Guidelines for the medical treatment of severe COVID-19 infection have evolved over the first years of the pandemic (1), but standards for the ventilator management of the acute respiratory distress syndrome (ARDS) that complicates COVID-19 are well-established (2). Unfortunately, despite cumulative evidence from randomized controlled trials in ARDS demonstrating the mortality benefit of low-tidal-volume ventilation (low Vt) (3), defined as 4–8 mL/kg predicted body weight, lower inspiratory pressure targets (plateau pressure [Pplat] less than 30 cm H2O) (4), as well as prone ventilation for moderate-severe ARDS (5,6), adoption of these interventions for non-COVID-19 ARDS prior to the COVID-19 pandemic was poor (7–11).
During the early months of the COVID-19 pandemic, some questioned the applicability of established ventilator management strategies for COVID-19 ARDS (“CARDS”) (12), whereas others affirmed the generalizability of evidence-based critical care to COVID-19 (13). Recent studies attempting to quantify the impact of these debates on clinical practice identify higher rates of adherence to low Vt within a single health system (14) or country (15) than that seen in non-COVID-19 ARDS. These data underscore an opportunity to identify patient- and hospital-level factors associated with adoption of use of low Vt, among other guideline-recommended interventions, and to assess their impact on patient outcomes.
We sought to characterize international hospital-level variation in use of ARDS management strategies with established mortality benefit within the Society of Critical Care Medicine’s Discovery Viral Infection and Respiratory Illness Universal Study (VIRUS) COVID-19 registry and to expand the current literature on variation in use of ARDS management strategies by identifying hospital- and patient-level factors associated with use. We hypothesized that: 1) CARDS management strategies would vary across hospitals and 2) better adherence to guideline-recommended ventilator management strategies would be associated with improved CARDS mortality.
MATERIALS AND METHODS
Study Population
We performed a retrospective cohort study of adults (greater than or equal to 18 yr), hospitalized with confirmed COVID-19 infection and receiving invasive mechanical ventilation for ARDS between February 15, 2020, and April 12, 2021, using the VIRUS registry. ARDS was defined by receipt of mechanical ventilation and a ratio of partial pressure of arterial oxygen divided by Fio2 (Pao2/Fio2 ratio) less than 300 during their hospitalization (Fig. 1). Consistent with Berlin definition of ARDS, all patients included received greater than or equal to 5 cm H2O of positive end-expiratory pressure. Although nearly 90% of patients with reported chest radiograph had bilateral infiltrates, due to high missingness of chest radiograph reporting (45%), notoriously poor reliability of chest radiograph interpretation for ARDS determination (16), and known pathologic and clinical association of severe COVID-19 infection with diffuse alveolar damage and ARDS (17), Berlin criteria of bilateral opacities on imaging were not required for study inclusion. Hospitals that enrolled fewer than 10 total patients in the registry or completed less than 80% of outcomes data for their registry were excluded to stabilize estimates of ARDS management strategies and restrict data to hospitals engaged in high-quality data entry. Data were collected from day of hospital admission or the date first available if admission data were missing. Patients with missing tidal volume, Pplat, or prone positioning data were excluded from the model assessing variation in use of “guideline-based care.”
Figure 1.:Cohort assembly of adults with acute respiratory distress syndrome due to COVID-19.
Outcomes
The primary outcome of interest was hospital-level variation in use of “guideline-based care” for ARDS management. “Guideline-based care” was determined by worst Pao2/Fio2 (P/F) ratio; among those with a P/F ratio between 100 and 300, use was defined as receipt of low Vt of 4–8 cc per kg ideal body weight and Pplat below 30 cm H2O. For patients with a P/F ratio less than 100, “guideline-based care” was additionally defined by receipt of prone positioning. Due to daily data entry into our database, the exact temporal relationship between patient illness severity and use of guideline-based ARDS management strategies is unknown. However, aligning with our primary focus on hospital-level practice patterns, our analysis captures whether a patient meeting criteria for ARDS ever received all the care strategies recommended for this diagnosis (low Vt, Pplat less than 30, and prone positioning for P:F less than 100). To address the possibility of patients dying before having a chance to receive all aspects of “guideline-based care,” a sensitivity analysis was performed among patients with greater than 1 day of mechanical ventilation. Additionally, to address the possibility that patients receiving extracorporeal membrane oxygenation (ECMO) may be more likely to receive low Vt, an additional sensitivity analysis was performed excluding patients with receipt of ECMO. For our exploratory analyses of the association of: 1) hospital-level use of “guideline-based care” and overall inhospital patient mortality and 2) hospital-level use of “guideline-based care” and patient discharge status to home, hospitals were grouped into quartiles of risk-adjusted use of “guideline-based care” based on the percentage of patients receiving recommended management strategies at each hospital (0–24%, 25–49%, 50–74%, and 75–100%), due to the nonlinear association of use with outcomes. Descriptive use of neuromuscular blockade (i.e., paralytics), inhaled pulmonary vasodilators, and ventilator strategies including airway pressure release ventilation is additionally reported.
Covariates
We adjusted models for potential confounding variables including patient characteristics (i.e., patient age, race, sex, body mass index [BMI], comorbid conditions, and admission code status), patient severity of illness (i.e., worst Sequential Organ Failure Assessment score and P/F ratio during admission), and hospital characteristics (i.e., geographic location where hospital is located [United States vs international], hospital type [academic vs nonacademic], ICU type [medical/COVID vs other], hospital capacity [overcapacity vs under capacity], and nurse-to-patient staffing ratios [low vs high]). Due to limitations of anonymity required for the VIRUS registry, further delineation of specific countries was not available. Academic hospitals were defined by hospitals at which residents and/or medical student were the first contact to provide ICU care. Overcapacity was defined by the number of COVID-19 patients at any given time outnumbering the number of ICU beds available at the hospital, and low nurse-to-patient staffing ratios were defined by greater than two patients per nurse (18,19). We additionally adjusted models for time of admission during the pandemic (early [before July 1, 2020] vs later [after July 1, 2020]) based on the availability of data from clinical trials for the management of COVID-19. Mortality assessments were adjusted for corticosteroid use given the survival benefit of dexamethasone in severe COVID-19 infection (20).
Data Source
The Society of Critical Care Medicine Discovery VIRUS COVID-19 registry (21,22) (NCT04323787) was approved by the Mayo Clinic (20-002610) and Boston University (H-40009) institutional review boards with waiver of informed consent due to the deidentified nature of the registry and the lack of interaction between study personnel, clinicians, and patients. Following local institutional review board approval and signed data use agreement, study data were recorded and managed using the Research Electronic Data Capture system (REDCap) (23). REDCap is a secure, web-based application designed to support data capture for research studies, providing: 1) an intuitive interface for validated data entry, 2) audit trails for tracking data manipulation and export procedures, 3) automated export procedures for seamless data downloads to common statistical packages, and 4) procedures for importing data from external sources. The study was determined to be exempt from Institutional Review Board. The study is registered on Clinicaltrials.gov: NCT04323787.
Statistical Analysis
Dichotomous and categorical variables are reported as counts and percentages. Continuous variables are reported using means withsd or median with interquartile range (IQR) based on the distribution. Trends of use of ARDS ventilator management strategies, associated measures of lung compliance, and patient and hospital characteristics were compared across quartiles of use of “guideline-based care” using the Cochran-Armitage test for categorical variables andF test on linear regression for continuous variables. Multilevel random effects modeling was performed, with each hospital included as a random intercept and above covariates as fixed effects, in order to determine the hospital risk-adjusted rates of “guideline-based care” for ARDS management. Variation in use of “guideline-based care” for ARDS management was described by the model intraclass correlation coefficient and the median odds ratio (24). After adjusting for patient- and hospital-level factors, the intraclass correlation coefficient summarizes the variation in guideline-based ARDS management attributable to the hospital itself. When considering an individual receiving care at a randomly selected hospital with higher versus lower use of “guideline-based care,” the median odds ratio represents the median increase in the odds of receiving “guideline-based ARDS care” at that higher use hospital. For the exploratory analyses of use of “guideline based care” on inhospital patient mortality and patient discharge status to home, hospitals were grouped into quartiles of risk-adjusted “guideline-based ARDS management” as the exposure of interest due to the nonlinear association of use with outcomes and convergence difficulties with random effects spline-based models.
Missing data were not imputed as they could not be considered to be missing completely at random. For missing covariate data due to the lack of entry by participating sites, as opposed to pending outcomes data of patients still hospitalized, complete case analysis was used to minimize introduction of “incomplete outcome data bias” (25). All tests were two-sided and conducted using a significance level of 0.05. Statistical analyses were conducted using statistical analysis software, SAS (Version 9.4, Cary, NC).
RESULTS
Patient and Hospital Characteristics
Fourteen hundred and ninety-five mechanically ventilated adults with COVID-19 infection and ARDS met criteria for inclusion in the study (Fig. 1). Over half the cohort (801; 54%) had severe ARDS as defined by P/F < 100. Median age was 64 (IQR, 54–72), two-thirds of patients (67%) were male, 654 (43.7%) were White and 298 (33.2%) were Black, and median BMI was 29.4 (IQR, 26–35). Patients were managed predominantly at U.S. hospitals (73%) with an academic affiliation (63%), and the majority had a full code status (93%) (Table 1). Distributions of academic hospital affiliation (p = 0.02), nurse-to-patient ratio (p = 0.007), and hospital capacity strain (p = 0.001) differed across quartiles of “guideline-based care” (Table 1).
TABLE 1. - Patient and Hospital Characteristics Stratified by Hospital Quartiles of Use of Guideline-Based Care
a | Characteristics | Overall | Q1 (0–24th %) | Q2 (25–49th %) | Q3 (50–74th %) | Q4 (75–100th %) | pb |
|---|
| n (%) | 1,495 (100) | 383 (25.6) | 366 (24.5) | 335 (24.5) | 411 (27.5) | |
| Patient characteristics |
| Admission month |
| March–June, 2020 | 528 (39.8) | 143 (55.4) | 92 (25.2) | 187 (57.5) | 106 (28.0) | 0.0002 |
| July 2020–April 2021 | 799 (60.2) | 115 (44.6) | 273 (74.8) | 138 (42.5) | 273 (72.0) | 0.0002 |
| Age (yr), median (IQR) | 64 (54–72) | 65 (53–72) | 65 (55–73) | 64 (54–71) | 63 (53–72) | 0.36 |
| Sex |
| Male | 998 (66.8) | 260 (68.1) | 231 (63.1) | 243 (72.5) | 264 (64.2) | 0.75 |
| Female | 496 (33.2) | 122 (31.9) | 135 (36.9) | 92 (27.5) | 147 (35.8) | 0.75 |
| Intersex | < 5 | < 5 | < 5 | < 5 | < 5 | - |
| Race |
| White Caucasian | 654 (43.7) | 134 (35.0) | 214 (58.5) | 134 (40.0) | 172 (41.8) | 0.81 |
| Black or African American | 298 (19.9) | 50 (13.1) | 67 (18.3) | 87 (26.0) | 94 (22.9) | < 0.0001 |
| Asian American | 156 (10.4) | 57 (14.9) | 34 (9.3) | 48 (14.3) | 17 (4.1) | < 0.0001 |
| Other | 387 (25.9) | 142 (37.1) | 51 (13.9) | 66 (19.7) | 128 (31.1) | 0.30 |
| Hispanic |
| Hispanic | 287 (19.2) | 113 (29.5) | 62 (16.9) | 44 (13.1) | 68 (16.5) | < 0.0001 |
| Non-Hispanic | 1,208 (80.8) | 270 (70.5) | 304 (83.1) | 291 (86.9) | 343 (83.5) | < 0.0001 |
| Height (cm), median (IQR)c | 170 (162–176) | 170 (164–177) | 168 (161–175) | 170 (163–176) | 168 (160–178) | 0.66 |
| Weight (kg), median (IQR)c | 85 (73–99 | 84 (75–96 | 85 (73–101 | 84 (75–97 | 85 (71–104 | 0.05 |
| Predicted weight (kg), median (IQR)c | 64.1 (56.9–70.7) | 65.9 (56.9–71.4) | 63.8 (54.7–70.7) | 65.9 (56.9–70.5) | 62.0 (55.2–70.7) | 0.29 |
| Body mass index (kg/m2), median (IQR)c | 29.4 (26.0–34.7) | 29.2 (25.8–33.3) | 29.5 (26.0–35.2) | 29.4 (25.4–34.7 | 29.7 (26.3–35.3) | 0.06 |
| Comorbidities |
| Coronary artery disease | 235 (15.7) | 80 (20.9) | 78 (21.3) | 30 (9.0) | 47 (11.4) | < 0.0001 |
| Congestive heart failure | 184 (12.3) | 63 (16.4) | 55 (15.0) | 27 (8.1) | 39 (9.5) | 0.0003 |
| Chronic pulmonary disease | 297 (19.9) | 19 (5.0) | 200 (54.6) | 32 (9.6) | 46 (11.2) | 0.006 |
| Asthma | 153 (10.2) | 26 (6.8) | 72 (19.7) | 21 (6.3) | 34 (8.3) | 0.23 |
| Chronic kidney disease | 215 (14.4) | 51 (13.3) | 77 (21.0) | 37 (11.0) | 50 (12.2) | 0.10 |
| Chronic dialysis | 36 (2.4) | 13 (3.4) | 10 (2.7) | 5 (1.5) | 8 (1.9) | 0.12 |
| Diabetes | 572 (38.3) | 155 (40.5) | 149 (40.7) | 119 (35.5) | 149 (36.3) | 0.11 |
| Liver disease | 54 (3.6) | 4 (1.0) | 32 (8.7) | 8 (2.4) | 10 (2.4) | 0.63 |
| HIV/AIDS | 15 (1.0) | 5 (1.3) | 2 (0.5) | 5 (1.5) | 3 (0.7) | 0.69 |
| Current smoker | 61 (4.2) | 12 (3.3) | 6 (1.7) | 22 (6.6) | 21 (5.2) | 0.03 |
| Former smoker | 349 (24.2) | 40 (11.0) | 129 (37.4) | 79 (23.7) | 101 (24.9) | 0.005 |
| Full code status at admission | 1,397 (93.4) | 348 (90.9) | 361 (98.6) | 320 (95.5) | 368 (89.5) | 0.16 |
| Maximum Sequential Organ Failure Assessment score, median (IQR)c | 9 (6–11) | 9 (7–12) | 9 (7–11) | 8 (6–11) | 9 (6–11) | 0.05 |
| Worst Pao2/Fio2 ratio during admission |
| < 300 | 145 (9.7) | 41 (10.7) | 25 (6.8) | 22 (6.6) | 57 (13.9) | 0.13 |
| < 200 | 549 (36.7) | 165 (43.1) | 77 (21.0) | 133 (39.7) | 174 (42.3) | 0.14 |
| < 100 | 801 (53.6) | 177 (46.2) | 264 (72.1) | 180 (53.7) | 180 (43.8) | 0.02 |
| Hospital characteristics |
| Hospital typea |
| Academic | 861 (62.8) | 257 (71.6) | 105 (32.4) | 259 (79.7) | 240 (66.3) | 0.02 |
| ICU typea |
| Medical or COVID ICU | 1,055 (77.0) | 359 (100) | 324 (100) | 245 (75.4) | 127 (35.1) | < 0.0001 |
| Hospital capacitya |
| Overcapacity | 880 (64.2) | 245 (68.2) | 266 (82.1) | 115 (35.4) | 254 (70.2) | 0.001 |
| Nurse staffing levela |
| Low nurse to patient ratio | 370 (27.0) | 73 (20.3) | 58 (17.9) | 168 (51.7) | 71 (19.6) | 0.007 |
| Hospital sitea |
| United States | 1,097 (73.4) | 259 (67.6) | 344 (94.0) | 154 (46.0) | 340 (82.7) | 0.72 |
IQR = interquartile range.
aGuideline-based care = low tidal volume and plateau pressure < 30 for 100 < Pao2/Fio2 (P/F) < 300 and additionally by prone positioning for P/F < 100. Academic hospital refers to hospital where resident and/or medical student is the first contact to provide ICU care, other ICU represents pediatric or mixed ICU, overcapacity refers to number of patients at peak of COVID greater than number of ICU beds, low nurse to patient ratio represents greater than two patients per nurse, and high nurse to patient ratio represents one to two patients per nurse and hospital site represents United States vs international hospitals.
bp represents significance of trend from quartile of hospitals with the lowest use of guideline-based care (Q1) up to quartile of hospitals with highest use of guideline-based care (Q4).
cCovariate missingness (% missing,n missing): height (13%,n = 265), weight (13%,n = 258), predicted weight (15%,n = 308), body mass index (13%,n = 271), and Sequential Organ Failure Assessment (5%,n = 105).
ARDS Ventilator Management Strategies
Among patients cared for at hospitals where “guideline-based care” variables were reported (hospitaln = 42) (Fig. 1), quartiles of risk-adjusted hospital-level use of “guideline-based care” were identified, and ARDS management strategies were compared (Table 2). Low Vt was used in the management of 79% of patients, inspiratory Pplat less than 30 cm H2O was achieved for 74% of patients, and more than half of patients with severe ARDS (62.3%) were managed with prone positioning. The distribution of use of these “guideline-based care” ARDS management strategies differed significantly across hospital quartiles of use (p < 0.0001;p < 0.0001;p = 0.001, respectively). Nearly half of patients were managed with adjunctive strategies including paralytics (56.3%) and inhaled pulmonary vasodilators (48.3%) with similarly significant differences across hospital quartiles of use (p < 0.0001 andp = 0.002, respectively). Use of these adjunctive strategies was lowest at hospitals in the second to lowest quartile of “guideline-based care” use (Table 2).
TABLE 2. - Acute Respiratory Distress Syndrome Management Strategies Stratified by Hospital Quartiles of Use of Guideline-Based Care
a | Overall | Q1 (0–24th %) | Q2 (25–49th %) | Q3 (50–74th %) | Q4 (75–100th %) | Pb |
|---|
| Acute respiratory distress syndrome management strategies | 1,495 (100) | 383 (25.6) | 366 (24.5) | 335 (24.5) | 411 (27.5) | |
|---|
| Tidal volume, mean (sd)a | 7.2 (1.5) | 8.1 (2.0) | 7.3 (1.2) | 6.9 (1.4) | 6.8 (1.1) | < 0.0001 |
| Plateau pressure (cm H2O), median (IQR)c | 26 (22–30) | 29 (24–30) | 28 (24–30) | 25 (22–29) | 24 (20–27) | < 0.0001 |
| Positive end-expiratory pressure (cm H2O), median (IQR)a,c | 11 (8–14) | 12 (10–14) | 10 (8–14) | 12 (10–14) | 12 (10–14) | 0.05 |
| Driving pressure (cm H2O), mean (sd)c | 15 (6) | 16 (6) | 17 (5) | 14 (5) | 13 (6) | < 0.0001 |
| Static compliance, mean (sd)c | 40 (40) | 44 (60) | 30 (12) | 40 (34) | 47 (48) | 0.007 |
| Ventilator mode,n (%) |
| Volume control | 780 (69.5) | 89 (49.7) | 209 (70.6) | 218 (67.7) | 264 (81.0) | < 0.0001 |
| Pressure control | 129 (11.5) | 52 (29.1) | 10 (3.4) | 58 (18.0) | 9 (2.8) | 0.001 |
| Pressure support | 25 (2.2) | 10 (5.6) | 1 (0.3) | 14 (4.3) | 0 (0.0) | 0.12 |
| Others | 157 (14.0) | 23 (12.8) | 54 (18.2) | 28 (8.7) | 52 (16.0) | 0.04 |
| Airway pressure release ventilationa | 89 (6.0) | 30 (7.8) | 30 (8.2) | 14 (4.2) | 15 (3.6) | 0.002 |
| Extracorporeal membrane oxygenation,n (%)a | 115 (7.7) | 38 (9.9) | 20 (5.5) | 33 (9.9) | 24 (5.8) | 0.16 |
| Paralytics,n (%) | 841 (56.3) | 236 (61.6) | 109 (29.8) | 226 (67.5) | 270 (65.7) | < 0.0001 |
| Inhaled pulmonary vasodilators,n (%) | 722 (48.3) | 209 (54.6) | 93 (25.4) | 190 (56.7) | 230 (56.0) | 0.002 |
| Prone positioning,n (%) | 858 (57.4) | 233 (60.8) | 164 (44.8) | 217 (64.8) | 244 (59.4) | 0.20 |
| Prone positioning, P:F < 100 | 499 (62.3) | 110 (62.2) | 126 (47.7) | 141 (78.3) | 122 (67.8) | 0.001 |
| Low tidal volume (4–8 cc/kg),n (%)c | 809 (79.0) | 85 (53.5) | 215 (77.1) | 246 (85.4) | 263 (88.3) | < 0.0001 |
| Plateau pressure < 30 cm H2O,n (%)c | 737 (74.4) | 64 (50.7) | 181 (67.5) | 217 (79.8) | 265 (86.9) | < 0.0001 |
| Guideline-based care, n (%)a,c | 458 (50.4) | 19 (14.1) | 100 (39.1) | 146 (59.8) | 193 (70.7) | < 0.0001 |
IQR = interquartile range.
aTidal volume = tidal volume (mL)/predicted weight (kg). Guideline-based care = low tidal volume and plateau pressure < 30 for 100 < Pao2/Fio2 (P/F) < 300 and additionally by prone positioning for P/F < 100.
bp represents significance of trend from quartile of hospitals with lowest use of guideline-based care (Q1) up to quartile of hospitals with highest use of guideline-based care (Q4).
cCovariate missingness (% missing,n missing): positive end-expiratory pressure (40%,n = 815), driving pressure (52% missing =n = 1,036), static compliance (52%,n = 1052), low tidal volume (32%,n = 471), plateau pressure (34%,n = 505), and guideline-based care (39%,n = 587).
Hospital Variation in Use of “Guideline-Based Care” for ARDS
In a complete case analysis of patients where all “guideline-based care” covariates were available (patientn = 739; hospitaln = 31), the rate of “guideline-based care” for ARDS varied between hospitals with a median hospital-level rate of (50%; IQR, 45–59%; range, 29–71%) (Fig. 2). After adjusting for patient demographic, and severity of illness characteristics and pandemic timing, hospital of admission contributed to 20% of the risk-adjusted variation in guideline-based care. The median odds ratio (MOR), which represents the median increase in the odds of a patient receiving “guideline-based” ARDS care when treated at a randomly selected hospital with practice patterns highly consistent with ARDS guidelines compared with a hospital with practice patterns less consistent with guidelines, was 2.4 (95% CI, 1.20–4.67). With the addition of hospital-level factors including academic affiliation, bed capacity, and nurse-to-patient staffing ratios, the contribution of hospital of admission to the risk-adjusted variation in use guideline-based care decreased from 20% to 14% with an MOR of 2.0 (95% CI, 1.15–3.43).
Figure 2.:Multivariable-adjusted variation in hospital-level use of guideline-based care for patients with acute respiratory distress syndrome acute respiratory distress syndrome, defined by receipt of low tidal volume ventilation (4–8-cc/kg ideal body weight) and plateau pressure below 30 cm H2O for 100 < Pao2/Fio2 ratio < 300 and additionally by prone positioning for Pao2/Fio2 ratio < 100 with adjustment for patient and hospital characteristics.
Factors Associated With Use of Guideline-Based Care
When accounting for both patient- and hospital-level factors that may explain variation in use of “guideline-based care,” female sex was associated with a 53% decreased odds of receiving “guideline-based care” (OR, 0.47; 95% CI, 0.33–0.68;p < 0.0001), and history of chronic dialysis was associated with an 81% decreased odds (OR, 0.19; 95% CI, 0.05–0.71;p = 0.01) of “guideline-based care” (Table 3). ARDS severity was also significantly associated with use of “guideline-based care” (p < 0.0001). Pplat targets less than 30 cm H2O were least commonly achieved among patients with severe ARDS where receipt of “guideline based care” was less likely compared with mild disease (Supplemental Table 1,https://links.lww.com/CCX/A920;Table 3). A smoking history was significantly associated with receipt of guideline-recommended therapies (OR, 1.76; 95% CI, 1.15–2.70;p = 0.01). Designated medical and/or COVID-19 ICUs were significantly less likely to use “guideline-based care” (OR, 0.26; 95% CI, 0.12–0.56;p = 0.001). Hospital capacity and nurse-to-patient ratios were not associated with receipt of “guideline-based care.” Compared with the early months of the pandemic, use of “guideline-based” care was similar in the later months of the pandemic (OR, 1.2; 95% CI, 0.8–1.9;p = 0.47) (Table 3). Results from sensitivity analyses among patients receiving mechanical ventilation for more than 1 day and excluding those who received ECMO were unchanged.
TABLE 3. - Patient- and Hospital-Level Factors Associated With Use of Guideline-Based Care for Acute Respiratory Distress Syndrome Due to COVID-19
| Characteristics | Guideline-Based Care;a Patient-Level Variable Model | Guideline-Based Care;a Patient- and Hospital-Level Variable Model |
|---|
| OR (95% CI) | p | OR (95% CI) | p |
|---|
| Age | 0.99 (0.97–1.00) | 0.11 | 0.99 (0.75–1.85) | 0.09 |
| Sex | | | | |
| Male | 1.00 (reference) | | 1.00 (reference) | |
| Female | 0.48 (0.33–0.69) | < 0.0001 | 0.47 (0.33–0.68) | < 0.0001 |
| Body mass index | 0.99 (0.96–1.01) | 0.49 | 0.99 (0.96–1.02) | 0.44 |
| Race |
| White | 1.00 (reference) | | 1.00 (reference) | |
| Black or African American | 0.69 (0.41–1.15) | 0.35 | 0.70 (0.41–1.19) | 0.20 |
| Asian | 0.53 (0.25–1.14) | 0.50 (0.23–1.05) |
| Other | 0.95 (0.56–1.63) | 1.01 (0.59–1.74) |
| Ethnicity |
| Non-Hispanic | 1.00 (reference) | | 1.00 (reference) | |
| Hispanic | 1.21 (0.71–2.07) | 0.48 | 1.20 (0.70–2.04) | 0.51 |
| Comorbidities |
| Coronary artery disease | 1.29 (0.77–2.15) | 0.33 | 1.32 (0.79–2.20) | 0.30 |
| Congestive heart failure | 1.13 (0.64–1.98) | 0.67 | 1.19 (0.67–2.09) | 0.55 |
| Chronic pulmonary disease | 1.15 (0.65–2.00) | 0.63 | 1.20 (0.69–2.10) | 0.52 |
| Asthma | 1.22 (0.71–2.10) | 0.47 | 1.22 (0.71–2.10) | 0.47 |
| Chronic kidney disease | 1.30 (0.78–2.15) | 0.31 | 1.33 (0.80–2.21) | 0.27 |
| Chronic dialysis | 0.20 (0.06–0.74) | 0.02 | 0.19 (0.05–0.71) | 0.01 |
| Diabetes mellitus | 1.05 (0.73–1.50) | 0.80 | 1.07 (0.74–1.53) | 0.73 |
| Liver disease | 0.78 (0.35–1.77) | 0.56 | 0.78 (0.34–1.75) | 0.54 |
| HIV/AIDS | 1.04 (0.22–4.99) | 0.96 | 1.06 (0.22–5.10) | 0.94 |
| Tobacco use |
| Current smoker | 0.94 (0.41–2.17) | 0.88 | 0.87 (0.38–2.04) | 0.76 |
| Former smoker | 1.78 (1.16–2.72) | 0.01 | 1.76 (1.15–2.70) | 0.01 |
| Admission code status |
| Full code | 1.41 (0.68–2.92) | 0.35 | 1.49 (0.72–3.12) | 0.29 |
| Acute respiratory distress syndrome severitya |
| P/F < 300 | 1.00 (reference) | | 1.00 (reference) | |
| P/F < 200 | 1.85 (0.88–3.86) | < 0.0001 | 1.91 (0.91–4.02) | < 0.0001 |
| P/F < 100 | 0.69 (0.33–1.43) | 0.71 (0.34–1.49) |
| Maximum Sequential Organ Failure Assessment score | 0.96 (0.90–1.01) | 0.12 | 0.96 (0.90–1.01) | 0.13 |
| Admission month |
| February–June 2020 | 1.00 (reference) | | | |
| July 2020–April 2021 | 1.27 (0.81–1.98) | 0.30 | 1.18 (0.75–1.85) | 0.47 |
| Hospital location |
| United States | 1.00 (reference) | | | |
| International | 0.97 (0.38–2.51) | 0.95 | 0.88 (0.34–2.29) | 0.80 |
| Hospital typea | | | | |
| Nonacademic | | | 1.00 (reference) | |
| Academic | | | 2.00 (0.91–4.42) | 0.09 |
| ICU typea | | | | |
| Other ICU | | | 1.00 (reference) | |
| Medical or COVID ICU | | | 0.26 (0.12–0.56) | 0.001 |
| Hospital capacitya | | | | |
| Undercapacity | | | 1.00 (reference) | |
| Overcapacity | | | 1.04 (0.46–2.32) | 0.93 |
| Nurse staffing levela | | | | |
| Low nurse to patient ratio | | | 1.00 (reference) | |
| High nurse to patient ratio | | | 1.12 (0.43–2.93) | 0.81 |
OR = odds ratio, P/F = Pao2/Fio2 ratio.
aGuideline-based care = low tidal volume and plateau pressure < 30 for 100 < P/F < 300 and additionally by prone positioning for P/F < 100. Academic hospital refers to hospital where resident and/or medical student is the first contact to provide ICU care, other ICU represents pediatric or mixed ICU, overcapacity refers to number of patients at peak of COVID greater than number of ICU beds, low nurse to patient ratio represents greater than two patients per nurse, and nurse to patient ratio represents one to two patients per nurse.
Association Between Use of Guideline-Based ARDS Care and Inhospital Mortality
Unadjusted inhospital mortality was 54%. Median-adjusted hospital-level mortality was 53% (IQR, 47–62%) with a nonsignificantly decreased risk of mortality for patients admitted to hospitals in the highest use evidence-based care quartile (49%) compared with the lowest use quartile (60%) (risk-adjusted OR for Q4 vs Q1, 0.7 [95% CI, 0.3–1.5]) (Supplemental Table 2,https://links.lww.com/CCX/A921). Further adjustment for hospital-level factors (ICU capacity, nurse-to-patient staffing ratios, and academic affiliations) resulted in little change to effect estimates (risk-adjusted OR for Q4 vs Q1, 0.7 [0.3–1.9]). Half of patients were discharged home (risk-adjusted median 54%; IQR, 45–61%), and receipt of “guideline-based care” was not significantly associated with discharge to home (Supplemental Table 3,https://links.lww.com/CCX/A922).
DISCUSSION
In this multicenter, international study of 1,495 patients with ARDS due to COVID-19, only half of patients received care consistent with ARDS guidelines. Although the majority of patients received either low Vt, values of Pplat less than 30 cm H2O, or prone ventilation for severe ARDS, combined use of these strategies among eligible patients was relatively poor. Despite established guidelines for ARDS management, we identified large practice variations in hospital-level use of guideline-recommended ARDS management strategies. After adjusting for patient characteristics and hospital resources, patients receiving care at a randomly selected hospital with practice patterns more consistent with ARDS guidelines had 2.0 times the odds of receiving these interventions compared with a patient being treated at a hospital with practice patterns less consistent with ARDS guidelines. Patients treated at hospitals with higher rates of “guideline-based care” had a nonsignificantly lower odds of mortality. These data provide important motivation to design strategies to align ARDS management with guideline recommendations for patients with COVID-19 and underscore the importance of considering hospital-level resources when evaluating capacity to adopt guidelines.
These data presented from patients with COVID-19 add novel information regarding ARDS practice patterns that can be interpreted in the context of prior surveys of intended ARDS practices among patients with COVID-19 (26) and observational studies of patients with ARDS prior to the COVID-19 pandemic. Our data demonstrate encouraging signs of relatively high use of each guideline-based management strategy for ARDS; however, variation in combined use of “guideline-based care” across hospitals highlights room for improvement. Rates of prone ventilation, which was provided to 62% of patients with severe ARDS, were double previously reported rates among patients without CARDS (i.e.16–33%) (9). This likely reflects clinician awareness of the benefits of prone ventilation early on in the pandemic, when there were no alternate therapies with mortality benefit (27). Although overall rates of use of low Vt and Pplat targets less than 30 cm H2O were high (78.9% and 74.4%, respectively), one-third of patients with mild ARDS did not receive low Vt and Pplat targets less than 30 cm H2O were less likely to be achieved among patients with severe ARDS. The discordance in adherence to overall “guideline-based care” among those with moderate ARDS but not severe disease seen in our study draws specific attention to patients with severe disease where adherence to Pplat targets may be particularly difficult due to poor lung compliance.
Within the highest use quartile of “guideline-based care,” academic affiliations and high nurse-to-patient staffing ratios (i.e., fewer patients per nurse) may have impacted adherence to best practices. The negative association between use of “guideline-based care” and medical or COVID ICUs compared with mixed or PICUs may represent the severity of cases designated to ICUs with expertise in disease pathophysiology when hospitals are caring for patients beyond capacity or controversies as to whether COVID-19 should be managed as traditional ARDS (28). Poor adoption of “guideline-based care” among women may be informed by prior studies demonstrating lower odds of lung-protective ventilation in women, likely as a result of bias in height assessments and default tidal volumes (8,29). Adjunctive therapies for ARDS management including paralytics and inhaled pulmonary vasodilators were used nearly as commonly as evidence-based practices, without predilection for use at low-performing “guideline-based care” hospitals, warranting further guidance of efficacy and appropriate use, specifically within the CARDS population.
Strengths of this study include evaluation of ARDS practices for patients with COVID-19 across an international sample of hospitals. Results should also be considered in the context of study limitations. First, P/F ratios used to classify severity of illness represent the most severe point in a patients’ illness. Second, serial measurements of Pao2 and Fio2 throughout a patients’ hospitalization were not available; therefore, association of ventilator management with time-varying ARDS severity may be limited. Additionally, “guideline-based care” strategies only had to be met at any time during mechanical ventilation. Daily data for each strategy were not available, so we are unable to comment on how frequently strategies were met simultaneously or separately. Fourth, categorization of continuous exposure variable of hospital-guideline base care into quartiles may lead to loss of information in the model; however, random effects spline-based models had convergence difficulties. Fifth, the impact of conservative versus liberal fluid management strategies on CARDS mortality could not be captured using this registry. Finally, residual confounding from unmeasured severity of illness may cannot be ruled out.
CONCLUSIONS
During the first year of the COVID-19 pandemic, half of patients received “guideline-based care” for ARDS management and use of “guideline-based care” varied widely across hospitals. Given the significant mortality risk associated with ARDS, efforts should focus toward identifying strategies that optimize adherence to care practices with established survival benefit.
ACKNOWLEDGMENTS
We thank Ms. Tresha Russell, Ms. Lynn Retford, Ms. Mary Reidy, and Ms. Colleen McNamara for their contributions to the Discovery VIRUS: COVID-19 Registry.
REFERENCES
2. Fan E, Del Sorbo L, Goligher EC, et al.; American Thoracic Society, European Society of Intensive Care Medicine, and Society of Critical Care Medicine: An Official American Thoracic Society/European Society of Intensive Care Medicine/Society of Critical Care Medicine Clinical Practice Guideline: Mechanical ventilation in adult patients with acute respiratory distress syndrome. Am J Respir Crit Care Med 2017; 195:1253–1263
3. Brower RG, Matthay MA, Morris A, et al.; Acute Respiratory Distress Syndrome Network: Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. N Engl J Med 2000; 342:1301–1308
4. Amato MB, Meade MO, Slutsky AS, et al.: Driving pressure and survival in the acute respiratory distress syndrome. N Engl J Med 2015; 372:747–755
5. Guérin C, Reignier J, Richard JC, et al.; PROSEVA Study Group: Prone positioning in severe acute respiratory distress syndrome. N Engl J Med 2013; 368:2159–2168
6. Sud S, Friedrich JO, Adhikari NKJ, et al.: Comparative effectiveness of protective ventilation strategies for moderate and severe acute respiratory distress syndrome. A network meta-analysis. Am J Respir Crit Care Med 2021; 203:1366–1377
7. Moss M, Huang DT, Brower RG, et al.; National Heart, Lung, and Blood Institute PETAL Clinical Trials Network: Early neuromuscular blockade in the acute respiratory distress syndrome. N Engl J Med 2019; 380:1997–2008
8. Walkey AJ, Wiener RS: Risk factors for underuse of lung-protective ventilation in acute lung injury. J Crit Care 2012; 27:323.e1–323.e9
9. Bellani G, Laffey JG, Pham T, et al.; LUNG SAFE Investigators; ESICM Trials Group: Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA 2016; 315:788–800
10. Guérin C, Beuret P, Constantin JM, et al.; investigators of the APRONET Study Group, the REVA Network, the Réseau recherche de la Société Française d’Anesthésie-Réanimation (SFAR-recherche) and the ESICM Trials Group: A prospective international observational prevalence study on prone positioning of ARDS patients: The APRONET (ARDS Prone Position Network) study. Intensive Care Med 2018; 44:22–37
11. Midega TD, Bozza FA, Machado FR, et al.; CHECKLIST-ICU Investigators and the Brazilian Research in Intensive Care Network (BRICNet): Organizational factors associated with adherence to low tidal volume ventilation: A secondary analysis of the CHECKLIST-ICU database. Ann Intensive Care 2020; 10:68
12. Marini JJ, Gattinoni L: Management of COVID-19 respiratory distress. JAMA 2020; 323:2329–2330
13. Rice TW, Janz DR: In defense of evidence-based medicine for the treatment of COVID-19 acute respiratory distress syndrome. Ann Am Thorac Soc 2020; 17:787–789
14. Levy E, Scott S, Tran T, et al.: Adherence to Lung protective ventilation in patients with coronavirus disease 2019. Crit Care Explor 2021; 3:e0512
15. Botta M, Tsonas AM, Pillay J, et al.; PRoVENT-COVID Collaborative Group: Ventilation management and clinical outcomes in invasively ventilated patients with COVID-19 (PRoVENT-COVID): A national, multicentre, observational cohort study. Lancet Respir Med 2021; 9:139–148
16. Sjoding MW, Hofer TP, Co I, et al.: Differences between patients in whom physicians agree and disagree about the diagnosis of acute respiratory distress syndrome. Ann Am Thorac Soc 2019; 16:258–264
17. Berlin DA, Gulick RM, Martinez FJ: Severe Covid-19. N Engl J Med 2020; 383:2451–2460
18. Law AC, Stevens JP, Hohmann S, et al.: Patient outcomes after the introduction of statewide ICU nurse staffing regulations. Crit Care Med 2018; 46:1563–1569
19. Needleman J, Buerhaus P, Mattke S, et al.: Nurse-staffing levels and the quality of care in hospitals. N Engl J Med 2002; 346:1715–1722
20. Horby P, Lim WS, Emberson JR, et al.; RECOVERY Collaborative Group: Dexamethasone in hospitalized patients with Covid-19. N Engl J Med 2021; 384:693–704
21. Walkey AJ, Kumar VK, Harhay MO, et al.: The Viral Infection and Respiratory Illness Universal Study (VIRUS): An international registry of coronavirus 2019-related critical illness. Crit Care Explor 2020; 2:e0113
22. Domecq JP, Lal A, Sheldrick CR, et al.; Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study (VIRUS): COVID-19 Registry Investigator Group: Outcomes of patients with coronavirus disease 2019 receiving organ support therapies: The International Viral Infection and Respiratory Illness Universal Study Registry. Crit Care Med 2021; 49:437–448
23. Harris PA, Taylor R, Thielke R, et al.: Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009; 42:377–381
24. Merlo J, Chaix B, Ohlsson H, et al.: A brief conceptual tutorial of multilevel analysis in social epidemiology: Using measures of clustering in multilevel logistic regression to investigate contextual phenomena. J Epidemiol Community Health 2006; 60:290–297
25. Jakobsen JC, Gluud C, Wetterslev J, et al.: When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts. BMC Med Res Methodol 2017; 17:162
26. Thompson AE, Ranard BL, Wei Y, et al.: Prone positioning in awake, nonintubated patients with COVID-19 hypoxemic respiratory failure. JAMA Intern Med 2020; 180:1537–1539
27. Azoulay E, de Waele J, Ferrer R, et al.: International variation in the management of severe COVID-19 patients. Crit Care 2020; 24:486
28. Gattinoni L, Chiumello D, Rossi S: COVID-19 pneumonia: ARDS or not? Crit Care 2020; 24:154
29. Weiss CH, Baker DW, Weiner S, et al.: Low tidal volume ventilation use in acute respiratory distress syndrome. Crit Care Med 2016; 44:1515–1522
APPENDIX
The journal is requested to PubMed index the list of The Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study (VIRUS): COVID-19 Registry Investigator Group: COVID-19 Registry Investigator Group as collaborative coauthors submitted along with this article: 1) Columbia: Clinica Medical SAS: Oscar Y. Gavidia, Felipe Pachon, and Yeimy A. Sanchez; 2) Egypt: Helwan University: Mohamed El Kassas, Mohamed Badr, Ahmed Tawheed, Ahmed Tawheed, and Hend Yahia; 3) Japan: Center Hospital of the National Center for Global Health and Medicine: Wataru Matsuda and Reina Suzuki; Jichi Medical University Saitama Medical Center: Reina Suzuki, Masamitsu Sanui, and Sho Horikita; Sapporo City General Hospital: Yuki Itagaki, Akira Kodate, Reina Suzuki, Akira Kodate, Yuki Takahashi, and Koyo Moriki; 4) Pakistan: Dow University Hospital: Muhammad Sohaib Asghar, Mashaal Syed, and Syed Anosh Ali Naqvi; 5) Russia: Kuban State Medical University with affiliation Territorial Hospital 2: Igor Borisovich Zabolotskikh, Konstantin Dmitrievich Zybin, Sergey Vasilevich Sinkov, and Tatiana Sergeevna Musaeva; 6) Saudi Arabia: King Saud University: Mohammed A. Almazyad, Mohammed I. Alarifi, Jara M. Macarambon, Ahmad Abdullah Bukhari, Hussain A. Albahrani, Kazi N. Asfina, and Kaltham M. Aldossary; 7) Serbia: CHC Bezanijska kosa, Belgrade: Marija Zdravkovic, Zoran Todorovic, Viseslav Popadic, and Slobodan Klasnja; University Hospital Center “Dr Dragisa Misovic-Dedinje”: Predrag D. Stevanovic, Dejan S. Stojakov, Duska K. Ignjatovic, Suzana C. Bojic, Marina M. Bobos, Irina B. Nenadic, Milica S. Zaric, Marko D. Djuric, and Vladimir R. Djukic; 8) Spain: Hospital Universitario La Paz: Santiago Yus and Belen C. Martin; 9) Turkey: Marmara University: Uluhan Sili, Huseyin Bilgin, and Pinar Ay; and 10) United States of America: Allina Health (Abbott Northwestern Hospital, United Hospital, and Mercy Hospital in Minnesota): Roman R. Melamed, David M. Tierney, Love A. Patel, Vino S. Raj, Barite U. Dawud, Narayana Mazumder, Abbey Sidebottom, Alena M. Guenther, Benjamin D. Krehbiel, Nova J. Schmitz, Stacy L. Jepsen, Lynn Sipsey, Anna Schulte, Whitney Wunderlich, and Cecely Hoyt; Advocate Christ Medical Center: Kenneth W. Dodd, Nicholas Goodmanson, Kathleen Hesse, Paige Bird, Chauncey Weinert, Nathan Schoenrade, Abdulrahman Altaher, Esmael Mayar, Matthew Aronson, Tyler Cooper, Monica Logan, Brianna Miner, and Gisele Papo; Al-Amiri and Jaber Al-Ahmed Hospitals, Kuwait Extracorporeal Life Support Program: Abdulrahman Al-Fares; Ascension St.Vincent Hospital: Anmol Kharbanda, Sunil Jhajhria, and Zachary Fyffe; Atrium Health Navicent: Amy B. Christie, Dennis W. Ashley, and Rajani Adiga; Augusta University Medical Center: Andrea Sikora Newsome, Christy C. Forehand, Rebecca Bruning, and Timothy W. Jones; Aultman Hospital: Moldovan Sabov, Fatema Zaidi, Fiona Tissavirasingham, and Dhatri Malipeddi; Beth Israel Deaconess Medical Center: Valerie M. Banner-Goodspeed, Somnath Bose, Lauren E. Kelly, Melisa Joseph, Marie McGourty, Krystal Capers, Benjamin Hoenig, Maria C. Karamourtopoulos, Anica C. Law, and Elias N. Baedorf Kassis; Boston University School of Medicine, Boston, MA: Allan J. Walkey, Sushrut S. Waikar, Michael A. Garcia, Mia Colona, Zoe Kibbelaar, Michael Leong, Daniel Wallman, Kanupriya Soni, Jennifer Maccarone, Joshua Gilman, Ycar Devis, Joseph Chung, Munizay Paracha, David N. Lumelsky, Madeline DiLorenzo, Najla Abdurrahman, and Shelsey Johnson; Buffalo General Medical Center: Kimberly Zammit, Patrick J. McGrath, William Loeffler, and Maya R. Chilbert; Centre Hospitalier Jolimont: Jean-Baptiste Mesland, Pierre Henin, Hélène Petre, Isabelle Buelens, and Anne-Catherine Gerard; Charleston Area Medical Center: Rayan E. Ihle, Shelda A. Martin, and Elaine A. Davis; Christus Spohn Shoreline Corpus Christi: Salim Surani, Joshua White, Aftab Khan, and Rahul Dhahwal; Clements University Hospital at UT Southwestern Medical Center: Sreekanth Cheruku, Farzin Ahmed, Christopher Deonarine, Ashley Jones, Mohammad-Ali Shaikh, David Preston, and Jeanette Chin; Cox Medical Center Springfield: Steven K. Daugherty, Sam Atkinson, and Kelly Shrimpton; Duke University Hospital: Raquel R. Bartz, Vijay Krishnamoorthy, Bryan Kraft, Aaron Pulsipher, Eugene Friedman, and Sachin Mehta; George Washington University: David P. Yamane, Ivy Benjenk, and Nivedita Prasanna; Howard University Hospital: Norma Smalls; Lahey Hospital & Medical Center: Katharine Nault; M Health- Fairview, University of Minnesota: Ronald A. Reilkoff, Julia A. Heneghan, Sarah Eichen, Lexie Goertzen, Scott Rajala, Ghislaine Feussom, and Ben Tang; Mayo Clinic Arizona : Rodrigo Cartin-Ceba, Ayan Sen, Amanda Palacios, and Giyth M. Mahdi; Mayo Clinic Rochester: Rahul Kashyap, Ognjen Gajic, Vikas Bansal, Aysun Tekin, Amos Lal, John C. O’Horo, Neha N. Deo, Mayank Sharma, Shahraz Qamar, Juan Pablo Domecq, Romil Singh, and Alex Niven; Mayo Clinic, Eau Claire : Abigail T. La Nou, Marija Bogojevic, and Barbara Mullen; Mayo Clinic, Florida: Devang Sanghavi, Pramod Guru, Pablo Moreno Franco, Karthik Gnanapandithan, Hollie Saunders, Zachary Fleissner, Juan Garcia, Alejandra Yu Lee Mateus, Siva Naga Yarrarapu, Nirmaljot Kaur, and Abhisekh Giri; Mayo Clinic, Mankato: Syed Anjum Khan, Nitesh Kumar Jain, and Thoyaja Koritala; Mercy Hospital, Saint Louis: Chakradhar Venkata, Miriam Engemann, and Annamarie Mantese; MetroHealth Medical Center: Yasir Tarabichi, Adam Perzynski, Christine Wang, and Dhatri Kotekal; OSF Saint Francis Medical Center: Bhagat S. Aulakh, Sandeep Tripathi, Jennifer A. Bandy, Lisa M. Kreps, Dawn R. Bollinger, and Jennifer A. Bandy; Parkview Health System, Fort Wayne: Roger Scott Stienecker, Andre G. Melendez, Tressa A. Brunner, Sue M. Budzon, Jessica L. Heffernan, Janelle M. Souder, Tracy L. Miller, and Andrea G. Maisonneuve; Sarasota Memorial Hospital: Antonia L. Vilella, Sara B. Kutner, Kacie Clark, and Danielle Moore; St. Joseph Mercy Ann Arbor, Ann Arbor: Harry L. Anderson III, Dixy Rajkumar, Ali Abunayla, and Jerrilyn Heiter; St. Joseph’s Candler Health System: Howard A. Zaren, Stephanie J. Smith, Grant C. Lewis, Lauren Seames, Cheryl Farlow, Judy Miller, and Gloria Broadstreet; St. Agnes Hospital: Anthony Martinez, Micheal Allison, Aniket Mittal, Rafael Ruiz, Aleta Skaanland, and Robert Ross; The Children’s Hospital at OU Medicine: Neha Gupta, Tracy L. Jones, Shonda C. Ayers, Amy B. Harrell, and Dr. Brent R. Brown; UC San Diego Medical Center—Hillcrest: Abdurrahman Husain, Atul Malhotra, and Qais Zawaydeh; University Clinical Hospital, Mostar, Bosnia, and Herzegovina: Dragana Markotić and Ivana Bošnjak; University Hospital San Antonio: Emily A. Vail, Susannah Nicholson, Rachelle B. Jonas, AnnaRose E. Dement, William Tang, Mark DeRosa, and Robert E. Villarreal; University of Iowa Carver College of Medicine: Patrick W. McGonagill, Colette Galet, Janice Hubbard, David Wang, Lauren Allan, Aditya Badheka, and Madhuradhar Chegondi; University of Miami Miller School of Medicine: Roger A. Alvarez, Amarilys Alarcon-Calderon, Marie Anne Sosa, Sunita K. Mahabir, and Mausam J. Patel; University of Pittsburgh: Faraaz Ali Shah, Byron Chuan, Sagar L. Rawal, and Manal Piracha; University of Utah Health: Joseph E. Tonna, Nicholas M. Levin, Kayte Suslavich, Rachel Tsolinas, Zachary T. Fica, and Chloe R. Skidmore; University of Vermont Larner College of Medicine: Renee D. Stapleton, Anne E. Dixon, Olivia Johnson, Sara S. Ardren, Stephanie Burns, Anna Raymond, Erika Gonyaw, Kevin Hodgdon, Chloe Housenger, Benjamin Lin, Karen McQuesten, Heidi Pecott-Grimm, Julie Sweet, and Sebastian Ventrone; Valleywise Health (formerly Maricopa Medical Center): Murtaza Akhter, Rania Abdul Rahman, and Mary Mulrow; Wake Forest University School of Medicine; Wake Forest Baptist Health Network: Ashish K. Khanna, Lynne Harris, Bruce Cusson, Jacob Fowler, David Vaneenenaam, Glen McKinney, Imoh Udoh, and Kathleen Johnson; and Wyoming Medical Center: Vishwanath Pattan, Jessica Papke, Ismail Jimada, Nida Mhid, and Samuel Chakola.