Characteristics of 12-Month Readmission for Hospitalized Patients with COPD: A Propensity Score Matched Analysis of Prospective Multicenter Study.

Purpose: Hospitalization for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is considered as severe exacerbations. Readmission for severe exacerbations is a crucial event for COPD patients. However, factors associated with readmission for severe exacerbations are incomplete. The study aimed to investigate different characteristics between the severe and non-severe exacerbation groups. Patients and Methods: Patients hospitalized for severe AECOPD were included in multi-centers, and their exacerbations in next 12 months after discharge were recorded. According to exacerbations, patients were separated into the severe-exacerbation group and the non-severe exacerbation group. Propensity-score matching (PSM) and multivariable analyses were performed to compare the baseline characteristics of two groups. The Hosmer-Lemeshow test and receiver operating characteristic curve were applied to evaluate how well the model could identify clusters. Results: The cohort included 550 patients with severe AECOPD across 27 study centers in China, and 465 patients were finally analyzed. A total of 41.5% of patients underwent readmission for AECOPD within 1 year. There were no significant differences in baseline characteristics between groups after PSM. Severe exacerbations in the 12 months were related to some factors, eg, the duration of COPD (13 vs 8 years, P<0.001), the COPD Assessment Test (CAT) score (20 vs 17, P<0.001), the blood eosinophil percentage (1.5 vs 2.0, P<0.05), and their inhaler therapies. Patients readmitted with AECOPD had a longer time of diagnosis (≥9 years), more symptoms (CAT ≥10), and lower blood eosinophils (Eos <2%). A clinical model was derived to help identify patients at risk of readmission with severe exacerbations. Conclusion: These analyses confirmed the relevance of COPD at admission with future severe exacerbations. A lower blood eosinophils percentage appears to be related to readmission when combined with clinical history. Further studies are needed to evaluate whether this study can predict the risk of exacerbations.

A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia in China

Background The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model. Findings We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results.