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Rhee C, Wang R, Song Y, et al. Risk Adjustment for Sepsis Mortality to Facilitate Hospital Comparisons Using Centers for Disease Control and Prevention's Adult Sepsis Event Criteria and Routine Electronic Clinical Data. Crit Care Explor. 2019;1(10):e0049doi: 10.1097/CCE.0000000000000049.
Rhee, C., Wang, R., Song, Y., Zhang, Z., Kadri, S. S., Septimus, E. J., Fram, D., Jin, R., Poland, R. E., Hickok, J., Sands, K., & Klompas, M. (2019). Risk Adjustment for Sepsis Mortality to Facilitate Hospital Comparisons Using Centers for Disease Control and Prevention's Adult Sepsis Event Criteria and Routine Electronic Clinical Data. Critical care explorations, 1(10), e0049. https://doi.org/10.1097/CCE.0000000000000049
Rhee, Chanu, et al. "Risk Adjustment for Sepsis Mortality to Facilitate Hospital Comparisons Using Centers for Disease Control and Prevention's Adult Sepsis Event Criteria and Routine Electronic Clinical Data." Critical care explorations vol. 1,10 (2019): e0049. doi: https://doi.org/10.1097/CCE.0000000000000049
Rhee C, Wang R, Song Y, Zhang Z, Kadri SS, Septimus EJ, Fram D, Jin R, Poland RE, Hickok J, Sands K, Klompas M. Risk Adjustment for Sepsis Mortality to Facilitate Hospital Comparisons Using Centers for Disease Control and Prevention's Adult Sepsis Event Criteria and Routine Electronic Clinical Data. Crit Care Explor. 2019 Oct 14;1(10):e0049. doi: 10.1097/CCE.0000000000000049. eCollection 2019 Oct. PMID: 32166230; PMCID: PMC7063887.
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Crit Care Explor. 2019 Oct 14;1(10):e0049. doi: 10.1097/CCE.0000000000000049. eCollection 2019 Oct.

Risk Adjustment for Sepsis Mortality to Facilitate Hospital Comparisons Using Centers for Disease Control and Prevention's Adult Sepsis Event Criteria and Routine Electronic Clinical Data.

Critical care explorations

Chanu Rhee, Rui Wang, Yue Song, Zilu Zhang, Sameer S Kadri, Edward J Septimus, David Fram, Robert Jin, Russell E Poland, Jason Hickok, Kenneth Sands, Michael Klompas

Affiliations

  1. Department of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, MA.
  2. Department of Medicine, Brigham and Women's Hospital, Boston, MA.
  3. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
  4. Department of Medical Oncology, Harvard Medical School/Dana Farber Cancer Institute, Boston, MA.
  5. Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD.
  6. Department of Internal Medicine, Texas A&M College of Medicine, Houston, TX.
  7. Commonwealth Informatics, Waltham, MA.
  8. Clinical Services Group, HCA Healthcare, Nashville, TN.
  9. Ondine Biomedical, Vancouver, BC, Canada.

PMID: 32166230 PMCID: PMC7063887 DOI: 10.1097/CCE.0000000000000049

Abstract

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.

DESIGN: Retrospective cohort study.

SETTING: One hundred thirty-six U.S. hospitals in Cerner HealthFacts (derivation dataset) and 137 HCA Healthcare hospitals (validation dataset).

PATIENTS: A total of 95,154 hospitalized adult patients (derivation) and 201,997 patients (validation) meeting Centers for Disease Control and Prevention Adult Sepsis Event criteria.

INTERVENTIONS: None.

MEASUREMENTS AND MAIN RESULTS: We 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.

CONCLUSIONS: Models 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.

Keywords: adult sepsis event; electronic health records; hospital benchmarking; risk adjustment; sepsis; surveillance

Conflict of interest statement

The authors have disclosed that they do not have any potential conflicts of interest.

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