loading page

A Survival Prediction Algorithm for Covid-19 Patients Admitted to a District General Hospital
  • +3
  • Ancy Fernandez,
  • nonyelum obiechina,
  • Justin Koh,
  • Anna Hong,
  • Angela Nandi,
  • Tim Reynolds
Ancy Fernandez
University Hospitals of Derby and Burton NHS Foundation Trust
Author Profile
nonyelum obiechina
University Hospitals of Derby and Burton NHS Foundation Trust
Author Profile
Justin Koh
University Hospitals of Derby and Burton NHS Foundation Trust
Author Profile
Anna Hong
University Hospitals of Derby and Burton NHS Foundation Trust
Author Profile
Angela Nandi
University Hospitals of Derby and Burton NHS Foundation Trust
Author Profile
Tim Reynolds
University Hospitals of Derby and Burton NHS Foundation Trust
Author Profile

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. Interval 0.689-0.784; P< 0.001). Further research adding extra markers, is underway.

Peer review status:ACCEPTED

12 Aug 2020Submitted to International Journal of Clinical Practice
13 Aug 2020Submission Checks Completed
13 Aug 2020Assigned to Editor
22 Sep 2020Reviewer(s) Assigned
27 Sep 2020Review(s) Completed, Editorial Evaluation Pending
03 Nov 20201st Revision Received
04 Nov 2020Submission Checks Completed
04 Nov 2020Assigned to Editor
04 Nov 2020Reviewer(s) Assigned
26 Nov 2020Review(s) Completed, Editorial Evaluation Pending
21 Dec 2020Editorial Decision: Accept