A recently developed pneumonia caused by SARS-CoV-2 has quickly spread across the world. Unfortunately, a simplified risk score that could easily be used in primary care or general practice settings has not been developed. The objective of this study is to identify a simplified risk score that could easily be used to quickly triage severe COVID-19 patients. All severe and critical adult patients with laboratory-confirmed COVID-19 on the West campus of Union Hospital, Wuhan, China, from 28 January 2020 to 29 February 2020 were included in this study. Clinical data and laboratory results were obtained. CURB-65 pneumonia score was calculated. Univariate logistic regressions were applied to explore risk factors associated with in-hospital death. We used the receiver operating characteristic curve and multivariate COX-PH model to analyse risk factors for in-hospital death. A total of 74 patients (31 died, 43 survived) were finally included in the study. We observed that compared with survivors, non-survivors were older and illustrated higher respiratory rate, neutrophil-to-lymphocyte ratio, D-dimer and lactate dehydrogenase (LDH), but lower SpO₂ as well as impaired liver function, especially synthesis function. CURB-65 showed good performance for predicting in-hospital death (area under curve 0.81, 95% confidence interval (CI) 0.71–0.91). CURB-65 ⩾ 2 may serve as a cut-off value for prediction of in-hospital death in severe patients with COVID-19 (sensitivity 68%, specificity 81%, F1 score 0.7). CURB-65 (hazard ratio (HR) 1.61; 95% CI 1.05–2.46), LDH (HR 1.003; 95% CI 1.001–1.004) and albumin (HR 0.9; 95% CI 0.81–1) were risk factors for in-hospital death in severe patients with COVID-19. Our study indicates CURB-65 may serve as a useful prognostic marker in COVID-19 patients, which could be used to quickly triage severe patients in primary care or general practice settings.

Background: Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. Objective: This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. Methods: A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. Results: The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. Conclusions: The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.

Objectives: This study aimed to identify the independent factors associated with depression in community-dwelling older adults in Wuhan, China.Methods: Four hundred and seventy older adults (aged ≥65 years) from four communities dwelling on Junshan Street in Wuhan, China were included in this study. Participants completed a questionnaire that asked questions pertaining to age, gender, educational level, income, living situation, care situation, social support, and social engagement. The 30-item Geriatric Depression Scale (GDS-30), the Fried frailty phenotype scale, the activities of daily living (ADL) scale, the mini nutritional assessment scale-short form (MNA-SF), and the Mini-cog scale were used to assess depression, frailty, self-care ability, malnutritional risk, and cognitive dysfunction, respectively. Differences in age, gender, educational level, income, living situation, care situation, social support, social engagement, ADL score, risk of malnutrition, frailty, and cognitive dysfunction between the non-depression (GDS-30 score <10 points) and depression groups (GDS-30 score ≥10 points) were compared using a chi-square test. Moreover, correlations between factors and depression were analyzed using Pearson’s correlation. Then, significant variables (p < 0.05) from the chi-square test were included in a multivariable logistic regression model to identify the independent factors associated with depression.Results: The incidence of depression among the participants was 14.04%. Age (p < 0.001), educational level (p < 0.001), living situation (p < 0.001), social support (p = 0.001), ADL score (p = 0.023), frailty (p < 0.001), and cognitive dysfunction (p < 0.001) were all significantly associated with depression, in which age, poor social support, frailty, and cognitive dysfunction were identified as independent factors.Conclusion: Improving social support and effective interventions for frailty and cognitive dysfunction may help relieve depression in community-dwelling older adults.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease characterized by hyperglycemia, insulin resistance, and insufficient insulin secretion. Sarcopenia, as a new complication of diabetes, is characterized by the loss of muscle mass and the progressive decline of muscle strength and function in T2DM patients, which has a serious impact on the physical and mental health of patients. Insulin resistance, mitochondrial dysfunction, and chronic inflammation are common mechanisms of diabetes and sarcopenia. Reasonable exercise training, nutrition supplement, and drug intervention may improve the quality of life of patients with diabetes combined with sarcopenia. This article reviews the relevant factors and management measures of sarcopenia in T2DM patients, in order to achieve early detection, diagnosis, and intervention.

Background: The coronavirus disease 2019 (COVID-19) has quickly spread across the world. However, the nutritional status of COVID-19 patients has not yet been extensively examined. Objectives: The aim of this study was to evaluate the nutritional status of COVID-19 patients and to identify factors independently associated with malnutrition risk. Methods: In this single-center, cross-sectional study, we analyzed data from 760 hospitalized COVID-19 patients between 29 January 2020 and 15 March 2020. Based on the Nutrition Risk Screening (NRS) 2002 score, we divided patients into the normal nutrition group (NRS score = 3). The associations of age, gender, symptoms, comorbidities, BMI, serum albumin and prealbumin concentrations, disease severity, activities of daily living (ADL) score, and clinical outcomes with malnutrition risk were analyzed. Multivariable logistic regression analysis was used to identify independent factors associated with malnutrition risk. Results: Of patients with COVID-19, 82.6% were at risk of malnutrition. There were statistical differences in the age, incidence of fever, BMI, serum albumin and prealbumin concentrations, ADL score, and disease severity between the 2 groups. Multivariable logistic regression analysis revealed that age >= 65 y (vs. <65 y; OR: 5.40; P < 0.001), serum albumin = 35 g/L; OR: 3.61; P < 0.001), serum prealbumin = 150 mg/L; OR: 2.88; P = 0.042), critical cases (vs. moderate cases; OR: 4.46; P < 0.001), ADL score 41-60 (vs. ADL score 100; OR: 4.50; P = 0.012), and ADL score <= 0 (vs. ADL score 100; OR: 9.49; P < 0.001) were significantly associated with the risk of malnutrition in COVID-19 patients. Conclusions: This study showed that prevalence of malnutrition risk was high in COVID-19 patients. Older age, low serum albumin and prealbumin concentrations, ADL score <60, and disease severity were independent factors associated with malnutrition risk.