Early screening and evaluation of sepsis in emergency department_West China Medical Journal

Authors：

XIA Yiqin ^1,2 ,  CAO Yu ^1,2

1. Department of Emergency Medicine, Laboratory of Emergency Medicine, West China School of Medicine / West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Disaster Medical Center, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

CAO Yu, Email: yuyuer@126.com

Keywords：

Sepsis; early screening; precision diagnosis and treatment

DOI：

10.7507/1002-0179.202210164

Video：

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Sepsis is a critical condition. The key factor affecting the survival of patient is whether standard treatment can be obtained timely. Because of the complexity of its pathogenesis and high heterogeneity, there is no special diagnosis method currently. Early identification is difficult. Delayed diagnosis and treatment is closely related to the mortality of patients. With the continuous updating of the guidelines, sepsis has been included in the “time window” disease, putting forward a great challenge to the early screening and evaluation of sepsis. This article aims to review the application of Sepsis-Related Organ Failure Assessment, sepsis biomarkers and artificial intelligence algorithms in early screening and evaluation of sepsis, so as to provide guidance tools for timely starting standardized treatment of sepsis.

Citation： XIA Yiqin, CAO Yu. Early screening and evaluation of sepsis in emergency department. West China Medical Journal, 2022, 37(11): 1607-1611. doi: 10.7507/1002-0179.202210164 Copy

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2.	Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet, 2020, 395(10219): 200-211.
3.	Markwart R, Saito H, Harder T, et al. Epidemiology and burden of sepsis acquired in hospitals and intensive care units: a systematic review and meta-analysis. Intensive Care Med, 2020, 46(8): 1536-1551.
4.	Seymour CW, Gesten F, Prescott HC, et al. Time to treatment and mortality during mandated emergency care for sepsis. N Engl J Med, 2017, 376(23): 2235-2244.
5.	Levy MM, Evans LE, Rhodes A. The surviving sepsis campaign bundle: 2018 update. Intensive Care Med, 2018, 44(6): 925-928.
6.	Rhee C, Chiotos K, Cosgrove SE, et al. Infectious diseases society of america position paper: recommended revisions to the national severe sepsis and septic shock early management bundle (SEP-1) sepsis quality measure. Clin Infect Dis, 2021, 72(4): 541-552.
7.	Obermaier M, Weigand MA, Popp E, et al. Sepsis in out-of-hospital emergency medicine. Notf Rett Med, 2021, 17: 1-11.
8.	Evans L, Rhodes A, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med, 2021, 47(11): 1181-1247.
9.	Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA, 2016, 315(8): 801-810.
10.	Freund Y, Lemachatti N, Krastinova E, et al. Prognostic accuracy of sepsis-3 criteria for in-hospital mortality among patients with suspected infection presenting to the emergency department. JAMA, 2017, 317(3): 301-308.
11.	Herwanto V, Shetty A, Nalos M, et al. Accuracy of quick sequential organ failure assessment score to predict sepsis mortality in 121 studies including 1, 716, 017 individuals: a systematic review and meta-analysis. Crit Care Explor, 2019, 1(9): e0043.
12.	Finkelsztein EJ, Jones DS, Ma KC, et al. Comparison of qSOFA and SIRS for predicting adverse outcomes of patients with suspicion of sepsis outside the intensive care unit. Crit Care, 2017, 21(1): 73.
13.	Anand V, Zhang Z, Kadri SS, et al. Epidemiology of quick sequential organ failure assessment criteria in undifferentiated patients and association with suspected infection and sepsis. Chest, 2019, 156(2): 289-297.
14.	王浩宇, 周亚雄, 曹钰, 等. 快速序贯器官衰竭评估评分筛检颅内感染致脓毒症的效力评价. 西部医学, 2016, 28(12): 1664-1668.
15.	Villar J, Short JH, Lighthall G. Lactate predicts both short- and long-term mortality in patients with and without sepsis. Infect Dis (Auckl), 2019, 12: 1178633719862776.
16.	Sinto R, Suwarto S, Lie KC, et al. PPrognostic accuracy of the quick Sequential Organ Failure Assessment (qSOFA)-lactate criteria for mortality in adults with suspected bacterial infection in the emergency department of a hospital with limited resources. Emerg Med J, 2020, 37(6): 363-369.
17.	Wright SW, Hantrakun V, Rudd KE, et al. Enhanced bedside mortality prediction combining point-of-care lactate and the quick Sequential Organ Failure Assessment (qSOFA) score in patients hospitalised with suspected infection in southeast Asia: a cohort study. Lancet Glob Health, 2022, 10(9): e1281-e1288.
18.	Shetty A, MacDonald SP, Williams JM, et al. Lactate ≥2 mmol/L plus qSOFA improves utility over qSOFA alone in emergency department patients presenting with suspected sepsis. Emerg Med Australas, 2017, 29(6): 626-634.
19.	Xia Y, Zou L, Li D, et al. The ability of an improved qSOFA score to predict acute sepsis severity and prognosis among adult patients. Medicine (Baltimore), 2020, 99(5): e18942.
20.	Guarino M, Perna B, De Giorgi A, et al. A 2-year retrospective analysis of the prognostic value of MqSOFA compared to lactate, NEWS and qSOFA in patients with sepsis. Infection, 2022, 50(4): 941-948.
21.	Guarino M, Gambuti E, Alfano F, et al. Predicting in-hospital mortality for sepsis: a comparison between qSOFA and modified qSOFA in a 2-year single-centre retrospective analysis. Eur J Clin Microbiol Infect Dis, 2021, 40(4): 825-831.
22.	Pierrakos C, Vincent JL. Sepsis biomarkers: a review. Crit Care, 2010, 14(1): R15.
23.	Rhodes A, Evans LE, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Med, 2017, 43(3): 304-377.
24.	Crouser ED, Parrillo JE, Seymour C, et al. Improved early detection of sepsis in the ED with a novel monocyte distribution width biomarker. Chest, 2017, 152(3): 518-526.
25.	Hausfater P, Robert Boter N, Morales Indiano C, et al. Monocyte distribution width (MDW) performance as an early sepsis indicator in the emergency department: comparison with CRP and procalcitonin in a multicenter international European prospective study. Crit Care, 2021, 25(1): 227.
26.	Piva E, Zuin J, Pelloso M, et al. Monocyte distribution width (MDW) parameter as a sepsis indicator in intensive care units. Clin Chem Lab Med, 2021, 59(7): 1307-1314.
27.	Binnie A, Walsh CJ, Hu P, et al. Epigenetic profiling in severe sepsis: a pilot study of DNA methylation profiles in critical illness. Crit Care Med, 2020, 48(2): 142-150.
28.	Lorente-Sorolla C, Garcia-Gomez A, Català-Moll F, et al. Inflammatory cytokines and organ dysfunction associate with the aberrant DNA methylome of monocytes in sepsis. Genome Med, 2019, 11(1): 66.
29.	Dakhlallah DA, Wisler J, Gencheva M, et al. Circulating extracellular vesicle content reveals de novo DNA methyltransferase expression as a molecular method to predict septic shock. J Extracell Vesicles, 2019, 8(1): 1669881.
30.	Goh KH, Wang L, Yeow AYK, et al. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nat Commun, 2021, 12(1): 711.
31.	Mao Q, Jay M, Hoffman JL, et al. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open, 2018, 8(1): e017833.
32.	Kamaleswaran R, Sataphaty SK, Mas VR, et al. Artificial intelligence may predict early sepsis after liver transplantation. Front Physiol, 2021, 12: 692667.
33.	Nemati S, Holder A, Razmi F, et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med, 2018, 46(4): 547-553.
34.	Yang M, Liu C, Wang X, et al. An explainable artificial intelligence predictor for early detection of sepsis. Crit Care Med, 2020, 48(11): e1091-e1096.

1. Shankar-Hari M, Phillips GS, Levy ML, et al. Developing a new definition and assessing new clinical criteria for septic shock: for the third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA, 2016, 315(8): 775-787.
2. Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet, 2020, 395(10219): 200-211.
3. Markwart R, Saito H, Harder T, et al. Epidemiology and burden of sepsis acquired in hospitals and intensive care units: a systematic review and meta-analysis. Intensive Care Med, 2020, 46(8): 1536-1551.
4. Seymour CW, Gesten F, Prescott HC, et al. Time to treatment and mortality during mandated emergency care for sepsis. N Engl J Med, 2017, 376(23): 2235-2244.
5. Levy MM, Evans LE, Rhodes A. The surviving sepsis campaign bundle: 2018 update. Intensive Care Med, 2018, 44(6): 925-928.
6. Rhee C, Chiotos K, Cosgrove SE, et al. Infectious diseases society of america position paper: recommended revisions to the national severe sepsis and septic shock early management bundle (SEP-1) sepsis quality measure. Clin Infect Dis, 2021, 72(4): 541-552.
7. Obermaier M, Weigand MA, Popp E, et al. Sepsis in out-of-hospital emergency medicine. Notf Rett Med, 2021, 17: 1-11.
8. Evans L, Rhodes A, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med, 2021, 47(11): 1181-1247.
9. Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA, 2016, 315(8): 801-810.
10. Freund Y, Lemachatti N, Krastinova E, et al. Prognostic accuracy of sepsis-3 criteria for in-hospital mortality among patients with suspected infection presenting to the emergency department. JAMA, 2017, 317(3): 301-308.
11. Herwanto V, Shetty A, Nalos M, et al. Accuracy of quick sequential organ failure assessment score to predict sepsis mortality in 121 studies including 1, 716, 017 individuals: a systematic review and meta-analysis. Crit Care Explor, 2019, 1(9): e0043.
12. Finkelsztein EJ, Jones DS, Ma KC, et al. Comparison of qSOFA and SIRS for predicting adverse outcomes of patients with suspicion of sepsis outside the intensive care unit. Crit Care, 2017, 21(1): 73.
13. Anand V, Zhang Z, Kadri SS, et al. Epidemiology of quick sequential organ failure assessment criteria in undifferentiated patients and association with suspected infection and sepsis. Chest, 2019, 156(2): 289-297.
14. 王浩宇, 周亚雄, 曹钰, 等. 快速序贯器官衰竭评估评分筛检颅内感染致脓毒症的效力评价. 西部医学, 2016, 28(12): 1664-1668.
15. Villar J, Short JH, Lighthall G. Lactate predicts both short- and long-term mortality in patients with and without sepsis. Infect Dis (Auckl), 2019, 12: 1178633719862776.
16. Sinto R, Suwarto S, Lie KC, et al. PPrognostic accuracy of the quick Sequential Organ Failure Assessment (qSOFA)-lactate criteria for mortality in adults with suspected bacterial infection in the emergency department of a hospital with limited resources. Emerg Med J, 2020, 37(6): 363-369.
17. Wright SW, Hantrakun V, Rudd KE, et al. Enhanced bedside mortality prediction combining point-of-care lactate and the quick Sequential Organ Failure Assessment (qSOFA) score in patients hospitalised with suspected infection in southeast Asia: a cohort study. Lancet Glob Health, 2022, 10(9): e1281-e1288.
18. Shetty A, MacDonald SP, Williams JM, et al. Lactate ≥2 mmol/L plus qSOFA improves utility over qSOFA alone in emergency department patients presenting with suspected sepsis. Emerg Med Australas, 2017, 29(6): 626-634.
19. Xia Y, Zou L, Li D, et al. The ability of an improved qSOFA score to predict acute sepsis severity and prognosis among adult patients. Medicine (Baltimore), 2020, 99(5): e18942.
20. Guarino M, Perna B, De Giorgi A, et al. A 2-year retrospective analysis of the prognostic value of MqSOFA compared to lactate, NEWS and qSOFA in patients with sepsis. Infection, 2022, 50(4): 941-948.
21. Guarino M, Gambuti E, Alfano F, et al. Predicting in-hospital mortality for sepsis: a comparison between qSOFA and modified qSOFA in a 2-year single-centre retrospective analysis. Eur J Clin Microbiol Infect Dis, 2021, 40(4): 825-831.
22. Pierrakos C, Vincent JL. Sepsis biomarkers: a review. Crit Care, 2010, 14(1): R15.
23. Rhodes A, Evans LE, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Med, 2017, 43(3): 304-377.
24. Crouser ED, Parrillo JE, Seymour C, et al. Improved early detection of sepsis in the ED with a novel monocyte distribution width biomarker. Chest, 2017, 152(3): 518-526.
25. Hausfater P, Robert Boter N, Morales Indiano C, et al. Monocyte distribution width (MDW) performance as an early sepsis indicator in the emergency department: comparison with CRP and procalcitonin in a multicenter international European prospective study. Crit Care, 2021, 25(1): 227.
26. Piva E, Zuin J, Pelloso M, et al. Monocyte distribution width (MDW) parameter as a sepsis indicator in intensive care units. Clin Chem Lab Med, 2021, 59(7): 1307-1314.
27. Binnie A, Walsh CJ, Hu P, et al. Epigenetic profiling in severe sepsis: a pilot study of DNA methylation profiles in critical illness. Crit Care Med, 2020, 48(2): 142-150.
28. Lorente-Sorolla C, Garcia-Gomez A, Català-Moll F, et al. Inflammatory cytokines and organ dysfunction associate with the aberrant DNA methylome of monocytes in sepsis. Genome Med, 2019, 11(1): 66.
29. Dakhlallah DA, Wisler J, Gencheva M, et al. Circulating extracellular vesicle content reveals de novo DNA methyltransferase expression as a molecular method to predict septic shock. J Extracell Vesicles, 2019, 8(1): 1669881.
30. Goh KH, Wang L, Yeow AYK, et al. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nat Commun, 2021, 12(1): 711.
31. Mao Q, Jay M, Hoffman JL, et al. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open, 2018, 8(1): e017833.
32. Kamaleswaran R, Sataphaty SK, Mas VR, et al. Artificial intelligence may predict early sepsis after liver transplantation. Front Physiol, 2021, 12: 692667.
33. Nemati S, Holder A, Razmi F, et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med, 2018, 46(4): 547-553.
34. Yang M, Liu C, Wang X, et al. An explainable artificial intelligence predictor for early detection of sepsis. Crit Care Med, 2020, 48(11): e1091-e1096.

West China Medical Journal

Early screening and evaluation of sepsis in emergency department

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