External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients

被引:345
作者
Wong, Andrew [1 ]
Otles, Erkin [2 ,3 ]
Donnelly, John P. [4 ]
Krumm, Andrew [4 ]
McCullough, Jeffrey [5 ]
DeTroyer-Cooley, Olivia [6 ]
Pestrue, Justin [6 ]
Phillips, Marie [7 ]
Konye, Judy [8 ]
Penoza, Carleen [8 ]
Ghous, Muhammad [4 ]
Singh, Karandeep [1 ,4 ]
机构
[1] Univ Michigan, Dept Internal Med, Med Sch, Ann Arbor, MI 48109 USA
[2] Univ Michigan, Med Scientist Training Program, Med Sch, Ann Arbor, MI 48109 USA
[3] Univ Michigan, Dept Ind & Operat Engn, Coll Engn, Ann Arbor, MI 48109 USA
[4] Univ Michigan, Dept Learning Hlth Sci, Med Sch, 1161H NIB,300 N Ingalls St, Ann Arbor, MI 48109 USA
[5] Univ Michigan, Sch Publ Hlth, Ann Arbor, MI 48109 USA
[6] Michigan Med, Dept Qual, Ann Arbor, MI USA
[7] Michigan Med, Hlth Informat Technol & Serv, Ann Arbor, MI USA
[8] Michigan Med, Nursing Informat, Ann Arbor, MI USA
基金
美国国家卫生研究院;
关键词
SYSTEMS;
D O I
10.1001/jamainternmed.2021.2626
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
IMPORTANCE The Epic Sepsis Model (ESM), a proprietary sepsis prediction model, is implemented at hundreds of US hospitals. The ESM's ability to identify patients with sepsis has not been adequately evaluated despite widespread use. OBJECTIVE To externally validate the ESM in the prediction of sepsis and evaluate its potential clinical value compared with usual care. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study was conducted among 27 697 patients aged 18 years or older admitted to Michigan Medicine, the academic health system of the University of Michigan, Ann Arbor, with 38 455 hospitalizations between December 6, 2018, and October 20, 2019. EXPOSURE The ESM score, calculated every 15 minutes. MAIN OUTCOMES AND MEASURES Sepsis, as defined by a composite of (1) the Centers for Disease Control and Prevention surveillance criteria and (2) International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnostic codes accompanied by 2 systemic inflammatory response syndrome criteria and 1 organ dysfunction criterion within 6 hours of one another. Model discrimination was assessed using the area under the receiver operating characteristic curve at the hospitalization level and with prediction horizons of 4, 8, 12, and 24 hours. Model calibration was evaluated with calibration plots. The potential clinical benefit associated with the ESM was assessed by evaluating the added benefit of the ESM score compared with contemporary clinical practice (based on timely administration of antibiotics). Alert fatigue was evaluated by comparing the clinical value of different alerting strategies. RESULTS We identified 27 697 patients who had 38 455 hospitalizations (21904 women [57%]; median age, 56 years [interquartile range, 35-69 years]) meeting indusion criteria, of whom sepsis occurred in 2552 (7%). The ESM had a hospitalization-level area under the receiver operating characteristic curve of 0.63 (95% CI, 0.62-0.64). The ESM identified 183 of 2552 patients with sepsis (7%) who did not receive timely administration of antibiotics, highlighting the low sensitivity of the ESM in comparison with contemporary clinical practice. The ESM also did not identify 1709 patients with sepsis (67%) despite generating alerts for an ESM score of 6 or higher for 6971 of all 38 455 hospitalized patients (18%), thus creating a large burden of alert fatigue. CONCLUSIONS AND RELEVANCE This external validation cohort study suggests that the ESM has poor discrimination and calibration in predicting the onset of sepsis. The widespread adoption of the ESM despite its poor performance raises fundamental concerns about sepsis management on a national level.
引用
收藏
页码:1065 / 1070
页数:6
相关论文
共 27 条
  • [1] Patient Outcomes and Cost-Effectiveness of a Sepsis Care Quality Improvement Program in a Health System*
    Afshar, Majid
    Arain, Erum
    Ye, Chen
    Gilbert, Emily
    Xie, Meng
    Lee, Josh
    Churpek, Matthew M.
    Durazo-Arvizu, Ramon
    Markossian, Talar
    Joyce, Cara
    [J]. CRITICAL CARE MEDICINE, 2019, 47 (10) : 1371 - 1379
  • [2] [Anonymous], 2014, NEW ENGL J MED, DOI DOI 10.1056/NEJMoa1401602
  • [3] Bennett T, ACCURACY EPIC SEPSIS
  • [4] The Nature and Variability of Automated Practice Alerts Derived from Electronic Health Records in a US Nationwide Critical Care Research Network
    Benthin, Cody
    Pannu, Sonal
    Khan, Akram
    Gong, Michelle
    [J]. ANNALS OF THE AMERICAN THORACIC SOCIETY, 2016, 13 (10) : 1784 - 1788
  • [5] Caldwell P, MOTHER JONES
  • [6] Centers for Disease Control and Prevention, Hospital Toolkit for Adult Sepsis Surveillance
  • [7] Calibration drift in regression and machine learning models for acute kidney injury
    Davis, Sharon E.
    Lasko, Thomas A.
    Chen, Guanhua
    Siew, Edward D.
    Matheny, Michael E.
    [J]. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2017, 24 (06) : 1052 - 1061
  • [8] Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis
    Delahanty, Ryan J.
    Alvarez, JoAnn
    Flynn, Lisa M.
    Sherwin, Robert L.
    Jones, Spencer S.
    [J]. ANNALS OF EMERGENCY MEDICINE, 2019, 73 (04) : 334 - 344
  • [9] Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation
    Downing, Norman Lance
    Rolnick, Joshua
    Poole, Sarah F.
    Hall, Evan
    Wessels, Alexander J.
    Heidenreich, Paul
    Shieh, Lisa
    [J]. BMJ QUALITY & SAFETY, 2019, 28 (09) : 762 - 768
  • [10] The impact of compliance with 6-hour and 24-hour sepsis bundles on hospital mortality in patients with severe sepsis: a prospective observational study
    Gao, F
    Melody, T
    Daniels, DF
    Giles, S
    Fox, S
    [J]. CRITICAL CARE, 2005, 9 (06) : R764 - R770