scholarly journals Granulocyte Transfusions in a Cohort of Neutropenic Patients with Life-Threatening Infections and Hematologic Diseases

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 3696-3696
Author(s):  
Asiri Ediriwickrema ◽  
Mrigender Virk ◽  
Jennifer Andrews

Granulocyte transfusions are inconsistently used despite observational evidence of their efficacy (Seidel et al, 2008). The Resolving Infection in Neutropenia with Granulocytes (RING) trial was incomplete due to poor enrollment and thus was underpowered to assess efficacy of granulocytes in adults with neutropenia and severe infection unresponsive to anti-microbials (Price et al, 2015). Post-hoc analysis showed a group of patients had better clinical responses with higher doses of granulocytes. We hypothesized that patients treated at Stanford University with higher doses of granulocytes had better clinical outcomes than those patients who received lower doses of granulocytes. Granulocyte transfusions are available at Stanford University from well repeat platelet donors who have recent negative infectious disease markers for transfusion-transmitted infections. Donors receive stimulation with steroids about 12 hours prior to collection. After IRB approval, a single center retrospective cohort study was done to assess all patients who had received granulocytes at Stanford University from August 2006 to June 2018. We queried the electronic health record and transfusion service information system for patient demographics, diagnosis, weight, dose and frequency of granulocyte infusion(s), time to ANC recovery >500 and the primary outcome of survival to hospital discharge. There were 45 patients identified within the transfusion service information system, but only 27 had available granulocyte dosing and clinical records for review. To evaluate for most important clinical parameters, random forest classification and regression was performed against survival to hospital discharge and time to neutropenia recovery post initiation of granulocyte infusions respectively (randomForest R package). Cox proportional hazard (coxph) models were determined using mean granulocyte dose per infusion (cells/kg/infusion), total granulocyte dose per admission (cells/kg), age, and duration of neutropenia against survival to discharge and resolution of neutropenia (survival and survminer R packages). Kaplan-Meier survival analysis was performed based on high and low mean granulocyte dosing (<0.6e9cells/kg, which is equivalent to 4 x10e10 in a 70kg patient). Twenty-seven patients age 3 to 80 years (median 38 years) with various hematologic disorders were treated with granulocyte transfusions (median 4 infusions, range 1 - 14). Fifteen of 27 (56%) patients survived to hospital discharge. Random forest classification identified mean granulocyte dose per infusion as the most influential feature in predicting survival to hospital discharge by both mean decrease in accuracy and mean decrease in Gini. Additional influential parameters included total granulocyte dose per kg, age, and duration of neutropenia. Random forest regression could not identify a clear feature that was most influential in predicting time to resolution of neutropenia. Coxph models did not identify a significant feature that predicted survival to discharge. Although all 3 patients who received high dose infusions survived compared to 50% surviving in the low dose group (median survival of 33 days), the results did not achieve significance (p = 0.15). Coxph models identified mean granulocyte dose per kg as significantly associated with resolution of neutropenia (HR 26.15, 95% CI 1.65-413.83, p = 0.021) whereas total granulocyte dose per kg over all infusions was associated with prolonged neutropenia (HR 0.69, 95% CI 0.52-0.92, p = 0.012). We present a series of granulocyte transfusions in 27 neutropenic patients with life-threatening infections not responsive to standard therapies. Though there was not a statistically significant difference in survival between those infused with high versus low dose granulocytes, the mean granulocyte dose infused per kg was associated with a resolution of neutropenia. This suggests that if one prescribes granulocytes for these patients, they should be higher dose (at least 0.6e9cells/kg). 1. Seidel MG, Peters C, Wacker A, et al. Study of granulocyte transfusions in neutropenic patients. Bone Marrow Transplant 2008;42:679-86. 2. Price TH, Boeckh M, Harrison RW, et al. Efficacy of transfusion with granulocytes from G-CSF/dexamethasone-treated donors in neutropenic patients with infection. Blood 2015;126:2153-61. Disclosures No relevant conflicts of interest to declare.

2016 ◽  
Vol 146 ◽  
pp. 370-385 ◽  
Author(s):  
Adam Hedberg-Buenz ◽  
Mark A. Christopher ◽  
Carly J. Lewis ◽  
Kimberly A. Fernandes ◽  
Laura M. Dutca ◽  
...  

Author(s):  
Ayesha Behzad ◽  
Muneeb Aamir ◽  
Syed Ahmed Raza ◽  
Ansab Qaiser ◽  
Syeda Yuman Fatima ◽  
...  

Wheat is the basic staple food, largely grown, widely used and highly demanded. It is used in multiple food products which are served as fundamental constituent to human body. Various regional economies are partially or fully dependent upon wheat production. Estimation of wheat area is essential to predict its contribution in regional economy. This study presents a comparative analysis of optical and active imagery for estimation of area under wheat cultivation. Sentinel-1 data was downloaded in Ground Range Detection (GRD) format and applied the Random Forest Classification using Sentinel Application Platform (SNAP) tools. We obtained a Sentinel-2 image for the month of March and applied supervised classification in Erdas Imagine 14. The random forest classification results of Sentinel-1 show that the total area under investigation was 1089km2 which was further subdivided in three classes including wheat (551km2), built-up (450 km2) and the water body (89 km2). Supervised classification results of Sentinel-2 data show that the area under wheat crop was 510 km2, however the built-up and waterbody were 477 km2, 102 km2 respectively. The integrated map of Sentinel-1 and Sentinel-2 show that the area under wheat was 531 km2 and the other features including water body and the built-up area were 95 km2 and 463 km2 respectively. We applied a Kappa coefficient to Sentinel-2, Sentinel-1 and Integrated Maps and found an accuracy of 71%, 78% and 85% respectively. We found that remotely sensed algorithms of classifications are reliable for future predictions.


2018 ◽  
Vol 5 (2) ◽  
pp. 175-185
Author(s):  
Akhmad Syukron ◽  
Agus Subekti

                                         AbstrakPenilaian kredit telah menjadi salah satu cara utama bagi sebuah lembaga keuangan untuk menilai resiko kredit,  meningkatkan arus kas, mengurangi kemungkinan resiko dan membuat keputusan manajerial. Salah satu permasalahan yang dihadapai pada penilaian kredit yaitu adanya ketidakseimbangan distribusi dataset. Metode untuk mengatasi ketidakseimbangan kelas yaitu dengan metode resampling, seperti menggunakan Oversampling, undersampling dan hibrida yaitu dengan menggabungkan kedua pendekatan sampling. Metode yang diusulkan pada penelitian ini adalah penerapan metode Random Over-Under Sampling Random Forest untuk meningkatkan kinerja akurasi klasifikasi penilaian kredit pada dataset German Credit.  Hasil pengujian menunjukan bahwa klasifikasi tanpa melalui proses resampling menghasilkan kinerja akurasi rata-rata 70 % pada semua classifier. Metode Random Forest memiliki nilai akurasi yang lebih baik dibandingkan dengan beberapa metode lainnya dengan nilai akurasi sebesar 0,76 atau 76%. Sedangkan klasifikasi dengan penerapan metode Random Over-under sampling Random Forest  dapat meningkatkan kinerja akurasi sebesar 14,1% dengan nilai akurasi sebesar 0,901 atau 90,1 %. Hasil penelitian menunjukan bahwa penerapan  resampling dengan metode Random Over-Under Sampling pada algoritma Random Forest dapat meningkatkan kinerja akurasi secara efektif pada klasifikasi  tidak seimbang untuk penilaian kredit pada dataset German Credit. Kata kunci: Penilaian Kredit, Random Forest, Klasifikasi, ketidakseimbangan kelas, Random Over-Under Sampling                                                  AbstractCredit scoring has become one of the main ways for a financial institution to assess credit risk, improve cash flow, reduce the possibility of risk and make managerial decisions. One of the problems faced by credit scoring is the imbalance in the distribution of datasets. The method to overcome class imbalances is the resampling method, such as using Oversampling, undersampling and hybrids by combining both sampling approaches. The method proposed in this study is the application of the Random Over-Under Sampling Random Forest method to improve the accuracy of the credit scoring classification performance on German Credit dataset. The test results show that the classification without going through the resampling process results in an average accuracy performance of 70% for all classifiers. The Random Forest method has a better accuracy value compared to some other methods with an accuracy value of 0.76 or 76%. While classification by applying the Random Over-under sampling + Random Forest method can improve accuracy performance 14.1% with an accuracy value of 0.901 or 90.1%. The results showed that the application of resampling using Random Over-Under Sampling method in the Random Forest algorithm can improve accuracy performance effectively on an unbalanced classification for credit scoring on German Credit dataset. Keywords: Imbalance Class, Credit Scoring, Random Forest, Classification, Resampling


2021 ◽  
Vol 12 ◽  
Author(s):  
Yuan Zhao ◽  
Zhao-Yu Fang ◽  
Cui-Xiang Lin ◽  
Chao Deng ◽  
Yun-Pei Xu ◽  
...  

In recent years, the application of single cell RNA-seq (scRNA-seq) has become more and more popular in fields such as biology and medical research. Analyzing scRNA-seq data can discover complex cell populations and infer single-cell trajectories in cell development. Clustering is one of the most important methods to analyze scRNA-seq data. In this paper, we focus on improving scRNA-seq clustering through gene selection, which also reduces the dimensionality of scRNA-seq data. Studies have shown that gene selection for scRNA-seq data can improve clustering accuracy. Therefore, it is important to select genes with cell type specificity. Gene selection not only helps to reduce the dimensionality of scRNA-seq data, but also can improve cell type identification in combination with clustering methods. Here, we proposed RFCell, a supervised gene selection method, which is based on permutation and random forest classification. We first use RFCell and three existing gene selection methods to select gene sets on 10 scRNA-seq data sets. Then, three classical clustering algorithms are used to cluster the cells obtained by these gene selection methods. We found that the gene selection performance of RFCell was better than other gene selection methods.


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