Simple risk scores to predict hospitalization or death in outpatients with COVID-19 including the Omicron variant (preprint)

Importance Outpatient physicians need guidance to support their clinical decisions regarding management of patients with COVID-19, specifically whether to hospitalize a patient or if managed as an outpatient, how closely to follow them. Objective To develop and prospectively validate a clinical prediction rule to predict the likelihood of hospitalization for outpatients with COVID-19 that does not require laboratory testing or imaging, including during the current Omicron wave. Design Derivation and temporal validation of a clinical prediction rule, and prospective validation of two externally derived clinical prediction rules. Setting Primary and urgent care clinics in a Pennsylvania health system. Participants Patients 12 years and older presenting to outpatient clinics who had a positive polymerase chain reaction test for COVID-19. Main outcomes and measures Classification accuracy (percentage in each risk group hospitalized) and area under the receiver operating characteristic curve (AUC). Results Overall, 4.0% of outpatients in the early derivation cohort (5843 patients presenting before 3/1/21), 4.2% in the late validation cohort (3806 patients presenting 3/1/21 to 9/30/21), and 1.9% in an Omicron cohort were ultimately hospitalized. We developed and temporally validated four simple risk scores. The base score included age, dyspnea, and the presence of a comorbidity, with the other scores adding fever, respiratory rate and/or oxygen saturation. All had very good overall accuracy (AUC 0.85-0.87) and classified at least half of patients into a low risk with a < 1% likelihood of hospitalization. Hospitalization rates in the Omicron cohort were 0.22%, 1.3% and 8.7% for the base score. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization than our novel risk scores. Conclusions and relevance A simple risk score applicable to outpatient and telehealth settings can classify over half of COVID-19 outpatients into a very low risk group with a 0.22% hospitalization risk in the Omicron cohort. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/ . Key points Question Is it possible to predict the eventual likelihood of hospitalization for outpatients with COVID-19 using simple non-laboratory based risk scores? Findings We created and temporally validated in the same population 4 risk scores with 3 to 5 predictors that do not require laboratory testing. Groups with low (0.34% to 0.89%), moderate (4.0% to 6.2%), and high-risk (19.2% to 25.2%) of hospitalization were identified. The risk scores were also accurate in an Omicron dominant cohort with hospitalization rates of 0.22% to 0.43% in the low-risk groups, 1.3% to 1.7% in the moderate risk groups, and 8.7% to 15.3% in the high risk groups. Meaning Simple risk scores can help support decisions about hospitalization in the outpatient setting.

Graduate students significantly more concerned than undergraduates about returning to campus in the era of COVID-19 (preprint)

Introduction: As students return to colleges and universities in the fall of 2020, it is important to understand their perception of risk and their desire for in person versus online learning, which may differ between undergraduate and graduate students. Methods: We anonymously surveyed 212 undergraduate and 134 graduate students in the College of Public Health, and 94 graduate students in the College of Education in late June, 2020. We asked them Likert style questions regarding their comfort returning to campus and their preferred learning strategies once back. We compared Strongly agree/Agree with Neutral/Disagree/Strongly disagree using a chi-square test. Results: Graduate students were significantly less likely to look forward to being on campus (38.3% doctoral vs 40.6% master's vs 77.7% undergraduate, p < 0.001), more likely to perceive themselves as high risk (43.3% doctoral vs 40.0% masters vs 17.5% undergraduate, p < 0.001), and were more likely to prefer all classwork online (66.7% doctoral vs 44.6% masters vs 20.8% undergraduate, p < 0.001). Graduate students were also less likely to prefer to be in the classroom as much as possible in the fall (59.2% doctoral vs 67.7% masters vs 74.5% undergraduate, p < 0.001). Most were not concerned about their ability to conduct research. Students generally supported wearing of facemasks indoors. Conclusions: There are important differences in perception of risk and desire for online versus in-person learning between undergraduate and graduate students. Faculty and administrators must acknowledge and address these differences as they prepare for return to campus in the fall.

Mandatory social distancing associated with increased doubling time: an example using hyperlocal data (preprint)

In a hyperlocal analysis of doubling time of COVID-19, we found that a county that implemented mandatory social distancing and other measures to reduce spread of infection saw an earlier increase in doubling time than surrounding counties.