Skip to main content

Advertisement

SpringerLink
Log in

Predictors of death and new disability after critical illness: a multicentre prospective cohort study

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

Abstract

Purpose

This study aimed to determine the prevalence and predictors of death or new disability following critical illness.

Methods

Prospective, multicentre cohort study conducted in six metropolitan intensive care units (ICU). Participants were adults admitted to the ICU who received more than 24 h of mechanical ventilation. The primary outcome was death or new disability at 6 months, with new disability defined by a 10% increase in the WHODAS 2.0.

Results

Of 628 patients with the primary outcome available (median age of 62 [49–71] years, 379 [61.0%] had a medical admission and 370 (58.9%) died or developed new disability by 6 months. Independent predictors of death or new disability included age [OR 1.02 (1.01–1.03), P = 0.001], higher severity of illness (APACHE III) [OR 1.02 (1.01–1.03), P < 0.001] and admission diagnosis. Compared to patients with a surgical admission diagnosis, patients with a cardiac arrest [OR (95% CI) 4.06 (1.89–8.68), P < 0.001], sepsis [OR (95% CI) 2.43 (1.32–4.47), P = 0.004], or trauma [OR (95% CI) 6.24 (3.07–12.71), P < 0.001] diagnosis had higher odds of death or new disability, while patients with a lung transplant [OR (95% CI) 0.21 (0.07–0.58), P = 0.003] diagnosis had lower odds. A model including these three variables had good calibration (Brier score 0.20) and acceptable discriminative power with an area under the receiver operating characteristic curve of 0.76 (95% CI 0.72–0.80).

Conclusion

Less than half of all patients mechanically ventilated for more than 24 h were alive and free of new disability at 6 months after admission to ICU. A model including age, illness severity and admission diagnosis has acceptable discriminative ability to predict death or new disability at 6 months.

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

Similar content being viewed by others

Data availability

Partial data set sharing according to individual requests for data access. Requests will be considered by the study's Management Committee. Requests for data sharing to be made to anzicrc@monash.edu and the corresponding author, carol.hodgson@monash.edu.

Code availability

Statistical code is available on request to anzicrc@monash.edu and the corresponding author, carol.hodgson@monash.edu.

References

  1. Meiring C, Dixit A, Harris S, MacCallum NS, Brealey DA, Watkinson PJ, Jones A, Ashworth S, Beale R, Brett SJ, Singer M, Ercole A (2018) Optimal intensive care outcome prediction over time using machine learning. PLoS ONE 13:e0206862

    Article  Google Scholar 

  2. Ball IM, Bagshaw SM, Burns KE, Cook DJ, Day AG, Dodek PM, Kutsogiannis DJ, Mehta S, Muscedere JG, Stelfox HT, Turgeon AF, Wells GA, Stiell IG (2016) A clinical prediction tool for hospital mortality in critically ill elderly patients. J Crit Care 35:206–212

    Article  Google Scholar 

  3. Kerckhoffs MC, Brinkman S, de Keizer N, Soliman IW, de Lange DW, van Delden JJM, van Dijk D (2020) The performance of acute versus antecedent patient characteristics for 1-year mortality prediction during intensive care unit admission: a national cohort study. Critical care (London, England) 24:330

    Article  Google Scholar 

  4. Krinsley JS, Wasser T, Kang G, Bagshaw SM (2017) Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients. Critical care (London, England) 21:110

    Article  Google Scholar 

  5. Marshall DC, Hatch RA, Gerry S, Young JD, Watkinson P (2020) Conditional survival with increasing duration of icu admission: an observational study of three intensive care databases. Crit Care Med 48:91–97

    Article  Google Scholar 

  6. Herridge MS (2007) Long-term outcomes after critical illness: past, present, future. Curr Opin Crit Care 13:473–475

    Article  Google Scholar 

  7. Herridge MS, Chu LM, Matte A, Tomlinson G, Chan L, Thomas C, Friedrich JO, Mehta S, Lamontagne F, Levasseur M, Ferguson ND, Adhikari NK, Rudkowski JC, Meggison H, Skrobik Y, Flannery J, Bayley M, Batt J, Santos CD, Abbey SE, Tan A, Lo V, Mathur S, Parotto M, Morris D, Flockhart L, Fan E, Lee CM, Wilcox ME, Ayas N, Choong K, Fowler R, Scales DC, Sinuff T, Cuthbertson BH, Rose L, Robles P, Burns S, Cypel M, Singer L, Chaparro C, Chow CW, Keshavjee S, Brochard L, Hebert P, Slutsky AS, Marshall JC, Cook D, Cameron JI (2016) The RECOVER program: disability risk groups and 1-year outcome after 7 or more days of mechanical ventilation. Am J Respir Crit Care Med 194:831–844

    Article  Google Scholar 

  8. Semler MW, Bernard GR, Aaron SD, Angus DC, Biros MH, Brower RG, Calfee CS, Colantuoni EA, Ferguson ND, Gong MN, Hopkins RO, Hough CL, Iwashyna TJ, Levy BD, Martin TR, Matthay MA, Mizgerd JP, Moss M, Needham DM, Self WH, Seymour CW, Stapleton RD, Thompson BT, Wunderink RG, Aggarwal NR, Reineck LA (2020) Identifying clinical research priorities in adult pulmonary and critical care: NHLBI working group report. Am J Respir Crit Care Med 202:511–523

    Article  CAS  Google Scholar 

  9. Needham DM, Davidson J, Cohen H, Hopkins RO, Weinert C, Wunsch H, Zawistowski C, Bemis-Dougherty A, Berney SC, Bienvenu OJ, Brady SL, Brodsky MB, Denehy L, Elliott D, Flatley C, Harabin AL, Jones C, Louis D, Meltzer W, Muldoon SR, Palmer JB, Perme C, Robinson M, Schmidt DM, Scruth E, Spill GR, Storey CP, Render M, Votto J, Harvey MA (2012) Improving long-term outcomes after discharge from intensive care unit: report from a stakeholders’ conference. Crit Care Med 40:502–509

    Article  Google Scholar 

  10. Haines KJ, Hibbert E, McPeake J, Anderson BJ, Bienvenu OJ, Andrews A, Brummel NE, Ferrante LE, Hopkins RO, Hough CL, Jackson J, Mikkelsen ME, Leggett N, Montgomery-Yates A, Needham DM, Sevin CM, Skidmore B, Still M, van Smeden M, Collins GS, Harhay MO (2020) Prediction models for physical, cognitive, and mental health impairments after critical illness: a systematic review and critical appraisal. Crit Care Med 48:1871–1880

    Article  Google Scholar 

  11. Lamas DJ, Lakin JR, Trindade AJ, Courtwright A, Goldberg H (2018) Looking beyond mortality in transplantation outcomes. N Engl J Med 379:1889–1891

    Article  Google Scholar 

  12. Higgins AM, Serpa Neto A, Bailey M, Barrett J, Bellomo R, Cooper DJ, Gabbe B, Linke N, Myles PS, Paton M, Philpot S, Shulman M, Young M, Hodgson CL (2021) The psychometric properties and minimal clinically important difference for disability assessment using WHODAS 2.0 in critically ill patients. Crit Care Resuscit 23:103–112

    Google Scholar 

  13. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, Mitnitski A (2005) A global clinical measure of fitness and frailty in elderly people. CMAJ 173:489–495

    Article  Google Scholar 

  14. Bagshaw SM, Stelfox HT, McDermid RC, Rolfson DB, Tsuyuki RT, Baig N, Artiuch B, Ibrahim Q, Stollery DE, Rokosh E, Majumdar SR (2014) Association between frailty and short- and long-term outcomes among critically ill patients: a multicentre prospective cohort study. CMAJ 186:E95-102

    Article  Google Scholar 

  15. Ustun TB, Chatterji S, Kostanjsek N, Rehm J, Kennedy C, Epping-Jordan J, Saxena S, von Korff M, Pull C (2010) Developing the world health organization disability assessment schedule 2.0. Bull World Health Organ 88:815–823

    Article  Google Scholar 

  16. Ustun TB, Kostanjesek N, Chatterji S, Rehm J. Measuring Health and Disability: Manual for WHO Disability Assessment Schedule (WHODAS 2.0). Geneva: World Health Organization; 2010

  17. Wilson ME, Barwise A, Heise KJ, Loftsgard TO, Dziadzko M, Cheville A, Majzoub A, Novotny PJ, Gajic O, Biehl M (2018) Long-term return to functional baseline after mechanical ventilation in the ICU. Crit Care Med 46:562–569

    Article  Google Scholar 

  18. Denehy L, Berney S, Whitburn L, Edbrooke L (2012) Quantifying physical activity levels of survivors of intensive care: a prospective observational study. Phys Ther 92:1507–1517

    Article  Google Scholar 

  19. Herridge MS, Cheung AM, Tansey CM, Matte-Martyn A, Diaz-Granados N, Al-Saidi F, Cooper AB, Guest CB, Mazer CD, Mehta S, Stewart TE, Barr A, Cook D, Slutsky AS (2003) One-year outcomes in survivors of the acute respiratory distress syndrome. N Engl J Med 348:683–693

    Article  Google Scholar 

  20. Ohtake PJ, Lee AC, Scott JC, Hinman RS, Ali NA, Hinkson CR, Needham DM, Shutter L, Smith-Gabai H, Spires MC, Thiele A, Wiencek C, Smith JM (2018) Physical impairments associated with post-intensive care syndrome: systematic review based on the world health organization’s international classification of functioning, disability and health framework. Phys Ther 98:631–645

    Article  Google Scholar 

  21. van der Schaaf M, Beelen A, Dongelmans DA, Vroom MB, Nollet F (2009) Functional status after intensive care: a challenge for rehabilitation professionals to improve outcome. J Rehabil Med 41:360–366

    Article  Google Scholar 

  22. Rabiee A, Nikayin S, Hashem MD, Huang M, Dinglas VD, Bienvenu OJ, Turnbull AE, Needham DM (2016) Depressive symptoms after critical illness: a systematic review and meta-analysis. Crit Care Med 44:1744–1753

    Article  Google Scholar 

  23. Choi J, Tate JA, Rogers MA, Donahoe MP, Hoffman LA (2016) Depressive symptoms and anxiety in intensive care unit (ICU) survivors after ICU discharge. Heart Lung 45:140–146

    Article  CAS  Google Scholar 

  24. Pandharipande PP, Girard TD, Jackson JC, Morandi A, Thompson JL, Pun BT, Brummel NE, Hughes CG, Vasilevskis EE, Shintani AK, Moons KG, Geevarghese SK, Canonico A, Hopkins RO, Bernard GR, Dittus RS, Ely EW (2013) Long-term cognitive impairment after critical illness. N Engl J Med 369:1306–1316

    Article  CAS  Google Scholar 

  25. Cuthbertson BH, Wunsch H (2016) Long-term outcomes after critical illness. the best predictor of the future is the past. Am J Respir Crit Care Med 194:132–134

    Article  Google Scholar 

  26. Garland A, Olafson K, Ramsey CD, Yogendran M, Fransoo R (2014) Distinct determinants of long-term and short-term survival in critical illness. Intensive Care Med 40:1097–1105

    Article  Google Scholar 

  27. Iwashyna TJ (2012) Trajectories of recovery and dysfunction after acute illness, with implications for clinical trial design. Am J Respir Crit Care Med 186:302–304

    Article  Google Scholar 

  28. Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, Sirio CA, Murphy DJ, Lotring T, Damiano A et al (1991) The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 100:1619–1636

    Article  CAS  Google Scholar 

  29. Moreno RP, Metnitz PG, Almeida E, Jordan B, Bauer P, Campos RA, Iapichino G, Edbrooke D, Capuzzo M, Le Gall JR, Investigators S (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

    Article  Google Scholar 

  30. Higgins TL, Teres D, Copes WS, Nathanson BH, Stark M, Kramer AA (2007) Assessing contemporary intensive care unit outcome: an updated Mortality Probability Admission Model (MPM0-III). Crit Care Med 35:827–835

    Article  Google Scholar 

  31. Johnson AE, Ghassemi MM, Nemati S, Niehaus KE, Clifton DA, Clifford GD (2016) Machine learning and decision support in critical care. Proc IEEE Inst Electr Electron Eng 104:444–466

    Article  Google Scholar 

  32. Marafino BJ, Park M, Davies JM, Thombley R, Luft HS, Sing DC, Kazi DS, DeJong C, Boscardin WJ, Dean ML, Dudley RA (2018) Validation of prediction models for critical care outcomes using natural language processing of electronic health record data. JAMA Netw Open 1:e185097

    Article  Google Scholar 

  33. Brummel NE, Bell SP, Girard TD, Pandharipande PP, Jackson JC, Morandi A, Thompson JL, Chandrasekhar R, Bernard GR, Dittus RS, Gill TM, Ely EW (2017) Frailty and subsequent disability and mortality among patients with critical illness. Am J Respir Crit Care Med 196:64–72

    Article  Google Scholar 

  34. Takaoka A, Heels-Ansdell D, Cook DJ, Kho ME (2021) The association between frailty and short-term outcomes in an intensive care unit rehabilitation trial: an exploratory analysis. J Frailty Aging 10:49–55

    CAS  PubMed  Google Scholar 

  35. Baldwin MR (2015) Measuring and predicting long-term outcomes in older survivors of critical illness. Minerva Anestesiol 81:650–661

    CAS  PubMed  Google Scholar 

  36. Denehy L, Hough CL (2017) Critical illness, disability, and the road home. Intensive Care Med 43:1881–1883

    Article  Google Scholar 

Download references

Acknowledgement

PREDICT Management Committee: C.L. Hodgson (Chair), M. Bailey, J.Barrett, R. Bellomo, D.J. Cooper, B.J. Gabbe, A.M. Higgins, N. Linke, P.S. Myles, M. Paton, S. Philpot, M. Shulman, M. Young

PREDICT Site Investigators (alphabetically by institution and all in Victoria, Australia): Alfred Hospital – K. Collins, D.J. Cooper, B. Jomon, R. Thompson, M. Young; Austin Hospital - R. Bellomo, M. Cousinery, G. Eastwood, R. Robbins; Cabrini Hospital - S. Philpot; S. Simpson; Dandenong Hospital – M. Paton; Epworth Hospital - J.Barrett; G. Hanlon; Monash Medical Centre - M. Paton, Y. Shehabi, C. Walker.

PREDICT Outcome Assessors (alphabetically): School of Public Health and Preventive Medicine, Monash University - P.Buckingham, L. Marsh, D. Sandler

PREDICT Coordinating Centre Staff: The Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventive Medicine, Monash University, Melbourne - M. Bailey, D.J. Cooper, A.M. Higgins, C. Hodgson, N. Linke, B. Fulcher, A. Serpa Neto.

Funding

The PREDICT Study was funded by the National Health and Medical Research Council of Australia (GNT1132976). Professor Hodgson is supported by a Heart Foundation Fellowship and a National Health and Medical Research Council Investigator Grant (GNT1173271). Professor Gabbe is supported by an Australian Research Council Future Fellowship (FT170100048). The funding agencies had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Author information

Authors and Affiliations

  1. Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd, Melbourne, VIC, 3004, Australia

    A. M. Higgins, A. Serpa Neto, M. Bailey, R. Bellomo, D. J. Cooper, N. Linke, M. Paton & C. L. Hodgson

  2. Department of Critical Care, The University of Melbourne, Melbourne, VIC, Australia

    A. Serpa Neto, M. Bailey & R. Bellomo

  3. Department of Intensive Care, Austin Health, Melbourne, VIC, Australia

    A. Serpa Neto & R. Bellomo

  4. Department of Critical Care Medicine, Hospital Israelita Albert Einstein, Sao Paulo, Brazil

    A. Serpa Neto

  5. Intensive Care Unit, Epworth Healthcare, Melbourne, VIC, Australia

    J. Barrett

  6. Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia

    J. Barrett

  7. Department of Intensive Care and Hyperbaric Medicine, The Alfred, Melbourne, VIC, Australia

    D. J. Cooper, M. Young & C. L. Hodgson

  8. School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia

    B. J. Gabbe, P. S. Myles & M. Shulman

  9. Department of Anaesthesiology and Perioperative Medicine, The Alfred, Melbourne, VIC, Australia

    P. S. Myles & M. Shulman

  10. Department of Physiotherapy, Monash Health, Melbourne, VIC, Australia

    M. Paton

  11. Intensive Care Unit, Cabrini Health, Melbourne, VIC, Australia

    S. Philpot

Authors
  1. A. M. Higgins
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Serpa Neto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Bailey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. Barrett
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. R. Bellomo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. D. J. Cooper
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. B. J. Gabbe
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. N. Linke
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. P. S. Myles
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. M. Paton
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. S. Philpot
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. M. Shulman
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. M. Young
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. C. L. Hodgson
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

on behalf of the PREDICT Study Investigators

Contributions

AMH, ASN and CLH had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: AMH, CLH, MB, JB, RB, DJC, BJG, PSM, SP, MS. Acquisition, analysis, or interpretation of data: AMH, CLH, ASN, MB, JB, RB, DJC, BJG, NL, PSL, MP, SP, MS, MY. Drafting of the manuscript: AMH, CLH, ASN, MB. Critical revision of the manuscript for important intellectual content:  AMH, CLH, ASN, MB, JB, RB, DJC, BJG, NL, PSL, MP, SP, MS, MY. Statistical analysis: AMH, ASN, MB. Obtained funding: AMH, CLH, MB, RB, DJC, PSM, MS. Administrative, technical, or material support: AMH, CLH, ASN, NL. Supervision: CLH, RB, DJC, BJG, PSM, MS.

Corresponding author

Correspondence to C. L. Hodgson.

Ethics declarations

Conflicts of interest

The authors have disclosed that they do not have any potential conflicts of interest.

Ethics approval

Ethics committee approval, including a waiver of consent for hospital data collection and opt-out consent for follow-up, was obtained at each site.

Additional information

Publisher's Note

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

The members of the PREDICT study group are listed in the Acknowledgement Section

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 4929 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Higgins, A.M., Neto, A.S., Bailey, M. et al. Predictors of death and new disability after critical illness: a multicentre prospective cohort study. Intensive Care Med 47, 772–781 (2021). https://doi.org/10.1007/s00134-021-06438-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00134-021-06438-7

Keywords

Search

Navigation