Survival prediction algorithms for COVID-19 patients admitted to a UK 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 were collected from all admissions to hospital for treatment of Covid-19. Data were manually extracted from the electronic patient record for statistical analysis. Results: Data, including outcome data (discharged alive/died), were extracted for 487 consecutive patients, admitted for treatment. Overall, patients who died were older, had very significantly lower Oxygen saturation (SpO2) on admission, required a higher inspired Oxygen concentration (IpO2) 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 5-parameter prediction algorithm we developed was: [Formula: see text] CONCLUSION: Age, IpO2 on admission, CRP, platelets and number of lungs consolidated were effective marker combinations that helped identify patients who would be likely to survive. The AUC under the ROC Plot was 0.8129 (95% confidence interval 0.0.773 - 0.853; P < .001).