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Study reveals novel risk scoring system to predict progression of COVID-19

risk_scoring_system risk_scoring_system
risk_scoring_system risk_scoring_system

What's new?

A new scoring system was found to be a highly informative risk stratification tool for recognizing SARS-CoV-2 people at raised risk of advancement to severe pneumonia.

A study published in Scientific Reports revealed a new risk scoring system that can assist in the identification of high-risk groups for advancement to severe pneumonia in coronavirus-infected patients, and also aid in the prevention of unwanted overusage of medical care in limited-resource settings. For early anticipation of advancement to severe pneumonia in COVID-19 patients, a risk prediction model was constructed and validated.

Overall, 561 people were randomly segregated into development cohort (N = 421) and validation cohort (N = 140). For determining four independent risk predictors for advancement to severe pneumonia, multivariate logistic regression was used. A risk scoring system (Keimyung University Daegu Dongsan Hospital [KDDH]) was built such that the scores given to each variable corresponded to its regression coefficients in the model.

For each patient, the risk scores were estimated. In total, 2 cohorts were specified: (i) high risk (9 to 20 points), and (ii) low risk (0 to 8 points). The C-statistics, specificity, and sensitivity of the KDDH scoring system in the development and validation cohorts are shown in Table 1:

Hence, this new risk scoring system can be used by clinicians to anticipate advancement to severe pneumonia in SARS-CoV-2 patients in earlier stages. Identification of coronavirus-infected people who are at elevated risk of developing severe symptoms in the early stages can lead to improved medical resource allocation, optimal use of limited medical resources, and better patient care, concluded the study authors.

Source:

Scientific Reports

Article:

A risk scoring system to predict progression to severe pneumonia in patients with Covid-19

Authors:

Ji Yeon Lee et al.

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