External validation of the Oldham composite Covid-19 associated mortality model (OCCAM), a prognostic model for death in patients hospitalised with Covid-19

Published Date: 19th June 2023

Publication Authors: McLeneghan D

Objective
External validation of the Oldham Composite Covid-19 associated Mortality Model (OCCAM), a prognostic model for Covid-19 mortality in hospitalised patients comprised of age, history of hypertension, current or previous malignancy, admission platelet count < 150 × 103/µL, admission CRP ≥ 100 µg/mL, acute kidney injury (AKI), and radiographic evidence of > 50% total lung field infiltrates.

Patients and Methods
Retrospective study assessing discrimination (c-statistic) and calibration of OCCAM for death in hospital or within 30 days of discharge. 300 adults admitted to six district general and teaching hospitals in North West England for treatment of Covid-19 between September 2020 and February 2021 were included.

Results
Two hundred and ninety-seven patients were included in the validation cohort analysis, with a mortality rate of 32.8%. The c-statistic was 0.794 (95% confidence interval 0.742–0.847) vs. 0.805 (95% confidence interval 0.766 – 0.844) in the development cohort. Visual inspection of calibration plots demonstrate excellent calibration across risk groups, with a calibration slope for the external validation cohort of 0.963.

Conclusion
The OCCAM model is an effective prognostic tool that can be utilised at the time of initial patient assessment to aid decisions around admission and discharge, use of therapeutics, and shared decision-making with patients. Clinicians should remain aware of the need for ongoing validation of all Covid-19 prognostic models in light of changes in host immunity and emerging variants.


Thompson, JV; McLeneghan, D et al. (2023). External validation of the Oldham composite Covid-19 associated mortality model (OCCAM), a prognostic model for death in patients hospitalised with Covid-19. Infectious Diseases Now. 53(6), p.104722. [Online]. Available at: https://doi.org/10.1016/j.idnow.2023.104722 [Accessed 11 July 2023]

 

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