Development and validation of an algorithm to identify severe sepsis onset from electronic medical records

Objective To develop and evaluate an automated algorithm to identify sepsis onset from the electronic medical records (EMR), referred to as time-zero (t0), to enable more accurate surveillance, quality improvement, and training related to SEP-1 aligned care.

We developed an algorithm to construct a comprehensive timeline of systemic inflammatory response syndrome (SIRS) criteria and organ dysfunction (OD) using structured data, and documentation of infection (DOI) using both structured data and unstructured clinical notes. The algorithm scans each timeline to detect the co-occurrence of SEP-1 components within a 6-hour window to determine t0. Algorithm performance was assessed using 2,030 manually abstracted adult sepsis cases from a large academic health system in southeast Michigan.

Using a subset of 516 abstracted cases, we show that including clinical notes achieves higher concordance of DOI with abstractors t0 (41.9%) compared to using antibiotic (27.9%) or culture order proxies (34.7%). Combining all three sources achieves the highest DOI concordance (44.4%). On average, the algorithm DOI time was significantly earlier than abstractors (mean: -0.30 h, 95% Cl: -0.51 to -0.09) across all 2,030 cases, resulting in a significantly earlier t0 (mean: -0.51 h, 95% CI: -0.65 to -0.36).

Automated approaches to analyze EMR data offer a scalable framework for SEP-1 monitoring, research, and quality improvement.

Incorporating unstructured clinical notes improves detection of infection suspicion and enhances concordance with manual abstraction of sepsis onset. Competing Interest Statement Ramin Homayouni is a co-founder and equity holder in Quire Inc., a health analytics company. He does not receive direct revenue from Quire Inc. Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board of the Beaumont Research Institute gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The source data for this study cannot be shared due to privacy concerns related to unstructured clinical notes. However, the Regular Expressions used for data extraction are available from the authors upon reasonable request.