ABSTRACT
BACKGROUND AND OBJECTIVES: The majority of coronavirus disease 2019 (COVID-19) cases are nonsevere, but severe cases have high mortality and need early detection and treatment. We aimed to develop a nomogram to predict the disease progression of nonsevere COVID-19 based on simple data that can be easily obtained even in primary medical institutions. METHODS: In this retrospective, multicenter cohort study, we extracted data from initial simple medical evaluations of 495 COVID-19 patients randomized (2:1) into a development cohort and a validation cohort. The progression of nonsevere COVID-19 was recorded as the primary outcome. We built a nomogram with the development cohort and tested its performance in the validation cohort. RESULTS: The nomogram was developed with the nine factors included in the final model. The area under the curve (AUC) of the nomogram scoring system for predicting the progression of nonsevere COVID-19 into severe COVID-19 was 0.875 and 0.821 in the development cohort and validation cohort, respectively. The nomogram achieved a good concordance index for predicting the progression of nonsevere COVID-19 cases in the development and validation cohorts (concordance index of 0.875 in the development cohort and 0.821 in the validation cohort) and had well-fitted calibration curves showing good agreement between the estimates and the actual endpoint events. CONCLUSIONS: The proposed nomogram built with a simplified index might help to predict the progression of nonsevere COVID-19; thus, COVID-19 with a high risk of disease progression could be identified in time, allowing an appropriate therapeutic choice according to the potential disease severity.
ABSTRACT
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. It causes acute respiratory distress syndrome and results in a high mortality rate if pneumonia is involved. 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, which facilitates the spread of the disease at the community level, and contributes to the overwhelming of medical resources in intensive care units. 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 global frontline 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 Unversity (approval number 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. These patients had SARS-CoV-2 RT-PCR test results and chest CT scans, both of which were used as the gold standard for the diagnosis of COVID-19 and COVID-19 pneumonia. In particular, the dataset included 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, and 122 asymptomatic cases who had positive RT-PCR test results, amongst whom 31 cases were diagnosed. We also integrated the function of a survey in nCapp to collect user feedback from frontline doctors. Findings We applied the statistical method of a multi-factor regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are fast and accessible: 'Residing or visiting history in epidemic regions', 'Exposure history to COVID-19 patient', 'Dry cough', 'Fatigue', 'Breathlessness', 'No body temperature decrease after antibiotic treatment', 'Fingertip blood oxygen saturation<=93%', 'Lymphopenia', and 'C-reactive protein (CRP) increased'. 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). To ensure the sensitivity of the model, we used a cutoff value of 0.09. The sensitivity and specificity of the model were 98.0% (95% CI: 96.9%, 99.1%) and 17.3% (95% CI: 15.0%, 19.6%), respectively, in the training dataset, and 96.5% (95% CI: 95.1%, 98.0%) and 18.8% (95% CI: 16.4%, 21.2%), respectively, in the validation dataset. In the subset of the 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, the model predicted 132 cases, accounting for 96.4% (95% CI: 91.7%, 98.8%) of the cases. In the subset of the 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, the model predicted 59 cases, accounting for 95.2% (95% CI: 86.5%, 99.0%) of the cases. Considering the specificity of the model, we used a cutoff value of 0.32. The sensitivity and specificity of the model were 83.5% (95% CI: 80.5%, 86.4%) and 83.2% (95% CI: 80.9%, 85.5%), respectively, in the training dataset, and 79.6% (95% CI: 76.4%, 82.8%) and 81.3% (95% CI: 78.9%, 83.7%), respectively, in the validation dataset, which is very close to the published AI model. The results of the online survey 'Questionnaire Star' showed that 90.9% of nCapp users in WeChat mini programs were 'satisfied' or 'very satisfied' with the tool. The WeChat mini program received a significantly higher satisfaction rate than other platforms, especially for 'availability and sharing convenience of the App' and 'fast speed of log-in and data entry'. 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. These patients require timely isolation or close medical supervision. By applying the model, medical resources can be allocated more reasonably, and missed diagnoses can be reduced. In addition, further education and interaction among medical professionals can improve the diagnostic efficiency for COVID-19, thus avoiding the transmission of the disease from asymptomatic patients at the community level.
Subject(s)
Respiratory Distress Syndrome , Pneumonia , Communicable Diseases , COVID-19 , LymphopeniaABSTRACT
BackgroundInformation regarding the impact of cardiovascular disease (CVD) on disease progression among patients with mild coronavirus disease 2019 (COVID-19) is limited. MethodsThis study evaluated the association of underlying CVD with disease progression in patients with mild COVID-19. The primary outcome was the need to be transferred to intensive care due to disease progression. The patients were divided with and without CVD as well as stable and intensive care groups. ResultsOf 332 patients with mild COVID-19, median age was 51 years (IQR, 40-59 years), and 200 (61.2%) were female. Of 48 (14.5%) patients with CVD, 23 (47.9%) progressed to severe disease status and required intensive care. Compared with patients without CVD, patients with CVD were older, and more likely to have fatigue, chest tightness, and myalgia. The rate of requiring intensive care was significantly higher among patients with CVD than in patients without CVD (47.92% vs. 12.4%; P<0.001). In subgroup analysis, rate of requiring intensive care was also higher among patients with either hypertension or coronary heart disease than in patients without hypertension or coronary heart disease. The multivariable regression model showed CVD served as an independent risk factor for intensive care (Odd ratio [OR], 2.652 [95% CI, 1.019-6.899]) after adjustment for various cofounders. ConclusionsPatients with mild COVID-19 complicating CVD in are susceptible to develop severe disease status and requirement for intensive care. Key PointsO_ST_ABSQuestionC_ST_ABSWhat is the impact of coexisting cardiovascular diseases (CVD) on disease progression in patients with mild COVID-19? FindingsAlthough most patients with mild COVID-19 were discharged alive from hospital, approximately 47.9% patients with coexisting CVD developed severe disease status and required intensive care. CVD is an independent risk factor of intensive care among patients with mild COVID-19. MeaningCoexisting CVD is associated with unfavorable outcomes among patients with mild COVID-19. Special monitoring is required for these patients to improve their outcome.
Subject(s)
COVID-19ABSTRACT
Objectives: To provide a reference for CT imaging changes for patients discharged from a Fangcang shelter hospital, a large-scale, temporary hospital for the centralized treatment of patients with mild to moderate Coronavirus disease 2019 (COVID-19) to provide essential functions (isolation, triage, basic medical care, frequent monitoring and rapid referral, essential living and social engagement) to them..Methods: Patients with mild to moderate COVID-19 admitted to the Wuchang Fangcang Shelter Hospital who had undergone pre-discharge and previous CT scans were included. Changes in the CT imaging features were defined as progression, no change, improvement or recovery. Basic patient information was obtained, and imaging signs were compared between the two CT scans.Results: A total of 83 patients were included. The median age was 53 years old. The course of disease was 28.3±10.7 days. CT imaging changes indicated progression, no change, improvement, and recovery in 3, 12, 66, and 2 patients, respectively. Between the two CT scans, the imaging signs showed a significant reduction in consolidation, a significant increase in fibrosis, and a reduction or / and thinning of ground-glass opacities. None of the patients showed signs of deterioration on follow-up and thus did not need to return to the hospital for treatment.Conclusion: In the COVID-19 Fangcang shelter hospital, given the shortage of medical staff and lack of medical resources, CT imaging diagnostic methods can be used to accurately discharge patients who had met the discharge criteria for isolation and observation from the Fangcang Shelter Hospital.