Electronic "Sniffer" Systems to Identify the Acute Respiratory Distress Syndrome

Abstract

Background: The acute respiratory distress syndrome (ARDS) results in substantial mortality but remains underdiagnosed in clinical practice. Automated ARDS "sniffer" systems, tools that can automatically analyze electronic medical record data, have been developed to improve recognition of ARDS in clinical practice.

Objectives: To perform a systematic review examining the evidence underlying automated sniffer systems for ARDS detection.

Data sources: MEDLINE and Scopus databases through November 2018 to identify studies of tools using routinely available clinical data to detect patients with ARDS.

Data extraction: Study design, tool description, and diagnostic performance were extracted by two reviewers. The Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate each study for risk of bias in four domains: patient selection, index test, reference standard, and study flow and timing.

Synthesis: Among 480 studies identified, 9 met inclusion criteria, and they evaluated six unique ARDS sniffer tools. Eight studies had derivation and/or temporal validation designs, with one also evaluating the effects of implementing a tool in clinical practice. A single study performed an external validation of previously published ARDS sniffer tools. Studies reported a wide range of sensitivities (43-98%) and positive predictive values (26-90%) for detection of ARDS. Most studies had potential for high risk of bias identified in their study design, including patient selection (five of nine), reference standard (four of nine), and flow and timing (three of nine). In the single external validation without any perceived risks of biases, the performance of ARDS sniffer tools was worse.

Conclusions: Sniffer systems developed to detect ARDS had moderate to high predictive value in their derivation cohorts, although most studies had the potential for high risks of bias in study design. Methodological issues may explain some of the variability in tool performance. There remains an ongoing need for robust evaluation of ARDS sniffer systems and their impact on clinical practice. Systematic review registered with PROSPERO (CRD42015026584).

Keywords: acute lung injury; acute respiratory distress syndrome; diagnostic tool; identification; systematic review.

Figure 1.

Flow diagram for literature search and results. *Other citations include those identified after performing a hand search of the references of articles identified in the original search. ARDS = acute respiratory distress syndrome.

Figure 2.

(A and B) Acute respiratory distress syndrome “sniffer” tool performance in each study and the corresponding number of study design domains with high risk of bias identified. ASSIST = ALI Selection System to Identify Subjects for Treatment/Trials tool.