Skip to main content
SpringerLink
Log in

Precision Delivery in Critical Care: Balancing Prediction and Personalization

  • Chapter
  • First Online:
Annual Update in Intensive Care and Emergency Medicine 2019

Part of the book series: Annual Update in Intensive Care and Emergency Medicine ((AUICEM))

  • 1735 Accesses

Abstract

Recent developments in healthcare data availability, advanced analytic algorithms, and high-performance computing have produced incredible enthusiasm about a new age of data-driven healthcare [1–8]. When it comes to clinical care specifically, ‘precision delivery’ is an emerging term to describe the “routine use of patients’ electronic health record (EHR) data to predict risk and personalize care to substantially improve value” (Table 2.1) [7, 9, 10]. While clinical risk prediction tools have a long history in critical care, novel machine learning applications can offer improved predictive performance by maximally leveraging large-scale, complex EHR and other data [5]. Perhaps, even more importantly, these approaches may help overcome the problem of heterogeneity, which is routinely noted to be a hallmark of critical illness as well as a major barrier to improved treatment [11–13]. In this chapter, we discuss the overarching concept of ‘precision delivery’, the important balance between clinical risk prediction and personalization, and the future challenges and applications of data-driven critical care delivery.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med. 2016;375:1216–9.

    Article  Google Scholar 

  2. Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA. 2013;309:1351–2.

    Article  CAS  Google Scholar 

  3. Celi LA, Mark RG, Stone DJ, Montgomery RA. “Big data” in the intensive care unit. Closing the data loop. Am J Respir Crit Care Med. 2013;187:1157–60.

    Article  Google Scholar 

  4. Naylor CD. On the prospects for a (deep) learning health care system. JAMA. 2018;320:1099–100.

    Article  Google Scholar 

  5. Hinton G. Deep learning—a technology with the potential to transform health care. JAMA. 2018;320:1101–2.

    Article  Google Scholar 

  6. Darcy AM, Louie AK, Roberts LW. Machine learning and the profession of medicine. JAMA. 2016;315:551–2.

    Article  CAS  Google Scholar 

  7. Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff (Millwood). 2014;33:1123–31.

    Article  Google Scholar 

  8. Liu VX. Toward the “plateau of productivity”: enhancing the value of machine learning in critical care. Crit Care Med. 2018;46:1196–7.

    Article  Google Scholar 

  9. Parikh RB, Kakad M, Bates DW. Integrating predictive analytics into high-value care: the dawn of precision delivery. JAMA. 2016;315:651–2.

    Article  CAS  Google Scholar 

  10. Parikh RB, Schwartz JS, Navathe AS. Beyond genes and molecules—a precision delivery initiative for precision medicine. N Engl J Med. 2017;376:1609–12.

    Article  Google Scholar 

  11. Cohen J, Vincent JL, Adhikari NK, et al. Sepsis: a roadmap for future research. Lancet Infect Dis. 2015;15:581–614.

    Article  Google Scholar 

  12. Seymour CW, Coopersmith CM, Deutschman CS, et al. Application of a framework to assess the usefulness of alternative sepsis criteria. Crit Care Med. 2016;44:e122–30.

    Article  Google Scholar 

  13. Prescott HC, Calfee CS, Thompson BT, Angus DC, Liu VX. Toward smarter lumping and smarter splitting: rethinking strategies for sepsis and acute respiratory distress syndrome clinical trial design. Am J Respir Crit Care Med. 2016;194:147–55.

    Article  Google Scholar 

  14. National Academies of Sciences, Engineering, and Medicine. The fourth industrial revolution: proceedings of a workshop-in brief. Washington: National Academies Press; 2017.

    Google Scholar 

  15. Schwab K. The fourth industrial revolution. New York: Crown Publishing Group; 2017.

    Google Scholar 

  16. Smith B, Linden G. Two decades of recommender systems at Amazon.com. IEEE Internet Comput. 2017;21:12–8.

    Article  Google Scholar 

  17. Gomez-Uribe CA, Hunt N. The Netflix recommender system: algorithms, business value, and innovation. ACM Trans Manage Inf Syst. 2016;6:1–19.

    Article  Google Scholar 

  18. Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning. New York: Springer Science+Business Media; 2017.

    Google Scholar 

  19. Kuhn M, Johnson K. Applied predictive modeling. New York: Springer; 2016.

    Google Scholar 

  20. Vincent JL. The future of critical care medicine: integration and personalization. Crit Care Med. 2016;44:386–9.

    Article  Google Scholar 

  21. Vincent JL. Critical care—where have we been and where are we going? Crit Care. 2013;17(Suppl 1):S2.

    PubMed  PubMed Central  Google Scholar 

  22. Vincent JL, Moreno R. Clinical review: scoring systems in the critically ill. Crit Care. 2010;14:207.

    Article  Google Scholar 

  23. Liu V. Keeping score of severity scores: taking the next step. Crit Care Med. 2016;44:639–40.

    Article  Google Scholar 

  24. Castella X, Artigas A, Bion J, Kari A. A comparison of severity of illness scoring systems for intensive care unit patients: results of a multicenter, multinational study. The European/North American Severity Study Group. Crit Care Med. 1995;23:1327–35.

    Article  CAS  Google Scholar 

  25. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13:818–29.

    Article  CAS  Google Scholar 

  26. Vincent JL, de Mendonca A, Cantraine F, et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on “sepsis-related problems” of the European Society of Intensive Care Medicine. Crit Care Med. 1998;26:1793–800.

    Article  CAS  Google Scholar 

  27. Ferreira FL, Bota DP, Bross A, Melot C, Vincent JL. Serial evaluation of the SOFA score to predict outcome in critically ill patients. JAMA. 2001;286:1754–8.

    Article  CAS  Google Scholar 

  28. 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.

    Article  CAS  Google Scholar 

  29. Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N. Scalable and accurate deep learning for electronic health records. NPJ Digital Med. 2018;18:1–10.

    Google Scholar 

  30. Osheroff J, Teich JM, Levick D, et al. Improving outomes with clinical decision support: an implementer’s guide. Chicago: HIMSS Publishing; 2012.

    Book  Google Scholar 

  31. Sendelbach S, Funk M. Alarm fatigue: a patient safety concern. AACN Adv Crit Care. 2013;24:378–86.

    Article  Google Scholar 

  32. Badawi O, Liu X, Hassan E, Amelung PJ, Swami S. Evaluation of ICU risk models adapted for use as continuous markers of severity of illness throughout the ICU stay. Crit Care Med. 2018;46:361–7.

    Article  Google Scholar 

  33. Weissman GE, Hubbard RA, Ungar LH, et al. Inclusion of unstructured clinical text improves early prediction of death or prolonged ICU stay. Crit Care Med. 2018;46:1125–32.

    Article  Google Scholar 

  34. Lee J, Maslove DM, Dubin JA. Personalized mortality prediction driven by electronic medical data and a patient similarity metric. PLoS One. 2015;10:e0127428.

    Article  Google Scholar 

  35. Sjoding MW, Liu VX. Can you read me now? Unlocking narrative data with natural language processing. Ann Am Thorac Soc. 2016;13:1443–5.

    Article  Google Scholar 

  36. Alam N, Hobbelink EL, van Tienhoven AJ, van de Ven PM, Jansma EP, Nanayakkara PW. The impact of the use of the early warning score (EWS) on patient outcomes: a systematic review. Resuscitation. 2014;85:587–94.

    Article  CAS  Google Scholar 

  37. McGaughey J, Alderdice F, Fowler R, Kapila A, Mayhew A, Moutray M. Outreach and Early Warning Systems (EWS) for the prevention of intensive care admission and death of critically ill adult patients on general hospital wards. Cochrane Database Syst Rev. 2007;Issue 3:CD005529.

    Google Scholar 

  38. Escobar GJ, LaGuardia JC, Turk BJ, Ragins A, Kipnis P, Draper D. Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record. J Hosp Med. 2012;7:388–95.

    Article  Google Scholar 

  39. Kipnis P, Turk BJ, Wulf DA, et al. Development and validation of an electronic medical record-based alert score for detection of inpatient deterioration outside the ICU. J Biomed Inform. 2016;64:10–9.

    Article  Google Scholar 

  40. Green M, Lander H, Snyder A, Hudson P, Churpek M, Edelson D. Comparison of the between the flags calling criteria to the MEWS, NEWS and the electronic Cardiac Arrest Risk Triage (eCART) score for the identification of deteriorating ward patients. Resuscitation. 2018;123:86–91.

    Article  Google Scholar 

  41. 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;190:649–55.

    Article  Google Scholar 

  42. Finlay GD, Rothman MJ, Smith RA. Measuring the modified early warning score and the Rothman index: advantages of utilizing the electronic medical record in an early warning system. J Hosp Med. 2014;9:116–9.

    Article  Google Scholar 

  43. Olenick EM, Zimbro KS, D'Lima GM, Ver Schneider P, Jones D. Predicting Sepsis risk using the “sniffer” algorithm in the electronic medical record. J Nurs Care Qual. 2017;32:25–31.

    Article  Google Scholar 

  44. Harrison AM, Thongprayoon C, Kashyap R, et al. Developing the surveillance algorithm for detection of failure to recognize and treat severe sepsis. Mayo Clin Proc. 2015;90:166–75.

    Article  Google Scholar 

  45. Alsolamy S, Al Salamah M, Al Thagafi M, et al. Diagnostic accuracy of a screening electronic alert tool for severe sepsis and septic shock in the emergency department. BMC Med Inform Decis Mak. 2014;14:105.

    Article  Google Scholar 

  46. 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.

    Article  Google Scholar 

  47. Herasevich V, Pieper MS, Pulido J, Gajic O. 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.

    Article  Google Scholar 

  48. Despins LA. Automated detection of sepsis using electronic medical record data: a systematic review. J Healthc Qual. 2017;39:322–33.

    Article  Google Scholar 

  49. 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.

    Article  Google Scholar 

  50. Henry KE, Hager DN, Pronovost PJ, Saria S. A targeted real-time early warning score (TREWScore) for septic shock. Sci Transl Med. 2015;7:299ra122.

    Article  Google Scholar 

  51. Wassenaar A, Schoonhoven L, Devlin JW, et al. Delirium prediction in the intensive care unit: comparison of two delirium prediction models. Crit Care. 2018;22:114.

    Article  Google Scholar 

  52. Lindroth H, Bratzke L, Purvis S, et al. Systematic review of prediction models for delirium in the older adult inpatient. BMJ Open. 2018;8:e019223.

    Article  Google Scholar 

  53. Marra A, Pandharipande PP, Shotwell MS, et al. Acute brain dysfunction: development and validation of a daily prediction model. Chest. 2018;154:293–301.

    Article  Google Scholar 

  54. Mestres Gonzalvo C, de Wit H, van Oijen BPC, et al. Validation of an automated delirium prediction model (DElirium MOdel (DEMO)): an observational study. BMJ Open. 2017;7:e016654.

    Article  Google Scholar 

  55. Hodgson LE, Roderick PJ, Venn RM, Yao GL, Dimitrov BD, Forni LG. Correction: the ICE-AKI study: impact analysis of a clinical prediction rule and electronic AKI alert in general medical patients. PLoS One. 2018;13:e0203183.

    Article  Google Scholar 

  56. Mohamadlou H, Lynn-Palevsky A, Barton C, et al. Prediction of acute kidney injury with a machine learning algorithm using electronic health record data. Can J Kidney Health Dis. 2018;5:1–9.

    Article  Google Scholar 

  57. Klein SJ, Brandtner AK, Lehner GF, et al. Biomarkers for prediction of renal replacement therapy in acute kidney injury: a systematic review and meta-analysis. Intensive Care Med. 2018;44:323–36.

    Article  CAS  Google Scholar 

  58. Haines RW, Lin SP, Hewson R, et al. Acute kidney injury in trauma patients admitted to critical care: development and validation of a diagnostic prediction model. Sci Rep. 2018;8:3665.

    Article  Google Scholar 

  59. Koyner JL, Adhikari R, Edelson DP, Churpek MM. Development of a multicenter ward-based AKI prediction model. Clin J Am Soc Nephrol. 2016;11:1935–43.

    Article  Google Scholar 

  60. Bauman ZM, Gassner MY, Coughlin MA, Mahan M, Watras J. Lung injury prediction score is useful in predicting acute respiratory distress syndrome and mortality in surgical critical care patients. Crit Care Res Pract. 2015;2015:157408.

    PubMed  PubMed Central  Google Scholar 

  61. Beitler JR, Schoenfeld DA, Thompson BT. Preventing ARDS: progress, promise, and pitfalls. Chest. 2014;146:1102–13.

    Article  Google Scholar 

  62. Levitt JE, Calfee CS, Goldstein BA, Vojnik R, Matthay MA. Early acute lung injury: criteria for identifying lung injury prior to the need for positive pressure ventilation. Crit Care Med. 2013;41:1929–37.

    Article  Google Scholar 

  63. Levitt JE, Bedi H, Calfee CS, Gould MK, Matthay MA. Identification of early acute lung injury at initial evaluation in an acute care setting prior to the onset of respiratory failure. Chest. 2009;135:936–43.

    Article  Google Scholar 

  64. LaFaro RJ, Pothula S, Kubal KP, et al. Neural network prediction of ICU length of stay following cardiac surgery based on pre-incision variables. PLoS One. 2015;10:e0145395.

    Article  Google Scholar 

  65. Verburg IW, Atashi A, Eslami S, et al. Which models can I use to predict adult ICU length of stay? A systematic review. Crit Care Med. 2017;45:e222–31.

    Article  Google Scholar 

  66. Escobar GJ, Baker JM, Kipnis P, et al. Prediction of recurrent Clostridium difficile infection using comprehensive electronic medical records in an integrated healthcare delivery system. Infect Control Hosp Epidemiol. 2017;38:1196–203.

    Article  Google Scholar 

  67. Zilberberg MD, Reske K, Olsen M, Yan Y, Dubberke ER. Development and validation of a recurrent Clostridium difficile risk-prediction model. J Hosp Med. 2014;9:418–23.

    Article  Google Scholar 

  68. Reveles KR, Mortensen EM, Koeller JM, et al. Derivation and validation of a Clostridium difficile infection recurrence prediction rule in a national cohort of veterans. Pharmacotherapy. 2018;38:349–56.

    Article  Google Scholar 

  69. Oh J, Makar M, Fusco C, et al. A generalizable, data-driven approach to predict daily risk of Clostridium difficile infection at two large academic health centers. Infect Control Hosp Epidemiol. 2018;39:425–33.

    Article  Google Scholar 

  70. Delucchi K, Famous KR, Ware LB, et al. Stability of ARDS subphenotypes over time in two randomised controlled trials. Thorax. 2018;73:439–45.

    Article  Google Scholar 

  71. Wong HR, Sweeney TE, Hart KW, Khatri P, Lindsell CJ. Pediatric sepsis endotypes among adults with sepsis. Crit Care Med. 2017;45:e1289–91.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kaiser Permanente Division of Research, Oakland, CA, USA

    V. X. Liu

  2. Department of Medicine and Institute for Social Research, University of Michigan and VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA

    H. C. Prescott

Authors
  1. V. X. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. C. Prescott
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. X. Liu .

Editor information

Editors and Affiliations

  1. Dept. of Intensive Care, Erasme Hospital, Université libre de Bruxelles, Brussels, Belgium

    Jean-Louis Vincent

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Liu, V.X., Prescott, H.C. (2019). Precision Delivery in Critical Care: Balancing Prediction and Personalization. In: Vincent, JL. (eds) Annual Update in Intensive Care and Emergency Medicine 2019. Annual Update in Intensive Care and Emergency Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-06067-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-06067-1_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-06066-4

  • Online ISBN: 978-3-030-06067-1

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation