Intensive Care Medicine

, Volume 43, Issue 1, pp 39–47 | Cite as

The effects of performance status one week before hospital admission on the outcomes of critically ill patients

  • Fernando G. Zampieri
  • Fernando A. Bozza
  • Giulliana M. Moralez
  • Débora D. S. Mazza
  • Alexandre V. Scotti
  • Marcelo S. Santino
  • Rubens A. B. Ribeiro
  • Edison M. Rodrigues Filho
  • Maurício M. Cabral
  • Marcelo O. Maia
  • Patrícia S. D’Alessandro
  • Sandro V. Oliveira
  • Márcia A. M. Menezes
  • Eliana B. Caser
  • Roberto S. Lannes
  • Meton S. Alencar Neto
  • Maristela M. Machado
  • Marcelo F. Sousa
  • Jorge I. F. Salluh
  • Marcio SoaresEmail author



To assess the impact of performance status (PS) impairment 1 week before hospital admission on the outcomes in patients admitted to intensive care units (ICU).


Retrospective cohort study in 59,693 patients (medical admissions, 67 %) admitted to 78 ICUs during 2013. We classified PS impairment according to the Eastern Cooperative Oncology Group (ECOG) scale in absent/minor (PS = 0–1), moderate (PS = 2) or severe (PS = 3–4). We used univariate and multivariate logistic regression analyses to investigate the association between PS impairment and hospital mortality.


PS impairment was moderate in 17.3 % and severe in 6.9 % of patients. The hospital mortality was 14.4 %. Overall, the worse the PS, the higher the ICU and hospital mortality and length of stay. In addition, patients with worse PS were less frequently discharged home. PS impairment was associated with worse outcomes in all SAPS 3, Charlson Comorbidity Index and age quartiles as well as according to the admission type. Adjusting for other relevant clinical characteristics, PS impairment was associated with higher hospital mortality (odds-ratio (OR) = 1.96 (95 % CI 1.63–2.35), for moderate and OR = 4.22 (3.32–5.35), for severe impairment). The effects of PS on the outcome were particularly relevant in the medium range of severity-of-illness. These results were consistent in the subgroup analyses. However, adding PS impairment to the SAPS 3 score improved only slightly its discriminative capability.


PS impairment was associated with worse outcomes independently of other markers of chronic health status, particularly for patients in the medium range of severity of illness.


Performance status Critical care Outcomes Markers of baseline health status 



This study was supported by the National Council for Scientific and Technological Development (CNPq) (Grant No 304240/2014-1), 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.

Compliance with ethical standards

Conflicts of interest

Dr. Soares and Dr. Salluh are founders and equity shareholders of Epimed Solutions®, which markets the Epimed Monitor System®, a cloud-based software for ICU management and benchmarking. The other authors declare that they have no conflict of interest.

Supplementary material

134_2016_4563_MOESM1_ESM.docx (295 kb)
Supplementary material 1 (DOCX 294 kb)


  1. 1.
    Moreno RP, Metnitz PGH, Almeida E et al (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 31:1345–1355. doi: 10.1007/s00134-005-2763-5 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Salluh JIF, Soares M (2014) ICU severity of illness scores: APACHE, SAPS and MPM. Curr Opin Crit Care 20:557–565. doi: 10.1097/MCC.0000000000000135 CrossRefPubMedGoogle Scholar
  3. 3.
    Zimmerman JE, Kramer AA, McNair DS, Malila FM (2006) Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today’s critically ill patients. Crit Care Med 34:1297–1310. doi: 10.1097/01.CCM.0000215112.84523.F0 CrossRefPubMedGoogle Scholar
  4. 4.
    Higgins TL, Teres D, Copes WS et al (2007) Assessing contemporary intensive care unit outcome: an updated Mortality Probability Admission Model (MPM0-III). Crit Care Med 35:827–835. doi: 10.1097/01.CCM.0000257337.63529.9F CrossRefPubMedGoogle Scholar
  5. 5.
    Zampieri FG, Colombari F (2014) The impact of performance status and comorbidities on the short-term prognosis of very elderly patients admitted to the ICU. BMC Anesthesiol 14:59. doi: 10.1186/1471-2253-14-59 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Rosolem MM, Rabello LSCF, Lisboa T et al (2012) Critically ill patients with cancer and sepsis: clinical course and prognostic factors. J Crit Care 27:301–307. doi: 10.1016/j.jcrc.2011.06.014 CrossRefPubMedGoogle Scholar
  7. 7.
    Torres VBL, Azevedo LCP, Silva UVA et al (2015) Sepsis-associated outcomes in critically ill patients with malignancies. Ann Am Thorac Soc 12:1185–1192. doi: 10.1513/AnnalsATS.201501-046OC PubMedGoogle Scholar
  8. 8.
    Bagshaw SM, Stelfox HT, McDermid RC et al (2014) Association between frailty and short- and long-term outcomes among critically ill patients: a multicentre prospective cohort study. CMAJ 186:E95–E102. doi: 10.1503/cmaj.130639 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Park C-M, Koh Y, Jeon K et al (2014) Impact of Eastern Cooperative Oncology Group Performance Status on hospital mortality in critically ill patients. J Crit Care 29:409–413. doi: 10.1016/j.jcrc.2014.01.016 CrossRefPubMedGoogle Scholar
  10. 10.
    Azoulay E, Mokart D, Pène F et al (2013) Outcomes of critically ill patients with hematologic malignancies: prospective multicenter data from France and Belgium–a groupe de recherche respiratoire en réanimation onco-hématologique study. J Clin Oncol 31:2810–2818. doi: 10.1200/JCO.2012.47.2365 CrossRefPubMedGoogle Scholar
  11. 11.
    Dolgin NH, Martins PNA, Movahedi B et al (2016) Functional status predicts postoperative mortality after liver transplantation. Clin Transplant. doi: 10.1111/ctr.12808 PubMedGoogle Scholar
  12. 12.
    Soares M, Bozza FA, Angus DC et al (2015) Organizational characteristics, outcomes, and resource use in 78 Brazilian intensive care units: the ORCHESTRA study. Intensive Care Med 41:2149–2160. doi: 10.1007/s00134-015-4076-7 CrossRefPubMedGoogle Scholar
  13. 13.
    Vincent JL, Moreno R, Takala J et al (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 22:707–710CrossRefPubMedGoogle Scholar
  14. 14.
    Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40:373–383CrossRefPubMedGoogle Scholar
  15. 15.
    Oken MM, Creech RH, Tormey DC et al (1982) Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol 5:649–655CrossRefPubMedGoogle Scholar
  16. 16.
    Stekhoven DJ, Bühlmann P (2012) MissForest–non-parametric missing value imputation for mixed-type data. Bioinformatics 28:112–118. doi: 10.1093/bioinformatics/btr597 CrossRefPubMedGoogle Scholar
  17. 17.
    Vesin A, Azoulay E, Ruckly S et al (2013) Reporting and handling missing values in clinical studies in intensive care units. Intensive Care Med 39:1396–1404. doi: 10.1007/s00134-013-2949-1 CrossRefPubMedGoogle Scholar
  18. 18.
    Pencina MJ, D’Agostino RB, Demler OV (2012) Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models. Stat Med 31:101–113. doi: 10.1002/sim.4348 CrossRefPubMedGoogle Scholar
  19. 19.
    Chirag R, Parikh HT (2014) Key concepts and limitations of statistical methods for evaluating biomarkers of kidney disease. J Am Soc Nephrol 25:1621CrossRefGoogle Scholar
  20. 20.
    Steyerberg EW, Vickers AJ, Cook NR et al (2010) Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21:128–138. doi: 10.1097/EDE.0b013e3181c30fb2 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Kerr KF, Wang Z, Janes H et al (2014) Net reclassification indices for evaluating risk prediction instruments: a critical review. Epidemiology 25:114–121. doi: 10.1097/EDE.0000000000000018 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  23. 23.
    Harrell FE Jr (2001) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer-Verlag, New York. doi: 10.1007/978-1-4757-3462-1 CrossRefGoogle Scholar
  24. 24.
    Wickham H (2009) ggplot2: elegant graphics for data analysis. Springer, New YorkCrossRefGoogle Scholar
  25. 25.
    Prigerson HG, Bao Y, Shah MA et al (2015) Chemotherapy use, performance status, and quality of life at the end of life. JAMA Oncol 1:778–784. doi: 10.1001/jamaoncol.2015.2378 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Soares M, Toffart A-C, Timsit J-F et al (2014) Intensive care in patients with lung cancer: a multinational study. Ann Oncol 25:1829–1835. doi: 10.1093/annonc/mdu234 CrossRefPubMedGoogle Scholar
  27. 27.
    Soares M, Caruso P, Silva E et al (2010) Characteristics and outcomes of patients with cancer requiring admission to intensive care units: a prospective multicenter study. Crit Care Med 38:9–15. doi: 10.1097/CCM.0b013e3181c0349e CrossRefPubMedGoogle Scholar
  28. 28.
    Cook NR (2007) Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 115:928–935. doi: 10.1161/CIRCULATIONAHA.106.672402 CrossRefPubMedGoogle Scholar
  29. 29.
    Vickers AJ, Cronin AM, Begg CB (2011) One statistical test is sufficient for assessing new predictive markers. BMC Med Res Methodol 11:13. doi: 10.1186/1471-2288-11-13 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Buccheri G, Ferrigno D, Tamburini M (1996) Karnofsky and ECOG performance status scoring in lung cancer: a prospective, longitudinal study of 536 patients from a single institution. Eur J Cancer 32A:1135–1141CrossRefPubMedGoogle Scholar
  31. 31.
    Soares M, Salluh JIF, Spector N, Rocco JR (2005) Characteristics and outcomes of cancer patients requiring mechanical ventilatory support for >24 hrs. Crit Care Med 33:520–526CrossRefPubMedGoogle Scholar
  32. 32.
    Forte DN, Vincent JL, Velasco IT, Park M (2012) Association between education in EOL care and variability in EOL practice: a survey of ICU physicians. Intensive Care Med 38:404–412. doi: 10.1007/s00134-011-2400-4 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg and ESICM 2016

Authors and Affiliations

  • Fernando G. Zampieri
    • 1
    • 2
  • Fernando A. Bozza
    • 3
    • 4
  • Giulliana M. Moralez
    • 3
    • 5
  • Débora D. S. Mazza
    • 6
  • Alexandre V. Scotti
    • 7
  • Marcelo S. Santino
    • 8
  • Rubens A. B. Ribeiro
    • 9
  • Edison M. Rodrigues Filho
    • 10
  • Maurício M. Cabral
    • 11
  • Marcelo O. Maia
    • 12
  • Patrícia S. D’Alessandro
    • 13
  • Sandro V. Oliveira
    • 14
  • Márcia A. M. Menezes
    • 15
  • Eliana B. Caser
    • 16
  • Roberto S. Lannes
    • 17
  • Meton S. Alencar Neto
    • 18
  • Maristela M. Machado
    • 19
  • Marcelo F. Sousa
    • 20
  • Jorge I. F. Salluh
    • 3
    • 21
  • Marcio Soares
    • 3
    • 21
    Email author
  1. 1.Research InstituteHospital do Coração (HCor)São PauloBrazil
  2. 2.Intensive Care UnitHospital Alemão Oswaldo CruzSão PauloBrazil
  3. 3.Department of Critical CareD’Or Institute for Research and EducationBotafogo, Rio de JaneiroBrazil
  4. 4.Instituto Nacional de Infectologia Evandro Chagas, Instituto Oswaldo Cruz-FiocruzRio de JaneiroBrazil
  5. 5.Intensive Care UnitHospital Estadual Getúlio VargasRio de JaneiroBrazil
  6. 6.Intensive Care UnitHospital São Luiz–Unidade JabaquaraSão PauloBrazil
  7. 7.Intensive Care Unit Hospital Israelita Albert SabinRio de JaneiroBrazil
  8. 8.Intensive Care Unit Hospital Barra D’OrRio de JaneiroBrazil
  9. 9.Intensive Care Unit Hospital AnchietaTaguatingaBrazil
  10. 10.Complexo Hospitalar Santa Casa de Misericórdia de Porto AlegrePorto AlegreBrazil
  11. 11.Intensive Care UnitHospital São MarcosRecifeBrazil
  12. 12.Intensive Care UnitHospital Santa LuziaBrasíliaBrazil
  13. 13.Intensive Care Unit Clínica São VicenteRio de JaneiroBrazil
  14. 14.Intensive Care UnitHospital BanguRio de JaneiroBrazil
  15. 15.Intensive Care UnitHospital Oeste D’OrRio de JaneiroBrazil
  16. 16.Intensive Care UnitHospital Unimed VitóriaVitóriaBrazil
  17. 17.Intensive Care UnitHospital Municipal Souza AguiarRio de JaneiroBrazil
  18. 18.Intensive Care UnitHospital Regional do CaririJuazeiro do NorteBrazil
  19. 19.Intensive Care UnitHospital Agenor PaivaSalvadorBrazil
  20. 20.Intensive Care UnitSanta Casa de Caridade de DiamantinaDiamantinaBrazil
  21. 21.Postgraduate Program of Internal MedicineUniversidade Federal do Rio de JaneiroRio de JaneiroBrazil

Personalised recommendations