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

Volumn 45, Issue 11, 2017, Pages 1805-1812

  Churpek, Matthew M ^a   Snyder, Ashley ^a   Sokol, Sarah ^a   Pettit, Natasha N ^a   Edelson, Dana P ^a  

^a UNIVERSITY OF CHICAGO   (United States)

Author keywords

Early warning scores; Multiple organ failure; Organ dysfunction scores; Quick Sepsis related Organ Failure Assessment; Sepsis; Sepsis related Organ Failure Assessment; Systemic inflammatory response syndrome

Indexed keywords

ANTIBIOTIC AGENT; ANTIINFECTIVE AGENT;

ADULT; ARTICLE; BLOOD CULTURE; CLINICAL OUTCOME; COHORT ANALYSIS; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; FEMALE; HOSPITAL MORTALITY; HOSPITAL PATIENT; HUMAN; INFECTION; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MIDDLE AGED; OBSERVATIONAL STUDY; OUTCOME ASSESSMENT; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SECONDARY ANALYSIS; SENSITIVITY AND SPECIFICITY; SEQUENTIAL ORGAN FAILURE ASSESSMENT SCORE; SYSTEMIC INFLAMMATORY RESPONSE SYNDROME; AGED; EARLY DIAGNOSIS; HEART ARREST; INTENSIVE CARE UNIT; MALE; MICROBIOLOGICAL EXAMINATION; MORTALITY; ORGAN DYSFUNCTION SCORE; PREDICTIVE VALUE; PROGNOSIS; RETROSPECTIVE STUDY; SEPSIS; STATISTICS AND NUMERICAL DATA; UNIVERSITY HOSPITAL;

ACADEMIC MEDICAL CENTERS; ADULT; AGED; ANTI-BACTERIAL AGENTS; BACTERIOLOGICAL TECHNIQUES; BLOOD CULTURE; COHORT STUDIES; EARLY DIAGNOSIS; FEMALE; HEART ARREST; HUMANS; INTENSIVE CARE UNITS; MALE; MIDDLE AGED; ORGAN DYSFUNCTION SCORES; PREDICTIVE VALUE OF TESTS; PROGNOSIS; RETROSPECTIVE STUDIES; ROC CURVE; SEPSIS; SYSTEMIC INFLAMMATORY RESPONSE SYNDROME;

EID: 85025678654     PISSN: 00903493     EISSN: 15300293     Source Type: Journal    
DOI: 10.1097/CCM.0000000000002648     Document Type: Article

References (22)

1. Liu V, Escobar GJ, Greene JD, et al: Hospital deaths in patients with sepsis from 2 independent cohorts. JAMA 2014; 312:90-92
2. Lagu T, Rothberg MB, Shieh MS, et al: Hospitalizations, costs, and outcomes of severe sepsis in the United States 2003 to 2007. Crit Care Med 2012; 40:754-761
3. Dellinger RP, Levy MM, Rhodes A, et al; Surviving Sepsis Campaign Guidelines Committee including the Pediatric Subgroup: Surviving sepsis campaign: International guidelines for management of severe sepsis and septic shock: 2012. Crit Care Med 2013; 41:580-637
4. 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. Crit Care Med 2001; 29:1303-1310
5. Iwashyna TJ, Odden A, Rohde J, et al. Identifying patients with severe sepsis using administrative claims: Patient-level validation of the angus implementation of the international consensus conference definition of severe sepsis. Medical care 2014; 52:e39-e43
6. Whittaker SA, Mikkelsen ME, Gaieski DF, et al: Severe sepsis cohorts derived from claims-based strategies appear to be biased toward a more severely ill patient population. Crit Care Med 2013; 41:945-953
7. 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; 315:762-774
8. Churpek MM, Snyder A, Han X, et al: qSOFA, SIRS, and early warning scores for detecting clinical deterioration in infected patients outside the ICU. Am J Respir Crit Care Med 2017; 195:906-911
9. Bone RC, Balk RA, Cerra FB, et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest 1992;101:1644-1655
10. Subbe CP, Kruger M, Rutherford P, et al: Validation of a modified Early Warning Score in medical admissions. QJM 2001; 94:521-526
11. Smith GB, Prytherch DR, Meredith P, et al: The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation 2013; 84:465-470
12. Churpek MM, Yuen TC, Winslow C, et al: Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med 2016; 44:368-374
13. Churpek MM, Zadravecz FJ, Winslow C, et al: Incidence and prognostic value of the systemic inflammatory response syndrome and organ dysfunctions in ward patients. Am J Respir Crit Care Med 2015; 192:958-964
14. Singer M, Deutschman CS, Seymour CW, et al: The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 2016; 315:801-810
15. Vincent JL, Opal SM, Marshall JC, et al: Sepsis definitions: Time for change. Lancet 2013; 381:774-775
16. Thiel SW, Rosini JM, Shannon W, et al: Early prediction of septic shock in hospitalized patients. J Hosp Med 2010; 5:19-25
17. Taylor RA, Pare JR, Venkatesh AK, et al:. Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach. Acad Emerg Med 2016; 23:269-278
18. Churpek MM, Yuen TC, Winslow C, et al: Multicenter development and validation of a risk stratification tool for ward patients. Am J Respir Crit Care Med 2014; 190:649-655
19. Kang MA, Churpek MM, Zadravecz FJ, et al: Real-time risk prediction on the wards: A feasibility study. Crit Care Med 2016; 44:1468-1473
20. Kurczewski L, Sweet M, McKnight R, et al: Reduction in time to first action as a result of electronic alerts for early sepsis recognition. Crit Care Nurs Q 2015; 38:182-187
21. Sawyer AM, Deal EN, Labelle AJ, et al: Implementation of a real-time computerized sepsis alert in nonintensive care unit patients. Crit Care Med 2011; 39:469-473
22. 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