Large Unstained Cell, Blast Suspect and Delta Neutrophil Index ll Analyzed with Automated Hematology Analyzer as Parameters for the Prediction of Acute Leukemia Relapse
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Background: Here we investigated the clinical utilities of blast suspect, large unstained cell (LUC), delta neutrophil index ll (DN ll), and delta neutrophil index l (DN l), analyzed in peripheral blood samples with automated hematology analyzers to predict the relapse of acute leukemia. Methods: We retrospectively reviewed the medical records of 112 patients, including 56 patients with acute leukemia relapse and 56 controls. Blast suspect, LUC, DN ll, and DN l were compared between the control and leukemia relapse groups. Results: Significant differences in blast suspect (P<0.001), LUC (P<0.001), DN ll (P<0.001), and DN l (P=0.002) were observed between the leukemia relapse and control groups. The areas under the curve (AUC) value was 0.927 for blast suspect (95% confidence interval [CI]: 0.8750.978, P<0.001), 0.868 for LUC (95% CI: 0.794–0.941, P<0.001), and 0.900 for DN ll (95% CI: 0.841–0.960, P<0.001). Logistic regression analysis for the prediction of leukemia relapse revealed odds ratio values of 1.52 (95% CI: 1.26–1.96, P=0.0002) for blast suspect, 1.66 (95% CI: 1.27–2.42, P=0.0019) for LUC, 1.16 (95% CI: 1.08–1.29, P=0.0014) for DN ll, and 1.05 (95% CI: 1.01–1.13, P=0.0845) for DN l. Conclusions: Multiple parameters provided by automated blood cell analyzers may serve as powerful ancillary tools for the prediction and diagnosis of leukemia relapse.
Abstract
Background: Here we investigated the clinical utilities of blast suspect, large unstained cell (LUC), delta neutrophil index ll (DN ll), and delta neutrophil index l (DN l), analyzed in peripheral blood samples with automated hematology analyzers to predict the relapse of acute leukemia. Methods: We retrospectively reviewed the medical records of 112 patients, including 56 patients with acute leukemia relapse and 56 controls. Blast suspect, LUC, DN ll, and DN l were compared between the control and leukemia relapse groups. Results: Significant differences in blast suspect (P<0.001), LUC (P<0.001), DN ll (P<0.001), and DN l (P=0.002) were observed between the leukemia relapse and control groups. The areas under the curve (AUC) value was 0.927 for blast suspect (95% confidence interval [CI]: 0.8750.978, P<0.001), 0.868 for LUC (95% CI: 0.794–0.941, P<0.001), and 0.900 for DN ll (95% CI: 0.841–0.960, P<0.001). Logistic regression analysis for the prediction of leukemia relapse revealed odds ratio values of 1.52 (95% CI: 1.26–1.96, P=0.0002) for blast suspect, 1.66 (95% CI: 1.27–2.42, P=0.0019) for LUC, 1.16 (95% CI: 1.08–1.29, P=0.0014) for DN ll, and 1.05 (95% CI: 1.01–1.13, P=0.0845) for DN l. Conclusions: Multiple parameters provided by automated blood cell analyzers may serve as powerful ancillary tools for the prediction and diagnosis of leukemia relapse.
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