ABSTRACT
Study Objectives: Asthma is a multifactorial disease in which complex environmental exposures contribute to asthma exacerbations, often requiring acute medical attention. Although asthma exacerbations follow a seasonal pattern, our ability to precisely predict the timing and magnitude of increased asthma admissions remains limited. Pediatric asthma-related emergency department (ED) visits historically follow a bimodal distribution with peak admissions occurring in the spring and fall, while being lowest during the summer. We aim to identify how the rates of pediatric ED visits for asthma at a large urban medical center are impacted by changes in environmental conditions. Method(s): A time series analysis for pediatric ED visits was analyzed from 1/1/2015 to 2/28/2021 at a New York City ED in a large hospital system (n=8,206). We compared admissions longitudinally relative to the same period the previous years. Here, an ensemble model will be developed using a data assimilation technique to retrospectively parameterize the incidence of asthma admissions using local ED admissions data in addition to viral respiratory illness lab markers, air pollutants, pollens, and meteorological conditions. This retrospective ensemble model is a data assimilation technique commonly applied in numerical weather prediction and has recently been applied to parameterize infectious diseases. Here, we will use it to parameterize the burden of asthma attributable to different environmental factors. To estimate the attributable fraction that respiratory infections and climatic factors such as air pollution or humidity and temperature have on asthma exacerbations, we will use viral lab tests to confirm respiratory infections, meteorological and air pollution conditions will be assessed to identify the relationship between exposure and exacerbations. The retrospective ensemble model will then be used as the basis for understanding the statistical relationships between the attributable fraction of environmental conditions and pediatric asthma admissions. Result(s): The classic bimodal peak for asthma related hospitalizations was not observed during the first year following the COVID-19 pandemic;pediatric asthma-related ED visits decreased by 78% between 3/1/20 and 2/28/21. During the initial 12 weeks following NYC's PAUSE order (March 22nd, 2020), a 92% reduction in admissions was observed, relative to the same period the previous year. Of the 8,206 pediatric patients seen for asthma that came through the ED, 1,827 were admitted to the hospital and 6,379 were sent home, indicating an admission rate of 22%. 18% of patients admitted for asthma had a clinical lab marker sent that returned positive to indicate a respiratory pathogen (positive RVP, etc). Conclusion(s): It is important to develop an inference system to provide insight on how changes in behavioral patterns and environmental exposures affect asthma-related health care utilization in order to plan current and future public health interventions in a timely manner. This clinical inference system is augmented here with the additional of an observational system via confirmed respiratory illness, helping strengthen our understanding of the burden of asthma that is attributable to respiratory infections. No, authors do not have interests to disclose Copyright © 2022
ABSTRACT
Rationale: The novel coronavirus disease 2019 (COVID-19) is a clinical syndrome caused by the SARS-CoV-2 virus and has resulted in widespread morbidity and mortality. Current treatment algorithms assess severity of illness and assign therapies based on oxygen requirement. We hypothesize that COVID-19 is a heterogenous syndrome and subphenotypes on admission may vary in both clinical course and response to therapy. Methods: We included all patients aged 18 years or older, admitted with laboratory-confirmed COVID-19 to the Mount Sinai Health System hospitals between March 1, and August 30, 2020. Demographic data, medical comorbidities, and clinical and laboratory data within 24 hours of the first COVID-19 admission were collected. The primary outcome, determined across all COVID-19 admissions, was mortality;secondary outcomes included intubation, intensive care unit (ICU) admission and length of stay. We employed latent class analysis to admission variables without consideration of clinical outcomes to determine the optimal number of COVID-19 subphenotypes and then assigned each patient to a subphenotype class. We then employed linear mixed effect models to determine if admission COVID-19 subphenotypes were associated with mortality, mechanical ventilation, ICU admission and length of stay. We then explored whether subphenotypes demonstrated a differential response to treatment with convalescent plasma and Tocilizumab. Results: A total of 4620 patients were included. These patients had a median age of 67 years (IQR 55-78), 56.9% were male, and 76.7% had 0 or 1 comorbid conditions. Latent class analysis identified that a six-subphenotype model was the best fit, based on cAIC, aBIC, entropy, and likelihood ratio. Each subphenotype was clinically distinct. Review of admission data identified a predominantly female, hyperinflammatory subphenotype;a renal failure subphenotype;a young, less hypoxic subphenotype;an older, more coagulopathic subphenotype;a younger, less coagulopathic subphenotype;and a multiorgan dysfunction subphenotype. As compared to the young, less hypoxic subphenotype, all subphenotypes had increased odds of mortality (Odds Ratio (OR) range from 3.2 to 51.6), intubation (OR range from 3.7 to 14.3) and ICU admission (OR range from 2.1 to 5.0) (Table 1). Exploratory analyses found significantly different mortality across subphenotypes treated by convalescent plasma and Tocilizumab. Conclusions: Six clinically distinct COVID-19 subphenotypes on admission were identified and were associated with varying odds of mortality, intubation and ICU admission. Exploratory analyses suggest that subphenotypes may differentially respond to treatment which may have implications for treatment algorithms.