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, 2 (3), 339-345
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Sepsis Surveillance: An Examination of Parameter Sensitivity and Alert Reliability

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Sepsis Surveillance: An Examination of Parameter Sensitivity and Alert Reliability

Robert C Amland et al. JAMIA Open.

Abstract

Objective: To examine performance of a sepsis surveillance system in a simulated environment where modifications to parameters and settings for identification of at-risk patients can be explored in-depth.

Materials and methods: This was a multiple center observational cohort study. The study population comprised 14 917 adults hospitalized in 2016. An expert-driven rules algorithm was applied against 15.1 million data points to simulate a system with binary notification of sepsis events. Three system scenarios were examined: a scenario as derived from the second version of the Consensus Definitions for Sepsis and Septic Shock (SEP-2), the same scenario but without systolic blood pressure (SBP) decrease criteria (near SEP-2), and a conservative scenario with limited parameters. Patients identified by scenarios as being at-risk for sepsis were assessed for suspected infection. Multivariate binary logistic regression models estimated mortality risk among patients with suspected infection.

Results: First, the SEP-2-based scenario had a hyperactive, unreliable parameter SBP decrease >40 mm Hg from baseline. Second, the near SEP-2 scenario demonstrated adequate reliability and sensitivity. Third, the conservative scenario had modestly higher reliability, but sensitivity degraded quickly. Parameters differed in predicting mortality risk and represented a substitution effect between scenarios.

Discussion: Configuration of parameters and alert criteria have implications for patient identification and predicted outcomes.

Conclusion: Performance of scenarios was associated with scenario design. A single hyperactive, unreliable parameter may negatively influence adoption of the system. A trade-off between modest improvements in alert reliability corresponded to a steep decline in condition sensitivity in scenarios explored.

Keywords: classification; decision support; ergonomics; expert systems; sepsis.

Figures

Figure 1.
Figure 1.
Sepsis surveillance model and corresponding organ system parameters by surveillance system scenario. SBP: systolic blood pressure; SEP-2: Consensus Definitions for Sepsis and Septic Shock; SIRS: Systemic Inflammatory Response Syndrome; MAP: mean arterial pressure.
Figure 2.
Figure 2.
Forest plot of organ system parameters on mortality by surveillance system scenario. INR: international normalized ratio; MAP: mean arterial pressure; MODS: multiple organ dysfunction; NEWS: National Early Warning Score; SBP: systolic blood pressure; SEP-2: Consensus Definitions for Sepsis and Septic Shock.

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