Skip to main content

Advertisement

SpringerLink
Log in

ICU staffing feature phenotypes and their relationship with patients’ outcomes: an unsupervised machine learning analysis

  • Original
  • Published:
Intensive Care Medicine Aims and scope Submit manuscript

Abstract

Purpose

To study whether ICU staffing features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical ventilation (MV) using cluster analysis directed by machine learning.

Methods

The following variables were included in the analysis: average bed to nurse, physiotherapist and physician ratios, presence of 24/7 board-certified intensivists and dedicated pharmacists in the ICU, and nurse and physiotherapist autonomy scores. Clusters were defined using the partition around medoids method. We assessed the association between clusters and hospital mortality using logistic regression and with ICU LOS and MV duration using competing risk regression.

Results

Analysis included data from 129,680 patients admitted to 93 ICUs (2014–2015). Three clusters were identified. The features distinguishing between the clusters were: the presence of board-certified intensivists in the ICU 24/7 (present in Cluster 3), dedicated pharmacists (present in Clusters 2 and 3) and the extent of nurse autonomy (which increased from Clusters 1 to 3). The patients in Cluster 3 exhibited the best outcomes, with lower adjusted hospital mortality [odds ratio 0.92 (95% confidence interval (CI), 0.87–0.98)], shorter ICU LOS [subhazard ratio (SHR) for patients surviving to ICU discharge 1.24 (95% CI 1.22–1.26)] and shorter durations of MV [SHR for undergoing extubation 1.61(95% CI 1.54–1.69)]. Cluster 1 had the worst outcomes.

Conclusion

Patients treated in ICUs combining 24/7 expert intensivist coverage, a dedicated pharmacist and nurses with greater autonomy had the best outcomes. All of these features represent achievable targets that should be considered by policy makers with an interest in promoting equal and optimal ICU care.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Soares M, Bozza FA, Angus DC, Japiassú AM, Viana WN, Costa R, Brauer L, Mazza BF, Corrêa TD, Nunes AL, Lisboa T, Colombari F, Maciel AT, Azevedo LC, Damasceno M, Fernandes HS, Cavalcanti AB, do Brasil PE, Kahn JM, Salluh JI (2015) Organizational characteristics, outcomes, and resource use in 78 Brazilian intensive care units: the ORCHESTRA study. Intensive Care Med 41(12):2149–2160

    Article  PubMed  Google Scholar 

  2. Soares M, Bozza FA, Azevedo LC, Silva UV, Corrêa TD, Colombari F, Torelly AP, Varaschin P, Viana WN, Knibel MF, Damasceno M, Espinoza R, Ferez M, Silveira JG, Lobo SA, Moraes AP, Lima RA, de Carvalho AG, do Brasil PE, Kahn JM, Angus DC, Salluh JI (2016) Effects of organizational characteristics on outcomes and resource use in patients with cancer admitted to intensive care units. J Clin Oncol 34(27):3315–3324

    Article  PubMed  Google Scholar 

  3. Sakr Y, Moreira CL, Rhodes A, Ferguson ND, Kleinpell R, Pickkers P, Kuiper MA, Lipman J, Vincent JL, Extended Prevalence of Infection in Intensive Care Study Investigators (2015) The impact of hospital and ICU organizational factors on outcome in critically ill patients: results from the extended prevalence of infection in intensive care study. Crit Care Med 43(3):519–526

    Article  PubMed  Google Scholar 

  4. Neuraz A, Guérin C, Payet C, Polazzi S, Aubrun F, Dailler F, Lehot JJ, Piriou V, Neidecker J, Rimmelé T, Schott AM, Duclos A (2015) Patient mortality is associated with staff resources and workload in the ICU: a multicenter observational study. Crit Care Med 43(8):1587–1594

    Article  PubMed  Google Scholar 

  5. Gershengorn HB, Harrison DA, Garland A, Wilcox ME, Rowan KM, Wunsch H (2017) Association of intensive care unit patient-to-intensivist ratios with hospital mortality. JAMA Intern Med 177(3):388–396

    Article  PubMed  Google Scholar 

  6. Wilcox ME, Chong CA, Niven DJ, Rubenfeld GD, Rowan KM, Wunsch H, Fan E (2013) Do intensivist staffing patterns influence hospital mortality following ICU admission? A systematic review and meta-analyses. Crit Care Med 41(10):2253–2274

    Article  PubMed  Google Scholar 

  7. Krapohl G, Manojlovich M, Redman R, Zhang L (2010) Nursing specialty certification and nursing-sensitive patient outcomes in the intensive care unit. Am J Crit Care 19(6):490–498

    Article  PubMed  Google Scholar 

  8. Sevransky JE, Checkley W, Herrera P, Pickering BW, Barr J, Brown SM, Chang SY, Chong D, Kaufman D, Fremont RD, Girard TD, Hoag J, Johnson SB, Kerlin MP, Liebler J, O’Brien J, O’Keefe T, Park PK, Pastores SM, Patil N, Pietropaoli AP, Putman M, Rice TW, Rotello L, Siner J, Sajid S, Murphy DJ, Martin GS, United States Critical Illness and Injury Trials Group-Critical Illness Outcomes Study Investigators (2015) Protocols and hospital mortality in critically ill patients: the United States critical illness and injury trials group critical illness outcomes study. Crit Care Med 43(10):2076–2084

    Article  PubMed  PubMed Central  Google Scholar 

  9. Writing Group for the CHECKLIST-ICU Investigators and the Brazilian Research in Intensive Care Network (BRICNet), Cavalcanti AB, Bozza FA, Machado FR, Salluh JI, Campagnucci VP, Vendramim P, Guimaraes HP, Normilio-Silva K, Damiani LP, Romano E, Carrara F, de Lubarino Diniz Souza J, Silva AR, Ramos GV, Teixeira C, da Brandão Silva N, Chang CC, Angus DC, Berwanger O (2016) Effect of a quality improvement intervention with daily round checklists, goal setting, and clinician prompting on mortality of critically ill patients: a randomized clinical Trial. JAMA 315(14):1480–1490

    Article  Google Scholar 

  10. Kim MM, Barnato AE, Angus DC, Fleisher LA, Kahn JM (2010) The effect of multidisciplinary care teams on intensive care unit mortality. Arch Intern Med 170(4):369–376

    Article  PubMed  PubMed Central  Google Scholar 

  11. Rose L, Nelson S, Johnston L, Presneill JJ (2007) Decisions made by critical care nurses during mechanical ventilation and weaning in an Australian intensive care unit. Am J Crit Care 16(5):434–443

    PubMed  Google Scholar 

  12. Hirzallah FM, Alkaissi A, do Céu Barbieri-Figueiredo M (2019) A systematic review of nurse-led weaning protocol for mechanically ventilated adult patients. Nurs Crit Care 24(2):89–96

    Article  PubMed  Google Scholar 

  13. Larson GE, McKeever S (2018) Nurse titrated analgesia and sedation in intensive care increases the frequency of comfort assessment and reduces midazolam use in paediatric patients following cardiac surgery. Aust Crit Care 31(1):31–36

    Article  PubMed  Google Scholar 

  14. Radosevich MA, Wanta BT, Meyer TJ, Weber VW, Brown DR, Smischney NJ, Diedrich DA (2017) Implementation of a goal-directed mechanical ventilation order set driven by respiratory therapists improves compliance with best practices for mechanical ventilation. J Intensive Care Med 1:885066617746089

    Google Scholar 

  15. Fagoni N, Piva S, Peli E, Turla F, Pecci E, Gualdoni L, Fiorese B, Rasulo F, Latronico N (2018) Comparison between a nurse-led weaning protocol and weaning based on physician’s clinical judgment in tracheostomized critically ill patients: a pilot randomized controlled clinical trial. Ann Intensive Care 8(1):11

    Article  PubMed  PubMed Central  Google Scholar 

  16. Zampieri FG, Soares M, Borges LP, Salluh JIF, Ranzani OT (2017) The Epimed Monitor ICU Database®: a cloud-based national registry for adult intensive care unit patients in Brazil. Rev Bras Ter Intensiva 29(4):418–426

    Article  PubMed  PubMed Central  Google Scholar 

  17. Zampieri FG, Bozza FA, Moralez GM et al (2017) The effects of performance status 1 week before hospital admission on the outcomes of critically ill patients. Intensive Care Med 43:39–47

    Article  PubMed  Google Scholar 

  18. Moreno RP, Metnitz PG, Almeida E, Jordan B, Bauer P, Campos RA, Iapichino G, Edbrooke D, Capuzzo M, Le Gall JR, SAPS 3 Investigators (2005) SAPS 3–From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission. Intensive Care Med 31(10):1345–1355

    Article  PubMed  PubMed Central  Google Scholar 

  19. Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H, Reinhart CK, Suter PM, Thijs LG (1996) The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on sepsis-related problems of the European Society of intensive care medicine. Intensive Care Med 22(7):707–710

    Article  CAS  PubMed  Google Scholar 

  20. van der Maaten LJP, Hinton GE (2008) Visualizing high-dimensional data using t-SNE. J Mach Learn Res 9:2579–2605

    Google Scholar 

  21. Gray B. cmprsk: Subdistribution Analysis of Competing Risks. 2014. R package version 2.2-7. https://CRAN.R-project.org/package=cmprsk

  22. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, STROBE Initiative (2007) The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Bull World Health Organ 85(11):867–872

    Article  Google Scholar 

  23. Orinovsky I, Raizman E (2018) Improvement of nutritional intake in intensive care unit patients via a nurse-led enteral nutrition feeding protocol. Crit Care Nurse 38(3):38–44

    Article  PubMed  Google Scholar 

  24. Al Ashry HS, Abuzaid AS, Walters RW, Modrykamien AM (2016) Effect of compliance with a nurse-led intensive care unit checklist on clinical outcomes in mechanically and nonmechanically ventilated patients. J Intensive Care Med 31(4):252–257

    Article  PubMed  Google Scholar 

  25. Armellino D, Quinn Griffin MT, Fitzpatrick JJ (2010) Structural empowerment and patient safety culture among registered nurses working in adult critical care units. J Nurs Manag 18(7):796–803

    Article  PubMed  Google Scholar 

  26. Fitzpatrick JJ, Campo TM, Graham G, Lavandero R (2010) Certification, empowerment, and intent to leave current position and the profession among critical care nurses. Am J Crit Care 19(3):218–226

    Article  PubMed  Google Scholar 

  27. Hauck A, Quinn Griffin MT, Fitzpatrick JJ (2011) Structural empowerment and anticipated turnover among critical care nurses. J Nurs Manag 19(2):269–276

    Article  PubMed  Google Scholar 

  28. Ganz FD, Raanan O, Khalaila R, Bennaroch K, Scherman S, Bruttin M, Sastiel Z, Fink NF, Benbenishty J (2013) Moral distress and structural empowerment among a national sample of Israeli intensive care nurses. J Adv Nurs 69(2):415–424

    Article  PubMed  Google Scholar 

  29. Papathanassoglou ED, Karanikola MN, Kalafati M, Giannakopoulou M, Lemonidou C, Albarran JW (2012) Professional autonomy, collaboration with physicians, and moral distress among European intensive care nurses. Am J Crit Care 21(2):e41–e52

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Council for Scientific and Technological Development (CNPq), Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ) and by departmental funds from the D’Or Institute for Research and Education. We dedicate this work to the memory of Dr. Dieter Eduardo Sielfeld Araya, ORCHESTRA Study investigator, who recently passed away.

Author information

Authors and Affiliations

  1. Graduate Program in Translational Medicine, Department of Critical Care, D’Or Institute for Research and Education, Rua Diniz Cordeiro, 30. Botafogo, Rio De Janeiro, 22281-100, Brazil

    Fernando G. Zampieri, Jorge I. F. Salluh, Fernando A. Bozza & Marcio Soares

  2. Research Institute, HCor-Hospital do Coração, São Paulo, Brazil

    Fernando G. Zampieri & Lucas P. Damiani

  3. Department of Research and Development, Epimed Solutions, Rio De Janeiro, Brazil

    Jorge I. F. Salluh & Lunna P. Borges

  4. ICU, Hospital Sírio Libanês, São Paulo, Brazil

    Luciano C. P. Azevedo

  5. Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Jeremy M. Kahn & Derek C. Angus

  6. Department of Health Policy & Management, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA

    Jeremy M. Kahn & Derek C. Angus

  7. ICU, Hospital Copa D’Or, Rio De Janeiro, Brazil

    William N. Viana

  8. ICU, Hospital Quinta D’Or, Rio De Janeiro, Brazil

    Roberto Costa

  9. Adult ICU, Hospital Israelita Albert Einstein, São Paulo, Brazil

    Thiago D. Corrêa

  10. ICU, Hospital Santa Paula, São Paulo, Brazil

    Dieter E. S. Araya

  11. ICU, Hospital Santa Luzia Rede D’Or São Luiz DF, Brasília, Brazil

    Marcelo O. Maia

  12. ICU, Hospital São Francisco, Ribeirão Preto, Brazil

    Marcus A. Ferez

  13. ICU, UDI Hospital, São Luís, Brazil

    Alexandre G. R. Carvalho

  14. ICU, Hospital São Lucas, Rio De Janeiro, Brazil

    Marcos F. Knibel

  15. ICU, Hospital Estadual Alberto Torres, São Gonçalo, Brazil

    Ulisses O. Melo

  16. ICU, Hospital Barra D’Or, Rio De Janeiro, Brazil

    Marcelo S. Santino

  17. ICU, Hospital Santa Rita, Santa Casa de Misericórdia de Porto Alegre, Porto Alegre, Brazil

    Thiago Lisboa

  18. ICU, Hospital Unimed Vitoria, Vitoria, Brazil

    Eliana B. Caser

  19. ICU, Hospital da Luz, Vila Mariana, São Paulo, Brazil

    Bruno A. M. P. Besen

  20. Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, Rio De Janeiro, Brazil

    Fernando A. Bozza

Authors
  1. Fernando G. Zampieri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jorge I. F. Salluh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luciano C. P. Azevedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jeremy M. Kahn
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lucas P. Damiani
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lunna P. Borges
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. William N. Viana
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Roberto Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Thiago D. Corrêa
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Dieter E. S. Araya
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Marcelo O. Maia
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Marcus A. Ferez
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Alexandre G. R. Carvalho
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Marcos F. Knibel
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Ulisses O. Melo
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Marcelo S. Santino
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Thiago Lisboa
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Eliana B. Caser
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Bruno A. M. P. Besen
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Fernando A. Bozza
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Derek C. Angus
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Marcio Soares
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

the ORCHESTRA Study Investigators

Corresponding author

Correspondence to Marcio Soares.

Ethics declarations

Conflicts of interest

JIFS and MS are founders and proprietors of Epimed Solutions®. LPB is an employee of Epimed Solutions®. FGZ has received grant for an investigator-initiated clinical trial from Bactiguard®, Sweden, which is unrelated to the aspects of this work. The other authors report no conflicts of interest to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Dieter E. S. Araya: In Memoriam.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 433 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zampieri, F.G., Salluh, J.I.F., Azevedo, L.C.P. et al. ICU staffing feature phenotypes and their relationship with patients’ outcomes: an unsupervised machine learning analysis. Intensive Care Med 45, 1599–1607 (2019). https://doi.org/10.1007/s00134-019-05790-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00134-019-05790-z

Keywords

Search

Navigation