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Volumn 28, Issue 9, 2019, Pages 693-696

To catch a killer: Electronic sepsis alert tools reaching a fever pitch?

Author keywords

alert; clinical decision support; machine learning; predictive model; sepsis

Indexed keywords

ALERT FATIGUE (HEALTH CARE); AWARENESS; CLINICAL DECISION SUPPORT SYSTEM; CLINICAL OUTCOME; COMPUTER MODEL; EDITORIAL; ELECTRONIC HEALTH RECORD; FEVER; HOSPITAL MORTALITY; HUMAN; MACHINE LEARNING; PREDICTIVE VALUE; PUBLIC HEALTH CAMPAIGN; SEPSIS; SEQUENTIAL ORGAN FAILURE ASSESSMENT SCORE; SYSTEMIC INFLAMMATORY RESPONSE SYNDROME;

EID: 85065296172     PISSN: 20445415     EISSN: None     Source Type: Journal    
DOI: 10.1136/bmjqs-2019-009463     Document Type: Editorial
Times cited : (14)

References (48)
  • 1
    • 85026775322 scopus 로고    scopus 로고
    • Recognizing sepsis as a global health priority-a WHO resolution
    • Reinhart K, Daniels R, Kissoon N, et al. Recognizing sepsis as a global health priority-a WHO resolution. N Engl J Med 2017;377:414-7.
    • (2017) N Engl J Med , vol.377 , pp. 414-417
    • Reinhart, K.1    Daniels, R.2    Kissoon, N.3
  • 3
    • 85031326158 scopus 로고    scopus 로고
    • Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014
    • Rhee C, Dantes R, Epstein L, et al. Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014. JAMA 2017;318:1241-9.
    • (2017) JAMA , vol.318 , pp. 1241-1249
    • Rhee, C.1    Dantes, R.2    Epstein, L.3
  • 4
    • 84903614329 scopus 로고    scopus 로고
    • Hospital deaths in patients with sepsis from 2 independent cohorts
    • Liu V, Escobar GJ, Greene JD, et al. Hospital deaths in patients with sepsis from 2 independent cohorts. JAMA 2014;312:90-2.
    • (2014) JAMA , vol.312 , pp. 90-92
    • Liu, V.1    Escobar, G.J.2    Greene, J.D.3
  • 5
    • 85056577940 scopus 로고    scopus 로고
    • Epidemiology and costs of sepsis in the United States-An analysis based on timing of diagnosis and severity Level
    • Paoli CJ, Reynolds MA, Sinha M, et al. Epidemiology and costs of sepsis in the United States-An analysis based on timing of diagnosis and severity Level. Critical Care Medicine 2018;46:1889-97.
    • (2018) Critical Care Medicine , vol.46 , pp. 1889-1897
    • Paoli, C.J.1    Reynolds, M.A.2    Sinha, M.3
  • 6
    • 0034958947 scopus 로고    scopus 로고
    • Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care
    • Angus DC, Linde-Zwirble WT, Lidicker J, et al. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Critical Care Medicine 2001;29:1303-10.
    • (2001) Critical Care Medicine , vol.29 , pp. 1303-1310
    • Angus, D.C.1    Linde-Zwirble, W.T.2    Lidicker, J.3
  • 7
    • 84924366276 scopus 로고    scopus 로고
    • Readmission diagnoses after hospitalization for severe sepsis and other acute medical conditions
    • Prescott HC, Langa KM, Iwashyna TJ. Readmission diagnoses after hospitalization for severe sepsis and other acute medical conditions. JAMA 2015;313:1055-7.
    • (2015) JAMA , vol.313 , pp. 1055-1057
    • Prescott, H.C.1    Langa, K.M.2    Iwashyna, T.J.3
  • 8
    • 84969961881 scopus 로고    scopus 로고
    • Late mortality after sepsis: Propensity matched cohort study
    • Prescott HC, Osterholzer JJ, Langa KM, et al. Late mortality after sepsis: propensity matched cohort study. BMJ 2016;353.
    • (2016) BMJ , vol.353
    • Prescott, H.C.1    Osterholzer, J.J.2    Langa, K.M.3
  • 9
    • 84876074082 scopus 로고    scopus 로고
    • Mortality and quality of life in the five years after severe sepsis
    • Cuthbertson BH, Elders A, Hall S, et al. Mortality and quality of life in the five years after severe sepsis. Crit Care 2013;17.
    • (2013) Crit Care , vol.17
    • Cuthbertson, B.H.1    Elders, A.2    Hall, S.3
  • 10
    • 84903837831 scopus 로고    scopus 로고
    • Increased 1-year healthcare use in survivors of severe sepsis
    • Prescott HC, Langa KM, Liu V, et al. Increased 1-year healthcare use in survivors of severe sepsis. Am J Respir Crit Care Med 2014;190:62-9.
    • (2014) Am J Respir Crit Care Med , vol.190 , pp. 62-69
    • Prescott, H.C.1    Langa, K.M.2    Liu, V.3
  • 11
    • 78049351929 scopus 로고    scopus 로고
    • Long-term cognitive impairment and functional disability among survivors of severe sepsis
    • Iwashyna TJ, Ely EW, Smith DM, et al. Long-term cognitive impairment and functional disability among survivors of severe sepsis. JAMA 2010;304:1787-94.
    • (2010) JAMA , vol.304 , pp. 1787-1794
    • Iwashyna, T.J.1    Ely, E.W.2    Smith, D.M.3
  • 13
    • 85071345743 scopus 로고    scopus 로고
    • Sepsis alliance [Accessed 31 Mar 2019]
    • Sepsis alliance. Available: https://www. sepsis. org/ itsabouttime/ [Accessed 31 Mar 2019].
  • 14
    • 84930383716 scopus 로고    scopus 로고
    • Diagnostic accuracy and effectiveness of automated electronic sepsis alert systems: A systematic review
    • Makam AN, Nguyen OK, Auerbach AD. Diagnostic accuracy and effectiveness of automated electronic sepsis alert systems: a systematic review. J. Hosp. Med. 2015;10:396-402.
    • (2015) J. Hosp. Med , vol.10 , pp. 396-402
    • Makam, A.N.1    Nguyen, O.K.2    Auerbach, A.D.3
  • 15
    • 84959317195 scopus 로고    scopus 로고
    • Assessment of clinical criteria for sepsis: For the third International consensus definitions for sepsis and septic shock (Sepsis-3)
    • Seymour CW, Liu VX, Iwashyna TJ, et al. Assessment of clinical criteria for sepsis: for the third International consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016;315:762-74.
    • (2016) JAMA , vol.315 , pp. 762-774
    • Seymour, C.W.1    Liu, V.X.2    Iwashyna, T.J.3
  • 16
    • 0026710191 scopus 로고
    • Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis
    • Bone RC, Balk RA, Cerra FB, et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Chest 1992;101:1644-55.
    • (1992) Chest , vol.101 , pp. 1644-1655
    • Bone, R.C.1    Balk, R.A.2    Cerra, F.B.3
  • 17
    • 84928485055 scopus 로고    scopus 로고
    • Systemic inflammatory response syndrome criteria in defining severe sepsis
    • Kaukonen K-M, Bailey M, Pilcher D, et al. Systemic inflammatory response syndrome criteria in defining severe sepsis. N Engl J Med 2015;372:1629-38.
    • (2015) N Engl J Med , vol.372 , pp. 1629-1638
    • Kaukonen, K.-M.1    Bailey, M.2    Pilcher, D.3
  • 18
    • 0037389094 scopus 로고    scopus 로고
    • SCCM/ESICM/ACCP/ ATS/SIS international sepsis definitions conference
    • Levy MM, Fink MP, Marshall JC, et al. SCCM/ESICM/ACCP/ ATS/SIS international sepsis definitions conference. Crit Care Med 2001;2003:1250-6.
    • (2001) Crit Care Med , vol.2003 , pp. 1250-1256
    • Levy, M.M.1    Fink, M.P.2    Marshall, J.C.3
  • 19
    • 85025678654 scopus 로고    scopus 로고
    • Investigating the impact of different suspicion of infection criteria on the accuracy of quick sepsis-related organ failure assessment, systemic inflammatory response syndrome, and early warning Scores
    • Churpek MM, Snyder A, Sokol S, et al. Investigating the impact of different suspicion of infection criteria on the accuracy of quick sepsis-related organ failure assessment, systemic inflammatory response syndrome, and early warning Scores. Critical Care Medicine 2017;45:1805-12.
    • (2017) Critical Care Medicine , vol.45 , pp. 1805-1812
    • Churpek, M.M.1    Snyder, A.2    Sokol, S.3
  • 20
    • 84938704873 scopus 로고    scopus 로고
    • A targeted real-time early warning score (TREWScore) for septic shock
    • Henry KE, Hager DN, Pronovost PJ, et al. A targeted real-time early warning score (TREWScore) for septic shock. Sci. Transl. Med. 2015;7.
    • (2015) Sci. Transl. Med , vol.7
    • Henry, K.E.1    Hager, D.N.2    Pronovost, P.J.3
  • 21
    • 85017113914 scopus 로고    scopus 로고
    • Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning
    • Horng S, Sontag DA, Halpern Y, et al. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS One 2017;12:e0174708.
    • (2017) PLoS One , vol.12 , pp. e0174708
    • Horng, S.1    Sontag, D.A.2    Halpern, Y.3
  • 22
    • 85051798815 scopus 로고    scopus 로고
    • Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU
    • 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:e017833.
    • (2018) BMJ Open , vol.8 , pp. e017833
    • Mao, Q.1    Jay, M.2    Hoffman, J.L.3
  • 23
    • 85029125816 scopus 로고    scopus 로고
    • Combining biomarkers with EMR data to identify patients in different phases of sepsis
    • Taneja I, Reddy B, Damhorst G, et al. Combining biomarkers with EMR data to identify patients in different phases of sepsis. Sci Rep 2017;7.
    • (2017) Sci Rep , vol.7
    • Taneja, I.1    Reddy, B.2    Damhorst, G.3
  • 24
    • 84960335394 scopus 로고    scopus 로고
    • Prediction of inhospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach
    • Taylor RA, Pare JR, Venkatesh AK, et al. Prediction of inhospital mortality in emergency department patients with sepsis: a local big data-driven, machine learning approach. Acad Emerg Med 2016;23:269-78.
    • (2016) Acad Emerg Med , vol.23 , pp. 269-278
    • Taylor, R.A.1    Pare, J.R.2    Venkatesh, A.K.3
  • 26
    • 85044927780 scopus 로고    scopus 로고
    • Big data and machine learning in health care
    • Beam AL, Kohane IS. Big data and machine learning in health care. JAMA 2018;319:1317-8.
    • (2018) JAMA , vol.319 , pp. 1317-1318
    • Beam, A.L.1    Kohane, I.S.2
  • 27
    • 85021635595 scopus 로고    scopus 로고
    • Machine learning and prediction in medicine-beyond the peak of inflated expectations
    • Chen JH, Asch SM. Machine learning and prediction in medicine-beyond the peak of inflated expectations. N Engl J Med 2017;376:2507-9.
    • (2017) N Engl J Med , vol.376 , pp. 2507-2509
    • Chen, J.H.1    Asch, S.M.2
  • 28
    • 85031322889 scopus 로고    scopus 로고
    • Discrimination and calibration of clinical prediction models: Users' guides to the medical literature
    • Alba AC, Agoritsas T, Walsh M, et al. Discrimination and calibration of clinical prediction models: users' guides to the medical literature. JAMA 2017;318:1377-84.
    • (2017) JAMA , vol.318 , pp. 1377-1384
    • Alba, A.C.1    Agoritsas, T.2    Walsh, M.3
  • 29
    • 84904856951 scopus 로고    scopus 로고
    • Accuracy of hospital standardized mortality rates: Effects of model calibration
    • Kipnis P, Liu V, Escobar GJ. Accuracy of hospital standardized mortality rates: effects of model calibration. Med Care 2014;52:378-84.
    • (2014) Med Care , vol.52 , pp. 378-384
    • Kipnis, P.1    Liu, V.2    Escobar, G.J.3
  • 31
    • 77949291292 scopus 로고    scopus 로고
    • Identifying the hospitalised patient in crisis"-A consensus conference on the afferent limb of Rapid Response Systems
    • DeVita MA, Smith GB, Adam SK, et al. "Identifying the hospitalised patient in crisis"-A consensus conference on the afferent limb of Rapid Response Systems. Resuscitation 2010;81:375-82.
    • (2010) Resuscitation , vol.81 , pp. 375-382
    • DeVita, M.A.1    Smith, G.B.2    Adam, S.K.3
  • 32
    • 85040255565 scopus 로고    scopus 로고
    • What this computer needs is a physician: Humanism and artificial intelligence
    • Verghese A, Shah NH, Harrington RA. What this computer needs is a physician: Humanism and artificial intelligence. JAMA 2018;319:19-20.
    • (2018) JAMA , vol.319 , pp. 19-20
    • Verghese, A.1    Shah, N.H.2    Harrington, R.A.3
  • 33
    • 84958692123 scopus 로고    scopus 로고
    • Integrating predictive analytics into high-value care: The dawn of precision delivery
    • Parikh RB, Kakad M, Bates DW. Integrating predictive analytics into high-value care: the dawn of precision delivery. JAMA 2016;315:651-2.
    • (2016) JAMA , vol.315 , pp. 651-652
    • Parikh, R.B.1    Kakad, M.2    Bates, D.W.3
  • 34
    • 44249118788 scopus 로고    scopus 로고
    • Improvement in process of care and outcome after a multicenter severe sepsis educational program in Spain
    • Ferrer R et al. Improvement in process of care and outcome after a multicenter severe sepsis educational program in Spain. JAMA 2008;299:2294-303.
    • (2008) JAMA , vol.299 , pp. 2294-2303
    • Ferrer, R.1
  • 35
    • 85009804040 scopus 로고    scopus 로고
    • Surviving sepsis campaign: International guidelines for management of sepsis and septic shock: 2016
    • Rhodes A, Evans LE, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Crit Care Med 2017;45:486-552.
    • (2017) Crit Care Med , vol.45 , pp. 486-552
    • Rhodes, A.1    Evans, L.E.2    Alhazzani, W.3
  • 36
    • 85020468010 scopus 로고    scopus 로고
    • Time to treatment and mortality during mandated emergency care for sepsis
    • Seymour CW, Gesten F, Prescott HC, et al. Time to treatment and mortality during mandated emergency care for sepsis. N Engl J Med Overseas Ed 2017;376:2235-44.
    • (2017) N Engl J Med Overseas Ed , vol.376 , pp. 2235-2244
    • Seymour, C.W.1    Gesten, F.2    Prescott, H.C.3
  • 37
    • 84994107923 scopus 로고    scopus 로고
    • Data that drive: Closing the loop in the learning hospital system
    • Liu VX, Morehouse JW, Baker JM, et al. Data that drive: closing the loop in the learning hospital system. J Hosp Med 2016;11 Suppl 1:S11-S17.
    • (2016) J Hosp Med , vol.11 , pp. S11-S17
    • Liu, V.X.1    Morehouse, J.W.2    Baker, J.M.3
  • 38
    • 85014637868 scopus 로고    scopus 로고
    • Evaluating the impact of a computerized surveillance algorithm and decision support system on sepsis mortality
    • Manaktala S, Claypool SR. Evaluating the impact of a computerized surveillance algorithm and decision support system on sepsis mortality. J Am Med Inform Assoc 2017;24:88-95.
    • (2017) J Am Med Inform Assoc , vol.24 , pp. 88-95
    • Manaktala, S.1    Claypool, S.R.2
  • 39
    • 85052147470 scopus 로고    scopus 로고
    • Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: A randomised clinical trial
    • Shimabukuro DW, Barton CW, Feldman MD, et al. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Resp Res 2017;4:e000234.
    • (2017) BMJ Open Resp Res , vol.4 , pp. e000234
    • Shimabukuro, D.W.1    Barton, C.W.2    Feldman, M.D.3
  • 40
    • 85049017507 scopus 로고    scopus 로고
    • Automated monitoring compared to standard care for the early detection of sepsis in critically ill patients
    • Warttig S, Alderson P, Evans DJW, et al. Automated monitoring compared to standard care for the early detection of sepsis in critically ill patients. Cochrane Database Syst Rev 2018;181.
    • (2018) Cochrane Database Syst Rev , vol.181
    • Warttig, S.1    Alderson, P.2    Evans, D.J.W.3
  • 41
    • 84920272451 scopus 로고    scopus 로고
    • Development, implementation, and impact of an automated early warning and response system for sepsis
    • Umscheid CA, Betesh J, VanZandbergen C, et al. Development, implementation, and impact of an automated early warning and response system for sepsis. J. Hosp. Med. 2015;10:26-31.
    • (2015) J. Hosp. Med , vol.10 , pp. 26-31
    • Umscheid, C.A.1    Betesh, J.2    VanZandbergen, C.3
  • 42
    • 84939141387 scopus 로고    scopus 로고
    • An electronic tool for the evaluation and treatment of sepsis in the ICU: A randomized controlled trial
    • Semler MW, Weavind L, Hooper MH, et al. An electronic tool for the evaluation and treatment of sepsis in the ICU: a randomized controlled trial. Crit Care Med 2015;43:1595-602.
    • (2015) Crit Care Med , vol.43 , pp. 1595-1602
    • Semler, M.W.1    Weavind, L.2    Hooper, M.H.3
  • 43
    • 85062968085 scopus 로고    scopus 로고
    • Electronic health record-based clinical decision support alert for severe sepsis: A randomised evaluation
    • Downing NL, Rolnick J, Poole SF, et al. Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation. BMJ Qual Saf 2019;28:762-8.
    • (2019) BMJ Qual Saf , vol.28 , pp. 762-768
    • Downing, N.L.1    Rolnick, J.2    Poole, S.F.3
  • 44
    • 84981316472 scopus 로고    scopus 로고
    • Validation of test performance and clinical time zero for an electronic health record embedded severe sepsis alert
    • Rolnick J, Downing NL, Shepard J, et al. Validation of test performance and clinical time zero for an electronic health record embedded severe sepsis alert. Appl Clin Inform 2016;7:560-72.
    • (2016) Appl Clin Inform , vol.7 , pp. 560-572
    • Rolnick, J.1    Downing, N.L.2    Shepard, J.3
  • 45
    • 84859865146 scopus 로고    scopus 로고
    • Why we still need randomized trials to compare effectiveness
    • Mauri L. Why we still need randomized trials to compare effectiveness. N Engl J Med 2012;366:1538-40.
    • (2012) N Engl J Med , vol.366 , pp. 1538-1540
    • Mauri, L.1
  • 46
    • 84940545085 scopus 로고    scopus 로고
    • Fusing randomized trials with big data: The key to self-learning health care systems
    • Angus DC. Fusing randomized trials with big data: the key to self-learning health care systems JAMA 2015;314:767-8.
    • (2015) JAMA , vol.314 , pp. 767-768
    • Angus, D.C.1
  • 47
    • 85021972547 scopus 로고    scopus 로고
    • Technologic Distractions (Part 1): Summary of approaches to manage alert quantity with intent to reduce alert fatigue and suggestions for alert fatigue metrics
    • Kane-Gill SL, O'Connor MF, Rothschild JM, et al. Technologic Distractions (Part 1): summary of approaches to manage alert quantity with intent to reduce alert fatigue and suggestions for alert fatigue metrics. Crit Care Med 2017;45:1481-8.
    • (2017) Crit Care Med , vol.45 , pp. 1481-1488
    • Kane-Gill, S.L.1    O'Connor, M.F.2    Rothschild, J.M.3
  • 48
    • 85008462865 scopus 로고    scopus 로고
    • Association between in-hospital critical illness events and outcomes in patients on the same ward
    • Volchenboum SL, Mayampurath A, Göksu-Gürsoy G, et al. Association between in-hospital critical illness events and outcomes in patients on the same ward. JAMA 2016;316:2674-5.
    • (2016) JAMA , vol.316 , pp. 2674-2675
    • Volchenboum, S.L.1    Mayampurath, A.2    Göksu-Gürsoy, G.3


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