Chanu Rhee1,2, Maximilian S Jentzsch1,3, Sameer S Kadri4, Christopher W Seymour5, Derek C Angus5, David J Murphy6, Greg S Martin6, Raymund B Dantes7, Lauren Epstein7, Anthony E Fiore7, John A Jernigan7, Robert L Danner4, David K Warren8, Edward J Septimus1,9, Jason Hickok10, Russell E Poland1,10, Robert Jin1, David Fram11, Richard Schaaf11, Rui Wang1, Michael Klompas1,2. 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. Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD. 5. The Clinical Research, Investigation and Systems Modeling of Acute illness (CRISMA) Center, Department of Critical Care and Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA. 6. Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Emory University School of Medicine, Emory Critical Care Center, Atlanta, GA. 7. Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, GA. 8. Department of Medicine, Washington University School of Medicine, St. Louis, MO. 9. Texas A&M Health Science Center College of Medicine, Houston, TX. 10. Clinical Services Group, HCA Healthcare, Nashville, TN. 11. Commonwealth Informatics, Waltham, MA.
Abstract
OBJECTIVES: Administrative claims data are commonly used for sepsis surveillance, research, and quality improvement. However, variations in diagnosis, documentation, and coding practices for sepsis and organ dysfunction may confound efforts to estimate sepsis rates, compare outcomes, and perform risk adjustment. We evaluated hospital variation in the sensitivity of claims data relative to clinical data from electronic health records and its impact on outcome comparisons. DESIGN, SETTING, AND PATIENTS: Retrospective cohort study of 4.3 million adult encounters at 193 U.S. hospitals in 2013-2014. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Sepsis was defined using electronic health record-derived clinical indicators of presumed infection (blood culture draws and antibiotic administrations) and concurrent organ dysfunction (vasopressors, mechanical ventilation, doubling in creatinine, doubling in bilirubin to ≥ 2.0 mg/dL, decrease in platelets to < 100 cells/µL, or lactate ≥ 2.0 mmol/L). We compared claims for sepsis prevalence and mortality rates between both methods. All estimates were reliability adjusted to account for random variation using hierarchical logistic regression modeling. The sensitivity of hospitals' claims data was low and variable: median 30% (range, 5-54%) for sepsis, 66% (range, 26-84%) for acute kidney injury, 39% (range, 16-60%) for thrombocytopenia, 36% (range, 29-44%) for hepatic injury, and 66% (range, 29-84%) for shock. Correlation between claims and clinical data was moderate for sepsis prevalence (Pearson coefficient, 0.64) and mortality (0.61). Among hospitals in the lowest sepsis mortality quartile by claims, 46% shifted to higher mortality quartiles using clinical data. Using implicit sepsis criteria based on infection and organ dysfunction codes also yielded major differences versus clinical data. CONCLUSIONS: Variation in the accuracy of claims data for identifying sepsis and organ dysfunction limits their use for comparing hospitals' sepsis rates and outcomes. Using objective clinical data may facilitate more meaningful hospital comparisons.
OBJECTIVES: Administrative claims data are commonly used for sepsis surveillance, research, and quality improvement. However, variations in diagnosis, documentation, and coding practices for sepsis and organ dysfunction may confound efforts to estimate sepsis rates, compare outcomes, and perform risk adjustment. We evaluated hospital variation in the sensitivity of claims data relative to clinical data from electronic health records and its impact on outcome comparisons. DESIGN, SETTING, AND PATIENTS: Retrospective cohort study of 4.3 million adult encounters at 193 U.S. hospitals in 2013-2014. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS:Sepsis was defined using electronic health record-derived clinical indicators of presumed infection (blood culture draws and antibiotic administrations) and concurrent organ dysfunction (vasopressors, mechanical ventilation, doubling in creatinine, doubling in bilirubin to ≥ 2.0 mg/dL, decrease in platelets to < 100 cells/µL, or lactate ≥ 2.0 mmol/L). We compared claims for sepsis prevalence and mortality rates between both methods. All estimates were reliability adjusted to account for random variation using hierarchical logistic regression modeling. The sensitivity of hospitals' claims data was low and variable: median 30% (range, 5-54%) for sepsis, 66% (range, 26-84%) for acute kidney injury, 39% (range, 16-60%) for thrombocytopenia, 36% (range, 29-44%) for hepatic injury, and 66% (range, 29-84%) for shock. Correlation between claims and clinical data was moderate for sepsis prevalence (Pearson coefficient, 0.64) and mortality (0.61). Among hospitals in the lowest sepsismortality quartile by claims, 46% shifted to higher mortality quartiles using clinical data. Using implicit sepsis criteria based on infection and organ dysfunction codes also yielded major differences versus clinical data. CONCLUSIONS: Variation in the accuracy of claims data for identifying sepsis and organ dysfunction limits their use for comparing hospitals' sepsis rates and outcomes. Using objective clinical data may facilitate more meaningful hospital comparisons.
Authors: Mitchell M Levy; Mitchell P Fink; John C Marshall; Edward Abraham; Derek Angus; Deborah Cook; Jonathan Cohen; Steven M Opal; Jean-Louis Vincent; Graham Ramsay Journal: Intensive Care Med Date: 2003-03-28 Impact factor: 17.440
Authors: Chanu Rhee; Sameer Kadri; Susan S Huang; Michael V Murphy; Lingling Li; Richard Platt; Michael Klompas Journal: Infect Control Hosp Epidemiol Date: 2015-11-03 Impact factor: 3.254
Authors: Dee W Ford; Andrew J Goodwin; Annie N Simpson; Emily Johnson; Nandita Nadig; Kit N Simpson Journal: Crit Care Med Date: 2016-02 Impact factor: 7.598
Authors: Priya A Prasad; Margaret C Fang; Yumiko Abe-Jones; Carolyn S Calfee; Michael A Matthay; Kirsten N Kangelaris Journal: Crit Care Med Date: 2020-02 Impact factor: 7.598
Authors: Edward J Schenck; Katherine L Hoffman; Clara Oromendia; Elizabeth Sanchez; Eli J Finkelsztein; Kyung Sook Hong; Joseph Kabariti; Lisa K Torres; John S Harrington; Ilias I Siempos; Augustine M K Choi; Thomas R Campion Journal: Ann Am Thorac Soc Date: 2021-11
Authors: Shimena R Li; Robert M Handzel; Daniel Tonetti; Jason Kennedy; Katherine Shapiro; Matthew R Rosengart; Daniel E Hall; Christopher Seymour; Edith Tzeng; Katherine M Reitz Journal: J Surg Res Date: 2022-03-21 Impact factor: 2.417
Authors: Mohammad Alrawashdeh; Michael Klompas; Steven Q Simpson; Sameer S Kadri; Russell Poland; Jeffrey S Guy; Jonathan B Perlin; Chanu Rhee Journal: Chest Date: 2022-01-20 Impact factor: 10.262
Authors: John Karlsson Valik; Logan Ward; Hideyuki Tanushi; Kajsa Müllersdorf; Anders Ternhag; Ewa Aufwerber; Anna Färnert; Anders F Johansson; Mads Lause Mogensen; Brian Pickering; Hercules Dalianis; Aron Henriksson; Vitaly Herasevich; Pontus Nauclér Journal: BMJ Qual Saf Date: 2020-02-06 Impact factor: 7.035
Authors: Chanu Rhee; Zhonghe Li; Rui Wang; Yue Song; Sameer S Kadri; Edward J Septimus; Huai-Chun Chen; David Fram; Robert Jin; Russell Poland; Kenneth Sands; Michael Klompas Journal: Open Forum Infect Dis Date: 2020-06-25 Impact factor: 3.835