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:ObjectivesIn 2018, the Centers for Disease Control and Prevention introduced the Adult Sepsis Event (ASE) definition, using electronic health records (EHRs) data for surveillance and sepsis quality improvement. However, data regarding ASE outside the United States remain limited. We therefore aimed to validate the diagnostic accuracy of the ASE and to assess the prevalence and mortality of sepsis using ASE.DesignRetrospective cohort study.SettingA single center in South Korea, with 2732 beds including 221 ICU beds.PatientsDuring the validation phase, adult patients who were hospitalized or visiting the emergency department between November 5 and November 11, 2019, were included. In the subsequent phase of epidemiologic analysis, we included adult patients who were admitted from January to December 2020.InterventionsNone.Measurements and main resultsASE had a sensitivity of 91.6%, a specificity of 98.3%, a positive predictive value (PPV) of 57.4%, and a negative predictive value of 99.8% when compared with the Sepsis-3 definition. Of 126,998 adult patient hospitalizations in 2020, 6,872 cases were diagnosed with sepsis based on the ASE (5.4% per year), and 893 patients were identified as having sepsis according to the International Classification of Diseases , 10th Edition (ICD-10) (0.7% per year). Hospital mortality rates were 16.6% (ASE) and 23.5% (ICD-10-coded sepsis). Monthly sepsis prevalence and hospital mortality exhibited less variation when diagnosed using ASE compared with ICD-10 coding (coefficient of variation [CV] for sepsis prevalence: 0.051 vs. 0.163, Miller test p < 0.001; CV for hospital mortality: 0.087 vs. 0.261, p = 0.001).ConclusionsASE demonstrated high sensitivity and a moderate PPV compared with the Sepsis-3 criteria in a Korean population. The prevalence of sepsis, as defined by ASE, was 5.4% per year and was similar to U.S. estimates. The prevalence of sepsis by ASE was eight times higher and exhibited less monthly variability compared with that based on the ICD-10 code.

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.

Similar Datasets

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