A Survival Prediction Algorithm for Covid-19 Patients Admitted to a District General Hospital

Abstract

OBJECTIVE: To collect and review data from consecutive patients admitted to Queen's Hospital, Burton on Trent for treatment of Covid-19 infection, with the aim of developing a predictive algorithm that can help identify those patients likely to survive. DESIGN: Consecutive patient data was collected from all admissions to hospital for treatment of Covid-19. Data was manually extracted from the electronic patient record for statistical analysis. RESULTS: Data, including outcome data (discharged alive / died) was extracted for 487 consecutive patients, admitted for treatment. Overall, patients who died were older, had very significantly lower Oxygen saturation (SpO2) on admission, and higher CRP as evidenced by a Bonferroni-corrected P<0.0056). Evaluated individually, platelets and lymphocyte count were not statistically significant but when used in a logistic regression to develop a predictive score, platelet count did add predictive value. The prediction algorithm we developed was: P(survival) = 1 1+e-1(-16.7104-3.3810LN(age)+6.5592LN(SpO2)-0.4584LN(CRP)+0.7183LN(Plt)) CONCLUSION: Age, SpO2 on Admmission, CRP and platelets were an effective marker combination that helped identify patients who would be likely to survive. The AUC under the ROC Plot was 0.737 (95% Conf.

Experiences of Latinx Individuals Hospitalized for COVID-19

“…Six authors conducted semistructured interviews in English or Spanish by telephone. The interview guide (eTable 1 in the Supplement) was based on a literature review of race disparities and the COVID-19 pandemic, with a particular focus on Latinx communities . Interviews were audio recorded and transcribed verbatim, and were conducted until thematic data saturation, defined as when a thorough understanding of the perspectives of Latinx respondents were obtained with few or no new concepts emerging in subsequent data collection…”