Dataset Information

Development and validation of a risk stratification model for screening suspected cases of COVID-19 in China.

ABSTRACT: How to quickly identify high-risk populations is critical to epidemic control. We developed and validated a risk prediction model for screening SARS-CoV-2 infection in suspected cases with an epidemiological history. A total of 1019 patients, ≥13 years of age, who had an epidemiological history were enrolled from fever clinics between January 2020 and February 2020. Among 103 (10.11%) cases of COVID-19 were confirmed. Multivariable analysis summarized four features associated with increased risk of SARS-CoV-2 infection, summarized in the mnemonic COVID-19-REAL: radiological evidence of pneumonia (1 point), eosinophils < 0.005 × 10⁹/L (1 point), age ≥ 32 years (2 points), and leukocytes < 6.05 × 10⁹ /L (1 point). The area under the ROC curve for the training group was 0.863 (95% CI, 0.813 - 0.912). A cut-off value of less than 3 points for COVID-19-REAL was assigned to define the low-risk population. Only 10 (2.70%) of 371 patients were proved to be SARS-CoV-2 positive, with a negative predictive value of 0.973. External validation was similar. This study provides a simple, practical, and robust screening model, COVID-19-REAL, able to identify populations at high risk for SARS-CoV-2 infection.

SUBMITTER: Ma J

PROVIDER: S-EPMC7425460 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Development and validation of a risk stratification model for screening suspected cases of COVID-19 in China.

Ma Jing J Shi Xiaowei X Xu Weiming W Lv Feifei F Wu Jian J Pan Qiaoling Q Yang Jinfeng J Yu Jiong J Cao Hongcui H Li Lanjuan L

Aging 20200729 14

How to quickly identify high-risk populations is critical to epidemic control. We developed and validated a risk prediction model for screening SARS-CoV-2 infection in suspected cases with an epidemiological history. A total of 1019 patients, ≥13 years of age, who had an epidemiological history were enrolled from fever clinics between January 2020 and February 2020. Among 103 (10.11%) cases of COVID-19 were confirmed. Multivariable analysis summarized four features associated with increased risk ...[more]

PMID: 32727933

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Project description:The shortage of recently approved vaccines against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has highlighted the need for evidence-based tools to prioritize healthcare resources for people at higher risk of severe coronavirus disease (COVID-19). Although age has been identified as the most important risk factor (particularly for mortality), the contribution of underlying comorbidities is often assessed using a pre-defined list of chronic conditions. Furthermore, the count of individual risk factors has limited applicability to population-based "stratify-and-shield" strategies. We aimed to develop and validate a COVID-19 risk stratification system that allows allocating individuals of the general population into four mutually-exclusive risk categories based on multivariate models for severe COVID-19, a composite of hospital admission, transfer to intensive care unit (ICU), and mortality among the general population. The model was developed using clinical, hospital, and epidemiological data from all individuals among the entire population of Catalonia (North-East Spain; 7.5 million people) who experienced a COVID-19 event (i.e., hospitalization, ICU admission, or death due to COVID-19) between March 1 and September 15, 2020, and validated using an independent dataset of 218,329 individuals with COVID-19 confirmed by reverse transcription-polymerase chain reaction (RT-PCR), who were infected after developing the model. No exclusion criteria were defined. The final model included age, sex, a summary measure of the comorbidity burden, the socioeconomic status, and the presence of specific diagnoses potentially associated with severe COVID-19. The validation showed high discrimination capacity, with an area under the curve of the receiving operating characteristics of 0.85 (95% CI 0.85-0.85) for hospital admissions, 0.86 (0.86-0.97) for ICU transfers, and 0.96 (0.96-0.96) for deaths. Our results provide clinicians and policymakers with an evidence-based tool for prioritizing COVID-19 healthcare resources in other population groups aside from those with higher exposure to SARS-CoV-2 and frontline workers.

Project description:ObjectivesCardiovascular conditions were shown to be predictive of clinical deterioration in hospitalised patients with coronavirus disease 2019 (COVID-19). Whether this also holds for outpatients managed in primary care is yet unknown. The aim of this study was to determine the incremental value of cardiovascular vulnerability in predicting the risk of hospital referral in primary care COVID-19 outpatients.DesignAnalysis of anonymised routine care data extracted from electronic medical records from three large Dutch primary care registries.SettingPrimary care.ParticipantsConsecutive adult patients seen in primary care for COVID-19 symptoms in the 'first wave' of COVID-19 infections (March 1 2020 to June 1 2020) and in the 'second wave' (June 1 2020 to April 15 2021) in the Netherlands.Outcome measuresA multivariable logistic regression model was fitted to predict hospital referral within 90 days after first COVID-19 consultation in primary care. Data from the 'first wave' was used for derivation (n = 5,475 patients). Age, sex, the interaction between age and sex, and the number of cardiovascular conditions and/or diabetes (0, 1, or ≥2) were pre-specified as candidate predictors. This full model was (i) compared to a simple model including only age and sex and its interaction, and (ii) externally validated in COVID-19 patients during the 'second wave' (n = 16,693).ResultsThe full model performed better than the simple model (likelihood ratio test p<0.001). Older male patients with multiple cardiovascular conditions and/or diabetes had the highest predicted risk of hospital referral, reaching risks above 15-20%, whereas on average this risk was 5.1%. The temporally validated c-statistic was 0.747 (95%CI 0.729-0.764) and the model showed good calibration upon validation.ConclusionsFor patients with COVID-19 symptoms managed in primary care, the risk of hospital referral was on average 5.1%. Older, male and cardiovascular vulnerable COVID-19 patients are more at risk for hospital referral.

Project description:BackgroundCorona Virus Disease 2019 (COVID-19) is currently a worldwide pandemic and has a huge impact on public health and socio-economic development. The purpose of this study is to explore the diagnostic value of the quantitative computed tomography (CT) method by using different threshold segmentation techniques to distinguish between patients with or without COVID-19 pneumonia.MethodsA total of 47 patients with suspected COVID-19 were retrospectively analyzed, including nine patients with positive real-time fluorescence reverse transcription polymerase chain reaction (RT-PCR) test (confirmed case group) and 38 patients with negative RT-PCR test (excluded case group). An improved 3D convolutional neural network (VB-Net) was used to automatically extract lung lesions. Eight different threshold segmentation methods were used to define the ground glass opacity (GGO) and consolidation. The receiver operating characteristic (ROC) curves were used to compare the performance of various parameters with different thresholds for diagnosing COVID-19 pneumonia.ResultsThe volume of GGO (VOGGO) and GGO percentage in the whole lung (GGOPITWL) were the most effective values for diagnosing COVID-19 at a threshold of - 300 HU, with areas under the curve (AUCs) of 0.769 and 0.769, sensitivity of 66.67 and 66.67%, specificity of 94.74 and 86.84%. Compared with VOGGO or GGOPITWL at a threshold of - 300 Hounsfield units (HU), the consolidation percentage in the whole lung (CPITWL) with thresholds at - 400 HU, - 350 HU, and - 250 HU were statistically different. There were statistical differences in the infection volume and percentage of the whole lung, right lung, and lobes between the two groups. VOGGO, GGOPITWL, and volume of consolidation (VOC) were also statistically different at the threshold of - 300 HU.ConclusionsQuantitative CT provides an image quantification method for the auxiliary diagnosis of COVID-19 and is expected to assist in confirming patients with COVID-19 pneumonia in suspected cases.

Project description:Novel coronavirus 2019 (COVID-19) infection is a global public health issue, that has now affected more than 200 countries worldwide and caused a second wave of pandemic. Severe adult respiratory syndrome-CoV-2 (SARS-CoV-2) pneumonia is associated with a high risk of mortality. However, prognostic factors predicting poor clinical outcomes of individual patients with SARS-CoV-2 pneumonia remain under intensive investigation. We conducted a retrospective, multicenter study of patients with SARS-CoV-2 who were admitted to four hospitals in Wuhan, China from December 2019 to February 2020. Mortality at the end of the follow up period was the primary outcome. Factors predicting mortality were also assessed and a prognostic model was developed, calibrated and validated. The study included 492 patients with SARS-CoV-2 who were divided into three cohorts: the training cohort (n = 237), the validation cohort 1 (n = 120), and the validation cohort 2 (n = 135). Multivariate analysis showed that five clinical parameters were predictive of mortality at the end of follow up period, including advanced age [odds ratio (OR), 1.1/years increase (p < 0.001)], increased neutrophil-to-lymphocyte ratio [(NLR) OR, 1.14/increase (p < 0.001)], elevated body temperature on admission [OR, 1.53/°C increase (p = 0.005)], increased aspartate transaminase [OR, 2.47 (p = 0.019)], and decreased total protein [OR, 1.69 (p = 0.018)]. Furthermore, the prognostic model drawn from the training cohort was validated with validation cohorts 1 and 2 with comparable area under curves (AUC) at 0.912, 0.928, and 0.883, respectively. While individual survival probabilities were assessed, the model yielded a Harrell's C index of 0.758 for the training cohort, 0.762 for the validation cohort 1, and 0.711 for the validation cohort 2, which were comparable among each other. A validated prognostic model was developed to assist in determining the clinical prognosis for SARS-CoV-2 pneumonia. Using this established model, individual patients categorized in the high risk group were associated with an increased risk of mortality, whereas patients predicted to be in the low risk group had a higher probability of survival.

Project description:Background: Predicting the risk of progression to severe coronavirus disease 2019 (COVID-19) could facilitate personalized diagnosis and treatment options, thus optimizing the use of medical resources. Methods: In this prospective study, 206 patients with COVID-19 were enrolled from regional medical institutions between December 20, 2019, and April 10, 2020. We collated a range of data to derive and validate a predictive model for COVID-19 progression, including demographics, clinical characteristics, laboratory findings, and cytokine levels. Variation analysis, along with the least absolute shrinkage and selection operator (LASSO) and Boruta algorithms, was used for modeling. The performance of the derived models was evaluated by specificity, sensitivity, area under the receiver operating characteristic (ROC) curve (AUC), Akaike information criterion (AIC), calibration plots, decision curve analysis (DCA), and Hosmer-Lemeshow test. Results: We used the LASSO algorithm and logistic regression to develop a model that can accurately predict the risk of progression to severe COVID-19. The model incorporated alanine aminotransferase (ALT), interleukin (IL)-6, expectoration, fatigue, lymphocyte ratio (LYMR), aspartate transaminase (AST), and creatinine (CREA). The model yielded a satisfactory predictive performance with an AUC of 0.9104 and 0.8792 in the derivation and validation cohorts, respectively. The final model was then used to create a nomogram that was packaged into an open-source and predictive calculator for clinical use. The model is freely available online at https://severeconid-19predction.shinyapps.io/SHINY/. Conclusion: In this study, we developed an open-source and free predictive calculator for COVID-19 progression based on ALT, IL-6, expectoration, fatigue, LYMR, AST, and CREA. The validated model can effectively predict progression to severe COVID-19, thus providing an efficient option for early and personalized management and the allocation of appropriate medical resources.

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Dataset Information

Development and validation of a risk stratification model for screening suspected cases of COVID-19 in China.

Publications

Development and validation of a risk stratification model for screening suspected cases of COVID-19 in China.

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