Identification of patients at high risk for brain death using an automated digital screening tool: a prospective diagnostic accuracy study

Daniela Schoene, Norman Freigang, Anne Trabitzsch, Konrad Pleul, Daniel P O Kaiser, Martin Roessler, Simon Winzer, Christian Hugo, Albrecht Günther, Volker Puetz, Kristian Barlinn, Daniela Schoene, Norman Freigang, Anne Trabitzsch, Konrad Pleul, Daniel P O Kaiser, Martin Roessler, Simon Winzer, Christian Hugo, Albrecht Günther, Volker Puetz, Kristian Barlinn

Abstract

Background: An automated digital screening tool (DETECT) has been developed to aid in the early identification of patients who are at risk of developing brain death during critical care.

Methods: This prospective diagnostic accuracy study included consecutive patients ≥ 18 years admitted to neurocritical care for primary or secondary acute brain injury. The DETECT screening tool searched routinely monitored patient data in the electronic medical records every 12 h for a combination of coma and absence of bilateral pupillary light reflexes. In parallel, daily neurological assessment was performed by expert neurointensivists in all patients blinded to the index test results. The primary target condition was the eventual diagnosis of brain death. Estimates of diagnostic accuracy along with their 95%-confidence intervals were calculated to assess the screening performance of DETECT.

Results: During the 12-month study period, 414 patients underwent neurological assessment, with 8 (1.9%) confirmed cases of brain death. DETECT identified 54 positive patients and sent 281 notifications including 227 repeat notifications. The screening tool had a sensitivity of 100% (95% CI 63.1-100%) in identifying patients who eventually developed brain death, with no false negatives. The mean time from notification to confirmed diagnosis of brain death was 3.6 ± 3.2 days. Specificity was 88.7% (95% CI 85.2-91.6%), with 46 false positives. The overall accuracy of DETECT for confirmed brain death was 88.9% (95% CI 85.5-91.8%).

Conclusions: Our findings suggest that an automated digital screening tool that utilizes routinely monitored clinical data may aid in the early identification of patients at risk of developing brain death.

Keywords: Brain death; Death by neurologic criteria; Diagnostic accuracy; Organ donor identification.

Conflict of interest statement

AT, CH, AG and KB received funding from the German organ procurement organization (DSO) and served as a paid consultant for the same organization. The other authors declare that they have no competing interests.

© 2023. The Author(s).

Figures

Fig. 1
Fig. 1
Study design. †Determination according to the guideline of the German Medical Association [14]. RASS Richmond Agitation Sedation Scale, GCS Glasgow Coma Scale, ICP intracranial pressure, CPP cerebral perfusion pressure, F/U follow up
Fig. 2
Fig. 2
Flow chart of the study participants. †According to the guideline of the German Medical Association [14]. TP true positive, TN true negative, FP false positive, FN false negative
Fig. 3
Fig. 3
Patient with large right-sided hemispheric infarction that resulted in cerebral edema, secondary intracerebral hemorrhage, intraventricular hemorrhage, hydrocephalus and transtentorial herniation. The patient was screened positive by DETECT on day 4 (6 a.m.) following admission to neurocritical care. A Early follow-up CT on day 2 used for independent neuroimaging assessment for the potential of developing brain death. B CT scan on day 5 when brain death was confirmed

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