Efigure 1A - Patient Flow Diagram (Chart Abstraction and Patient Recruitment

Supplementary Appendix 1

Contents

Acknowledgements

Supplementary Methods

Figures

eFigure 1a - Patient Flow Diagram (Chart abstraction and patient recruitment

eFigure 1b - Longitudinal Cohort Patient Flow Diagram (Patient follow up)

eFigure 2 - Physical Function Among Survivors.

eFigure 3 - Combined Outcome of Mortality and Change of Physical Function from Baseline at Different Time Points in the Longitudinal Cohort by Baseline Physical Function

Tables

eTable 1 - Items Included in the Frailty Index

eTable2a - Logistic Regression Models Predicting Survival at 12 Months After ICU Admission.

eTable 3a - Linear Regression Model Predicting 12-month SF-36 Physical Function Scores Among Survivors.

References

Acknowledgements

We would like to thank Jennifer Korol and other staff at the Clinical Evaluation Research Unit at the Kingston General Hospital for their role in coordinating the study. We would also like to thank the following colleagues who participated in data collection for this study (number in brackets refers to number of patients enrolled at each site):

Hôpital du Sacré-Coeur de Montréal (71), Montréal, Québec: Martin Albert, Patrice Deroy, Caroline Chagnon, Huber Simard.

Hôpital de L’Enfant-Jésus (50), Québec City, Québec: Alexis Turgeon, Chantal Gagné, Marie Thibodeau, Suzy Lavoie, Michèle D’Amours

Hôpital Laval (50), Québec City, Québec: Francois Lellouche, Marie-Claude Ferland, Patrick Dussault.

Royal University Hospital (49), Saskatoon, Saskatchewan: John Reid, Rose Rumanski, Susan Hattori

University of Alberta Hospital (48), Edmonton, Alberta : Sean Bagshaw; Karen O’Hara Banack; Nadia Baig; Sheri Cholin, Samantha Taylor

St. Paul’s Hospital (46), Vancouver, British Columbia: Peter Dodek, Betty Jean Ashley.

Kingston General Hospital (39), Kingston, Ontario: John Muscedere, Susan Fleury, Nicole O’Callaghan.

Ottawa Hospital, Civic Campus (32), Ottawa, Ontario: Richard Hodder, Mary Jo Lewis, Erin Murphy, Tracy McCardle

Royal Alexandra Hospital (31), Edmonton, Alberta: Jim Kutsogiannis, Patrica Thompson, Kirby Scott, Reagan Bartel, Darlene Jossy

Foothills Hospital (29), Calgary, Alberta: Tom Stelfox, Stephanie Todd, Crystal Wilson, Jamie Boyd

St. Boniface Hospital Winnipeg (29), Winnipeg, Manitoba: Allan Garland, Kim Wiebe, Nicole Martens, Wellam Yu Ko

Hôpital Maisonneuve-Rosemont (24), Montréal, Québec: Stéphane Ahern, Johanne Harvey, Brigitte Beauregard

Mount Sinai Hospital (19), Toronto, Ontario: Sangeeta Mehta, Maedean Brown, Scott Kim, Alex Day.

Royal Columbian (17), New Westminster, BC: Sean Keenan, Miroslav Svetik, Jacquie Stevenson

Sunnybrook Health Sciences Centre (17), Toronto, Ontario: Rob Fowler, Nicole Marinoff, Derek Parsotam, AshwatiRaghunath

St. Michael’s Hospital MSICU/NTICU(14/0), Toronto, Ontario: Karen Burns, Orla Smith, Yoon Lee, Kerri Porretta, Julia Lee, Marlene Santos

Peter Lougheed Hospital (11), Calgary, Alberta: Tom Stelfox, Cheryl Dielissen, Stephanie Todd

Toronto General Hospital (11), Toronto, Ontario: Neil Lazar; Paulina Farias, Andrea Matte, Emily Stern

St. Joseph’s Healthcare (11), Hamilton, Ontario: Deborah Cook, Ellen McDonald, France Clarke, Nicole Zytaruk, Christina Maria Katsios

Victoria General Hospital (5), Victoria, British Columbia: Gordon Wood, Fiona Auld, Leslie Atkins, Peggy Leonard

Royal Jubilee Hospital (5), Victoria, British Columbia: Gordon Wood, Fiona Auld, Leslie Atkins, Peggy Leonard

Winnipeg Health Sciences Centre (2), Winnipeg, Manitoba: Allan Garland, Wendy Janz, Mandi Siddiqui.

Supplementary Methods

From proxy responses to the comprehensive geriatric assessment, we calculated the Frailty Index. The Frailty Index identified 43 deficits at baseline (Appendix eTable 1), each coded as 0 (absent), 1 (present), or 0·5 (where intermediate values were possible).1The score was calculated as the cumulative proportion of deficits present (minimum score 0; maximum score 1·0). The score was considered a continuous variable for modeling but was then graded as fit (> 0 and ≤ 0·2), mild frailty (> 0·2 and < 0·4) or moderate/severe frailty (≥ 0·4) for descriptive purposes.2At baseline, we also recorded patients’ baseline physical function (see Short Form-36 (SF-36) details below), and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) using proxy responses from family members. The IQCODE, an unweighted average of 16 items, identifies changes in patients’ cognitive performance; this instrument has high reliability and good validity.3-5 A score > 3 indicates that some cognitive decline has occurred.3

In both the hospital and longitudinal cohorts, variables collected from hospital records at the time of ICU admission were age, sex, admission diagnosis, admission type (medical vs. surgical), Acute Physiology and Chronic Health Evaluation (APACHE) II score6, Functional Comorbidity Index7, and Charlson Comorbidity Index.8We also measured multiple organ dysfunction using sequential organ failure assessment (SOFA) scores at admission and daily in ICU.9All patients were followed prospectively to document duration of mechanical ventilation, and ICU and hospital lengths of stay.

Figures

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eFigure 1a - Patient Flow Diagram (Chart abstraction and patient recruitment)

eFigure 1b - Longitudinal Cohort Patient Flow Diagram (Patient follow up)

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eFigure 2 - Physical Function Among Survivors.

Panel A: SF-36 Physical Functioning Domain

Panel B: SF-36 Physical Component Score

Legend: The average age-sex matched Canadian norms are depicted by the red dotted line.The differences between values for the patients and Canadian norms are statistically significant (at <0.05 level) at all time points (month 3,6,9 and 12) and this applies to both PF and PCS.

eFigure 3 - Combined Outcome of Mortality and Change of Physical Function from Baseline at Different Time Points in the Longitudinal Cohort by Baseline Physical Function

Tables

eTable 1 - Items Included in the Frailty Index

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eTable2a - Logistic Regression Models Predicting Survival at 12 Months After ICU Admission.

eTable 3a - Linear RegressionModel Predicting 12-month SF-36 Physical Function Scores Among Survivors.

Predictors with p≤0.05 are in bold.

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References

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9.Moreno R, Vincent JL, Matos R, et al. The use of maximum SOFA score to quantify organ dysfunction/failure in intensive care. Results of a prospective multicenter study. Working group on Sepsis related Problems of the ESICM. Intensive Care Med 1999; 25:686–96.

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