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2.
J Natl Compr Canc Netw ; 20(2): 160-166, 2022 02.
Article in English | MEDLINE | ID: covidwho-1675165

ABSTRACT

BACKGROUND: Most safety and efficacy trials of the SARS-CoV-2 vaccines excluded patients with cancer, yet these patients are more likely than healthy individuals to contract SARS-CoV-2 and more likely to become seriously ill after infection. Our objective was to record short-term adverse reactions to the COVID-19 vaccine in patients with cancer, to compare the magnitude and duration of these reactions with those of patients without cancer, and to determine whether adverse reactions are related to active cancer therapy. PATIENTS AND METHODS: A prospective, single-institution observational study was performed at an NCI-designated Comprehensive Cancer Center. All study participants received 2 doses of the Pfizer BNT162b2 vaccine separated by approximately 3 weeks. A report of adverse reactions to dose 1 of the vaccine was completed upon return to the clinic for dose 2. Participants completed an identical survey either online or by telephone 2 weeks after the second vaccine dose. RESULTS: The cohort of 1,753 patients included 67.5% who had a history of cancer and 12.0% who were receiving active cancer treatment. Local pain at the injection site was the most frequently reported symptom for all respondents and did not distinguish patients with cancer from those without cancer after either dose 1 (39.3% vs 43.9%; P=.07) or dose 2 (42.5% vs 40.3%; P=.45). Among patients with cancer, those receiving active treatment were less likely to report pain at the injection site after dose 1 compared with those not receiving active treatment (30.0% vs 41.4%; P=.002). The onset and duration of adverse events was otherwise unrelated to active cancer treatment. CONCLUSIONS: When patients with cancer were compared with those without cancer, few differences in reported adverse events were noted. Active cancer treatment had little impact on adverse event profiles.


Subject(s)
COVID-19 , Neoplasms , COVID-19 Vaccines , Humans , Neoplasms/drug therapy , Prospective Studies , RNA, Messenger , SARS-CoV-2
3.
Acad Radiol ; 29(1): 129-136, 2022 01.
Article in English | MEDLINE | ID: covidwho-1525649

ABSTRACT

Coronavirus disease 2019 (COVID-19) has significantly disrupted medical education around the world and created the risk of students missing vital education and experience previously held within actively engaging in-person activities by switching to online leaning and teaching activities. To retain educational yield, active learning strategies, such as microlearning and visual learning tools are increasingly utilized in the new digital format. This article will introduce the challenges of a digital learning environment, review the efficacy of applying microlearning and visual learning strategies, and demonstrate tools that can reinforce radiology education in this constantly evolving digital era such as innovative tablet apps and tools. This will be key in preserving and augmenting essential medical teaching in the currently trying socially and physically distant times of COVID-19 as well as in similar future scenarios.


Subject(s)
COVID-19 , Education, Medical , Radiology , Humans , Radiography , SARS-CoV-2
5.
Am J Health Syst Pharm ; 78(19): 1798-1799, 2021 Sep 22.
Article in English | MEDLINE | ID: covidwho-1440600
6.
Applied Artificial Intelligence ; : 1-22, 2021.
Article in English | Academic Search Complete | ID: covidwho-1402196

ABSTRACT

One of the most challenging aspects of the emergent coronavirus disease 2019 (COVID-19) pandemic caused by infection of severe acute respiratory syndrome coronavirus 2 has been the need for massive diagnostic tests to detect and track infection rates at the population level. Current tests such as reverse transcription-polymerase chain reaction can be low-throughput and labor intensive. An ultra-fast and accurate mode of detecting COVID-19 infection is crucial for healthcare workers to make informed decisions in fast-paced clinical settings. The high-dimensional, feature-rich components of Raman spectra and validated predictive power for identifying human disease, cancer, as well as bacterial and viral infections pose the potential to train a supervised classification machine learning algorithm on Raman spectra of patient serum samples to detect COVID-19 infection. We developed a novel stacked subsemble classifier model coupled with an iteratively validated and automated feature selection and engineering workflow to predict COVID-19 infection status from Raman spectra of 250 human serum samples, with a 10-fold cross-validated classification accuracy of 98.0% (98.6% precision and 98.5% recall). Furthermore, we benchmarked nine machine learning and artificial neural network models when evaluated using eight standalone performance metrics to assess whether ensemble methods offered any improvement from baseline machine learning models. Using a rank-normalized scores derived from the performance metrics, the stacked subsemble model ranked higher than the Multi-layer Perceptron, which in turn ranked higher than the eight other machine learning models. This study serves as a proof of concept that stacked ensemble machine learning models are a powerful predictive tool for COVID-19 diagnostics. [ABSTRACT FROM AUTHOR] Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

7.
Am J Health Syst Pharm ; 78(6): 527-529, 2021 03 05.
Article in English | MEDLINE | ID: covidwho-1132427
8.
Sensors (Basel) ; 21(4)2021 Feb 21.
Article in English | MEDLINE | ID: covidwho-1112769

ABSTRACT

Infrared thermography for camera-based skin temperature measurement is increasingly used in medical practice, e.g., to detect fevers and infections, such as recently in the COVID-19 pandemic. This contactless method is a promising technology to continuously monitor the vital signs of patients in clinical environments. In this study, we investigated both skin temperature trend measurement and the extraction of respiration-related chest movements to determine the respiratory rate using low-cost hardware in combination with advanced algorithms. In addition, the frequency of medical examinations or visits to the patients was extracted. We implemented a deep learning-based algorithm for real-time vital sign extraction from thermography images. A clinical trial was conducted to record data from patients on an intensive care unit. The YOLOv4-Tiny object detector was applied to extract image regions containing vital signs (head and chest). The infrared frames were manually labeled for evaluation. Validation was performed on a hold-out test dataset of 6 patients and revealed good detector performance (0.75 intersection over union, 0.94 mean average precision). An optical flow algorithm was used to extract the respiratory rate from the chest region. The results show a mean absolute error of 2.69 bpm. We observed a computational performance of 47 fps on an NVIDIA Jetson Xavier NX module for YOLOv4-Tiny, which proves real-time capability on an embedded GPU system. In conclusion, the proposed method can perform real-time vital sign extraction on a low-cost system-on-module and may thus be a useful method for future contactless vital sign measurements.


Subject(s)
Deep Learning , Intensive Care Units , Thermography/instrumentation , Vital Signs , Humans
9.
J Thorac Cardiovasc Surg ; 162(6): 1654-1664, 2021 12.
Article in English | MEDLINE | ID: covidwho-1108501

ABSTRACT

OBJECTIVE: As the Coronavirus Disease 2019 pandemic continues, appropriate management of thoracic complications from Coronavirus Disease 2019 needs to be determined. Our objective is to evaluate which complications occurring in patients with Coronavirus Disease 2019 require thoracic surgery and to report the early outcomes. METHODS: This study is a single-institution retrospective case series at New York University Langone Health Manhattan campus evaluating patients with confirmed Coronavirus Disease 2019 infection who were hospitalized and required thoracic surgery from March 13 to July 18, 2020. RESULTS: From March 13 to August 8, 2020, 1954 patients were admitted to New York University Langone Health for Coronavirus Disease 2019. Of these patients, 13 (0.7%) required thoracic surgery. Two patients (15%) required surgery for complicated pneumothoraces, 5 patients (38%) underwent pneumatocele resection, 1 patient (8%) had an empyema requiring decortication, and 5 patients (38%) developed a hemothorax that required surgery. Three patients (23%) died after surgery, 9 patients (69%) were discharged, and 1 patient (8%) remains in the hospital. No healthcare providers were positive for Coronavirus Disease 2019 after the surgeries. CONCLUSIONS: Given the 77% survival, with a majority of patients already discharged from the hospital, thoracic surgery is feasible for the small percent of patients hospitalized with Coronavirus Disease 2019 who underwent surgery for complex pneumothorax, pneumatocele, empyema, or hemothorax. Our experience also supports the safety of surgical intervention for healthcare providers who operate on patients with Coronavirus Disease 2019.


Subject(s)
COVID-19/surgery , Empyema, Pleural/surgery , Hemothorax/surgery , Pandemics , Pneumothorax/surgery , Thoracic Surgical Procedures/methods , Adult , Aged , COVID-19/complications , COVID-19/epidemiology , Empyema, Pleural/diagnosis , Empyema, Pleural/etiology , Female , Follow-Up Studies , Hemothorax/diagnosis , Hemothorax/etiology , Humans , Male , Middle Aged , New York/epidemiology , Pneumothorax/diagnosis , Pneumothorax/etiology , RNA, Viral/analysis , Retrospective Studies , SARS-CoV-2/genetics , Tomography, X-Ray Computed , Treatment Outcome
10.
Ann Acad Med Singap ; 50(1): 61-76, 2021 01.
Article in English | MEDLINE | ID: covidwho-1100577

ABSTRACT

INTRODUCTION: Teleophthalmology may assist the healthcare sector in adapting to limitations imposed on clinical practice by a viral pandemic. A scoping review is performed in this study to assess the current applications of teleophthalmology for its suitability to diagnose, monitor or manage ophthalmological conditions with accuracy. METHODS: A search of PubMed was conducted for teleophthalmology-related articles published from 1 January 2018 to 4 May 2020. Only articles that focused on the use of teleophthalmology in terms of diagnosis and management, as well as its benefits and detriments, were included. The Mixed Methods Appraisal Tool (MMAT) was used to assess the quality of the included articles. RESULTS: A total of 38 articles were assessed at the full-text level. There were 2 qualitative studies and 1 quantitative randomised controlled trial, while the majority were either quantitative descriptive studies (19, 50.0%) or quantitative non-randomised studies (16, 42.1%). Overall, 8 studies described reducing manpower requirements, 4 described reducing direct patient-doctor contact, 17 described storage of medical imaging and clinical data, and 9 described real-time teleconferencing. The MMAT analysis revealed limitations in appropriate sampling strategy in both quantitative non-randomised studies (9 of 16, 56.3%) and quantitative descriptive studies (9 of 19, 47.4%). Cost-effectiveness of teleophthalmology was not performed in any included study. CONCLUSION: This current review of the various aspects of teleophthalmology describes how it may potentially assist the healthcare sector to cope with the limitations imposed by a viral pandemic through technology. Further research is required to evaluate the cost-effectiveness of the various strategies.


Subject(s)
COVID-19/epidemiology , Ophthalmology/organization & administration , Telemedicine/organization & administration , COVID-19/prevention & control , COVID-19/transmission , Humans
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