ABO Group As a Thrombotic Risk Factor In Children With Acute Lymphoblastic Leukemia: A Retrospective Study Of 523 Pediatric Patients
Bibliographic record
Abstract
Abstract Introduction Children with acute lymphoblastic leukemia (ALL) are at high risk of thrombotic complications, resulting from multiple risk factors (including malignancy, medications, central venous access devices (CVADs) and inherent host characteristics). Several studies have investigated the role of ABO blood groups in the occurrence of thromboembolic events in adults. Non-O blood group has been associated with an increased risk of venous thromboembolism (VTE), with a compounding effect in the presence of thrombophilia or cancer. We hypothesized that among children with ALL receiving a standardized protocol, there would be an increased risk of thrombotic events in non-O blood group compared to O group patients. Methods We retrospectively reviewed medical charts of all children with ALL treated in our tertiary care center between 1995 and 2013 and identified those with an objectively confirmed VTE. Children were included in 3 different, but similar multiagent protocols (DFCI 95-01, 00-01 and 05-01). Patients were classified into O and non-O blood groups. Leukemia phenotype, risk category, age, gender, timing of thrombosis, localisation and thrombotic workup were also collected. Statistical analysis Risk factor for VTE was defined as a prevalence. Univariate and multivariate logistic regression models were applied to assess the association between potential risk factors and the occurrence of VTE. Results are presented as adjusted OR and 95% confidence intervals. All statistical tests were two-sided. Results Of 523 children with ALL, 50 (9.6%) had thrombosis. Blood group distribution showed 38 (76%) patients in the non-O and 12 (24%) in the O group, compared to 302 (58%) non-O and 221 (42%) O in the total cohort. Except for gender, univariate predictors of VTE were all significant (age ≥ 10 years, P = 0.023, high risk category, P = 0.001, T phenotype, P = <0.001, non-O blood group, P = 0.006, male, P = 0.79). In multivariate analysis, non-O blood group, phenotype and risk category remained significant, but not age nor gender: age ≥ 10 years, OR 1.12 [95% CI 0.53,2.34]; high risk, OR 2.45 [95% CI 1.13,5.34]; T phenotype, OR 2.48 [95% CI 1.11,5.55]; non-O blood group, OR 2.93 [95% CI 1.46, 5.87]; male, OR 0.83[ 95% CI 0.45, 1.54] (table I). The vast majority of VTE occurred at an upper limb CVAD site (64%). Other sites were lower limb (14%), cerebral venous sinus thrombosis (14%), pulmonary embolism (6%), intracardiac (6%) and multiple sites (10%). VTE mainly occurred during the induction (18%) and consolidation (72%) phases of therapy. Thrombotic workup was done in 33/50 patients, with 11 abnormal results (mostly non-diagnostic low Protein C or S levels and positive lupus anticoagulants). Positive family history was found in 2 cases, but not consistently explored. No patients had Factor V Leiden or II G20210A mutation. Discussion The estimated prevalence of VTE in children with cancer is 8% (compared with 0.7-1.4/100 000 in the general pediatric population (Athale and Chan, 2003). A large number of studies have evaluated the epidemiology and risk factors of thrombotic events in children with ALL (Caruso et al 2006, Farinasso et al, 2007, Grace et al, 2011), confirming the multifactorial etiology of VTE in these children. Significant associations between ABO blood group and VTE have been reported in several studies (Dentali et al, 2012, T.Ohira et al, 2006 and 2007, Wu O et al, 2008, Wiggins KL et al, 2009). A few studies have investigated ABO blood group as a risk factor for VTE in adult malignancies (Streiff et al, 2004,). To date, none have explored it in childhood cancer. Our results clearly identify non-O blood group as a significant independent risk factor for VTE in children with ALL. Prospective studies are needed to confirm these data. The mechanisms underlying the increased risk of VTE in non-O blood group patients remain to be elucidated. Disclosures: No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".