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1.
ssrn; 2020.
Preprint en Inglés | PREPRINT-SSRN | ID: ppzbmed-10.2139.ssrn.3638427

RESUMEN

Effectively and efficiently diagnosing COVID-19 patients with accurate clinical type is essential to achieve optimal outcomes for the patients as well as reducing the risk of overloading the healthcare system. Currently, severe and non-severe COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 invasion in different types. In this study, we recruited 214 confirmed COVID-19 patients in non-severe and 148 in severe type, from Wuhan, China. The patients’ comorbidity and symptoms (including 26 features), and laboratory testing results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest (RF) models based on features in each modality were developed and validated to classify COVID-19 clinical types. Using comorbidity/symptom and laboratory results as input independently, RF models achieved >90% and >95% predictive accuracy, respectively. Input features’ importance based on Gini impurity were further evaluated and top five features from each modality were identified (age, hypertension, cardiovascular disease, gender, diabetes; D-Dimer, hsTNI, absolute neutrophil count, IL-6, and LDH, in descending order). Combining top 10 multimodal features, RF model achieved >99% predictive accuracy. These findings shed light on how the human body reacts to SARS-CoV-2 invasion as a unity and provide insights on effectively evaluating COVID-19 patient’s severity and developing personalized treatment plans accordingly. We suggest that symptoms and comorbidities can be used as an initial screening tool for triaging, while laboratory results are applied when accuracy is the priority.Funding Statement: This study was jointly supported by the National Science Foundation for Young Scientists of China (81703201), the Natural Science Foundation for Young Scientists of Jiangsu Province (BK20171076), the Jiangsu Provincial Medical Innovation Team (CXTDA2017029), the Jiangsu Provincial Medical Youth Talent program (QNRC2016548), the Jiangsu Preventive Medicine Association program (Y2018086), the Lifting Program of Jiangsu Provincial Scientific and Technological Association, and the Jiangsu Government Scholarship for Overseas Studies.Declaration of Interests: The authors declare no competing interests in this study.Ethics Approval Statement: Patient-specific identifying information (e.g., name, address of residence) was removed from data collected for this study. This study was evaluated and approved by the IRB committee of Union Hospital, Wuhan, China (approval number: 2020-IEC-J-345).


Asunto(s)
COVID-19 , Enfermedades Cardiovasculares
2.
medrxiv; 2020.
Preprint en Inglés | medRxiv | ID: ppzbmed-10.1101.2020.08.18.20176776

RESUMEN

Effectively identifying COVID-19 patients using non-PCR clinical data is critical for the optimal clinical outcomes. Currently, there is a lack of comprehensive understanding of various biomedical features and appropriate technical approaches to accurately detecting COVID-19 patients. In this study, we recruited 214 confirmed COVID-19 patients in non-severe (NS) and 148 in severe (S) clinical type, 198 non-infected healthy (H) participants and 129 non-COVID viral pneumonia (V) patients. The participants' clinical information (23 features), lab testing results (10 features), and thoracic CT scans upon admission were acquired as three input feature modalities. To enable late fusion of multimodality data, we developed a deep learning model to extract a 10-feature high-level representation of the CT scans. Exploratory analyses showed substantial differences of all features among the four classes. Three machine learning models (k-nearest neighbor kNN, random forest RF, and support vector machine SVM) were developed based on the 43 features combined from all three modalities to differentiate four classes (NS, S, V, and H) at once. All three models had high accuracy to differentiate the overall four classes (95.4%-97.7%) and each individual class (90.6%-99.9%). Multimodal features provided substantial performance gain from using any single feature modality. Compared to existing binary classification benchmarks often focusing on single feature modality, this study provided a novel and effective breakthrough for clinical applications. Findings and the analytical workflow can be used as clinical decision support for current COVID-19 and other clinical applications with high-dimensional multimodal biomedical features.


Asunto(s)
COVID-19 , Neumonía Viral , Discapacidades para el Aprendizaje
3.
medrxiv; 2020.
Preprint en Inglés | medRxiv | ID: ppzbmed-10.1101.2020.05.31.20115196

RESUMEN

The COVID-19 pandemic has brought an unprecedented crisis to the global health sector1. When recovering COVID-19 patients are discharged in accordance with throat or nasal swab protocols using reverse transcription polymerase chain reaction (RT-PCR), the potential risk of re-introducing the infection source to humans and the environment must be resolved 2,3,4. Here we show that 20% of COVID-19 patients, who were ready for a hospital discharge based on current guidelines, had SARS-CoV-2 in their exhaled breath (~105 RNA copies/m3). They were estimated to emit about 1400 RNA copies into the air per minute. Although fewer surface swabs (1.3%, N=318) tested positive, medical equipment frequently contacted by healthcare workers and the work shift floor were contaminated by SARS-CoV-2 in four hospitals in Wuhan. All air samples (N=44) appeared negative likely due to the dilution or inactivation through natural ventilation (1.6-3.3 m/s) and applied disinfection. Despite the low risk of cross environmental contamination in the studied hospitals, there is a critical need for strengthening the hospital discharge standards in preventing re-emergence of COVID-19 spread.


Asunto(s)
COVID-19
4.
medrxiv; 2020.
Preprint en Inglés | medRxiv | ID: ppzbmed-10.1101.2020.05.18.20105841

RESUMEN

Effectively and efficiently diagnosing COVID-19 patients with accurate clinical type is essential to achieve optimal outcomes of the patients as well as reducing the risk of overloading the healthcare system. Currently, severe and non-severe COVID-19 types are differentiated by only a few clinical features, which do not comprehensively characterize complicated pathological, physiological, and immunological responses to SARS-CoV-2 invasion in different types. In this study, we recruited 214 confirmed COVID-19 patients in non-severe and 148 in severe type, from Wuhan, China. The patients' comorbidity and symptoms (26 features), and blood biochemistry (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest (RF) models using features in each modality were developed and validated to classify COVID-19 clinical types. Using comorbidity/symptom and biochemistry as input independently, RF models achieved >90% and >95% predictive accuracy, respectively. Input features' importance based on Gini impurity were further evaluated and top five features from each modality were identified (age, hypertension, cardiovascular disease, gender, diabetes; D-Dimer, hsTNI, neutrophil, IL-6, and LDH). Combining top 10 multimodal features, RF model achieved >99% predictive accuracy. These findings shed light on how the human body reacts to SARS-CoV-2 invasion as a unity and provide insights on effectively evaluating COVID-19 patient's severity and developing treatment plans accordingly. We suggest that symptoms and comorbidities can be used as an initial screening tool for triaging, while biochemistry and features combined are applied when accuracy is the priority.


Asunto(s)
Enfermedades Cardiovasculares , Diabetes Mellitus , Hipertensión , Enfermedad del Bosque de Kyasanur , COVID-19
5.
arxiv; 2020.
Preprint en Inglés | PREPRINT-ARXIV | ID: ppzbmed-2005.06546v1

RESUMEN

The COVID-19 is sweeping the world with deadly consequences. Its contagious nature and clinical similarity to other pneumonias make separating subjects contracted with COVID-19 and non-COVID-19 viral pneumonia a priority and a challenge. However, COVID-19 testing has been greatly limited by the availability and cost of existing methods, even in developed countries like the US. Intrigued by the wide availability of routine blood tests, we propose to leverage them for COVID-19 testing using the power of machine learning. Two proven-robust machine learning model families, random forests (RFs) and support vector machines (SVMs), are employed to tackle the challenge. Trained on blood data from 208 moderate COVID-19 subjects and 86 subjects with non-COVID-19 moderate viral pneumonia, the best result is obtained in an SVM-based classifier with an accuracy of 84%, a sensitivity of 88%, a specificity of 80%, and a precision of 92%. The results are found explainable from both machine learning and medical perspectives. A privacy-protected web portal is set up to help medical personnel in their practice and the trained models are released for developers to further build other applications. We hope our results can help the world fight this pandemic and welcome clinical verification of our approach on larger populations.


Asunto(s)
COVID-19 , Neumonía
6.
medrxiv; 2020.
Preprint en Inglés | medRxiv | ID: ppzbmed-10.1101.2020.04.03.20052175

RESUMEN

Background: Respiratory and faecal aerosols play a suspected role in transmitting the SARS-CoV-2 virus. We performed extensive environmental sampling in a dedicated hospital building for Covid-19 patients in both toilet and non-toilet environments, and analysed the associated environmental factors. Methods: We collected data of the Covid-19 patients. 107 surface samples, 46 air samples, two exhaled condensate samples, and two expired air samples were collected were collected within and beyond the four three-bed isolation rooms. We reviewed the environmental design of the building and the cleaning routines. We conducted field measurement of airflow and CO2 concentrations. Findings: The 107 surface samples comprised 37 from toilets, 34 from other surfaces in isolation rooms (ventilated at 30-60 L/s), and 36 from other surfaces outside isolation rooms in the hospital. Four of these samples were positive, namely two ward door-handles, one bathroom toilet-seat cover and one bathroom door-handle; and three were weakly positive, namely one bathroom toilet seat, one bathroom washbasin tap lever and one bathroom ceiling-exhaust louvre. One of the 46 air samples was weakly positive, and this was a corridor air sample. The two exhaled condensate samples and the two expired air samples were negative. Interpretation: The faecal-derived aerosols in patients' toilets contained most of the detected SARS-CoV-2 virus in the hospital, highlighting the importance of surface and hand hygiene for intervention.


Asunto(s)
COVID-19
7.
Chinese Journal of Radiological Medicine and Protection ; (12): E005-E005, 2020.
Artículo en Chino | WPRIM (Pacífico Occidental), WPRIM (Pacífico Occidental) | ID: covidwho-11840

RESUMEN

X-ray imaging is an important method for the diagnosis of corona virus disease(COVID-19), but there is a risk of nosocomial infection during X-ray imaging diagnosis. By analyzing the process of X-ray imaging diagnosis and the possible infection factors in hospital, Jiangsu province took the lead in issuing the Guideline for the nosocomial infection prevention and control of X-ray imaging diagnosis of COVID-19. This guideline clarifies the basic requirements for controlling infections during X-ray imaging diagnosis, the specific measures for staff protection, disinfection of personnel and places, and the protection and disinfection of subjects, which is instructive for field work. It is worth noting that while focusing on controlling infections, the principle of optimal protection for medical exposure cannot be ignored.

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