Clinical significance of decreased or loss of ABO blood group expression in acute myeloid leukaemia: A single-centre retrospective study.

BACKGROUND AND OBJECTIVES: Decreased or loss of ABO blood group antigen expression has been observed in acute myeloid leukaemia (AML) patients. We studied the clinical significance of this group in AML patients. MATERIALS AND METHODS: This was a retrospective, single-centre cohort study in which the data were retrieved from April 2009 to December 2019. A total of 1592 AML patients with normal ABO blood group antigen (Group I) and 65 patients of decreased or loss of ABO blood group antigen (Group II) group were enrolled. Data were collected at the time of initial admission for pathological diagnosis. To interrogate the underlying mechanism, publicly available The Cancer Genome Atlas AML data were downloaded. RESULTS: Group II consisted of 3.9% (65/1657) of AML patients. The 90-day survival (D90) probability was higher for Group II with a mean survival of 86.4 days compared to 80.6 days for Group I (p = 0.047). Group II had higher haematocrit (28.6 vs. 27.4%) and lower d-dimer, fibrinogen degradation production and C-reactive protein. Publicly available data revealed that among 11 CpG methylation sites within the ABO gene, 4 sites with elevated methylation level were associated with improved D90 survival probability and demonstrated an inverse correlation with ABO gene expression. Lower expression of the ABO gene showed improved survival trends for D90 (p = 0.058) and 180-day survival (p = 0.072). CONCLUSION: AML with decreased expression or loss of ABO blood group showed better early survival during D90. Transfusion support for this subgroup of AML patients should be meticulously performed considering serum typing.

Effectiveness of leukapheresis on early survival in acute myeloid leukemia: An observational propensity score matching cohort study.

BACKGROUND: The association between leukapheresis (LK) as a treatment option for hyperleukocytosis (HL) in patients with acute myeloid leukemia (AML) remains controversial. METHODS: Data were extracted from the electronic medical record for 2801 patients with AML between April 2009 and December 2019. LK was performed when the leukocyte count was ≥100 × 109 /L at the time initial bone marrow examination. RESULTS: A comparison between the patients with HL in the non-LK (n = 1579) and LK (n = 208) groups revealed survival probabilities (%) of 93.2% and 90.4% (P = .130) for day 30 (D30), 85.4% and 84.2% (P = .196) for D60, and 83.6% and 80.8% (P = .258) for D90, respectively. After propensity score matching, a comparison between the patients with HL in the non-LK (n = 192) and LK (n = 192) groups revealed survival probabilities (%) of 83.9% and 91.2% (P = .030) for D30, 75.0% and 84.9% (P = .015) for day 60 (D60), and 62.4% and 81.3% (P = .034) for day 90 (D90), respectively. After D150, the observed effect of LK appeared to be mitigated without a survival benefit. DISCUSSION: LK was associated with improved early survival outcomes at D30, D60, and D90 among patients with AML exhibiting HL. Thus, it may be considered a treatment option for reducing cell mass in such patients.

Transfusion support in hematopoietic stem cell transplantation.

Transfusion support for hematopoietic stem cell transplantation (HSCT) is an essential part of supportive care, and compatible blood should be transfused into recipients. As leukocyte antigen (HLA) matching is considered first and as the blood group does not impede HSCT, major, minor, bidirectional, and RhD incompatibilities occur that might hinder transfusion and cause adverse events. Leukocyte reduction in blood products is frequently used, and irradiation should be performed for blood products, except for plasma. To mitigate incompatibility and adverse events, local transfusion guidelines, hospital transfusion committees, and patient management should be considered.

Machine Learning Predicts 30-Day Outcome among Acute Myeloid Leukemia Patients: A Single-Center, Retrospective, Cohort Study.

Acute myeloid leukemia (AML) is a clinical emergency requiring treatment and results in high 30-day (D30) mortality. In this study, the prediction of D30 survival was studied using a machine learning (ML) method. The total cohort consisted of 1700 survivors and 130 non-survivors at D30. Eight clinical and 42 laboratory variables were collected at the time of diagnosis by pathology. Among them, six variables were selected by a feature selection method: induction chemotherapy (CTx), hemorrhage, infection, C-reactive protein, blood urea nitrogen, and lactate dehydrogenase. Clinical and laboratory data were entered into the training model for D30 survival prediction, followed by testing. Among the tested ML algorithms, the decision tree (DT) algorithm showed higher accuracy, the highest sensitivity, and specificity values (95% CI) of 90.6% (0.918-0.951), 70.4% (0.885-0.924), and 92.1% (0.885-0.924), respectively. DT classified patients into eight specific groups with distinct features. Group 1 with CTx showed a favorable outcome with a survival rate of 97.8% (1469/1502). Group 6, with hemorrhage and the lowest fibrinogen level at diagnosis, showed the worst survival rate of 45.5% (25/55) and 20.5 days. Prediction of D30 survival among AML patients by classification of patients with DT showed distinct features that might support clinical decision-making.