ABSTRACT
Coronavirus Disease 2019 (COVID-19) has become a world-wide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods for clinicians to promptly identify high-risk patients. Here we developed a risk score using clinical data from 1,479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital in Wuhan (validation cohort 1) and 432 inpatients from the Third People’s Hospital Shenzhen (validation cohort 2). The risk score is based on three biomarkers readily available in routine blood samples and can be easily translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 days in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients upon admission can clearly be differenciated into low, medium or high risk, with an AUC score of 0.9551. In summary, a simple risk score was validated to predict death in patients infected with COVID-19 and was validated in independent cohorts.
Subject(s)
COVID-19 , DeathABSTRACT
Background: False negative results of SARS-CoV-2 nucleic acid detection pose threats to COVID-19 patients and medical workers alike. Objective: To develop multivariate models to determine clinical characteristics that contribute to false negative results of SARS-CoV-2 nucleic acid detection, and use them to predict false negative results as well as time windows for testing positive. Design: Retrospective Cohort Study (Ethics number of Tongji Hospital: No. IRBID: TJ-20200320) Setting: A database of outpatients in Tongji Hospital (University Hospital) from 15 January 2020 to 19 February 2020. Patients: 1,324 outpatients with COVID-19 Measurements: Clinical information on CT imaging reports, blood routine tests, and clinic symptoms were collected. A multivariate logistic regression was used to explain and predict false negative testing results of SARS-CoV-2 detection. A multivariate accelerated failure model was used to analyze and predict delayed time windows for testing positive. Results: Of the 1,324 outpatients who diagnosed of COVID-19, 633 patients tested positive in their first SARS-CoV-2 nucleic acid test (47.8%), with a mean age of 51 years (SD=14.9); the rest, which had a mean age of 47 years (SD=15.4), tested negative in the first test. Ground glass opacity in a CT imaging report was associated with a lower chance of false negatives (aOR, 0.56), and reduced the length of time window for testing positive by 26%. Consolidation was associated with a higher chance of false negatives (aOR, 1.57), and extended the length of time window for testing positive by 44%. In blood routine tests, basophils (aOR, 1.28) and eosinophils (aOR, 1.29) were associated with a higher chance of false negatives, and were found to extend the time window for testing positive by 23% and 41%, respectively. Age and gender also affected the significantly. Limitation: Data were generated in a large single-center study. Conclusion: Testing outcome and positive window of SARS-CoV-2 detection for COVID-19 patients were associated with CT imaging results, blood routine tests, and clinical symptoms. Taking into account relevant information in CT imaging reports, blood routine tests, and clinical symptoms helped reduce a false negative testing outcome. The predictive AFT model, what we believe to be one of the first statistical models for predicting time window of SARS-CoV-2 detection, could help clinicians improve the accuracy and efficiency of the diagnosis, and hence, optimizes the timing of nucleic acid detection and alleviates the shortage of nucleic acid detection kits around the world. Primary Funding Source: None.
Subject(s)
COVID-19ABSTRACT
Summary Background The recent outbreak of the novel coronavirus in December 2019 (COVID-19) has activated top-level response nationwide. We developed a new treatment model based on the online-to-offline (O2O) model for the home isolated patients, because in the early stages the medical staff were insufficient to cope with so many patients. Methods In this single-centered, retrospective study, we enrolled 48 confirmed/suspected COVID-19 patients who underwent home isolation in Wuhan between January 6 and January 31, 2020. By WeChat and online document editing all patients were treated with medical observation scale. The clinical indications such as Fever, Muscle soreness, Dyspnea and Lack of strength were collected with this system led by medical staff in management, medicine, nursing, rehabilitation and psychology. Findings The mean(SD) age of 48 patients was 39.08(13.88) years, 35(72.9%) were women. Compared with non-hospitalized patients, inpatients were older([≥]8805;70years, 2.4% vs 33.3%, P<0.04). All inpatients had fever, 50% inpatients had coughs and showed infiltration in both lungs at the time of diagnosis. 33.3% inpatients exhibited negative changes in their CT results at initial diagnosis. The body temperature of non-hospitalized patients with mild symptoms returned to normal by day 4-5. While dyspnea peaked on day 6 for non-hospitalized patients with mild symptoms, it persisted in hospitalized patients and exacerbated over time. The lack of strength and muscle soreness were both back to normal by day 4 for non-hospitalized patients. Interpretation Monitoring the trends of symptoms is more important for identifying severe cases. Excessive laboratory data and physical examination are not necessary for the evaluation of patients with mild symptoms. The system we developed is the first to convert the subjective symptoms of patients into objective scores. This type of O2O, subjective-to-objective strategy may be used in regions with similar highly infectious diseases to minimize the possibility of infection among medical staff.