The Investigation of Pulmonary Function Changes of COVID-19 Patients in Three Months.

Background: Novel coronavirus disease 2019 (COVID-19) was discovered in December 2019 and has infected more than 80 million people worldwide, and more than 50 million people have achieved a clinical cure. In this study, the pulmonary function results of patients after clinical medicine for three months were reported. Objective: To investigate the effect of COVID-19 on lung function in patients. Methods: A retrospective analysis was performed on 56 COVID-19-infected patients who were cured after the clinical treatment at Taizhou Public Health Medical Center in Zhejiang Province from January 31, 2020, to March 10, 2020. At discharge and three months after discharge, lung function was measured, including inspiratory vital capacity (IVC), forced vital capacity (FVC), forced expiratory volume in first second (FEV1), forced expiratory volume in first second to inspiratory vital capacity (FEV1/IVC), maximum mid-expiratory flow rate (MEF), peak expiratory flow rate (PEF), and carbon monoxide dispersion (DLCO). Results: At discharge, there were 37 patients (66.1%) with pulmonary dysfunction, 22 patients (39.3%) with ventilation dysfunction, 31 cases (55.4%) with small airway dysfunction, and 16 cases (28.6%) with restricted ventilation dysfunction combined with small airway dysfunction. At 3 months after discharge, 24 of the 56 patients still had pulmonary dysfunction and all of them had small airway dysfunction, of which 10 patients (17.9%) were restricted ventilation dysfunction combined with small airway dysfunction. DLCO was measured three months after discharge. Twenty-nine patients (51.8%) had mild to moderate diffuse dysfunction. All pulmonary function indexes of 56 patients recovered gradually after 3 months after release, except FEV1/IVC, and the difference was statistically significant (P < 0.05). There were 41 patients of normal type (73.2%) and 15 patients of severe type (26.8%). Among the 15 severe patients, 8 patients (53.3%) had ventilation dysfunction at discharge, 9 patients (60%) had small airway dysfunction, 4 patients (26.7%) still had ventilation dysfunction 3 months after discharge, 7 patients (46.7%) had small airway dysfunction, and 10 patients (66.7%) had diffuse dysfunction. Among the 41 common type patients, 14 patients (34.1%) had ventilation dysfunction at discharge, 22 patients (53.7%) had small airway dysfunction, 6 patients (14.6%) still had ventilation dysfunction 3 months after discharge, 17 patients (41.5%) had small airway dysfunction, and 19 patients (46.3%) had diffuse dysfunction. Patients with severe COVID-19 had more pulmonary impairment and improved pulmonary function than normal patients. Conclusion: COVID-19 infection can cause lung function impairment, manifested as restricted ventilation dysfunction, small airway dysfunction, and diffuse dysfunction. The pulmonary function of most patients was improved 3 months after clinical cure and discharge, and some patients remained with mild to moderate diffuse dysfunction and small airway dysfunction.

Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine.

Coronavirus disease-2019 (COVID-19) has made the world more cautious about widespread viruses, and a tragic pandemic that was caused by a novel coronavirus has harmed human beings in recent years. The new coronavirus pneumonia outbreak is spreading rapidly worldwide. We collect arterial blood samples from 51 patients with a COVID-19 diagnosis. Blood gas analysis is performed using a Siemens RAPID Point 500 blood gas analyzer. To accurately determine the factors that play a decisive role in the early recognition and discrimination of COVID-19 severity, a prediction framework that is based on an improved binary Harris hawk optimization (HHO) algorithm in combination with a kernel extreme learning machine is proposed in this paper. This method uses specular reflection learning to improve the original HHO algorithm and is referred to as HHOSRL. The experimental results show that the selected indicators, such as age, partial pressure of oxygen, oxygen saturation, sodium ion concentration, and lactic acid, are essential for the early accurate assessment of COVID-19 severity by the proposed feature selection method. The simulation results show that the established methodlogy can achieve promising performance. We believe that our proposed model provides an effective strategy for accurate early assessment of COVID-19 and distinguishing disease severity. The codes of HHO will be updated in https://aliasgharheidari.com/HHO.html.

An Effective Machine Learning Approach for Identifying Non-Severe and Severe Coronavirus Disease 2019 Patients in a Rural Chinese Population: The Wenzhou Retrospective Study.

This paper has proposed an effective intelligent prediction model that can well discriminate and specify the severity of Coronavirus Disease 2019 (COVID-19) infection in clinical diagnosis and provide a criterion for clinicians to weigh scientific and rational medical decision-making. With indicators as the age and gender of the patients and 26 blood routine indexes, a severity prediction framework for COVID-19 is proposed based on machine learning techniques. The framework consists mainly of a random forest and a support vector machine (SVM) model optimized by a slime mould algorithm (SMA). When the random forest was used to identify the key factors, SMA was employed to train an optimal SVM model. Based on the COVID-19 data, comparative experiments were conducted between RF-SMA-SVM and several well-known machine learning algorithms performed. The results indicate that the proposed RF-SMA-SVM not only achieves better classification performance and higher stability on four metrics, but also screens out the main factors that distinguish severe COVID-19 patients from non-severe ones. Therefore, there is a conclusion that the RF-SMA-SVM model can provide an effective auxiliary diagnosis scheme for the clinical diagnosis of COVID-19 infection.