Jonathan S Austrian1,2, Catherine T Jamin3, Glenn R Doty2, Saul Blecker1,4. 1. Department of Medicine, New York University Langone Medical Center, New York, NY, USA. 2. Medical Center Information Technology, New York University Langone Medical Center, New York, NY, USA. 3. Department of Emergency Medicine, New York University Langone Medical Center, New York, NY, USA. 4. Department of Population Health, New York University School of Medicine, New York, NY, USA.
Abstract
Objective: The purpose of this study was to determine whether an electronic health record-based sepsis alert system could improve quality of care and clinical outcomes for patients with sepsis. Materials and Methods: We performed a patient-level interrupted time series study of emergency department patients with severe sepsis or septic shock between January 2013 and April 2015. The intervention, introduced in February 2014, was a system of interruptive sepsis alerts triggered by abnormal vital signs or laboratory results. Primary outcomes were length of stay (LOS) and in-hospital mortality; other outcomes included time to first lactate and blood cultures prior to antibiotics. We also assessed sensitivity, positive predictive value (PPV), and clinician response to the alerts. Results: Mean LOS for patients with sepsis decreased from 10.1 to 8.6 days (P < .001) following alert introduction. In adjusted time series analysis, the intervention was associated with a decreased LOS of 16% (95% CI, 5%-25%; P = .007, with significance of α = 0.006) and no change thereafter (0%; 95% CI, -2%, 2%). The sepsis alert system had no effect on mortality or other clinical or process measures. The intervention had a sensitivity of 80.4% and a PPV of 14.6%. Discussion: Alerting based on simple laboratory and vital sign criteria was insufficient to improve sepsis outcomes. Alert fatigue due to the low PPV is likely the primary contributor to these results. Conclusion: A more sophisticated algorithm for sepsis identification is needed to improve outcomes.
Objective: The purpose of this study was to determine whether an electronic health record-based sepsis alert system could improve quality of care and clinical outcomes for patients with sepsis. Materials and Methods: We performed a patient-level interrupted time series study of emergency department patients with severe sepsis or septic shock between January 2013 and April 2015. The intervention, introduced in February 2014, was a system of interruptive sepsis alerts triggered by abnormal vital signs or laboratory results. Primary outcomes were length of stay (LOS) and in-hospital mortality; other outcomes included time to first lactate and blood cultures prior to antibiotics. We also assessed sensitivity, positive predictive value (PPV), and clinician response to the alerts. Results: Mean LOS for patients with sepsis decreased from 10.1 to 8.6 days (P < .001) following alert introduction. In adjusted time series analysis, the intervention was associated with a decreased LOS of 16% (95% CI, 5%-25%; P = .007, with significance of α = 0.006) and no change thereafter (0%; 95% CI, -2%, 2%). The sepsis alert system had no effect on mortality or other clinical or process measures. The intervention had a sensitivity of 80.4% and a PPV of 14.6%. Discussion: Alerting based on simple laboratory and vital sign criteria was insufficient to improve sepsis outcomes. Alert fatigue due to the low PPV is likely the primary contributor to these results. Conclusion: A more sophisticated algorithm for sepsis identification is needed to improve outcomes.
Authors: Brian W Patterson; Michael S Pulia; Shashank Ravi; Peter L T Hoonakker; Ann Schoofs Hundt; Douglas Wiegmann; Emily J Wirkus; Stephen Johnson; Pascale Carayon Journal: Ann Emerg Med Date: 2019-01-03 Impact factor: 5.721
Authors: Zhongheng Zhang; Lin Chen; Ping Xu; Qing Wang; Jianjun Zhang; Kun Chen; Casey M Clements; Leo Anthony Celi; Vitaly Herasevich; Yucai Hong Journal: NPJ Digit Med Date: 2022-07-19
Authors: Hoyt Burdick; Eduardo Pino; Denise Gabel-Comeau; Andrea McCoy; Carol Gu; Jonathan Roberts; Sidney Le; Joseph Slote; Emily Pellegrini; Abigail Green-Saxena; Jana Hoffman; Ritankar Das Journal: BMJ Health Care Inform Date: 2020-04
Authors: Antje Wulff; Sara Montag; Bianca Steiner; Michael Marschollek; Philipp Beerbaum; André Karch; Thomas Jack Journal: BMJ Open Date: 2019-06-19 Impact factor: 2.692
Authors: Meera Joshi; Hutan Ashrafian; Sonal Arora; Sadia Khan; Graham Cooke; Ara Darzi Journal: J Med Internet Res Date: 2019-12-20 Impact factor: 5.428
Authors: Kate Honeyford; Graham S Cooke; Anne Kinderlerer; Elizabeth Williamson; Mark Gilchrist; Alison Holmes; Ben Glampson; Abdulrahim Mulla; Ceire Costelloe Journal: J Am Med Inform Assoc Date: 2020-02-01 Impact factor: 4.497