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

  • Fernando G. Zampieri
  • Jorge I. F. Salluh
  • Luciano C. P. Azevedo
  • Jeremy M. Kahn
  • Lucas P. Damiani
  • Lunna P. Borges
  • William N. Viana
  • Roberto Costa
  • Thiago D. Corrêa
  • Dieter E. S. Araya
  • Marcelo O. Maia
  • Marcus A. Ferez
  • Alexandre G. R. Carvalho
  • Marcos F. Knibel
  • Ulisses O. Melo
  • Marcelo S. Santino
  • Thiago Lisboa
  • Eliana B. Caser
  • Bruno A. M. P. Besen
  • Fernando A. Bozza
  • Derek C. Angus
  • Marcio SoaresEmail author
  • the ORCHESTRA Study Investigators
Original

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.

Keywords

Intensive care unit Outcomes Cluster analysis Nurse autonomy Staffing features ICU organization 

Notes

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.

Compliance with ethical standards

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.

Supplementary material

134_2019_5790_MOESM1_ESM.docx (433 kb)
Supplementary material 1 (DOCX 433 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Fernando G. Zampieri
    • 1
    • 2
  • Jorge I. F. Salluh
    • 1
    • 3
  • Luciano C. P. Azevedo
    • 4
  • Jeremy M. Kahn
    • 5
    • 6
  • Lucas P. Damiani
    • 2
  • Lunna P. Borges
    • 3
  • William N. Viana
    • 7
  • Roberto Costa
    • 8
  • Thiago D. Corrêa
    • 9
  • Dieter E. S. Araya
    • 10
  • Marcelo O. Maia
    • 11
  • Marcus A. Ferez
    • 12
  • Alexandre G. R. Carvalho
    • 13
  • Marcos F. Knibel
    • 14
  • Ulisses O. Melo
    • 15
  • Marcelo S. Santino
    • 16
  • Thiago Lisboa
    • 17
  • Eliana B. Caser
    • 18
  • Bruno A. M. P. Besen
    • 19
  • Fernando A. Bozza
    • 1
    • 20
  • Derek C. Angus
    • 5
    • 6
  • Marcio Soares
    • 1
    Email author
  • the ORCHESTRA Study Investigators
  1. 1.Graduate Program in Translational Medicine, Department of Critical CareD’Or Institute for Research and EducationRio De JaneiroBrazil
  2. 2.Research Institute, HCor-Hospital do CoraçãoSão PauloBrazil
  3. 3.Department of Research and DevelopmentEpimed SolutionsRio De JaneiroBrazil
  4. 4.ICU, Hospital Sírio LibanêsSão PauloBrazil
  5. 5.Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care MedicineUniversity of Pittsburgh School of MedicinePittsburghUSA
  6. 6.Department of Health Policy & ManagementUniversity of Pittsburgh Graduate School of Public HealthPittsburghUSA
  7. 7.ICU, Hospital Copa D’OrRio De JaneiroBrazil
  8. 8.ICU, Hospital Quinta D’OrRio De JaneiroBrazil
  9. 9.Adult ICU, Hospital Israelita Albert EinsteinSão PauloBrazil
  10. 10.ICU, Hospital Santa PaulaSão PauloBrazil
  11. 11.ICU, Hospital Santa Luzia Rede D’Or São Luiz DFBrasíliaBrazil
  12. 12.ICU, Hospital São FranciscoRibeirão PretoBrazil
  13. 13.ICU, UDI HospitalSão LuísBrazil
  14. 14.ICU, Hospital São LucasRio De JaneiroBrazil
  15. 15.ICU, Hospital Estadual Alberto TorresSão GonçaloBrazil
  16. 16.ICU, Hospital Barra D’OrRio De JaneiroBrazil
  17. 17.ICU, Hospital Santa Rita, Santa Casa de Misericórdia de Porto AlegrePorto AlegreBrazil
  18. 18.ICU, Hospital Unimed VitoriaVitoriaBrazil
  19. 19.ICU, Hospital da LuzSão PauloBrazil
  20. 20.Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo CruzRio De JaneiroBrazil

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