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BackgroundThe 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. GoalThis 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. MethodsWith 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. FindingsWe 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. DiscussionWith 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.
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ImportanceHeart injury can be easily induced by viral infection such as adenovirus and enterovirus. However, whether coronavirus disease 2019 (COVID-19) causes heart injury and hereby impacts mortality has not yet been fully evaluated. ObjectiveTo explore whether heart injury occurs in COVID-19 on admission and hereby aggravates mortality later. Design, Setting, and ParticipantsA single-center retrospective cohort study including 188 COVID-19 patients admitted from December 25, 2019 to January 27, 2020 in Wuhan Jinyintan Hospital, China; follow up was completed on February 11, 2020. ExposuresHigh levels of heart injury indicators on admission (hs-TNI; CK; CK-MB; LDH; -HBDH). Main Outcomes and MeasuresMortality in hospital and days from admission to mortality (survival days). ResultsOf 188 patients with COVID-19, the mean age was 51.9 years (standard deviation: 14.26; range: 21[~]83 years) and 119 (63.3%) were male. Increased hs-TnI levels on admission tended to occur in older patients and patients with comorbidity (especially hypertension). High hs-TnI on admission ([≥] 6.126 pg/mL), even within the clinical normal range (0[~]28 pg/mL), already can be associated with higher mortality. High hs-TnI was associated with increased inflammatory levels (neutrophils, IL-6, CRP, and PCT) and decreased immune levels (lymphocytes, monocytes, and CD4+ and CD8+ T cells). CK was not associated with mortality. Increased CK-MB levels tended to occur in male patients and patients with current smoking. High CK-MB on admission was associated with higher mortality. High CK-MB was associated with increased inflammatory levels and decreased lymphocytes. Increased LDH and -HBDH levels tended to occur in older patients and patients with hypertension. Both high LDH and -HBDH on admission were associated with higher mortality. Both high LDH and -HBDH were associated with increased inflammatory levels and decreased immune levels. hs-TNI level on admission was negatively correlated with survival days (r= -0.42, 95% CI= -0.64[~]-0.12, P=0.005). LDH level on admission was negatively correlated with survival days (r= -0.35, 95% CI= -0.59[~]-0.05, P=0.022). Conclusions and RelevanceHeart injury signs arise in COVID-19, especially in older patients, patients with hypertension and male patients with current smoking. COVID-19 virus might attack heart via inducing inflammatory storm. High levels of heart injury indicators on admission are associated with higher mortality and shorter survival days. COVID-19 patients with signs of heart injury on admission must be early identified and carefully managed by cardiologists, because COVID-19 is never just confined to respiratory injury. Key pointsO_ST_ABSQuestionC_ST_ABSDoes coronavirus disease 2019 (COVID-19) cause heart injury and hereby impact mortality? FindingsIn this retrospective cohort study including 188 patients with COVID-19, patients with high levels of high-sensitivity cardiac troponin I (hs-TNI) on admission had significantly higher mortality (50.0%) than patients with moderate or low levels of hs-TNI (10.0% or 9.1%). hs-TNI level on admission was significantly negatively correlated with survival days (r= -0.42, 95% CI= -0.64[~]-0.12, P=0.005). MeaningCOVID-19 patients with signs of heart injury on admission must be early identified and carefully managed by cardiologists, in order to maximally prevent or rescue heart injury-related mortality in COVID-19.
