Standardising Training of Nurses in an Evidence-Based Psychosocial Intervention for Perinatal Depression: Randomized Trial of Electronic vs. Face-to-Face Training in China.

BACKGROUND: Rates of perinatal depression in China are high. The Thinking Healthy Programme is a WHO-endorsed, evidence-based psychosocial intervention for perinatal depression, requiring five days of face-to-face training by a specialist trainer. Given the paucity of specialist trainers and logistical challenges, standardized training of large numbers of nurses is a major challenge for scaling up. We developed an electronic training programme (e-training) which eliminates the need for specialist-led, face-to-face training. The aim of this study was to evaluate the effectiveness of the e-training compared to conventional face-to-face training in nursing students. METHODS: A single blind, non-inferiority, randomized controlled trial was conducted. One hundred nursing students from two nursing schools were randomly assigned to either e-training or conventional face-to-face training. RESULTS: E-training was not inferior to specialist-led face-to-face training immediately post-training [mean ENhancing Assessment of Common Therapeutic factors (ENACT) score (M) 45.73, standard deviation (SD) 4.03 vs. M 47.08, SD 4.53; mean difference (MD) -1.35, 95% CI; (-3.17, 0.46), p = 0.14]. There was no difference in ENACT scores at three months [M = 42.16, SD 4.85 vs. M = 42.65, SD 4.65; MD = -0.481, 95% CI; (-2.35, 1.39), p = 0.61]. CONCLUSIONS: E-training is a promising tool with comparative effectiveness to specialist-led face-to-face training. E-training can be used for training of non-specialists for evidence-based psychosocial interventions at scale and utilized where there is a shortage of specialist trainers, but practice under supervision is necessary to maintain competence. However, continued practice under supervision may be necessary to maintain competence.

Kinetics of anti-SARS-CoV-2 IgG antibody levels and potential influential factors in subjects with COVID-19: A 11-month follow-up study.

We aim to study kinetics of anti-SARS-CoV-2 IgG antibody levels in subjects with COVID-19 for up to 11 months and the potential influential factors. The study was a prospective longitudinal study. The analyses were based on 77 serum/plasma samples with a mean of 4 samples per participant (range 1 - 18) in 20 participants with at least one positive Polymerase Chain Reaction testing result from 19 March 2020 up to 10 February 2021. Among the subjects (median age 34.5 years, 65% male), IgG level declined with the follow-up time (per month; geometric mean ratio [GMR] 0.73; 95% CI, 0.72 - 0.74). In a small sample of subjects from the general population with COVID-19, IgG levels declined non-linearly from month 2 to 11 with individual heterogeneity in quantity and changing speed and may be associated with gender, race and the loss of smell and taste.

A nomogram predicting severe COVID-19 based on a large study cohort from China.

BACKGROUND: The use of accurate prediction tools and early intervention are important for addressing severe coronavirus disease 2019 (COVID-19). However, the prediction models for severe COVID-19 available to date are subject to various biases. This study aimed to construct a nomogram to provide accurate, personalized predictions of the risk of severe COVID-19. METHODS: This study was based on a large, multicenter retrospective derivation cohort and a validation cohort. The derivation cohort consisted of 496 patients from Jiangsu Province, China, between January 10, 2020, and March 15, 2020, and the validation cohort contained 105 patients from Huangshi, Hunan Province, China, between January 21, 2020, and February 29, 2020. A nomogram was developed with the selected predictors of severe COVID-19, which were identified by univariate and multivariate logistic regression analyses. We evaluated the discrimination of the nomogram with the area under the receiver operating characteristic curve (AUC) and the calibration of the nomogram with calibration plots and Hosmer-Lemeshow tests. RESULTS: Three predictors, namely, age, lymphocyte count, and pulmonary opacity score, were selected to develop the nomogram. The nomogram exhibited good discrimination (AUC 0.93, 95% confidence interval [CI] 0.90-0.96 in the derivation cohort; AUC 0.85, 95% CI 0.76-0.93 in the validation cohort) and satisfactory agreement. CONCLUSIONS: The nomogram was a reliable tool for assessing the probability of severe COVID-19 and may facilitate clinicians stratifying patients and providing early and optimal therapies.

Associations between CT pulmonary opacity score on admission and clinical characteristics and outcomes in patients with COVID-19.

This study investigated associations between chest computed tomography (CT) pulmonary opacity score on admission and clinical features and outcomes in COVID-19 patients. The retrospective multi-center cohort study included 496 COVID-19 patients in Jiangsu province, China diagnosed as of March 15, 2020. Patients were divided into four groups based on the quartile of pulmonary opacity score: ≤ 5%, 6-20%, 21-40% and 41% +. CT pulmonary opacity score was independently associated with age, single onset, fever, cough, peripheral capillary oxygen saturation, lymphocyte count, platelet count, albumin level, C-reactive protein (CRP) level and fibrinogen level on admission. Patients with score ≥ 41% had a dramatic increased risk of severe or critical illness [odds ratio (OR), 15.58, 95% confidence interval (CI) 3.82-63.53), intensive care unit (ICU)] admission (OR, 6.26, 95% CI 2.15-18.23), respiratory failure (OR, 19.49, 95% CI 4.55-83.40), and a prolonged hospital stay (coefficient, 2.59, 95% CI 0.46-4.72) compared to those with score ≤ 5%. CT pulmonary opacity score on admission, especially when ≥ 41%, was closely related to some clinical characteristics and was an independent predictor of disease severity, ICU admission, respiratory failure and long hospital stay in patients with COVID-19.

Age differences in clinical features and outcomes in patients with COVID-19, Jiangsu, China: a retrospective, multicentre cohort study.

OBJECTIVES: To determine the age-specific clinical presentations and incidence of adverse outcomes among patients with COVID-19 in Jiangsu, China. DESIGN AND SETTING: Retrospective, multicentre cohort study performed at 24 hospitals in Jiangsu, China. PARTICIPANTS: 625 patients with COVID-19 enrolled between 10 January and 15 March 2020. RESULTS: Of the 625 patients (median age, 46 years; 329 (52.6%) men), 37 (5.9%) were children (18 years or younger), 261 (41.8%) young adults (19-44 years), 248 (39.7%) middle-aged adults (45-64 years) and 79 (12.6%) elderly adults (65 years or older). The incidence of hypertension, coronary heart disease, chronic obstructive pulmonary disease and diabetes comorbidities increased with age (trend test, p<0.0001, p=0.0003, p<0.0001 and p<0.0001, respectively). Fever, cough and shortness of breath occurred more commonly among older patients, especially the elderly, compared with children (χ2 test, p=0.0008, 0.0146 and 0.0282, respectively). The quadrant score and pulmonary opacity score increased with age (trend test, both p<0.0001). Older patients had many significantly different laboratory parameters from younger patients. Elderly patients had the highest proportion of severe or critically-ill cases (33.0%, χ2 test p<0.0001), intensive care unit use (35.4%, χ2 test p<0.0001), respiratory failure (31.6%, χ2 test p<0.0001) and the longest hospital stay (median 21 days, Kruskal-Wallis test p<0.0001). CONCLUSIONS: Elderly (≥65 years) patients with COVID-19 had the highest risk of severe or critical illness, intensive care use, respiratory failure and the longest hospital stay, which may be due partly to their having a higher incidence of comorbidities and poor immune responses to COVID-19.

Clinical characteristics and risk factors of patients with severe COVID-19 in Jiangsu province, China: a retrospective multicentre cohort study.

BACKGROUND: Coronavirus Disease-2019 (COVID-19) pandemic has become a major health event that endangers people health throughout China and the world. Understanding the factors associated with COVID-19 disease severity could support the early identification of patients with high risk for disease progression, inform prevention and control activities, and potentially reduce mortality. This study aims to describe the characteristics of patients with COVID-19 and factors associated with severe or critically ill presentation in Jiangsu province, China. METHODS: Multicentre retrospective cohort study of all individuals with confirmed Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infections diagnosed at 24 COVID-19-designated hospitals in Jiangsu province between the 10th January and 15th March 2020. Demographic, clinical, laboratory, and radiological data were collected at hospital admission and data on disease severity were collected during follow-up. Patients were categorised as asymptomatic/mild/moderate, and severe/critically ill according to the worst level of COVID-19 recorded during hospitalisation. RESULTS: A total of 625 patients, 64 (10.2%) were severe/critically ill and 561 (89.8%) were asymptomatic/mild/moderate. All patients were discharged and no patients died. Patients with severe/critically ill COVID-19 were more likely to be older, to be single onset (i.e. not belong to a cluster of cases in a family/community, etc.), to have a medical history of hypertension and diabetes; had higher temperature, faster respiratory rates, lower peripheral capillary oxygen saturation (SpO2), and higher computer tomography (CT) image quadrant scores and pulmonary opacity percentage; had increased C-reactive protein, fibrinogen, and D-dimer on admission; and had lower white blood cells, lymphocyte, and platelet counts and albumin on admission than asymptomatic/mild/moderate cases. Multivariable regression showed that odds of being a severe/critically ill case were associated with age (year) (OR 1.06, 95%CI 1.03-1.09), lymphocyte count (109/L) (OR 0.25, 95%CI 0.08-0.74), and pulmonary opacity in CT (per 5%) on admission (OR 1.31, 95%CI 1.15-1.51). CONCLUSIONS: Severe or critically ill patients with COVID-19 is about one-tenths of patients in Jiangsu. Age, lymphocyte count, and pulmonary opacity in CT on admission were associated with risk of severe or critically ill COVID-19.

Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China.

OBJECTIVES: To determine the patterns of chest computed tomography (CT) evolution according to disease severity in a large coronavirus disease 2019 (COVID-19) cohort in Jiangsu Province, China. METHODS: This retrospective cohort study was conducted from January 10, 2020, to February 18, 2020. All patients diagnosed with COVID-19 in Jiangsu Province were included, retrospectively. Quantitative CT measurements of pulmonary opacities including volume, density, and location were extracted by deep learning algorithm. Dynamic evolution of these measurements was investigated from symptom onset (day 1) to beyond day 15. Comparison was made between severity groups. RESULTS: A total of 484 patients (median age of 47 years, interquartile range 33-57) with 954 CT examinations were included, and each was assigned to one of the three groups: asymptomatic/mild (n = 63), moderate (n = 378), severe/critically ill (n = 43). Time series showed different evolution patterns of CT measurements in the groups. Following disease onset, posteroinferior subpleural area of the lung was the most common location for pulmonary opacities. Opacity volume continued to increase beyond 15 days in the severe/critically ill group, compared with peaking on days 13-15 in the moderate group. Asymptomatic/mild group had the lowest opacity volume which almost resolved after 15 days. The opacity density began to drop from day 10 to day 12 for moderately ill patients. CONCLUSIONS: Volume, density, and location of the pulmonary opacity and their evolution on CT varied with disease severity in COVID-19. These findings are valuable in understanding the nature of the disease and monitoring the patient's condition during the course of illness. KEY POINTS: • Volume, density, and location of the pulmonary opacity on CT change over time in COVID-19. • The evolution of CT appearance follows specific pattern, varying with disease severity.