Supplemental Digital Content
Prone positioning and survival in mechanically ventilated patients with COVID-19- related respiratory failure
Kusum S. Mathews, MD, MPH, MSCR; Howard Soh, MD; Shahzad Shaefi, MD, MPH;
Wei Wang, PhD; Sonali Bose, MD; Steven Coca, MD; Shruti Gupta, MD, MPH; Salim S.
Hayek, MD; Anand Srivastava, MD, MPH; Samantha K. Brenner, MD, MPH; Jared Radbel, MD; Adam Green, MD, MBA; Anne Sutherland, MD; Amanda Leonberg-Yoo, MD, MS; Alexandre Shehata, MD; Edward Schenck, MD; Samuel A.P. Short, BA;
Miguel A. Hernán, MD, DrPH; Lili Chan, MD, MSCR; David E. Leaf, MD, MMSc; for the STOP-COVID Investigators
Table of Contents
1. STOP-COVID Investigators List 2. Supplementary Methods
3. Supplemental Table 1. List of participating sites with STOP-COVID patients who underwent prone positioning ventilation during the first 14 days of ICU admission 4. Supplemental Table 2. Baseline characteristics before and after applying inverse
probability of treatment weighting
5. Supplemental Table 3. Multivariable Cox model for death among patients included in the target trial emulation of early proning initiation versus non-early proning initiation 6. Supplemental Figure 1: Timing of prone positioning ventilation initiation over the first
14 days of ICU admission for all patients requiring invasive mechanical ventilation and moderate-to-severe hypoxemia within the first two days of ICU admission.
7. References
1. STOP-COVID Investigators List STOP-COVID INVESTIGATORS
Baylor College of Medicine: Carl P. Walther*, Samaya J. Anumudu
Baylor University Medical Center: Justin Arunthamakun*, Kathleen F. Kopecky, Gregory P. Milligan, Peter A. McCullough, Thuy-Duyen Nguyen
Beth Israel Deaconess Medical Center: Shahzad Shaefi*, Megan L. Krajewski, Sidharth Shankar, Ameeka Pannu, Juan D. Valencia
Boston Medical Center: Sushrut S. Waikar*, Zoe A. Kibbelaar
Cook County Health: Ambarish M. Athavale*, Peter Hart, Shristi Upadhyay, Ishaan Vohra
Cooper University Health Care: Adam Green*, Jean-Sebastien Rachoin, Christa A.
Schorr, Lisa Shea
Duke University Medical Center: Daniel L. Edmonston*, Christopher L. Mosher
Hackensack Meridian Health Mountainside Medical Center: Alexandre M. Shehata*, Zaza Cohen, Valerie Allusson, Gabriela Bambrick-Santoyo, Noor ul aain Bhatti, Bijal Mehta, Aquino Williams
Hackensack Meridian Health Hackensack University Medical Center: Samantha K.
Brenner*, Patricia Walters, Ronaldo C. Go, Keith M. Rose
Harvard T.H. Chan School of Public Health: Miguel A. Hernán Harvard University: Rebecca Lisk, Amy M. Zhou, Ethan C. Kim
Icahn School of Medicine at Mount Sinai: Lili Chan*, Kusum S. Mathews*, Steven G.
Coca, Deena R. Altman, Aparna Saha, Howard Soh, Huei Hsun Wen, Sonali Bose, Emily A. Leven, Jing G. Wang, Gohar Mosoyan, Girish N. Nadkarni, Pattharawin Pattharanitima, Emily J. Gallagher
Indiana University School of Medicine/Indiana University Health: Allon N.
Friedman*, John Guirguis, Rajat Kapoor, Christopher Meshberger, Katherine J. Kelly Johns Hopkins Hospital: Chirag R. Parikh*, Brian T. Garibaldi, Celia P. Corona- Villalobos, Yumeng Wen, Steven Menez, Rubab F. Malik, Elena Cervantes, Samir Gautam
Kings County Hospital Center: Mary C. Mallappallil*, Jie Ouyang, Sabu John, Ernie Yap, Yohannes Melaku, Ibrahim Mohamed, Siddartha Bajracharya, Isha Puri, Mariah Thaxton, Jyotsna Bhattacharya, John Wagner, Leon Boudourakis
Loma Linda University: H. Bryant Nguyen*, Afshin Ahoubim
Mayo Clinic, Arizona: Leslie F. Thomas*, Dheeraj Reddy Sirganagari Mayo Clinic, Florida: Pramod K. Guru*
Medical College of Wisconsin: Yan Zhou,* Paul A. Bergl, Jesus Rodriguez, Jatan A.
Shah, Mrigank S. Gupta
MedStar Georgetown University Hospital: Princy N. Kumar*, Deepa G. Lazarous, Seble G. Kassaye
Montefiore Medical Center/Albert Einstein College of Medicine: Michal L.
Melamed*, Tanya S. Johns. Ryan Mocerino, Kalyan Prudhvi, Denzel Zhu, Rebecca V.
Levy, Yorg Azzi, Molly Fisher, Milagros Yunes, Kaltrina Sedaliu, Ladan Golestaneh, Maureen Brogan, Neelja Kumar, Michael Chang, Jyotsana Thakkar
New York-Presbyterian Queens Hospital: Ritesh Raichoudhury*, Akshay Athreya, Mohamed Farag
New York-Presbyterian/Weill Cornell Medical Center: Edward J. Schenck*, Soo Jung Cho, Maria Plataki, Sergio L. Alvarez-Mulett, Luis G. Gomez-Escobar, Di Pan, Stefi Lee, Jamuna Krishnan, William Whalen
New York University Langone Hospital: David Charytan*, Ashley Macina, Sobaata Chaudhry, Benjamin Wu, Frank Modersitzki
Northwestern Memorial Hospital: Northwestern University Feinberg School of Medicine - Anand Srivastava*, Alexander S. Leidner, Carlos Martinez, Jacqueline M.
Kruser, Richard G. Wunderink, Alexander J. Hodakowski
Ochsner Medical Center: Juan Carlos Q. Velez*, Eboni G. Price-Haywood, Luis A.
Matute-Trochez, Anna E. Hasty, Muner MB. Mohamed
Oregon Health and Science University Hospital: Rupali S. Avasare*, David Zonies*
Partners Healthcare: Brigham and Women’s Hospital, Brigham and Women’s Faulkner Hospital, Massachusetts General Hospital, and Newton Wellesley Hospital - David E.
Leaf*, Shruti Gupta*, Meghan E. Sise, Erik T. Newman, Samah Abu Omar, Kapil K.
Pokharel, Shreyak Sharma, Harkarandeep Singh, Simon Correa, Tanveer Shaukat, Omer Kamal, Wei Wang, Heather Yang, Jeffery O. Boateng, Meghan Lee, Ian A.
Strohbehn, Jiahua Li, Ariel L. Mueller
ProMedica Health System: Roberta Redfern,* Nicholas S. Cairl, Gabriel Naimy, Abeer Abu-Saif, Danyell Hall, Laura Bickley
Renown Health: Chris Rowan*, Farah Madhai-Lovely*
Rush University Medical Center: Vasil Peev*, Jochen Reiser, John J. Byun, Andrew Vissing, Esha M. Kapania, Zoe Post, Nilam P. Patel, Joy-Marie Hermes
Rutgers/New Jersey Medical School: Anne K. Sutherland*, Amee Patrawalla, Diana G. Finkel, Barbara A. Danek, Sowminya Arikapudi, Jeffrey M. Paer, Peter Cangialosi, Mark Liotta
Rutgers/Robert Wood Johnson Medical School: Jared Radbel*, Jag Sunderram, Sonika Puri, Jayanth S. Vatson, Matthew T. Scharf, Ayesha Ahmed, Ilya Berim,
Stanford Healthcare: Stanford University School of Medicine – Shuchi Anand*, Joseph E. Levitt, Pablo Garcia
Temple University Hospital: Suzanne M. Boyle*, Rui Song, Ali Arif
Thomas Jefferson Health: Jingjing Zhang*, Sang Hoon Woo, Xiaoying Deng, Goni Katz-Greenberg, Katharine Senter
Tulane Medical Center: Moh’d A. Sharshir*, Vadym V. Rusnak
United Health Services Hospitals: Muhammad Imran Ali, Terri Peters, Kathy Hughes University of Colorado Anschutz Medical Campus: Anip Bansal*, Amber S. Podoll, Michel Chonchol, Sunita Sharma, Ellen L. Burnham
University Hospitals Cleveland Medical Center: Arash Rashidi*, Rana Hejal
University of Alabama-Birmingham Hospital: Eric Judd*, Laura Latta, Ashita Tolwani University of California-Davis Medical Center: Timothy E. Albertson*, Jason Y.
Adams
University of California-Los Angeles Medical Center: Ronald Reagan-UCLA Medical Center - Steven Y. Chang*, Rebecca M. Beutler; Santa Monica-UCLA Medical Center – Carl E. Schulze
University of California-San Diego Medical Center: Etienne Macedo*, Harin Rhee University of California-San Francisco Medical Center: Kathleen D. Liu*, Vasantha K. Jotwani
University of Chicago Medical Center: Jay L. Koyner*
University of Florida Health-Gainesville: Chintan V. Shah*
University of Florida-Health-Jacksonville: Vishal Jaikaransingh*
University of Illinois Hospital and Health Sciences System: Stephanie M. Toth- Manikowski*, Min J. Joo*, James P. Lash
University of Kentucky Medical Center: Javier A. Neyra*, Nourhan Chaaban
University Medical Center of Southern Nevada: Alfredo Iardino, Elizabeth H. Au, Jill H. Sharma
University of Miami Health System: Marie Anne Sosa*, Sabrina Taldone, Gabriel Contreras, David De La Zerda, Alessia Fornoni, Hayley B. Gershengorn
University of Michigan: Salim S. Hayek*, Pennelope Blakely, Hanna Berlin, Tariq U.
Azam, Husam Shadid, Michael Pan, Patrick O’ Hayer, Chelsea Meloche, Rafey Feroze, Rayan Kaakati, Danny Perry, Abbas Bitar, Elizabeth Anderson, Kishan J. Padalia, Christopher Launius, John P. Donnelly, Andrew J. Admon
University of North Carolina School of Medicine: Jennifer E. Flythe*, Matthew J.
Tugman, Emily H. Chang
University of Oklahoma Health Sciences Center: Brent R. Brown*
University of Pennsylvania Health System: Amanda K. Leonberg-Yoo*, Ryan C.
Spiardi, Todd A. Miano, Meaghan S. Roche, Charles R. Vasquez
University of Pittsburgh Medical Center: Amar D. Bansal*, Natalie C. Ernecoff, Sanjana Kapoor, Siddharth Verma, Huiwen Chen
University of Tennessee Health Science Center and Memphis VA Medical Center/Methodist University Hospital – Csaba P. Kovesdy*, Miklos Z. Molnar*, Ambreen Azhar
University of Texas Southwestern Medical Center and Parkland Health and
Hospital System: S. Susan Hedayati*, Mridula V. Nadamuni, Shani Shastri, Duwayne L. Willett
University of Vermont Larner College of Medicine: Samuel A.P. Short
University of Virginia Health System: Amanda D. Renaghan*, Kyle B. Enfield
University of Washington Medical Center: Pavan K. Bhatraju*, A. Bilal Malik Vanderbilt University Medical Center: Matthew W. Semler
Washington University in St. Louis/Barnes Jewish Hospital: Anitha Vijayan*, Christina Mariyam Joy, Tingting Li, Seth Goldberg, Patricia F. Kao
Wellforce Health System: Lowell General Hospital - Greg L. Schumaker*, Tufts
Medical Center - Nitender Goyal*, Anthony J. Faugno, Greg L. Schumaker, Caroline M.
Hsu, Asma Tariq, Leah Meyer, Ravi K. Kshirsagar, Daniel E. Weiner
Westchester Medical Center: Marta Christov*, Jennifer Griffiths, Sanjeev Gupta, Aromma Kapoor
Yale School of Medicine: Perry Wilson,* Tanima Arora, Ugochukwu Ugwuowo
*Site Principal Investigator
2. Supplemental Methods
Statistical Analysis Plan for STOP-COVID Proning Paper Data Collection and Validation
STOP-COVID patients were screened for enrollment based on the presence of laboratory-confirmed COVID-19 infection and ICU admission; eligible patients were enrolled consecutively. Data were collected using REDCap, a secure, HIPAA-compliant, web-based application. Wherever possible, data were captured using checkboxes rather manual entry to minimize keystroke errors. For data that required keystroke entry (e.g., laboratory values), we implemented validation ranges to flag potential errors in real-time. We also implemented automated data validation rules to flag errors in dates (e.g., if the date of death was entered as being before the date of ICU admission).
Finally, all data were manually reviewed, and values that appeared incongruent or out of range were manually validated by confirming the accuracy of the data with the
collaborator who entered it.
Overview of Target Trial Approach
We sought to determine whether critically ill patients with hypoxemic respiratory failure from COVID-19 who are initiated on early prone positioning ventilation in the first two days of ICU admission have improved survival compared to patients who are not initiated on early proning.
Target Trial Specification and Emulation
We emulated a hypothetical target trial in which patients are eligible if they meet each of the following criteria:
Inclusion criteria:
1. Adults (≥18 years old) with laboratory-confirmed COVID-19 who were admitted to a participating ICU between March 4 and May 15, 2020
2. Admitted to a hospital that proned at least one patient with COVID-19 during the above time period
3. Hypoxemic respiratory failure, defined as receipt of invasive mechanical ventilation and PaO2:FiO2 ratio ≤200 on ICU days 1 or 2 on the day of or prior to proning initiation
Exclusion criteria:
1. Initiated on prone positioning ventilation prior to ICU admission 2. Initiated extracorporeal membrane oxygenation on ICU day 1
3. Cardiac arrest, sustained ventricular tachycardia, or ventricular fibrillation on ICU day 1
4. Pregnancy
Patients meeting these criteria were classified according to whether proning was initiated within the first two days of ICU admission. ICU day one was defined as the 24- hour period spanning from midnight to midnight on the day of ICU admission. ICU day 2
was defined as the following day. After treatment assignment, decisions regarding initiation, continuation, or discontinuation of proning were left to the primary team’s discretion. Patients were followed until hospital discharge, death, or June 22, 2020 – the date on which the study database for the current analysis was locked.
The primary analysis compares the survival among patients who initiated on prone positioning ventilation vs. not initiated on proning in the first two days of ICU admission.
Survival time was defined as the interval from ICU admission to death, censored at hospital discharge or the date of last follow-up, whichever came first. Hazard ratios and 95% confidence intervals (CIs) were estimated using a Cox model.
Inverse probability weighting
To adjust for confounding we fit a logistic regression model with proning initiation as the outcome conditional on the covariates listed below. These covariates were selected based on clinical judgment, as they were thought to be potentially associated with a clinician’s decision to initiate proning and with survival. We used the model’s predicted probabilities to calculate stabilized inverse probability (IP) weights.1 We used a robust (sandwich) variance estimator to account for potential replications of patients induced by IP treatment weighting, which results in conservative (wider) 95% CIs. We evaluated standardized differences across each measured covariate before and after applying the weighting (Figure 2).2,3
Sensitivity Analyses
We conducted two prespecified and four post-hoc sensitivity analyses. First, we included the covariates below in an unweighted Cox model. Second, to eliminate the potential for immortal time bias,4,5 we assigned eligible individuals to either proning or no proning initiation on ICU day 1, and we repeated the process for eligible individuals on ICU day 2. Our final estimates were obtained by pooling the data from the emulation of the nested target trials on ICU days 1 and 2, using IP weighting as described above.
Patients initiated on proning only appeared in the pooled dataset up to and including the day that proning was initiated. For example, a patient who initiated proning on ICU day 1 did not have a corresponding observation corresponding to ICU day 2. A patient who initiated proning on ICU day 2, meanwhile, appeared as both a non-proned patient on ICU day 1 and as a proned patient on ICU day 2. We did not collect data on the duration of proning. Thus, our analyses are limited to initiation versus no initiation of proning.
Third, as an alternative approach to reduce the potential for immortal time bias, we excluded patients who died in the first two days of ICU admission. Fourth, we repeated the primary analysis while further adjusting for the percentage of cohort patients who received prone positioning ventilation within the first two days of ICU admission at each site by including this new variable in the IP weighted model. Fifth, we repeated the primary analysis but censored patients on day 30. Sixth, we repeated the primary analysis, censored patients on day 30, and assumed that individuals discharged alive before day 30 remained alive for the full 30 days.
Secondary Target Trial Emulations
We conducted two additional target trial emulations to assess the effect of proning on survival in patients with more severe hypoxemia. These emulations were similar to the primary analysis above, but with hypoxemia defined as receipt of invasive mechanical ventilation and a PaO2/FiO2 ratio ≤150 or ≤100 mm Hg during the first two days of ICU admission.
List of model covariates A. Baseline covariates
1) Age: 18-49; 50-59; 60-69; ≥70 2) Male sex
3) Race: white versus non-white (including other/unknown) 4) Body mass index (kg/m2): <25; 25-29; 30-34; ≥35; unknown
5) Chronic lung disease: chronic obstructive pulmonary disease, asthma, or other lung disease
6) Current smoker
7) Coronary artery disease 8) Congestive heart failure
9) Active malignancy, defined as any malignancy other than non-melanoma skin cancer that was treated in the prior year
10) Days from symptom onset to ICU admission: ≤7 versus >7 B. Severity-of-illness covariates
1) Renal, liver, and coagulation components of the Sequential Organ Failure Assessment (SOFA) score:6
Categoriesa
0 1 2 3 4
SOFA Renal (Cr [mg/dl], UOP [ml/day],b acute RRT, and ESRD)
Cr<1.2 mg/dl
Cr 1.2- 1.9 mg/dl
Cr 2-3.4 mg/dl
Cr 3.5-4.9 or UOP<500
Cr ≥5 or UOP<200 or acute RRT or
ESRD
SOFA Liver (Bilirubin, mg/dl)c <1.2 1.2-1.9 ≥2 --- ---
SOFA Coagulation (Platelets, K/mm3)c ≥150 100-149 <100 --- --- Abbreviations: Cr, creatinine (mg/dl); ESRD, end stage renal disease; RRT, renal replacement therapy;
SOFA, Sequential Organ Failure Assessment; UOP, urine output.
a Missing data were categorized as 0.
b If the UOP was missing, the category was assigned according to the Cr
c Liver and coagulation SOFA scores of 2, 3, or 4 were binned due to low frequency of events in categories “3” and “4”.
2) PaO2:FiO2 ratio on ICU days 1 or 2 as follows: ≤100; 100-150; >150-200 mm Hg (if more than one value was available, the lowest value was used)
3) Shock on ICU day 1: defined as receipt of ≥2 vasopressors
5) White blood cell count (per mm3): <4000, 4000–11,900, ≥12,000; missing 6) Lymphocyte count (per mm3): <1000; ≥1000; missing
7) Inflammation. Three mutually exclusive categories were created: inflamed, non- inflamed, or missing. Inflamed was defined as either of the following: C-reactive protein
>100 mg/L or ferritin >1,000 ng/mL on ICU days 1 or 2 (if more than one value was
available, the highest one was used). Non-inflamed was defined as at least one value that was below the threshold and no value that was above the threshold for the above parameters. Missing was defined as C-reactive protein and ferritin both being missing.
8) D-Dimer (ng/ml) on ICU days 1 or 2: <1000; 1000-2499; 2500-10,000; >10,000;
missing; (if more than one value was available, the highest value was used) 9) Concurrent therapies administered on ICU day 1 (each assessed individually):
corticosteroids, neuromuscular blockade, therapeutic anticoagulation, tocilizumab C. Other covariates
1) Number of pre-COVID ICU beds (not including surge capacity): <50, 50-99, ≥100 2) Regional density of COVID-19, assessed by categorizing hospitals into quartiles according to the regional (county) density of COVID-19 cases present on the median date of ICU admission for the patients that were contributed by that hospital.
Specifically, we calculated the number of COVID-19 cases per 100,000 population in the county in which each of the participating hospital’s main campus is located. We then categorized hospitals according to quartiles of the density of COVID-19 cases.
Missing Data
The renal, liver, and coagulation components of the SOFA score were categorized as
“0” if missing.7-9 Otherwise, missing data were not imputed. Rather, for variables with missing data (e.g., body mass index) we created a separate missing category, since data may not have been missing at random. Further, the missingness of a variable could have clinical relevance (e.g., a healthier patient may not have certain physiologic or laboratory values assessed as frequently), which could affect treatment decisions.
Data regarding body mass index were missing for 96 (4.1%) patients (23 (3.3%) and 73 (4.5%) among proned and not proned patients, respectively)
Data regarding white blood cell count were missing for 86 (3.7%) patients (30 (4.3%) and 56 (3.4%) among proned and not proned patients, respectively).
Lymphocyte count was missing in 386 (16.5%) patients (118 (16.8%) and 268 (16.4%) among proned and not proned patients, respectively).
Data regarding inflammation were missing for 302 (12.9%) patients (70 (10.0%) and 232 (14.2%) among proned and not proned patients, respectively).
Data regarding D-dimer were missing for 663 (28.4%) patients (176 (25.1%) and 487 (29.8%) among proned and not proned patients, respectively).
C-reactive protein was missing in 814 (34.8%) patients (201 (28.6%) and 613 (37.5%) among proned and not proned patients, respectively).
Ferritin was missing in 894 (38.2%) patients (219 (31.2%) and 675 (41.3%) among proned and not proned patients, respectively).
Overall, 1.9% of the data were missing.
All other covariates had complete data.
3. Supplemental Table 1. List of participating sites with STOP-COVID patients who underwent prone positioning ventilation during the first 14 days of ICU admission
Northeast
Beth Israel Deaconess Medical Center Brigham and Women's Faulkner Hospital Brigham and Women's Hospital
Cooper University Health Care (Inspira Mullica Hill, Inspira Vineland) Hackensack Meridian Health Hackensack University Medical Center Hackensack Mountainside Hospital
Johns Hopkins Hospital (Johns Hopkins Suburban, Johns Hopkins Bayview) Kings County Hospital Center
Lowell General Hospital
Massachusetts General Hospital
MedStar Georgetown University Hospital Montefiore Medical Center (Montefiore Weiler) Mount Sinai (Mount Sinai Brooklyn)
Newton Wellesley Hospital
New York Presbyterian Queens Hospital
New York-Presbyterian/Weill Cornell Medical Center
New York University Langone Hospital (NYU Langone Brooklyn, NYU Langone Winthrop) Rutgers/New Jersey Medical School
Rutgers/Robert Wood Johnson Medical School Temple University Hospital
Jefferson Health (Jefferson Cherry Hill, Methodist, Washington Township, and Stratford) Tufts Medical Center
United Health Services Hospitals
University of Pennsylvania Health System (Penn Presbyterian Medical Center, Princeton Medical Center)
University of Pittsburgh Medical Center (UPMC Presbyterian, St. Margaret, Passavant, Jameson, McKeesport)
Westchester Medical Center Yale University Medical Center South
Baylor College of Medicine, Houston
Baylor University Medical Center/Baylor Scott and White Health (Baylor Scott and White Medical Center White Temple, Centennial, College Station, Round Rock)
Duke University Medical Center Mayo Clinic, Florida
Memphis VA Medical Center Methodist University Hospital
Ochsner Medical Center (Ochsner Medical Center Baptist, Westbank) Tulane Medical Center
University of Alabama-Birmingham Hospital University of Florida Health-Gainesville University of Florida Health-Jacksonville University of Miami Health System
University of North Carolina Hospitals (Chatham Hospital)
University of Texas Southwestern Medical Center (UT Southwestern Medical Center Parkland Hospital)
University of Virginia Health System
Midwest
Barnes-Jewish Hospital (Christian Hospital, Progress West Hospital, Barnes-Jewish St.
Peters Hospital) Froedtert Hospital
Indiana University Health Methodist Hospital Mayo Clinic, Rochester
Northwestern Memorial Hospital (Lake Forest Hospital, Delnor Community Hospital) ProMedica Health System (ProMedica Bay Park, Monroe Regional, Bixby, and Flower Hospitals, Beaumont Hospital)
Rush University Medical Center (Rush Copley Medical Center)
University Hospitals Cleveland Medical Center (University Hospitals Elyria, Geneva, St John, Ahuja, Conneaut, and Bedford, Southwest General Hospital)
University of Chicago Medical Center
University of Illinois Hospital and Health Sciences System University of Kentucky Hospital
University of Michigan Hospital
University of Oklahoma Health Sciences Center West
Loma Linda University Medical Center (Bear Valley Hospital) Mayo Clinic, Arizona
Oregon Health and Science University Hospital (PeaceHealth Southwest Medical Center) Renown Health
Stanford University Medical Center (Stanford ValleyCare) University of California-Davis Medical Center
University of California-Los Angeles Medical Center (Santa Monica Hospital) University of California-San Diego Medical Center
University of California-San Francisco Medical Center UCHealth University of Colorado
University Medical Center of Southern Nevada
*All listed sites refer to main campus hospitals. Satellite hospitals are noted in parentheses
4. Supplemental Table 2. Baseline characteristics before and after applying inverse probability of treatment weighting
Pre-IP Weighting Post-IP Weighting
Covariates Proned early
(N=702)
Not proned early (N=1636)
Proned early Not proned early Demographic characteristics
Age – median (IQR) 60 (51-69) 63 (53-72) 61.0 (52.0-70.0) 62.0 (53.0-71.0)
Age – no. (%)
18–49 162 (23.1) 292 (17.8) 20.1% 19.5%
50–59 169 (24.1) 361 (22.1) 23.8% 22.9%
60–69 209 (29.8) 480 (29.3) 29.3% 29.4%
≥70 162 (23.0) 503 (30.7) 26.9% 28.3%
Male sex – no. (%) 474 (67.5) 1053 (64.4) 65.6% 65.6%
White race – no. (%) 271 (38.6) 620 (37.9) 36.5% 37.7%
Body mass index (kg/m2) – median (IQR) 31.5 (27.4-37.2) 30.6 (26.7-35.9) 31.1 (27.3-36.5) 30.8 (26.9-36.2) Body mass index (kg/m2) – no. (%)
<25 77 (11.0) 245 (15.0) 12.0% 13.6%
25-29 204 (29.0) 477 (29.2) 30.6% 29.3%
30-34 174 (24.8) 414 (25.3) 24.5% 24.9%
≥35 224 (31.9) 427 (26.1) 29.0% 28.0%
Coexisting conditions – no. (%)
Coronary artery disease 73 (10.4) 227 (13.9) 12.2% 12.7%
Congestive heart failure 42 (6.0) 163 (10.0) 7.8% 8.7%
Any lung disease 135 (19.2) 367 (22.4) 20.7% 21.3%
Current smoker 36 (5.1) 83 (5.1) 4.2% 4.9%
Active malignancy 21 (4.0) 72 (4.4) 3.9% 4.0%
Symptom onset to ICU admission – no. (%)
≤7 days 330 (47.0) 924 (56.5) 46.6% 46.4%
>7 days 372 (53.0) 712 (43.5) 53.4% 53.6%
Severity-of-illness on ICU admission – no. (%) Renal SOFA score
0 (Cr <1.2 mg/dl) 425 (60.5) 898 (54.9) 56.7% 56.5%
1 (Cr 1.2-1.9 mg/dl) 157 (22.4) 396 (24.2) 23.5% 23.7%
2 (Cr 2-3.4 mg/dl) 67 (9.5) 174 (10.6) 10.2% 10.2%
3 (Cr 3.5-4.9 mg/dl or UOP 201-500ml/24h) 28 (4.0) 94 (5.7) 5.8% 5.3%
4 (Cr ≥5 mg/dl, UOP≤200ml/24h, acute RRT, or ESRDa)
25 (3.6) 74 (4.5) 3.8% 4.2%
Liver SOFA scoreb
0 (Bilirubin <1.2 mg/dl) 632 (90.0) 1473 (90.0) 89.6% 89.8%
1 (Bilirubin 1.2-1.9 mg/dl) 48 (6.8) 120 (7.2) 8.0% 7.5%
2-4 (Bilirubin ≥2 mg/dl) 22 (3.1) 43 (2.6) 2.4% 2.7%
Coagulation SOFA scoreb
0 (≥150 K/mm3) 603 (85.9) 1337 (81.7) 83.4% 83.0%
1 (100-149 K/mm3) 75 (10.7) 222 (13.6) 12.1% 12.7%
2-4 (<100 K/mm3) 24 (3.4) 77 (4.7) 4.4% 4.3%
PaO2/FiO2, mm Hgc – no. (%)
Ventilated and PaO2/FiO2 151-200 94 (13.4) 330 (20.2) 16.6% 18.0%
Ventilated and PaO2/FiO2 100-150 208 (29.6) 584 (35.7) 33.4% 33.7%
Ventilated and PaO2/FiO2 ≤100 400 (57.0) 722 (44.1) 50.1% 48.3%
Shock (%) – no. (%) 114 (16.2) 208 (12.7) 14.5% 14.0%
White blood cell count (per mm3) – no. (%)
<4000 31 (4.4) 77 (4.7) 5.2% 4.8%
4000-11,900 448 (63.8) 1087 (66.4) 65.4% 65.7%
≥12000 193 (27.5) 416 (25.4) 25.5% 25.9%
Lymphocyte count (per mm3) – no. (%)
<1000 408 (58.1) 927 (56.7) 57.0% 57.0%
≥1000 176 (25.1) 441 (27.0) 26.0% 26.5%
Inflammationd – no. (%)
Inflamed 551 (78.5) 1186 (72.5) 74.3% 74.3%
Non-Inflamed 81 (11.5) 218 (13.3) 12.6% 12.8%
D-Dimer (ng/ml) – no. (%)
<1000 144 (20.5) 328 (20.0) 21.4% 20.4%
1000-2499 137 (19.5) 337 (20.6) 19.4% 20.2%
2500-10,000 150 (21.4) 296 (18.1) 19.8% 19.2%
>10,000 95 (13.5) 188 (14.5) 11.5% 11.9%
Therapies administered – no. (%)
Corticosteroids 149 (21.2) 217 (13.3) 15.6% 15.5%
Therapeutic anticoagulation 107 (15.2) 265 (16.2) 15.6% 15.8%
Neuromuscular blockade 176 (25.0) 218 (13.3) 17.4% 17.0%
Tocilizumab 54 (7.7) 94 (5.7) 6.6% 6.4%
Hospital characteristics ICU bed size – no. (%)
<60 372 (53.0) 605 (37.0) 43.1% 42.1%
60-119 199 (28.3) 686(41.9) 36.4% 37.7%
>120 131 (18.7) 345 (21.1) 20.5% 20.2%
Regional density of COVID-19, quartilee – no. (%)
1 41 (5.8) 147 (9.0) 8.5% 8.1%
2 141 (20.1) 308 (18.8) 20.1% 19.4%
3 150 (21.4) 447 (27.3) 24.7% 25.4%
4 370 (52.7) 734 (44.9) 46.7% 47.1%
Abbreviations: Cr, creatinine; ESRD, end-stage renal disease; ICU, intensive care unit; IP, inverse probability; IQR, interquartile range; RRT, renal replacement therapy; SOFA, Sequential Organ Failure Assessment; UOP, Urine output
a Includes both acute RRT as well as ESRD requiring RRT.
b Categories 2, 3, and 4 for the liver and coagulation components of the SOFA score were combined due to low frequency of events in categories 3 and 4.
c PaO2/FiO2 refers to the ratio of the partial pressure of arterial oxygen (PaO2) over the fraction of inspired oxygen (FiO2), and was only assessed in patients receiving invasive mechanical ventilation.
d Inflamed was defined as at least one of the following on ICU days 1 or 2: C-reactive protein >100 mg/L or ferritin >1,000 ng/mL. Non-inflamed was defined as at least one value below the thresholds above. The thresholds above were selected based on prior studies.(35-37)
e Regional density of COVID-19 was assessed by categorizing hospitals into quartiles according to the regional (county) density of COVID-19 cases present on the median date of ICU admission for the patients that were contributed by that hospital.
Data regarding body mass index were missing for 23 proned (3.3%) and 73 non-proned patients (4.5%).
Data regarding white blood cell count were missing for 30 P proned PV (4.3%) and 56 non-proned patients (3.4%).
Data regarding lymphocyte count was missing for 118 proned (16.8%) and 268 non-proned patients (16.4%).
Data regarding inflammation were missing for 70 proned (10.0%) and 232 non-proned patients (14.2%).
Data regarding D-dimer were missing for 176 proned (25.1%) and 487 non-proned patients (29.8%).
5. Supplemental Table 3. Multivariable Cox model for death among patients included in the target trial emulation of early proning initiation versus non- early proning initiation
Covariate Hazard Ratio
(95%CI) for Death
Demographics and co-existing conditions Age (years)
18-49 (REF) 1
50-59 1.03 (0.82-1.28)
60-69 1.34 (1.09-1.66)
≥70 1.82 (1.47-2.27)
Male sex 1.15 (1.01-1.32)
White race 0.84 (0.74-0.95)
Body mass index (kg/m2)
<25 (REF) 1
25-29 0.95 (0.78-1.14)
30-34 0.92 (0.75-1.12)
≥35 1.36 (1.00-1.85)
Coronary artery disease 1.39 (1.17-1.66)
Congestive heart failure 1.04 (0.83-1.30)
Any lung disease 1.20 (1.03-1.40)
Current smoker 0.99 (0.74-1.34)
Active malignancy 1.52 (1.17-1.98)
Symptom onset ≤ 7 days 1.17 (1.03-1.33)
Severity of illnessa Renal SOFA score
0 (Cr <1.2 mg/dl) (REF) 1
1 (Cr 1.2-1.9 mg/dl) 1.29 (1.11-1.50)
2 (Cr 2-3.4 mg/dL) 1.58 (1.30-1.92)
3 (Cr 3.5-4.9 mg/dL or UOP 200-500 ml/24 hours) 1.38 (1.04-1.82) 4 (Cr ≥ 5 mg/dL or UOP ≤200ml/24 hours or acute RRT or ESRD) 1.59 (1.21-2.10) Liver SOFA score
0 (Bilirubin <1.2 mg/dl) (REF) 1
1 (Bilirubin 1.2-1.9 mg/dl) 1.18 (0.96-1.43)
2-4 (Bilirubin ≥2 mg/dl) 1.84 (1.32-2.58)
Coagulation SOFA score
0 (Platelet count ≥ 150 K/mm3) (REF) 1
1 (Platelet count 100-149 K/mm3) 1.08 (0.90-1.30)
2-4 (Platelet count <100 K/mm3) 1.46 (1.10-1.93)
PaO2/FiO2, mm Hg
≤100 (REF) 1
101-150 0.78 (0.68-0.89)
151-200 0.67 (0.56-0.79)
Shock 0.99 (0.83-1.19)
White blood cell count (per mm3)
<4000 1.06 (0.76-1.48)
4000-11,900 0.85 (0.74-0.98)
≥12,000 (REF) 1
Missing 1.12 (0.79-1.61)
Lymphocyte count (per mm3)
<1000 (REF) 1
≥1000 0.87 (0.74-1.01)
Missing 0.83 (0.69-1.01)
Inflammationb
Inflamed 0.91 (0.76-1.09)
Non-inflamed (REF) 1
Missing 0.96 (0.75-1.23)
D-dimer (ng/ml)
<1000 (REF) 1
1000-2499 1.30 (1.05-1.61)
2500-10,000 1.52 (1.23-1.88)
>10,000 1.83 (1.44-2.32)
Missing 1.41 (1.14-1.75)
Therapies administered
Corticosteroids 1.07 (0.91-1.26)
Therapeutic anticoagulation 1.04 (0.89-1.22)
Neuromuscular blockade 1.01 (0.86-1.20)
Tocilizumab 0.91 (0.70-1.18)
Hospital characteristics ICU bed size
<60 1.95 (1.59-2.39)
60-119 1.13 (0.92-1.39)
≥120 (REF) 1
Regional density of COVID-19, quartile
1 (REF) 1
2 0.98 (0.74-1.30)
3 0.93 (0.72-1.21)
4 1.43 (1.12-1.82)
Early proning 0.81 (0.70-0.93)
Table Legend
a Severity of illness data were assessed on the day of proning initiation or non-initiation.
b Inflamed was defined as at least one of the following on ICU days 1 or 2: C-reactive protein >100 mg/L or ferritin
>1,000 ng/mL. Non-inflamed was defined as at least one value below the thresholds above. The thresholds above were selected based on prior studies.(34-36)
Abbreviations: ESRD, end-stage renal disease; PaO2/FiO2, partial pressure of arterial oxygen over the fraction of inspired oxygen; REF, reference group; RRT, renal replacement therapy; SOFA, Sequential Organ Failure Assessment; UOP, urine output.
6. Supplemental Figure 1: Timing of prone positioning ventilation initiation over the first 14 days of ICU admission for all patients requiring invasive
mechanical ventilation and with moderate-to-severe hypoxemia (PaO2/FiO2 ≤ 200 mg Hg) within the first two days of ICU admission
*Six patients were proned after ICU day 14 (days 31, 32, 32, 33, 35, and 35) and not shown here
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