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Clinical Predictive Models for COVID-19: Systematic Study
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TLDR
Clinical predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19 and, therefore, help inform care and prioritize resources.Abstract:
Coronavirus Disease 2019 (COVID-19) is a rapidly emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the rapid human-to-human transmission of SARS-CoV-2, many healthcare systems are at risk of exceeding their healthcare capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds and mechanical ventilators. Predictive algorithms could potentially ease the strain on healthcare systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalised or admitted to the ICU. Here, we study clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test, require hospitalisation or intensive care. To evaluate the predictive performance of our models, we perform a retrospective evaluation on clinical and blood analysis data from a cohort of 5644 patients. Our experimental results indicate that our predictive models identify (i) patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI: 67%, 81%) and a specificity of 49% (95% CI: 46%, 51%), (ii) SARS-CoV-2 positive patients that require hospitalisation with 0.92 AUC (95% CI: 0.81, 0.98), and (iii) SARS-CoV-2 positive patients that require critical care with 0.98 AUC (95% CI: 0.95, 1.00). In addition, we determine which clinical features are predictive to what degree for each of the aforementioned clinical tasks. Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19, and therefore help inform care and prioritise resources.read more
Citations
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Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study
Fei Zhou,Ting Yu,Ronghui Du,Guohui Fan,Ying Liu,Zhibo Liu,Jie Xiang,Yeming Wang,Bin Song,Xiaoying Gu,Xiaoying Gu,Lulu Guan,Yuan Wei,Li Hui,Xudong Wu,Jiuyang Xu,Shengjin Tu,Yi Zhang,Hua Chen,Bin Cao +19 more
TL;DR: Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future.
Posted Content
Classification supporting COVID-19 diagnostics based on patient survey data.
Joanna Henzel,Joanna Tobiasz,Michał Kozielski,Małgorzata Bach,Pawel Foszner,Aleksandra Gruca,Mateusz Kania,Justyna Mika,Anna Papiez,Aleksandra Werner,Joanna Zyla,Jerzy Jaroszewicz,Joanna Polanska,Marek Sikora +13 more
TL;DR: As a part of the presented research, logistic regression and XGBoost classifiers, that allow for effective screening of patients for COVID-19, were generated and provided the basis for the DECODE service, which can serve as support in screening patients with CO VID-19 disease.
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Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study.
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