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1.
Clin Obes ; 12(3): e12514, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1700258

ABSTRACT

The association between body mass index (BMI) and poor COVID-19 outcomes in patients has been demonstrated across numerous studies. However, obesity-related comorbidities have also been shown to be associated with poor outcomes. The purpose of this study was to determine whether BMI or obesity-associated comorbidities contribute to elevated COVID-19 severity in non-elderly, hospitalized patients with elevated BMI (≥25 kg/m2 ). This was a single-center, retrospective cohort study of 526 hospitalized, non-elderly adult (aged 18-64) COVID-19 patients with BMI ≥25 kg/m2 in suburban New York from March 6 to May 11, 2020. The Edmonton Obesity Staging System (EOSS) was used to quantify the severity of obesity-related comorbidities. EOSS was compared with BMI in multivariable regression analyses to predict COVID-19 outcomes. We found that higher EOSS scores were associated with poor outcomes after demographic adjustment, unlike BMI. Specifically, patients with increased EOSS scores had increased odds of acute kidney injury (adjusted odds ratio [aOR] = 6.40; 95% CI 3.71-11.05), intensive care unit admission (aOR = 10.71; 95% CI 3.23-35.51), mechanical ventilation (aOR = 3.10; 95% CI 2.01-4.78) and mortality (aOR = 5.05; 95% CI 1.83-13.90). Obesity-related comorbidity burden as determined by EOSS was a better predictor of poor COVID-19 outcomes relative to BMI, suggesting that comorbidity burden may be driving risk in those hospitalized with elevated BMI.


Subject(s)
COVID-19 , Adult , Body Mass Index , COVID-19/epidemiology , Comorbidity , Humans , Middle Aged , Obesity/complications , Obesity/epidemiology , Retrospective Studies , Risk Factors
2.
JAMIA Open ; 3(4): 518-522, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-1147144

ABSTRACT

OBJECTIVES: We develop a dashboard that leverages electronic health record (EHR) data to monitor intensive care unit patient status and ventilator utilization in the setting of the COVID-19 pandemic. MATERIALS AND METHODS: Data visualization software is used to display information from critical care data mart that extracts information from the EHR. A multidisciplinary collaborative led the development. RESULTS: The dashboard displays institution-level ventilator utilization details, as well as patient-level details such as ventilator settings, organ-system specific parameters, laboratory values, and infusions. DISCUSSION: Components of the dashboard were selected to facilitate the determination of resources and simultaneous assessment of multiple patients. Abnormal values are color coded. An overall illness assessment score is tracked daily to capture illness severity over time. CONCLUSION: This reference guide shares the architecture and sample reusable code to implement a robust, flexible, and scalable dashboard for monitoring ventilator utilization and illness severity in intensive care unit ventilated patients.

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