Development of the Canadian COVID-19 Emergency Department Rapid Response Network population-based registry: a methodology study

ObjectivesThe aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy.DesignRetrospective, population-based registry study.SettingSwedish health services.Primary and secondary outcome measuresAll cause 30-day mortality.MethodsElectronic health records (EHRs) and administrative data were used to train six supervised machine learning models to predict all-cause mortality within 30 days in patients discharged from EDs in southern Sweden, Europe.ParticipantsThe models were trained using 65 776 ED visits and validated on 55 164 visits from a separate ED to which the models were not exposed during training.ResultsThe outcome occurred in 136 visits (0.21%) in the development set and in 83 visits (0.15%) in the validation set. The model with highest discrimination attained ROC–AUC 0.95 (95% CI 0.93 to 0.96), with sensitivity 0.87 (95% CI 0.80 to 0.93) and specificity 0.86 (0.86 to 0.86) on the validation set.ConclusionsMultiple models displayed excellent discrimination on the validation set and outperformed available indexes for short-term mortality prediction interms of ROC–AUC (by indirect comparison). The practical utility of the models increases as the data they were trained on did not require costly de novo collection but were real-world data generated as a by-product of routine care delivery.

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Divergence in the epidemiological estimates of traumatic brain injury in the United States: comparison of two national databases

Journal of Neurosurgery ◽

10.3171/2020.7.jns201896 ◽

2020 ◽

pp. 1-10

Author(s):

Brittany M. Stopa ◽

Maya Harary ◽

Ray Jhun ◽

Arun Job ◽

Saef Izzy ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Emergency Department ◽

Brain Injury ◽

Data Bank ◽

The United States ◽

National Sample ◽

Population Based ◽

Weighted Estimates ◽

True Incidence ◽

Ed Visits

OBJECTIVETraumatic brain injury (TBI) is a leading cause of morbidity and mortality in the US, but the true incidence of TBI is unknown.METHODSThe National Trauma Data Bank National Sample Program (NTDB NSP) was queried for 2007 and 2013, and population-based weighted estimates of TBI-related emergency department (ED) visits, hospitalizations, and deaths were calculated. These data were compared to the 2017 Centers for Disease Control and Prevention (CDC) report on TBI, which used the Healthcare Cost and Utilization Project’s National (“Nationwide” before 2012) Inpatient Sample and National Emergency Department Sample.RESULTSIn the NTDB NSP the incidence of TBI-related ED visits was 59/100,000 in 2007 and 62/100,000 in 2013. However, in the CDC report there were 534/100,000 in 2007 and 787/100,000 in 2013. The CDC estimate for ED visits was 805% higher in 2007 and 1169% higher in 2013. In the NTDB NSP, the incidence of TBI-related deaths was 5/100,000 in 2007 and 4/100,000 in 2013. In the CDC report, the incidence was 18/100,000 in both years. The CDC estimate for deaths was 260% higher in 2007 and 325% higher in 2013.CONCLUSIONSThe databases disagreed widely in their weighted estimates of TBI incidence: CDC estimates were consistently higher than NTDB NSP estimates, by an average of 448%. Although such a discrepancy may be intuitive, this is the first study to quantify the magnitude of disagreement between these databases. Given that research, funding, and policy decisions are made based on these estimates, there is a need for a more accurate estimate of the true national incidence of TBI.

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