopenia, sepsis, leukocytosis and treatment toxicity. Before the introduction of all-trans retinoic acid (ATRA), patients with acute promyelocytic leukemia (APL) were at particularly high risk for DIC and fatal hemorrhage. As methods to manage acute leukemia have improved, overall survival (OS) and relapse-free survival (RFS) have also improved, but little data is available on changes in the rate of hemorrhagic death in patients with leukemia. For example, although ATRA has been found to increase the remission rate, prolong survival and decrease hemorrhagic complications in APL, its effect on hemorrhagic death rates is not known.4, 5, 6, 7

Among the hemorrhagic complications in acute leukemia, intracranial hemorrhage (ICH) is the most serious and most common,8, 9 severely shortening the survival of patients with acute leukemia. Most fatal intracranial hemorrhages (FICH) occur during the early course of the disease (within 10 days of diagnosis), suggesting that early identification and management is crucial for preventing FICH. Early hemorrhagic death in patients with APL has been reported to be influenced by higher blast cell count at diagnosis, higher hemorrhagic score, lower plasma fibrinogen level and lower platelet counts.10, 11 It is now known, however, whether these factors can be generalized to all patients with acute leukemia, whether some of these factors may affect FICH but not other hemorrhagic complications and whether risk strategies can reduce the incidence of FICH. To answer these questions, it is necessary to perform risk analyses for FICH and to develop an adequate risk model. To our knowledge, however, no model has been constructed to predict FICH.

We have previously reported the clinical features of FICH in patients with acute leukemia.12 Based on these findings, we performed a retrospective analysis of FICH in newly diagnosed acute leukemia patients to identify the factors affecting FICH and to determine whether any group of acute leukemia patients is at high risk for FICH. From these findings, we developed a risk score model to predict FICH, and we have evaluated the feasibility and validity of our model.

Patients and methods

Patients

We examined the features of FICH in 792 patients with acute leukemia newly diagnosed between July 1989 and March 2003 at Asan Medical Center, Seoul. Until December 1999, acute leukemia was diagnosed and classified according to the French–American–British (FAB) classification system;13 after December 1999, patients were classified according to the FAB and World Health Organization classifications.14 Fatal intracranial hemorrhages was defined as an ICH that was a leading cause of death. Disease status at FICH was categorized by whether or not first complete remission (CR) was achieved. All patients were given appropriate induction chemotherapy as soon as possible after confirming a diagnosis of acute leukemia.

Clinical data

All data were collected retrospectively, including performance status, presenting symptoms, hemorrhage score, history of previous hematological disease and laboratory data. Laboratory data included complete blood counts, blood chemistry, coagulation test results, and results of bone marrow aspirate/biopsy, chromosome spreads and flow cytometry. Coagulation tests consisted of prothrombin time (PT), activated partial thromboplastin time (aPTT), plasma fibrinogen, fibrin degradation product and D-dimer.

Data regarding FICH were acquired from the acute leukemia registry at diagnosis, or from reviewing medical records. The data included the date of FICH, laboratory test results at the time of FICH, imaging studies, management of FICH, clinical signs and symptoms. In all cases, radiological imaging results, such as those of computed tomogram or magnetic resonance images, were required to confirm FICH. Patients only suspected of having FICH clinically were excluded from analysis.

Induction chemotherapies consisted of cytarabine+anthracycline based regimens in acute myeloid leukemia (AML) and VPDL (vincristine+prednisone+daunorubicin+L-asparaginase) in acute lymphoid leukemia (ALL). Induction chemotherapy during the pre-ATRA era was the same for APL as for AML, but it was changed to an ATRA-containing regimen beginning in July 1994.

The hemorrhage score was a modification of previously described methods.15 At diagnosis, patients were examined for clinically evident hemorrhage. One point each was assigned for the presence of cutaneous hemorrhage, gum bleeding, epistaxis, gastrointestinal tract bleeding or genitourinary bleeding. For each patient, the minimum score was 0 point and the maximum was 5. Prothrombin time (PT) was expressed as international normalized ratio (INR). Fever was considered present when body temperature exceeded 38.2°C.

The analyzed risk factors included APL vs acute leukemia other than APL, hemorrhage score (0 vs 1), ALL vs non-ALL, gender (male vs female), age (<40 vs 40 years), white blood cell (WBC) counts (<50 000 vs 50 000/mm3), platelets (<35 000 vs 35 000/mm3), plasma fibrinogen (<250 vs 250 mg/dl), peripheral blood blasts (<70 vs 70%), PT (<1.5 vs 1.5 INR), aPTT (<48 vs 48 s), performance status (<70 vs 70%), performance of induction chemotherapy (done vs not done) and presence of fever (none vs present). For each risk factor, the values applied were based on their median values in study groups and adjusted after testing various values for greater survival differences.

End points and survival time

The starting point was the day of diagnosis. The primary end points were OS and FICH-free survival. Survival time was the time interval from diagnosis to events such as death from any cause, FICH or to last follow-up date. Therefore, FICH-free survival was defined as the time from diagnosis to onset of FICH; in patients without FICH, FICH-free survival was defined as the time from diagnosis to death or last follow-up.

Risk score model for fatal intracranial hemorrhage

The risk score was calculated according to the number of risk factors found to be significant in multivariate analysis. Patients were subsequently stratified by risk score for a test of validity. After estimation of FICH-free survival, similar risk scores were combined to make simplified risk groups with greater survival differences. Other risk score models were developed, based on the sum of risk factors multiplied by their RRs and many modified models were developed. All-risk score models were tested to estimate their efficacies. After many comparisons among models, we selected the RS model of greatest simplicity and statistical significance.

Statistical analysis

For continuous variables, data are given as medians (ranges) and means±s.d.; s.e.m.'s, however, were used instead of s.d. when comparing groups. For nominal variables, data are reported as the number (percent) of patients, unless specified otherwise. Continuous variables were dichotomized and coded into binary variables to make various categorical comparisons. The Kaplan–Meier method was use to estimate the probability of OS and FICH-free survival. The log-rank test was used to compare the difference in survival probability between two groups. For FICH-free survival, we used both the log-rank test and the Tarone–Ware test. Univariate analysis of FICH-free survival was performed by the Kaplan–Meier method for each risk factor. Multivariate prognostic analyses and RR were determined by the Cox proportional-hazard models, using all covariates with P<0.1 by univariate analysis, plus age and gender. P-values are presented by log-rank test, and linear by linear associations were tested by the χ2 test. All P-values are two-tailed and considered significant when less than 0.05.

Results

Basic characteristics of patients with acute leukemia

Of the 792 patients assessed up to 1 August, 2003, 419 (52. 9%) had died, and 67 (8.5%) were lost to follow-up. The median follow-up in the remainder was 45.6 months (range, 0.7–180.6 months). Overlapped causes of death included graft rejection or failure in two patients (0.5%); hemorrhage in 79 (18.9%); infection in 163 (38.9%); interstitial pneumonitis in 45 (10.7%); acute respiratory distress syndrome in 19 (4.5%); acute graft-versus-host disease in 3 (0.7%); recurrence or persistence of acute leukemia in 149 (35.6%); organ failure in 50 (11.9%); and accidental death in 2 (0.5%).

The median age of our patient cohort was 41 years (range, 14–84 years), with a mean of 42.0±17 years. Of the 792 patients, 415 (52.4%) were male and 377 (47.6%) were female. At the time of diagnosis, 158 patients (19.9%) presented with various hemorrhagic problems. There were 188 (23.7%) patients with ALL, 575 (72.6%) with AML, 28 (3.5%) with acute biphenotyphic leukemia and one (0.1%) with unclassified acute leukemia.

Characteristics of patients with fatal intracranial hemorrhage

Of the 79 patients who died of hemorrhages, accounting for 9.8% of all deaths, FICH occurred in 41 (51.9%), 27 from early FICH and 14 of late FICH. The probability of OS in acute leukemia patients with FICH was significantly shorter than in those without FICH (median, 19.64 vs 0.2 months; P<0.0001; RR, 5.1; 95% confidence interval (CI), 3.7–7.1). Median days from diagnosis to hemorrhage were two (range, 0–1826 days). Median time from hemorrhage to death was very short, 2 days (range, 0–20 days). Acute promyelocytic was the most common FAB subtype (18/41, 43.9%). Table 1 summarizes the characteristics of all 41 acute leukemia patients who experienced FICH.

Table 1 Basic characteristics of patients with FICH

Univariate and multivariate analyses

Univariate analyses revealed that female gender (P=0.006). APL (P<0.001), hemorrhage score 1 (P0.001), non-ALL (P=0.032), leukocytosis (P=0.016), thrombocytopenia (P=0.003), PT (P<0.001) and aPTT (P=0.015) were significantly associated with FICH (Table 2). In contrast, age (P=0.567), plasma fibrinogen concentration (P=0.182), peripheral blood blast (P=0.698), performance status (P=0.457), performance of induction chemotherapy (P=0.226) and presence of fever (P=0.714) were not related to FICH. Multivariate analysis showed that only female gender (RR=5.234, P<0.001), APL (RR=4.057, P=0.003), leukocytosis (RR=3.301, P=0.004), thrombocytopenia (RR=3.283, P=0.005) and prolonged PT (RR=3.291, P=0.016) were significantly associated with FICH (Table 3). Figure 1 shows Kaplan–Meier estimation of probabilities of FICH-free survival in relation to gender, APL, peripheral WBC, platelet and PT.

Table 2 Univariate analysis of factors associated with fatal intracranial hemorrhage
Table 3 Multivariate analysis of factors associated with fatal intracranial hemorrhage
Figure 1
figure1

Probabilities of fatal intracranial hemorrhage-free survival in relation to (a) female gender (n=415 vs 377), (b) acute promyelocytic leukemia (n=337 vs 455), (c) leukocytosis, (d) thrombocytopenia, and (e) prothrombin time (n=434 vs 272) (Kaplan–Meier curves). Abbreviations: INR, international normalized ratio; PT, prothrombin time; WBC, white blood cell.

Risk score model for fatal intracranial hemorrhage

Patients were assigned a number (0 or 1) for each of five risk factors: female gender, APL, leukocytosis, (peripheral WBC count >50 000 cells/μl), low platelet count (<35 000 cells/μl) and prolonged PT (>1.5). Thus, all risk scores ranged from 0 to 5. Our RS model had three prognostic groups: a low-risk group (LRG) for RS of 0 or 1; an intermediate-risk group (IRG) for RS of 2 or 3; and a high-risk group (HRG) for RS of 4 or 5. Table 4 shows the results obtained when the risk score model was applied to FICH. There was a linear-by-linear correlation between risk groups and frequency of FICH (P<0.001). The frequencies of FICH in the LRG, IRG and HRG groups were 3/476 (0.6%), 30/301 (10.0%) and 8/15 (53.3%), respectively. Estimated median FICH-free survival was reached only in the HRG group, 0.13 months. FICH-free survival showed that the three groups were well separated from each other (Table 5, Figure 2; P<0.0001 for LRG vs IRG, LRG vs HRG, and IRG vs HRG). When the risk score model was applied to OS, the estimated median OS times in the LRG, IRG and HRG groups were 20.03, 13.03 and 0.26 months, respectively. Although there were no differences in OS between the LRG and ILG groups (P=0.0783) and between the IRG and HRG groups (P=0.1203), there was a significant difference between the LRG and HRG groups (P=0.0189) (Figure 3).

Table 4 Results of risk score model
Table 5 Application of risk score model to FICH-free survival and OS
Figure 2
figure2

Application of risk score model to fatal intracranial hemorrhage-free survival (Kaplan–Meier curve). Low-risk group (thin dotted line, n=670), intermediate-risk group (solid line, n=94) and high-risk group (thick dotted line, n=28, P<0.0001).

Figure 3
figure3

Application of risk score model to overall survival (Kaplan–Meier curve). Low-risk group (thin dotted line, n=670), intermediate-risk group (solid line, n=94) and high-risk group (thick dotted line, n=28).

Discussion

The frequency of FICH seen in our patients, 5.2% (41/792 patients with acute leukemia, accounting for 9.8% of all deaths), is comparable to those reported elsewhere.9, 16, 17, 18, 19, 20, 21, 22, 23, 24 Most FICH occurred soon after diagnosis, 27 within 7 days, and 13 patients presented with FICH at diagnosis. Because the median interval from FICH to death was 2 days, we estimate that 1.6% of patients with acute leukemia die from FICH without any opportunity for proper management. Although early mortality from FICH is inevitable in patients presenting with FICH, early detection and management of this condition will improve the survival of patients with acute leukemia. Thus, risk analysis and the development of a risk model to predict patients at high risk for FICH are meaningful.

Hemorrhagic complications can come from the disease itself or can be a complication of chemotherapy. Chemotherapies for acute leukemia result in severe cytopenia of long duration, thus increasing the risk of FICH. We therefore divided FICH into early and late categories, with late FICH usually occurring during chemotherapy-related cytopenia. Early FICH can also interfere with effective treatment of acute leukemia. To delineate the difference between early and late FICH, we used 7 days after diagnosis as a cutoff, because at least this length of time was required for completion of induction chemotherapy. Because our results were similar in early and late FICH, we concluded that a single formula would be applicable, regardless of the time of occurrence of FICH. Our risk score model was a strong predictor of both overall and early FICH (data not shown).

Risk factor analysis revealed that female gender, APL, leukocytosis, thrombocytopenia and prolonged PT were the risk factors for FICH. In contrast, other reports have suggested the significance of serum fibrinogen level.11, 25, 26, 27, 28, 29, 30 Most of these reports, however, concentrated on patients with DIC or APL. In our patient population, serum fibrinogen concentration did not correlate well with FICH, perhaps owing to its varied etiologies. In addition, viral infection of brain endothelial cells has been thought to cause cerebral hemorrhages,31 but this could not be confirmed clinically. Thrombocytopenia, or platelet counts ranging from 20 000 to 40 000/μl, has been found to be a cause of cerebral hemorrhages,16, 30, 32, 33 and several centers have a restrictive platelet transfusion policy, with thresholds at 10 000 to 20 000/μl for patients with acute leukemia.34, 35 In contrast, we found that platelet counts less than 35 000/μl increased the risk of FICH. Thus, our RS model may enable us to determine thresholds for platelet transfusion.

High WBC or blast counts have been reported to increase the risk of hemorrhage in acute leukemia.36, 37, 38 Although our univariate analysis showed that WBC and blast counts were associated with FICH, these terms dropped out on multivariate analysis. Thus, although leukocytosis or high blast count is associated with hemorrhagic risk, the proportion of blasts may not affect FICH.

Our finding that APL was a risk factor for FICH is in agreement with many previous reports.20, 23, 39, 40, 41, 42, 43 In contrast to the improvement in survival and decrease of hemorrhagic complications resulting from the introduction of ATRA, changes in hemorrhagic mortality have been reported to be minimal.10 In our study, FICH was not decreased after ATRA use (data not shown). Thus, despite the introduction of ATRA, APL is still a major risk factor for FICH. In agreement with previous findings, we also observed that prolonged PT was a risk factor for FICH.28, 30 We were surprised, however, to find that female gender was a high-risk factor for FICH. Although female gender and the incidence of cerebral hemorrhage may be related owing to the use of oral contraceptives and hormone replacement therapy, results to date have not been convincing.44, 45, 46 In recent epidemiological studies, age-standardized death rates for ICH stroke among women were equal to lower than those among men in all racial/ethnic groups, but women had a higher risk of death from subarachnoid hemorrhage.47, 48 Therefore, female sex hormone may increase the hazard of coagulopathy in acute leukemia, and women may be at greater risk for some subtypes of cerebral hemorrhage.

Most of the risk score models we tested were based primarily on the method of summing risk factors weighted by RR obtained from multivariate analysis and its variants. These results, however, were very complicated, and no better than the results obtained from the simple summation of risk factors. Risk scores and risk groups had a linear-by-linear association with frequency of FICH. When we applied our RS model to FICH-free survival, it was able to discriminate significantly among the three risk groups (LRG, IRG and HRG). Moreover, its ability to predict EFICH was acceptable. When applied to OS, the model showed that survival was different among the risk groups, with HRG having poorer survival than LRG. As this may mean that the group predicted to be at high risk by our model had a sufficient number of cases of FICH to shorten OS, we will have to confirm the validity of our model in prospective studies. Even after excluding patients presenting with FICH, our model was shown to be valid. In addition, our finding that early and late FICH had similar clinical features suggests that risk factors for FICH are similar regardless of its time of onset.

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Correspondence to K-H Lee.

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Kim, H., Lee, JH., Choi, SJ. et al. Risk score model for fatal intracranial hemorrhage in acute leukemia. Leukemia 20, 770–776 (2006). https://doi.org/10.1038/sj.leu.2404148

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Keywords

  • acute leukemia
  • risk
  • cerebral hemorrhage

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