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

Abstract

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.