Open AccessSurvival prediction algorithms for COVID‐19 patients admitted to a UK district general hospital
Author(s) -
Fernandez Ancy,
Obiechionyelum,
Koh Justin,
Hong Anna,
Nandi Angela,
Reynolds Timothy M.
Publication year - 2021
Publication title -
international journal of clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.756
H-Index - 98
eISSN - 1742-1241
pISSN - 1368-5031
DOI - 10.1111/ijcp.13974
Subject(s) - medicine , confidence interval , logistic regression , covid-19 , predictive value , receiver operating characteristic , predictive value of tests , bonferroni correction , algorithm , pediatrics , emergency medicine , statistics , mathematics , disease , computer science , infectious disease (medical specialty)
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:P ( death ) = 1 1 + e ‐ 1 ( ‐ 22.8449 + 4.1124 LN ( age ) + 2.0421 LN ( IpO 2 ) + 0.2770 LN ( CRP ) ‐ 0.7738 LN ( Plt ) + 0.7625 # consolidated _ lungs )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).