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Identification of Pediatric Sepsis for Epidemiologic Surveillance Using Electronic Clinical Data
Scott L Weiss, Fran Balamuth1,2, Marianne Chilutti3
1Pediatric Sepsis Program at the Children's Hospital of Philadelphia, Philadelphia, PA.
Insights
A new surveillance algorithm accurately identifies pediatric sepsis using clinical data, revealing a rise in sepsis incidence without changes in mortality. This method overcomes limitations of traditional coding practices for reliable epidemiological tracking.
Area of Science:
- Pediatric critical care medicine
- Clinical informatics
- Epidemiology
Background:
- Accurate identification of pediatric sepsis is crucial for effective treatment and public health monitoring.
- Existing methods relying on diagnosis and claims-based coding are susceptible to changes in practices, limiting epidemiological accuracy.
- A robust surveillance system is needed to track pediatric sepsis trends reliably.
Purpose of the Study:
- To derive and validate a surveillance algorithm for identifying pediatric sepsis episodes using routine clinical data.
- To apply the validated algorithm to study longitudinal trends in pediatric sepsis incidence and mortality.
- To overcome the limitations of diagnostic and claims-based coding in sepsis surveillance.
Main Methods:
- Retrospective observational study conducted at a single academic children's hospital.
- Development and validation of a surveillance algorithm using clinical data to detect infection and organ dysfunction.
- Application of the algorithm to analyze 8 years (2011-2019) of hospital encounter data, excluding NICU and cardiac center patients.
Main Results:
- The derived surveillance algorithm demonstrated good performance: sensitivity 78%, specificity 76% (derivation); sensitivity 84%, specificity 65% (validation).
- Positive predictive value was 41% and negative predictive value was 94% in the derivation cohort.
- Risk-adjusted sepsis incidence increased significantly over the study period (IRR 1.07 per year), while mortality remained stable (OR 0.98 per year).
Conclusions:
- A clinical data-driven surveillance algorithm offers an objective, efficient, and reliable method for monitoring pediatric sepsis.
- The study identified a significant increase in pediatric sepsis incidence, independent of coding or diagnostic practice changes.
- Pediatric sepsis mortality remained stable during the study period, suggesting potential improvements in management or earlier detection.
Objectives:
A method to identify pediatric sepsis episodes that is not affected by changing diagnosis and claims-based coding practices does not exist. We derived and validated a surveillance algorithm to identify pediatric sepsis using routine clinical data and applied the algorithm to study longitudinal trends in sepsis epidemiology.
Design:
Retrospective observational study.
Setting:
Single academic children's hospital.
Patients:
All emergency and hospital encounters from January 2011 to January 2019, excluding neonatal ICU and cardiac center.
Exposure:
Sepsis episodes identified by a surveillance algorithm using clinical data to identify infection and concurrent organ dysfunction.
Interventions:
None.
Measurements And Main Results:
A surveillance algorithm was derived and validated in separate cohorts with suspected sepsis after clinician-adjudication of final sepsis diagnosis. We then applied the surveillance algorithm to determine longitudinal trends in incidence and mortality of pediatric sepsis over 8 years. Among 93,987 hospital encounters and 1,065 episodes of suspected sepsis in the derivation period, the surveillance algorithm yielded sensitivity 78% (95% CI, 72-84%), specificity 76% (95% CI, 74-79%), positive predictive value 41% (95% CI, 36-46%), and negative predictive value 94% (95% CI, 92-96%). In the validation period, the surveillance algorithm yielded sensitivity 84% (95% CI, 77-92%), specificity of 65% (95% CI, 59-70%), positive predictive value 43% (95% CI, 35-50%), and negative predictive value 93% (95% CI, 90-97%). Notably, most "false-positives" were deemed clinically relevant sepsis cases after manual review. The hospital-wide incidence of sepsis was 0.69% (95% CI, 0.67-0.71%), and the inpatient incidence was 2.8% (95% CI, 2.7-2.9%). Risk-adjusted sepsis incidence, without bias from changing diagnosis or coding practices, increased over time (adjusted incidence rate ratio per year 1.07; 95% CI, 1.06-1.08; p < 0.001). Mortality was 6.7% and did not change over time (adjusted odds ratio per year 0.98; 95% CI, 0.93-1.03; p = 0.38).
Conclusions:
An algorithm using routine clinical data provided an objective, efficient, and reliable method for pediatric sepsis surveillance. An increased sepsis incidence and stable mortality, free from influence of changes in diagnosis or billing practices, were evident.
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