Accuracy of the Veterans Health Administration COVID-19 (VACO) Index for predicting short-term mortality among 1307 US academic medical centre inpatients and 427 224 US Medicare patients.
BACKGROUND
The Veterans Health Administration COVID-19 (VACO) Index predicts 30-day all-cause mortality in patients with COVID-19 using age, sex and pre-existing comorbidity diagnoses. The VACO Index was initially developed and validated in a nationwide cohort of US veterans-we now assess its accuracy in an academic medical centre and a nationwide US Medicare cohort.


METHODS
With measures and weights previously derived and validated in US national Veterans Health Administration (VA) inpatients and outpatients (n=13 323), we evaluated the accuracy of the VACO Index for estimating 30-day all-cause mortality using area under the receiver operating characteristic curve (AUC) and calibration plots of predicted versus observed mortality in inpatients at a single US academic medical centre (n=1307) and in Medicare inpatients and outpatients aged 65+ (n=427 224).


RESULTS
30-day mortality varied by data source: VA 8.5%, academic medical centre 17.5%, Medicare 16.0%. The VACO Index demonstrated similar discrimination in VA (AUC=0.82) and academic medical centre inpatient population (AUC=0.80), and when restricted to patients aged 65+ in VA (AUC=0.69) and Medicare inpatient and outpatient data (AUC=0.67). The Index modestly overestimated risk in VA and Medicare data and underestimated risk in Yale New Haven Hospital data.


CONCLUSIONS
The VACO Index estimates risk of short-term mortality across a wide variety of patients with COVID-19 using data available prior to or at the time of diagnosis. The VACO Index could help inform primary and booster vaccination prioritisation, and indicate who among outpatients testing positive for SARS-CoV-2 should receive greater clinical attention or scarce treatments.
['King|Joseph T|JT|http://orcid.org/0000-0003-2823-9649', 'Yoon|James S|JS|', 'Bredl|Zachary M|ZM|', 'Habboushe|Joseph P|JP|', 'Walker|Graham A|GA|', 'Rentsch|Christopher T|CT|', 'Tate|Janet P|JP|', 'Kashyap|Nitu M|NM|', 'Hintz|Richard C|RC|', 'Chopra|Aneesh P|AP|', 'Justice|Amy C|AC|']
COVID-19 mortality epidemiology public health policy