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2.
Leuk Lymphoma ; 62(14): 3516-3520, 2021 12.
Article in English | MEDLINE | ID: covidwho-1354196

ABSTRACT

Patients with acute leukemia (AL) have a high mortality rate from coronavirus disease 2019 (COVID-19). However, studies including patients with AL and COVID-19 are few. Fifty-one patients with AL and COVID-19 were included in our study. The mortality rate was 17/51 (29.4%). In all cases, death was associated with COVID-19 pneumonia. The major driver of outcome was the disease status (worse outcome was observed in newly diagnosed (OR, 6.00; 95% CI, 1.133 - 15.188) and patients with bone marrow aplasia (OR 4.148 [95% CI 1.133 - 15.188])). Higher mortality rate was associated with lower platelet count, prolonged PT, higher ISTH DIC score, CRP and LDH. Moreover, careful risk-benefit assessment regarding the continuation of anticancer therapy is required in patients receiving nonintensive and supportive therapy. Considering the high frequency of intrahospital viral transmission (50.98%), isolation of AL patients in single rooms, and permanent symptom monitoring and testing should be prioritized.


Subject(s)
COVID-19 , Leukemia , Humans , Leukemia/diagnosis , Leukemia/epidemiology , Leukemia/therapy , Risk Factors , SARS-CoV-2
3.
Curr Oncol Rep ; 23(10): 114, 2021 08 03.
Article in English | MEDLINE | ID: covidwho-1338274

ABSTRACT

PURPOSE OF REVIEW: The spread of the novel coronavirus SARS-CoV-2 and its associated disease, coronavirus disease of 2019 (COVID-19), has significantly derailed cancer care. Patients with leukemia are more likely to have severe infection and increased rates of mortality. There is paucity of information on how to modify care of leukemia patients in view of the COVID-19 risks and imposed restrictions. We review the available literature on the impact of COVID-19 on different types of leukemia patients and suggest general as well as disease-specific recommendations on care based on available evidence. RECENT FINDINGS: The COVID-19 infection impacts leukemia subtypes in variable ways and the standard treatments for leukemia have similarly, varying effects on the course of COVID-19 infection. Useful treatment strategies include deferring treatment when possible, use of less intensive regimens, outpatient targeted oral agents requiring minimal monitoring, and prioritization of curative or life-prolonging strategies. Reducing health care encounters, rational transfusion standards, just resource allocation, and pre-emptive advance care planning will serve the interests of leukemia patients. Ad hoc modifications based on expert opinions and extrapolations of previous well-designed studies are the way forward to navigate the crisis. This should be supplanted with more rigorous prospective evidence.


Subject(s)
COVID-19/epidemiology , Leukemia/therapy , COVID-19/prevention & control , COVID-19/therapy , Humans , Leukemia/classification , Leukemia/diagnosis , Leukemia/epidemiology , Patient Care , Risk Factors , SARS-CoV-2
5.
Nature ; 594(7862): 265-270, 2021 06.
Article in English | MEDLINE | ID: covidwho-1246377

ABSTRACT

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.


Subject(s)
Blockchain , Clinical Decision-Making/methods , Confidentiality , Datasets as Topic , Machine Learning , Precision Medicine/methods , COVID-19/diagnosis , COVID-19/epidemiology , Disease Outbreaks , Female , Humans , Leukemia/diagnosis , Leukemia/pathology , Leukocytes/pathology , Lung Diseases/diagnosis , Machine Learning/trends , Male , Software , Tuberculosis/diagnosis
6.
J Healthc Eng ; 2020: 6648574, 2020.
Article in English | MEDLINE | ID: covidwho-991957

ABSTRACT

For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients' lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.


Subject(s)
Deep Learning , Diagnosis, Computer-Assisted , Internet of Things , Leukemia/classification , Leukemia/diagnosis , Pattern Recognition, Automated , Algorithms , COVID-19/epidemiology , Cloud Computing , Databases, Factual , Diagnostic Imaging , Humans , Leukemia, Lymphocytic, Chronic, B-Cell/diagnosis , Leukemia, Myelogenous, Chronic, BCR-ABL Positive/diagnosis , Leukemia, Myeloid, Acute/diagnosis , Machine Learning , Neural Networks, Computer , Precursor Cell Lymphoblastic Leukemia-Lymphoma/diagnosis , Telemedicine
7.
Pediatr Infect Dis J ; 39(7): e142-e145, 2020 07.
Article in English | MEDLINE | ID: covidwho-261323

ABSTRACT

We report the first case of coronavirus disease 2019 (COVID-19) comorbid with leukemia in a patient hospitalized in Beijing, China. The patient showed a prolonged manifestation of symptoms and a protracted diagnosis period of COVID-19. It is necessary to extend isolation time, increase the number of nucleic acid detections and conduct early symptomatic treatment for children with both COVID-19 and additional health problems.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/blood , Leukemia/virology , Pneumonia, Viral/blood , Beijing/epidemiology , COVID-19 , Child, Preschool , China/epidemiology , Coronavirus Infections/pathology , Coronavirus Infections/virology , Humans , Leukemia/diagnosis , Leukemia/therapy , Male , Pandemics , Pneumonia, Viral/pathology , Pneumonia, Viral/virology , SARS-CoV-2
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