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1423. A Simple Electronic Medical Record-Based Predictors of Illness Severity in Sepsis (SEPSIS) Score
BACKGROUND: Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort. METHODS: A simple electronic medical record-based pre...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10679058/ http://dx.doi.org/10.1093/ofid/ofad500.1260 |
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author | Cressman, Alex Wen, Bijun Saha, Sudipta Jun, Hae Young Waters, Riley Lail, Sharan Jabeen, Aneela Koppula, Radha Lapointe-Shaw, Lauren Sheehan, Kathleen Daneman, Nick Verma, Amol Razak, Fahad MacFadden, Derek |
author_facet | Cressman, Alex Wen, Bijun Saha, Sudipta Jun, Hae Young Waters, Riley Lail, Sharan Jabeen, Aneela Koppula, Radha Lapointe-Shaw, Lauren Sheehan, Kathleen Daneman, Nick Verma, Amol Razak, Fahad MacFadden, Derek |
author_sort | Cressman, Alex |
collection | PubMed |
description | BACKGROUND: Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort. METHODS: A simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors: Creatinine, Bilirubin, Platelet Count, Lactate) using a retrospective cohort study of patients admitted to internal medicine services from April 2010 - March 2015, across four hospitals in Toronto, Canada. We identified patients with sepsis based upon receipt of antibiotics and positive (non-screening) cultures on admission. Chart review was conducted to extract components of qSOFA and NEWS2 scores. The primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a contemporary cohort (2015 to 2019) of patients receiving systemic antibiotics. RESULTS: Our initial cohort included 1,890 patients with a median age of 72 years (IQR: 56-83). 9% of the cohort died during hospitalization, 13% were admitted to ICU at 72 hours, and mean LOS was 12.7 days (SD: 21.5). In the initial and the contemporary (2015-2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay >14 days, were similar between scores, in the initial and contemporary cohorts. All scores were generally well calibrated for predicting mortality (Figure 1). [Figure: see text] CONCLUSION: The EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score’s simplicity, it may prove a useful tool for clinical and research applications. DISCLOSURES: All Authors: No reported disclosures |
format | Online Article Text |
id | pubmed-10679058 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-106790582023-11-27 1423. A Simple Electronic Medical Record-Based Predictors of Illness Severity in Sepsis (SEPSIS) Score Cressman, Alex Wen, Bijun Saha, Sudipta Jun, Hae Young Waters, Riley Lail, Sharan Jabeen, Aneela Koppula, Radha Lapointe-Shaw, Lauren Sheehan, Kathleen Daneman, Nick Verma, Amol Razak, Fahad MacFadden, Derek Open Forum Infect Dis Abstract BACKGROUND: Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort. METHODS: A simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors: Creatinine, Bilirubin, Platelet Count, Lactate) using a retrospective cohort study of patients admitted to internal medicine services from April 2010 - March 2015, across four hospitals in Toronto, Canada. We identified patients with sepsis based upon receipt of antibiotics and positive (non-screening) cultures on admission. Chart review was conducted to extract components of qSOFA and NEWS2 scores. The primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a contemporary cohort (2015 to 2019) of patients receiving systemic antibiotics. RESULTS: Our initial cohort included 1,890 patients with a median age of 72 years (IQR: 56-83). 9% of the cohort died during hospitalization, 13% were admitted to ICU at 72 hours, and mean LOS was 12.7 days (SD: 21.5). In the initial and the contemporary (2015-2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay >14 days, were similar between scores, in the initial and contemporary cohorts. All scores were generally well calibrated for predicting mortality (Figure 1). [Figure: see text] CONCLUSION: The EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score’s simplicity, it may prove a useful tool for clinical and research applications. DISCLOSURES: All Authors: No reported disclosures Oxford University Press 2023-11-27 /pmc/articles/PMC10679058/ http://dx.doi.org/10.1093/ofid/ofad500.1260 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Abstract Cressman, Alex Wen, Bijun Saha, Sudipta Jun, Hae Young Waters, Riley Lail, Sharan Jabeen, Aneela Koppula, Radha Lapointe-Shaw, Lauren Sheehan, Kathleen Daneman, Nick Verma, Amol Razak, Fahad MacFadden, Derek 1423. A Simple Electronic Medical Record-Based Predictors of Illness Severity in Sepsis (SEPSIS) Score |
title | 1423. A Simple Electronic Medical Record-Based Predictors of Illness Severity in Sepsis (SEPSIS) Score |
title_full | 1423. A Simple Electronic Medical Record-Based Predictors of Illness Severity in Sepsis (SEPSIS) Score |
title_fullStr | 1423. A Simple Electronic Medical Record-Based Predictors of Illness Severity in Sepsis (SEPSIS) Score |
title_full_unstemmed | 1423. A Simple Electronic Medical Record-Based Predictors of Illness Severity in Sepsis (SEPSIS) Score |
title_short | 1423. A Simple Electronic Medical Record-Based Predictors of Illness Severity in Sepsis (SEPSIS) Score |
title_sort | 1423. a simple electronic medical record-based predictors of illness severity in sepsis (sepsis) score |
topic | Abstract |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10679058/ http://dx.doi.org/10.1093/ofid/ofad500.1260 |
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