| Administrative claims data |
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Susceptible to ascertainment bias from decreasing thresholds to diagnose and code for sepsis over time Explicit sepsis codes have low sensitivity relative to medical record reviews Limited ability to distinguish hospital onset vs community-onset disease Limited comparability between hospitals due to variability in diagnosis and coding practices
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Retrospective audits
Example:
CMS SEP-1 measure |
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Resource-intensive; generally only applied to a fraction of a hospital’s sepsis population Still susceptible to ascertainment bias, particularly if cases are selected for audit based on administrative data Variability among abstractors and hospitals for determining presence of sepsis and time zero may limit interpretation of trends in a hospital and comparisons with other hospitals
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Prospective registries
Example:
New York hospitals tracking patients triggering sepsis screens/protocols |
More accurate and rigorous than using administrative data alone Allows for precise identification of time zero and processes of care relative to time zero Can capture majority of a hospital’s sepsis population if hospitals have structured electronic health record–based sepsis screens and protocols
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Resource-intensive Still susceptible to ascertainment bias due to decreasing threshold to diagnose sepsis and organ dysfunction as well as more sensitive sepsis screens and protocols Sepsis screens or protocols can miss true sepsis cases and flag some patients who do not actually have infection or sepsis Limited comparability between hospitals due to differing sepsis screening criteria
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| Adult Sepsis Event surveillance using electronic health record data |
More objective than other methods due to standardised criteria for presumed infection and organ dysfunction Focus on patients treated for infection with sustained courses of antibiotics (≥4 antibiotic days) mitigates ascertainment bias from increasingly aggressive sepsis screening over time capturing more patients with ‘suspected infection’ Can be automated and applied to an entire hospital’s population Can identify day of sepsis onset May allow for more comparability across hospitals due to standardisation
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Requires electronic health record data with microbiology data, chemistry laboratories and medications to apply Implementation requires information technology expertise and resources Misclassification possible (but higher sensitivity and comparable positive predictive value compared with administrative data) Still dependent on clinician interventions (blood cultures, antibiotics, vasopressors, mechanical ventilation, laboratory tests)
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