Prevalence of Metabolic Syndrome in the United States National Health and Nutrition Examination Survey (NHANES) 2011-2018 (preprint)

Importance Metabolic syndrome (MetS) is a cluster of risk factors presaging the development of cardiovascular diseases and diabetes. It is a risk factor for severe coronavirus disease 2019 (COVID-19). Objective To estimate the prevalence of MetS in the US National Health and Nutrition Examination Survey (NHANES) 2011-2018. Design, Setting, and Participants This cohort study included 22370 eligible participants aged ≥20 years from the NHANES 2011-2018. Main Outcome and Measure MetS was defined as the presence of at least three of these components: central obesity, reduced high-density lipoprotein, elevated triglycerides, elevated blood pressure and elevated fasting blood glucose. The prevalence of MetS was estimated taking into account the complex sampling. The time trend was evaluated using logistic regression. Annual percentage changes (APC) were used to measure the trends in MetS prevalence. Results The prevalence of MetS was 36.2% (95% CI, 32.3-40.3), 34.8% (95% CI, 32.3-37.4), 39.9% (95% CI, 36.6-43.2) and 38.3% (95% CI, 35.3-41.3) in 2011-2, 2013-4, 2015-6, 2017-8, respectively ( P for trend =.08). Among the MetS components, the prevalence of elevated glucose increased from 48.7% (95% CI, 45.9-51.5) in 2011-2 to 64.3% (95% CI, 61.0-67.4) in 2017-8 [ P for trend Conclusion and Relevance The prevalence of MetS remained stable from 2011 to 2018, but increased among non-Hispanic Asians. Lifestyle modification is needed to prevent metabolic syndrome and the associated risks of diabetes and cardiovascular disease.

Development of a predictive risk model for severe COVID-19 disease using population-based administrative data (preprint)

Background: Recent studies have reported numerous significant predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk score for prompt risk stratification. The objective is to develop a simple risk score for severe COVID-19 disease using territory-wide healthcare data based on simple clinical and laboratory variables. Methods: Consecutive patients admitted to Hong Kong public hospitals between 1st January and 22nd August 2020 diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8th September 2020. Results: COVID-19 testing was performed in 237493 patients and 4445 patients (median age 44.8 years old, 95% CI: [28.9, 60.8]); 50% male) were tested positive. Of these, 212 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, hypertension, stroke, diabetes mellitus, ischemic heart disease/heart failure, respiratory disease, renal disease, increases in neutrophil count, monocyte count, sodium, potassium, urea, alanine transaminase, alkaline phosphatase, high sensitive troponin-I, prothrombin time, activated partial thromboplastin time, D-dimer and C-reactive protein, as well as decreases in lymphocyte count, base excess and bicarbonate levels. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. Conclusions: A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.

Identifying main and interaction effects of risk factors to predict intensive care admission in patients hospitalized with COVID-19: a retrospective cohort study in Hong Kong (preprint)

Background: The coronavirus disease 2019 (COVID-19) has become a pandemic, placing significant burdens on the healthcare systems. In this study, we tested the hypothesis that a machine learning approach incorporating hidden nonlinear interactions can improve prediction for Intensive care unit (ICU) admission. Methods: Consecutive patients admitted to public hospitals between 1st January and 24th May 2020 in Hong Kong with COVID-19 diagnosed by RT-PCR were included. The primary endpoint was ICU admission. Results: This study included 1043 patients (median age 35 (IQR: 32-37; 54% male). Nineteen patients were admitted to ICU (median hospital length of stay (LOS): 30 days, median ICU LOS: 16 days). ICU patients were more likely to be prescribed angiotensin converting enzyme inhibitors/angiotensin receptor blockers, anti-retroviral drugs lopinavir/ritonavir and remdesivir, ribavirin, steroids, interferon-beta and hydroxychloroquine. Significant predictors of ICU admission were older age, male sex, prior coronary artery disease, respiratory diseases, diabetes, hypertension and chronic kidney disease, and activated partial thromboplastin time, red cell count, white cell count, albumin and serum sodium. A tree-based machine learning model identified most informative characteristics and hidden interactions that can predict ICU admission. These were: low red cells with 1) male, 2) older age, 3) low albumin, 4) low sodium or 5) prolonged APTT. A five-fold cross validation confirms superior performance of this model over baseline models including XGBoost, LightGBM, random forests, and multivariate logistic regression. Conclusions: A machine learning model including baseline risk factors and their hidden interactions can accurately predict ICU admission in COVID-19.