Agreement of claims-based methods for identifying sepsis with clinical criteria in the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort
| Autor: | Hong Zhao, Lisandro D. Colantonio, John W. Baddley, Yuling Dai, John P. Donnelly, Paul Muntner, Henry E. Wang, Monika M. Safford |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
medicine.medical_specialty Epidemiology Health Informatics Population health Definitions Medicare Sensitivity and Specificity White People Sepsis Cohort Studies 03 medical and health sciences 0302 clinical medicine health services administration Validation Medicine Humans 030212 general & internal medicine health care economics and organizations Aged Aged 80 and over lcsh:R5-920 Geography business.industry Septic shock Mortality rate Organ dysfunction Health services research Reproducibility of Results 030208 emergency & critical care medicine medicine.disease United States 3. Good health Black or African American Stroke Emergency medicine Cohort Female Health Services Research medicine.symptom lcsh:Medicine (General) business Medicaid Algorithms Research Article |
| Zdroj: | BMC Medical Research Methodology BMC Medical Research Methodology, Vol 20, Iss 1, Pp 1-12 (2020) |
| ISSN: | 1471-2288 |
| Popis: | Background Claims-based algorithms are commonly used to identify sepsis in health services research because the laboratory features required to define clinical criteria may not be available in administrative data. Methods We evaluated claims-based sepsis algorithms among adults in the US aged ≥65 years with Medicare health insurance enrolled in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. Suspected infections from baseline (2003–2007) through December 31, 2012 were analyzed. Two claims-based algorithms were evaluated: (1) infection plus organ dysfunction diagnoses or sepsis diagnoses (Medicare-Implicit/Explicit) and (2) Centers for Medicare and Medicaid Services Severe Sepsis/Septic Shock Measure diagnoses (Medicare-CMS). Three classifications based on clinical criteria were used as standards for comparison: (1) the sepsis-related organ failure assessment (SOFA) score (REGARDS-SOFA), (2) “quick” SOFA (REGARDS-qSOFA), and (3) Centers for Disease Control and Prevention electronic health record criteria (REGARDS-EHR). Results There were 2217 suspected infections among 9522 participants included in the current study. The total number of suspected infections classified as sepsis was 468 for Medicare-Implicit/Explicit, 249 for Medicare-CMS, 541 for REGARDS-SOFA, 185 for REGARDS-qSOFA, and 331 for REGARDS-EHR. The overall agreement between Medicare-Implicit/Explicit and REGARDS-SOFA, REGARDS-qSOFA, and REGARDS-EHR was 77, 79, and 81%, respectively, sensitivity was 46, 53, and 57%, and specificity was 87, 82, and 85%. Comparing Medicare-CMS and REGARDS-SOFA, REGARDS-qSOFA, and REGARDS-EHR, agreement was 77, 87, and 85%, respectively, sensitivity was 27, 41, and 36%, and specificity was 94, 92, and 93%. Events meeting the REGARDS-SOFA classification had a lower 90-day mortality rate (140.7 per 100 person-years) compared with the Medicare-CMS (296.1 per 100 person-years), REGARDS-qSOFA (238.6 per 100 person-years), Medicare-Implicit/Explicit (219.4 per 100 person-years), and REGARDS-EHR classifications (201.8 per 100 person-years). Conclusion Claims-based sepsis algorithms have high agreement and specificity but low sensitivity when compared with clinical criteria. Both claims-based algorithms identified a patient population with similar 90-day mortality rates as compared with classifications based on qSOFA and EHR criteria but higher mortality relative to SOFA criteria. |
| Databáze: | OpenAIRE |
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