Cost and mortality impact of an algorithm-driven sepsis prediction system

Journal of Medical Economics

Volumn 20, Issue 6, 2017, Pages 646-651

  Calvert, Jacob ^a   Hoffman, Jana ^a   Barton, Christopher ^b   Shimabukuro, David ^b   Ries, Michael ^c   Chettipally, Uli ^b,d   Kerem, Yaniv ^e,f   Jay, Melissa ^a   Mataraso, Samson ^g   Das, Ritankar ^a  

^a DASCENA INC   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c part of Advocate Aurora Health   (United States)

^d KAISER PERMANENTE   (United States)

^e STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^f KAISER PERMANENTE MEDICAL CENTER   (United States)

^g University of California   (United States)

Author keywords

Algorithm; Clinical decision support systems; Computer assisted diagnosis; Length of stay; Medical informatics; Mortality reduction; Sepsis

Indexed keywords

ALGORITHM; ARTICLE; COHORT ANALYSIS; COST; COST CONTROL; DISEASE SEVERITY; FALSE POSITIVE RESULT; HOSPITAL COST; HUMAN; LENGTH OF STAY; MORTALITY; PREDICTION; SENSITIVITY AND SPECIFICITY; SEPSIS; SYSTEMIC INFLAMMATORY RESPONSE SYNDROME; AGE; CLINICAL PROTOCOL; COST BENEFIT ANALYSIS; ECONOMICS; ORGAN DYSFUNCTION SCORE; SEVERITY OF ILLNESS INDEX; VITAL SIGN;

ANTIINFECTIVE AGENT; BIOLOGICAL MARKER;

AGE FACTORS; ALGORITHMS; ANTI-BACTERIAL AGENTS; BIOMARKERS; CLINICAL PROTOCOLS; COST-BENEFIT ANALYSIS; HUMANS; LENGTH OF STAY; ORGAN DYSFUNCTION SCORES; SENSITIVITY AND SPECIFICITY; SEPSIS; SEVERITY OF ILLNESS INDEX; VITAL SIGNS;

EID: 85017001414     PISSN: 13696998     EISSN: 1941837X     Source Type: Journal    
DOI: 10.1080/13696998.2017.1307203     Document Type: Article

References (34)

1. Hoyert DL, Heron MP, Murphy SL, et al. Deaths:final data for 2003: National vital statistics reports 2006;54:1-120
2. Murphy SL, Sherry L, Xu J, et al. Deaths:final data for 2010:National vital statistics reports. Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System 2013;61:1-117
3. Wood KA, Angus DC., Pharmoeconomic implications of new therapies in sepsis. Pharmoeconomics 2004;22:895-906
4. Stevenson EK, Rubenstein AR, Radin et al. Two decades of mortality trends among patients with severe sepsis:a comparative meta-analysis. Crit Care Med 2014;42:625
5. Chalupka AN, Talmor D., The economics of sepsis. Crit Care Clin 2012;28:57-76
6. Levy MM, Fink MP, Marshall JC, et al. 2001 sccm/esicm/accp/ats/sis international sepsis definitions conference. Intensive Care Med 2003;29:530-8
7. Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 2016;315:801-10
8. 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
9. Rangel-Frausto MS, Pittet D, Costigan M, et al. The natural history of the systemic inflammatory response syndrome (SIRS):a prospective study. JAMA 1995;273:117-23
10. Rivers E, Nguyen B, Havstad S, et al. Early goal-directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med 2001;345:1368-77
11. Nguyen HB, Corbett SW, Steele R, et al. Implementation of a bundle of quality indicators for the early management of severe sepsis and septic shock is associated with decreased mortality. Crit Care Med 2007;35:1105-12
12. Kumar A, Roberts D, Wood KE, et al. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit Care Med 2006;34:1589-96
13. Tafelski S, Nachtigall I, Deja M, et al. Computer-assisted decision support for changing practice in severe sepsis and septic shock. J Int Med Res 2010;38:1605-16
14. 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
15. Nelson JL, Smith BL, Jared JD, et al. Prospective trial of real-time electronic surveillance to expedite early care of severe sepsis. Ann Emerg Med 2011;57:500-4
16. Herasevich V, Pieper MS, Pulido J, et al. Enrollment into a time sensitive clinical study in the critical care setting:results from computerized septic shock sniffer implementation. J Am Med Inform Assoc 2011;18:639-44
17. Nguyen SQ, Mwakalindile E, Booth JS, et al. Automated electronic medical record sepsis detection in the emergency department. PeerJ 2014;2:e343
18. Calvert JS, Price DA, Chettipally UK, et al. A computational approach to early sepsis detection. Comp Biol Med 2016;74:69-73
19. Calvert JS, Price DA, Chettipally UK, et al. A computational approach to mortality prediction of alcohol use disorder inpatients. Comp Biol Med 2016;75:74-9
20. Calvert JS, Price DA, Barton CW, Chettipally UK, Das R. Discharge recommendation based on a novel technique of homeostatic analysis. J Am Med Inform Assoc 2017;24:24–9
21. Calvert JS, Price DA, Chettipally UK, et al. High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Ann Med Surg 2016;8:50-5
22. Desautels T, Calvert J, Hoffman J, et al. Prediction of sepsis in the intensive care unit with minimal electronic health record data:a machine learning approach. JMIR Med Inform 2016;4:3
23. Subbe CP, Kruger M, Rutherford P, et al. Validation of a modified early warning score in medical admissions. QJM 2001;94:521-6
24. Le Gall JR, Lemeshow S, Saulnier F., A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA 1993;270:2957-63
25. Vincent JL, Moreno R, Takala J, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. Intens Care Med 1996;22:707-10
26. Ferrer R, Martin-Loeches I, Phillips G, et al. Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour:results from a guideline-based performance improvement program. Crit Care Med 2014;42:1749-55
27. Torsvik M, Gustad LT, Mehl A, et al. Early identification of sepsis inpatients by ward nurses increases 30-day survival. Crit Care 2016;20:244
28. Rappleye E. Average cost per inpatient day across 50 states. Becker’s Healthcare. Chicago, IL:Financial Management, 2015. Archived at:http://www.webcitation.org/6pBNGh9Wc. Accessed November 4, 2016
29. Critical Care Statistics. Society of Critical Care Medicine. Mount Prospect, IL:Communications, 2015. http://www.sccm.org/Communications/Pages/CriticalCareStats.aspx. Accessed November 4, 2016
30. Mayr FB, Yende S, Angus DC., Epidemiology of severe sepsis. Virulence 2014;5:4-11
31. Wallace DJ, Angus DC, Seymour CW, et al. Critical care bed growth in the United States:a comparison of regional and national trends. Am J Respir Crit Care Med 2015;191:410-16
32. Phillip R. Lee Institute for Health Policy Studies. ICU outcomes (mortality and length of stay) methods, data collection tool and data. San Francisco, CA. Accessed November 4, 2016
33. Sendelbach S, Funk M., Alarm fatigue:a patient safety concern. AACN Adv Crit Care 2013;24:378-86
34. Torio C, Moore B. New U.S. Government report reveals annual cost of hospital treatment of sepsis has grown by $3.4 billion. San Diego, CA:Sepsis Alliance, 2016. Archived at:http://www.webcitation.org/6pBRMzYAp. Accessed November 3, 2016