This Viewpoint reviews the pathophysiological and observational basis for speculating that angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) might worsen clinical outcomes for patients with COVID-19, and summarizes guidance from specialty societies to continue the drugs in patients who need them pending more definitive evidence of harm.

Coronavirus disease 2019 (COVID-19) has disproportionately affected certain vulnerable populations. Studies noted higher rates of certain comorbidities such as hypertension, diabetes mellitus, and chronic obstructive pulmonary disease in patients infected with COVID-19 with severe disease. 1 Additionally, areas with more racial/ethnic minorities and higher rates of poverty have been shown to have higher rates of COVID-19 hospitalization and death. 2 After adjustment for comorbidities, age has been independently associated with increased mortality due to COVID-19. 3 However, limited attention has been given to children, who appear to have lower risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and mortality.In this issue of JAMA, Bunyavanich et al 4 identify a possible factor that may be related to lower rates of SARS-CoV-2 infection in children. The authors evaluated gene expression in nasal epithelial samples collected as part of a study involving patients with asthma from 2015 to 2018. The nasal epithelium is one of the first sites of infection with SARS-CoV-2, and the investigators probed for the expression of the cell surface enzyme angiotensin-converting enzyme 2 (ACE2), which has been proven to bind to SARS-CoV-2 spike protein and promote internalization of the virus into human cells. 5 Among a cohort of 305 patients aged 4 to 60 years, older children (10-17 years old; n = 185), young adults (18-24 years old; n = 46), and adults (≥25 years old; n = 29) all had higher expression of ACE2 in the nasal epithelium compared with younger children (4-9 years old; n = 45), and ACE2 expression was higher with each subsequent age group after adjusting for sex and asthma.Numerous studies have highlighted the low rates of SARS-CoV-2 infection in children compared with adults. Children have been shown to have fewer and less severe symptoms compared with adults. 6,7 This leads to the question of whether low rates of SARS-CoV-2 infection in children are due to low rates of testing in children, or if children are less susceptible to infection. An evaluation of 1286 close contacts of index cases in China found that infection rates in children were comparable with or slightly higher than in younger adults (aged 30-49 years) but were significantly lower than in older patients (aged ≥60 years). 8 This finding suggests that children seem to have similar rates of becoming infected compared with middle-aged adults following close contact with a person infected with SARS-CoV-2. In contrast, a targeted screening approach in Iceland found SARS-CoV-2 in 6.7% of children younger than 10 years old (n = 564) compared with in 13.7% of people aged 10 years or older (n = 8635). A population-wide screening approach not

As predicting the trajectory of COVID-19 is challenging, machine learning models could assist physicians in identifying high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVID-19 patient data from the Mass General Brigham (MGB) Healthcare database, we developed and internally validated models using patients presenting to the Emergency Department (ED) between March-April 2020 (n = 3597) and further validated them using temporally distinct individuals who presented to the ED between May-August 2020 (n = 1711). We show that ensemble-based models perform better than other model types at predicting both 5-day ICU admission and 28-day mortality from COVID-19. CRP, LDH, and O2 saturation were important for ICU admission models whereas eGFR <60 ml/min/1.73 m2, and neutrophil and lymphocyte percentages were the most important variables for predicting mortality. Implementing such models could help in clinical decision-making for future infectious disease outbreaks including COVID-19.

Understanding the underlying mechanisms of COVID-19 progression and the impact of various pharmaceutical interventions is crucial for the clinical management of the disease. We developed a comprehensive mathematical framework based on the known mechanisms of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, incorporating the renin−angiotensin system and ACE2, which the virus exploits for cellular entry, key elements of the innate and adaptive immune responses, the role of inflammatory cytokines, and the coagulation cascade for thrombus formation. The model predicts the evolution of viral load, immune cells, cytokines, thrombosis, and oxygen saturation based on patient baseline condition and the presence of comorbidities. Model predictions were validated with clinical data from healthy people and COVID-19 patients, and the results were used to gain insight into identified risk factors of disease progression including older age; comorbidities such as obesity, diabetes, and hypertension; and dysregulated immune response. We then simulated treatment with various drug classes to identify optimal therapeutic protocols. We found that the outcome of any treatment depends on the sustained response rate of activated CD8+ T cells and sufficient control of the innate immune response. Furthermore, the best treatment—or combination of treatments—depends on the preinfection health status of the patient. Our mathematical framework provides important insight into SARS-CoV-2 pathogenesis and could be used as the basis for personalized, optimal management of COVID-19.