Dataset Information

Identification of Pediatric Sepsis for Epidemiologic Surveillance Using Electronic Clinical Data.

ABSTRACT:

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.

SUBMITTER: Weiss SL

PROVIDER: S-EPMC7008717 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of Pediatric Sepsis for Epidemiologic Surveillance Using Electronic Clinical Data.

Weiss Scott L SL Balamuth Fran F Chilutti Marianne M Ramos Mark Jason MJ McBride Peter P Kelly Nancy-Ann NA Payton K Joy KJ Fitzgerald Julie C JC Pennington Jeffrey W JW

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 20200201 2

<h4>Objectives</h4>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.<h4>Design</h4>Retrospective observational study.<h4>Setting</h4>Single academic children's hospital.<h4>Patients</h4>All emergency and hospital encounters ...[more]

PMID: 32032262

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Project description:Variability in hospital-level sepsis mortality rates may be due to differences in case mix, quality of care, or diagnosis and coding practices. Centers for Disease Control and Prevention's Adult Sepsis Event definition could facilitate objective comparisons of sepsis mortality rates between hospitals but requires rigorous risk-adjustment tools. We developed risk-adjustment models for Adult Sepsis Events using administrative and electronic health record data.DesignRetrospective cohort study.SettingOne hundred thirty-six U.S. hospitals in Cerner HealthFacts (derivation dataset) and 137 HCA Healthcare hospitals (validation dataset).PatientsA total of 95,154 hospitalized adult patients (derivation) and 201,997 patients (validation) meeting Centers for Disease Control and Prevention Adult Sepsis Event criteria.InterventionsNone.Measurements and main resultsWe created logistic regression models of increasing complexity using administrative and electronic health record data to predict in-hospital mortality. An administrative model using demographics, comorbidities, and coded markers of severity of illness at admission achieved an area under the receiver operating curve of 0.776 (95% CI, 0.770-0.783) in the Cerner cohort, with diminishing calibration at higher baseline risk deciles. An electronic health record-based model that integrated administrative data with laboratory results, vasopressors, and mechanical ventilation achieved an area under the receiver operating curve of 0.826 (95% CI, 0.820-0.831) in the derivation cohort and 0.827 (95% CI, 0.824-0.829) in the validation cohort, with better calibration than the administrative model. Adding vital signs and Glasgow Coma Score minimally improved performance.ConclusionsModels incorporating electronic health record data accurately predict hospital mortality for patients with Adult Sepsis Events and outperform models using administrative data alone. Utilizing laboratory test results, vasopressors, and mechanical ventilation without vital signs may achieve a good balance between data collection needs and model performance, but electronic health record-based models must be attentive to potential variability in data quality and availability. With ongoing testing and refinement of these risk-adjustment models, Adult Sepsis Event surveillance may enable more meaningful comparisons of hospital sepsis outcomes and provide an important window into quality of care.

Project description:ObjectivesPrior studies have reported that hospital-onset sepsis is associated with higher mortality rates than community-onset sepsis. Most studies, however, have used inconsistent case-finding methods and applied limited risk-adjustment for potential confounders. We used consistent sepsis criteria and detailed electronic clinical data to elucidate the epidemiology and mortality associated with hospital-onset sepsis.DesignRetrospective cohort study.Setting136 U.S. hospitals in the Cerner HealthFacts dataset.PatientsAdults hospitalized in 2009-2015.InterventionsNone.Measurements and main resultsWe identified sepsis using Centers for Disease Control and Prevention Adult Sepsis Event criteria and estimated the risk of in-hospital death for hospital-onset sepsis versus community-onset sepsis using logistic regression models. In patients admitted without community-onset sepsis, we estimated risk of death associated with hospital-onset sepsis using Cox regression models with sepsis as a time-varying covariate. Models were adjusted for baseline characteristics and severity of illness. Among 2.2 million hospitalizations, there were 95,154 sepsis cases: 83,620 (87.9%) community-onset sepsis and 11,534 (12.1%) hospital-onset sepsis (0.5% of hospitalized cohort). Compared to community-onset sepsis, hospital-onset sepsis patients were younger (median 66 vs 68 yr) but had more comorbidities (median Elixhauser score 14 vs 11), higher Sequential Organ Failure Assessment scores (median 4 vs 3), higher ICU admission rates (61% vs 44%), longer hospital length of stay (median 19 vs 8 d), and higher in-hospital mortality (33% vs 17%) (p < 0.001 for all comparisons). On multivariate analysis, hospital-onset sepsis was associated with higher mortality versus community-onset sepsis (odds ratio, 2.1; 95% CI, 2.0-2.2) and patients admitted without sepsis (hazard ratio, 3.0; 95% CI, 2.9-3.2).ConclusionsHospital-onset sepsis complicated one in 200 hospitalizations and accounted for one in eight sepsis cases, with one in three patients dying in-hospital. Hospital-onset sepsis preferentially afflicted ill patients but even after risk-adjustment, they were twice as likely to die as community-onset sepsis patients; in patients admitted without sepsis, hospital-onset sepsis tripled the risk of death. Hospital-onset sepsis is an important target for surveillance, prevention, and quality improvement initiatives.

Dataset Information

Identification of Pediatric Sepsis for Epidemiologic Surveillance Using Electronic Clinical Data.

Objectives

Design

Setting

Patients

Exposure

Interventions

Measurements and main results

Conclusions

Publications

Identification of Pediatric Sepsis for Epidemiologic Surveillance Using Electronic Clinical Data.

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