Racial Bias in Pulse Oximetry Measurement Among Patients About to Undergo Extracorporeal Membrane Oxygenation in 2019-2020: A Retrospective Cohort Study.

BACKGROUND: Pulse oximeters may produce less accurate results in non-White patients. RESEARCH QUESTION: Do pulse oximeters detect arterial hypoxemia less effectively in Black, Hispanic, and/or Asian patients than in White patients in respiratory failure and about to undergo extracorporeal membrane oxygenation (ECMO)? STUDY DESIGN AND METHODS: Data on adult patients with respiratory failure readings 6 h before ECMO were provided by the Extracorporeal Life Support Organization registry. Data was collected from 324 centers between January 2019 and July 2020. Our primary analysis was of rates of occult hypoxemia-low arterial oxygen saturation (Sao2 ≤ 88%) on arterial blood gas measurement despite a pulse oximetry reading in the range of 92% to 96%. RESULTS: The rate of pre-ECMO occult hypoxemia, that is, arterial oxygen saturation (Sao2) ≤ 88%, was 10.2% (95% CI, 6.2%-15.3%) for 186 White patients with peripheral oxygen saturation (Spo2) of 92% to 96%; 21.5% (95% CI, 11.3%-35.3%) for 51 Black patients (P = .031 vs White); 8.6% (95% CI, 3.2%-17.7%) for 70 Hispanic patients (P = .693 vs White); and 9.2% (95% CI, 3.5%-19.0%) for 65 Asian patients (P = .820 vs White). Black patients with respiratory failure had a statistically significantly higher risk of occult hypoxemia with an OR of 2.57 (95% CI, 1.12-5.92) compared with White patients (P = .026). The risk of occult hypoxemia for Hispanic and Asian patients was equivalent to that of White patients. In a secondary analysis of patients with Sao2 ≤ 88% despite Spo2 > 96%, Black patients had more than three times the risk compared with White patients (OR, 3.52; 95% CI, 1.12-11.10; P = .032). INTERPRETATION: Compared with White patients, the prevalence of occult hypoxemia was higher in Black patients than in White patients about to undergo ECMO for respiratory failure, but it was comparable in Hispanic and Asian patients compared with White patients.

Deep learning to detect acute respiratory distress syndrome on chest radiographs: a retrospective study with external validation.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common, but under-recognised, critical illness syndrome associated with high mortality. An important factor in its under-recognition is the variability in chest radiograph interpretation for ARDS. We sought to train a deep convolutional neural network (CNN) to detect ARDS findings on chest radiographs. METHODS: CNNs were pretrained on 595 506 radiographs from two centres to identify common chest findings (eg, opacity and effusion), and then trained on 8072 radiographs annotated for ARDS by multiple physicians using various transfer learning approaches. The best performing CNN was tested on chest radiographs in an internal and external cohort, including a subset reviewed by six physicians, including a chest radiologist and physicians trained in intensive care medicine. Chest radiograph data were acquired from four US hospitals. FINDINGS: In an internal test set of 1560 chest radiographs from 455 patients with acute hypoxaemic respiratory failure, a CNN could detect ARDS with an area under the receiver operator characteristics curve (AUROC) of 0·92 (95% CI 0·89-0·94). In the subgroup of 413 images reviewed by at least six physicians, its AUROC was 0·93 (95% CI 0·88-0·96), sensitivity 83·0% (95% CI 74·0-91·1), and specificity 88·3% (95% CI 83·1-92·8). Among images with zero of six ARDS annotations (n=155), the median CNN probability was 11%, with six (4%) assigned a probability above 50%. Among images with six of six ARDS annotations (n=27), the median CNN probability was 91%, with two (7%) assigned a probability below 50%. In an external cohort of 958 chest radiographs from 431 patients with sepsis, the AUROC was 0·88 (95% CI 0·85-0·91). When radiographs annotated as equivocal were excluded, the AUROC was 0·93 (0·92-0·95). INTERPRETATION: A CNN can be trained to achieve expert physician-level performance in ARDS detection on chest radiographs. Further research is needed to evaluate the use of these algorithms to support real-time identification of ARDS patients to ensure fidelity with evidence-based care or to support ongoing ARDS research. FUNDING: National Institutes of Health, Department of Defense, and Department of Veterans Affairs.

Comparing Clinical Features and Outcomes in Mechanically Ventilated Patients with COVID-19 and Acute Respiratory Distress Syndrome.

Rationale: Patients with severe coronavirus disease (COVID-19) meet clinical criteria for the acute respiratory distress syndrome (ARDS), yet early reports suggested they differ physiologically and clinically from patients with non-COVID-19 ARDS, prompting treatment recommendations that deviate from standard evidence-based practices for ARDS. Objectives: To compare respiratory physiology, clinical outcomes, and extrapulmonary clinical features of severe COVID-19 with non-COVID-19 ARDS. Methods: We performed a retrospective cohort study, comparing 130 consecutive mechanically ventilated patients with severe COVID-19 with 382 consecutive mechanically ventilated patients with non-COVID-19 ARDS. Initial respiratory physiology and 28-day outcomes were compared. Extrapulmonary manifestations (inflammation, extrapulmonary organ injury, and coagulation) were compared in an exploratory analysis. Results: Comparison of patients with COVID-19 and non-COVID-19 ARDS suggested small differences in respiratory compliance, ventilatory efficiency, and oxygenation. The 28-day mortality was 30% in patients with COVID-19 and 38% in patients with non-COVID-19 ARDS. In adjusted analysis, point estimates of differences in time to breathing unassisted at 28 days (adjusted subdistributional hazards ratio, 0.98 [95% confidence interval (CI), 0.77-1.26]) and 28-day mortality (risk ratio, 1.01 [95% CI, 0.72-1.42]) were small for COVID-19 versus non-COVID-19 ARDS, although the confidence intervals for these estimates include moderate differences. Patients with COVID-19 had lower neutrophil counts but did not differ in lymphocyte count or other measures of systemic inflammation. Conclusions: In this single-center cohort, we found no evidence for large differences between COVID-19 and non-COVID-19 ARDS. Many key clinical features of severe COVID-19 were similar to those of non-COVID-19 ARDS, including respiratory physiology and clinical outcomes, although our sample size precludes definitive conclusions. Further studies are needed to define COVID-19-specific pathophysiology before a deviation from evidence-based treatment practices can be recommended.

Creating an Intensive Care Unit From a Postanesthesia Care Unit for the COVID-19 Surge at the Veterans Affairs Ann Arbor Healthcare System.

OBJECTIVES: To prepare for the predicted surge of patients with COVID-19 in Southeast Michigan, the US Department of Veterans Affairs Ann Arbor Healthcare System engineered, built, and staffed a 12-bed intensive care unit (ICU) from the existing postanesthesia care unit (PACU). OBSERVATIONS: Considerations including floor planning, reversal of airflow, strict airborne precautions, sealing off a dedicated nursing station, and developing an infection control plan in an open care unit. A staffing model was created that included anesthesiologist intensivists, advanced practice providers, residents, certified registered nurse anesthetists, and perioperative nurses working alongside ICU trained nurses. Challenges arose in infection control, communication, mechanical ventilation using anesthesia machines, providing renal replacement therapy, and maintaining patient privacy in an open unit. CONCLUSIONS: This article describes the setup, challenges, and solutions that allowed the creation of the PACU-ICU to help serve veterans and civilians during a time of unprecedented strain on the health care system due to COVID-19.