Decision‐tree algorithm for optimized hematopoietic progenitor cell–based predictions in peripheral blood stem cell mobilization

Wu, Chia Yun; Chiou, Tzeon Jye; Liu, Chun Yu; Lin, Feng; Lin, Jeong‐Shi; Hung, Ming-Hui; Hsiao, Liang Tsai; Yen, Chueh Chuan; Gau, Jyh Pyng; Yen, Hsiu Ju; Hung, Giun Yi; Hsu, Hui‐Chun; Tzeng, Cheng Hwai; Liu, Jing Hwang; Yu, Yuan Bin

doi:10.1111/trf.13666

Cited by 9 publications

(11 citation statements)

References 47 publications

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“…The risk of net loan default brings serious consequences to the P2P platform, such as slow return of capital, fragmentation of capital chain, bankruptcy, and liquidation of enterprises. These consequences bring about economic losses that P2P companies and investors cannot recover and also cast a shadow on the healthy and sustainable development of the entire P2P industry [8]. In the analysis of P2P network credit management, it is seen that the risk nature of the credit risk in the network loan platform and the traditional bank lending is the same, because the borrower is unable to achieve the initial repayment agreement under various factors, making the interest of the net loan company damaged.…”

Section: State Of the Artmentioning

confidence: 99%

RETRACTED ARTICLE: P2P net loan default risk based on Spark and complex network analysis based on wireless network element data environment

Tong

Chen

2019

J Wireless Com Network

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P2P net loan is the latest financial lending platform business, which is a new way of borrowing under the background of rapid development of the mobile Internet. Since the beginning of the new century, net loan default has caused P2P companies to break up funds and operate without continuity, which has become an important factor affecting the healthy development of the industry. Therefore, starting from the actual management situation of P2P net loan platform, the default risk of net loan was studied based on Spark technology in wireless network environment. The decision tree data mining algorithm was introduced to construct the early warning model of the net loan default risk, which achieved effective control of risk links. From the fuzzy characteristics that affected the uncertain factors of net loan credit, a hybrid algorithm model of C4.5 decision tree optimization was established. The simulation results show that the hybrid optimization model has good application value.

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Section: State Of the Artmentioning

confidence: 99%

RETRACTED ARTICLE: P2P net loan default risk based on Spark and complex network analysis based on wireless network element data environment

Tong

Chen

2019

J Wireless Com Network

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“…These markers can be regarded as reflecting a left shift of leukocytes, rather than a direct measurement of stem cells in a sample. On the other hand, the Sysmex hematology analyzer uses a special lysis reagent for differentiation of immature cells [9]. Therefore, it is possible that HPCs reflect the immature cell population more specifically than parameters from the Siemens and Coulter analyzers.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, hematopoietic progenitor cells (HPCs) measured by the Sysmex hematology analyzer (Sysmex, Kobe, Japan) have been used as an estimate of immature cells in PB and as a marker for initiation of apheresis [7,8]. The HPC count by the Sysmex analyzer is determined by the combination of cell size and density, along with a resistance characteristic of immature cells to a lysis reagent [9]. However, HPCs can only be utilized in laboratories using the Sysmex analyzer.…”

Section: Introductionmentioning

confidence: 99%

Identification of cell morphology parameters from automatic hematology analyzers to predict the peripheral blood CD34-positive cell count after mobilization

et al. 2017

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Optimal timing of apheresis initiation is important for maximizing the hematopoietic stem cell (HSC) yield. This study aimed to identify useful parameters from automatic hematology analyzers for predicting the peripheral blood CD34+ cell count after mobilization. We prospectively enrolled 53 healthy donors and 72 patients, and evaluated 43 cell morphology parameters from Unicel DxH800 (Beckman Coulter, USA) and Advia 2120i (Siemens Healthcare Diagnostics, USA). The correlation of each parameter with the CD34+ cell count in pre-apheresis blood samples was analyzed. The delta neutrophil index (DNI) from Advia 2120i, standard deviation of volume of neutrophils and monocytes (SD-V-NE and SD-V-MO), standard deviation of conductivity of neutrophils and monocytes (SD-C-NE and SD-C-MO), mean conductivity of neutrophils and monocytes (MN-C-NE and MN-C-MO), and standard deviation of axial light loss of neutrophils and monocytes (SD-AL2-NE and SD-AL2-MO) from DxH800 showed significant correlations with the CD34+ cell count. SD-V-NE, SD-C-NE, and SD-C-MO showed good or fair area under the curve values for the prediction of the CD34+ cell count. SD-V-NE, SD-C-NE, and SD-C-MO from DxH800 will provide rapid, useful information for the initiation of apheresis after mobilization.

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“…Acid-citrate-dextrose (ACD) was used as an anticoagulant with an ACD-to-whole blood ratio of 1:13 to 1:14 [ 25 , 26 ]. The leukapheresis began when peripheral blood hematopoietic progenitor cells reached 20 × 10 6 cells/L [ 16 , 27 , 28 ].…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, only 31.3% of the patients (71 of 227) had MM [ 15 ]. Wu et al [ 16 ] reported a decision-tree algorithm for prediction of PMs in a cohort with positive and negative predictive values of 92.3% and 70.0%, respectively. Nonetheless, only 19% of those patients had MM.…”

Section: Introductionmentioning

confidence: 99%

Risk Factors and Outcomes of Stem Cell Mobilization Failure in Multiple Myeloma Patients

Hsu

Tsai

Liu

et al. 2022

Transfus Med Hemother

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Introduction: Autologous hematopoietic stem cell transplantation (ASCT) is a well-established treatment for patients with multiple myeloma (MM), and adequate stem cell collection must be assured before ASCT. However, prediction of poor mobilizers (PMs) is still difficult despite several risk factors for mobilization failure having been identified. Methods: We retrospectively analyzed MM patients at Taipei Veterans General Hospital in Taiwan who underwent stem cell collection between October 2006 and August 2020. A CD34+ cell collection of <1 × 106 cells/kg was defined as a mobilization failure. The primary endpoint was mobilization failure. The secondary endpoint was overall survival (OS). Odds ratios (ORs) and 95% confidence intervals (CIs) for mobilization failure were calculated using a logistic regression model. The cumulative incidence of mortality was estimated using the Kaplan-Meier method. Results: In the multivariate analysis, absolute monocyte count <500/µL (adjusted OR 10.75, 95% CI: 1.82–63.57, p = 0.009), platelet count <150,000/µL (adjusted OR 12.49, 95% CI: 2.65–58.89, p = 0.001) before mobilization, and time interval from diagnosis to stem cell harvest ≥180 days (adjusted OR 7.69, 95% CI: 1.61–36.87, p = 0.011) were risk factors for PMs. PM patients had poorer OS compared to patients with successful stem cell collection in the univariate analysis (log-rank test p = 0.027). The predicted probability of PMs was estimated by the multiple logistic regression model with a sensitivity of 84.6% and a specificity of 84.0%. Conclusion: Absolute monocyte count <500/µL, platelet count <150,000/µL, and treatment duration more than 180 days before stem cell mobilization are risk factors for unsuccessful stem cell collection. Our prediction models have high sensitivity and specificity for mobilization failure prediction and allow for early interventions for possible PMs.

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Decision‐tree algorithm for optimized hematopoietic progenitor cell–based predictions in peripheral blood stem cell mobilization

Abstract: A CART algorithm incorporating host predictors and HPC and MNC counts improves predictions in a successful harvest and might reduce the necessity of monitoring peripheral CD34+ cells.

Cited by 9 publications

References 47 publications

RETRACTED ARTICLE: P2P net loan default risk based on Spark and complex network analysis based on wireless network element data environment

RETRACTED ARTICLE: P2P net loan default risk based on Spark and complex network analysis based on wireless network element data environment

Identification of cell morphology parameters from automatic hematology analyzers to predict the peripheral blood CD34-positive cell count after mobilization

Risk Factors and Outcomes of Stem Cell Mobilization Failure in Multiple Myeloma Patients

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