Objectives To study abnormality of spirometry, six-minute walk distance, and chest radiograph among patients recovered from Coronavirus Disease 2019 (COVID-19). Methods and study design A prospective cohort study was conducted in 87 COVID-19 confirmed cases who recovered and discharged from a medical school hospital in Thailand. At the follow-up visit on day 60 after onset of symptoms, patients underwent an evaluation by spirometry (FVC, FEV1, FEV1/FVC, FEF25-75, and PEF), a six-minute-walk test (6MWT), and a chest radiograph. Results There were 35 men and 52 women, with a mean age of 39.6±11.8 years and the mean body mass index (BMI) was 23.8±4.3 kg/m2. Of all, 45 cases had mild symptoms; 35 had non-severe pneumonia, and 7 had severe pneumonia. Abnormality in spirometry was observed in 15 cases (17.2%), with 8% of restrictive defect and 9.2% of obstructive defect. Among the patients with an abnormal spirometry, the majority of the cases were in the severe pneumonia group (71.4%), compared with 15.6% in the non-severe pneumonia group, and 10.2% in the mild symptom group (p = 0.001). The mean six-minute-walk distance (6MWD) in the mild symptom and non-severe pneumonia groups was 538±56.8 and 527.5±53.5 meters, respectively. Although the severe pneumonia group tended to have a shorter mean 6-min walking distance, but this was not statistically significant (p = 0.118). Twelve patients (13.8%) had abnormal chest radiographs that showed residual fibrosis. This abnormality was more common in the severe pneumonia group (85.7%) and in others (7.5%) (p<0.001). Conclusions Abnormal spirometry was noted in 17.2% of COVID-19 survivors with both restrictive and obstructive defects. Severe COVID-19 pneumonia patients had higher prevalence rates of abnormal spirometry and residual fibrosis on the chest radiographs when compared to patients in the mild symptom and non-severe pneumonia groups.

Objectives The coronavirus disease 2019 (COVID-19) has become a worst pandemic. The clinical characteristics vary from asymptomatic to fatal. This study aims to examine the association between body mass index (BMI) levels and the severity of COVID-19. Methods and study design A cohort study included 147 adult patients with confirmed COVID-19 were categorized into 4 groups by BMI levels on admission: <18.5 (underweight), 18.5–22.9 (normal weight), 23.0–24.9 (overweight), and ≥25.0 kg/m2 (obese). Rates of pneumonia, severe pneumonia, acute kidney injury (AKI), and ICU stay during hospitalization across BMI group was determined. Logistic regression analysis was used to determine the association between BMI and severe pneumonia. Results Of the totals, patients having a BMI <18.5, 18.5–22.9, 23.0–24.9, and ≥25.0 kg/m2 were 12.9%, 38.1%, 17.7%, and 31.3%, respectively. The rates of pneumonia and severe pneumonia tended to be higher in patients with higher BMI, whereas the rates of AKI and ICU stay were higher in patients with BMI <18.5 kg/m2 and ≥ 25 kg/m2, when compared to patients with normal BMI. After controlling for age, sex, diabetes, hypertension and dyslipidemia in the logistic regression analysis, having a BMI ≥25.0 kg/m2 was associated with higher risk of severe pneumonia (OR 4.73; 95% CI, 1.50–14.94; p = 0.003) compared to having a BMI 18.5–22.9 kg/m2. During admission, elevated hemoglobin and alanine aminotransferase levels on day 7 and 14 of illness were associated with higher BMI levels. In contrast, rising of serum creatinine levels was observed in underweight patients on days 12 and 14 of illness. Conclusions Obesity in patients with COVID-19 was associated with severe pneumonia and adverse outcomes such as AKI, transaminitis and ICU stay. Underweight patients should be closely monitored for AKI. Further studies in body composition are warranted to explore the links between adiposity and COVID-19 pathogenesis.

Recently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration in COVID-19 patients after the recent resolution of disease using deep learning. The study was a prospective study of 76 post COVID-19 patients and 40 healthy individuals. The diagnoses of post COVID-19 patients were based on more than the eighth week after onset of symptoms. Voice samples of an ‘ah’ sound, coughing sound and a polysyllabic sentence were collected and preprocessed to log-mel spectrogram. Transfer learning using the VGG19 pre-trained convolutional neural network was performed with all voice samples. The performance of the model using the polysyllabic sentence yielded the highest classification performance of all models. The coughing sound produced the lowest classification performance while the ability of the monosyllabic ‘ah’ sound to predict the recent COVID-19 fell between the other two vocalizations. The model using the polysyllabic sentence achieved 85% accuracy, 89% sensitivity, and 77% specificity. In conclusion, deep learning is able to detect the subtle change in voice features of COVID-19 patients after recent resolution of the disease.