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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-305541

ABSTRACT

The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7,772 (75.9%) recovered, and 2,237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality ( p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities >90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer;for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.

2.
Clin Infect Dis ; 73(7): e1855-e1862, 2021 10 05.
Article in English | MEDLINE | ID: covidwho-1455257

ABSTRACT

BACKGROUND: Increased body mass index (BMI) has been associated with a higher risk of severe coronavirus disease 2019 (COVID-19) infections. However, whether obesity is a risk factor for contracting COVID-19 has hardly been investigated so far. METHODS: We examined the association between BMI level and the risk of COVID-19 infection in a nationwide case-control study comprised of 3788 case patients confirmed to have COVID-19 between 24 January and 9 April 2020 and 15 152 controls matched by age and sex, who were aged 20 years or more and underwent National Health Insurance Service (NHIS) health examinations between 2015-2017, using data from the Korean NHIS with linkage to the Korea Centers for Disease Control and Prevention data. Our primary exposure of interest was BMI level, categorized into 4 groups: <18.5 (underweight), 18.5-22.9 (normal weight), 23-24.9 (overweight), and ≥25 kg/m2 (obese). RESULTS: Of the entire 18 940 study participants, 11 755 (62.1%) were women, and the mean age of the study participants was 53.7 years (standard deviation, 13.8). In multivariable logistic regression models adjusted for sociodemographic, comorbidity, laboratory, and medication data, there was a graded association between higher BMI levels and higher risk of COVID-19 infection. Compared to normal-weight individuals, the adjusted odds ratios in the overweight and obese individuals were 1.13 (95% confidence interval [CI], 1.03-1.25) and 1.26 (95% CI, 1.15-1.39), respectively. This association was robust across age and sex subgroups. CONCLUSIONS: Higher BMI levels were associated with a higher risk of contracting COVID-19.


Subject(s)
COVID-19 , Adult , Body Mass Index , Case-Control Studies , Female , Humans , Middle Aged , Republic of Korea/epidemiology , Risk Factors , SARS-CoV-2 , Young Adult
3.
Sci Rep ; 10(1): 18716, 2020 10 30.
Article in English | MEDLINE | ID: covidwho-894420

ABSTRACT

The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7772 (75.9%) recovered, and 2237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities > 90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.


Subject(s)
Coronavirus Infections/mortality , Machine Learning , Pneumonia, Viral/mortality , Adult , Aged , Aged, 80 and over , COVID-19 , Female , Humans , Male , Middle Aged , Models, Statistical , Mortality/trends , Pandemics , Republic of Korea
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