Use of short-term MCS in the United States has increased rapidly, whereas rates of in-hospital mortality have decreased. These changes have taken place in the context of declining hospital costs associated with short-term MCS.

Purpose To describe the trajectory of respiratory failure in COVID-19 and explore factors associated with risk of invasive mechanical ventilation (IMV). Materials and methods A retrospective, observational cohort study of 112 inpatient adults diagnosed with COVID-19 between March 12 and April 16, 2020. Data were manually extracted from electronic medical records. Multivariable and Univariable regression were used to evaluate association between baseline characteristics, initial serum markers and the outcome of IMV. Results Our cohort had median age of 61 (IQR 45–74) and was 66% male. In-hospital mortality was 6% (7/112). ICU mortality was 12.8% (6/47), and 18% (5/28) for those requiring IMV. Obesity (OR 5.82, CI 1.74–19.48), former (OR 8.06, CI 1.51–43.06) and current smoking status (OR 10.33, CI 1.43–74.67) were associated with IMV after adjusting for age, sex, and high prevalence comorbidities by multivariable analysis. Initial absolute lymphocyte count (OR 0.33, CI 0.11–0.96), procalcitonin (OR 1.27, CI 1.02–1.57), IL-6 (OR 1.17, CI 1.03–1.33), ferritin (OR 1.05, CI 1.005–1.11), LDH (OR 1.57, 95% CI 1.13–2.17) and CRP (OR 1.13, CI 1.06–1.21), were associated with IMV by univariate analysis. Conclusions Obesity, smoking history, and elevated inflammatory markers were associated with increased need for IMV in patients with COVID-19.

Purpose: To describe the trajectory of respiratory failure in COVID-19 and explore factors associated with risk of invasive mechanical ventilation (IMV). Materials and Methods: A retrospective, observational cohort study of 112 inpatient adults diagnosed with COVID-19 between March 12 and April 16, 2020. Data were manually extracted from electronic medical records. Multivariable and Univariable regression were used to evaluate association between baseline characteristics, initial serum markers and the outcome of IMV. Results: Our cohort had median age of 61 (IQR 45-74) and was 66% male. In-hospital mortality was 6% (7/112). ICU mortality was 12.8% (6/47), and 18% (5/28) for those requiring IMV. Obesity (OR 5.82, CI 1.74-19.48), former (OR 8.06, CI 1.51-43.06) and current smoking status (OR 10.33, CI 1.43-74.67) were associated with IMV after adjusting for age, sex, and high prevalence comorbidities by multivariable analysis. Initial absolute lymphocyte count (OR 0.33, CI 0.11-0.96), procalcitonin (OR 1.27, CI 1.02-1.57), IL-6 (OR 1.17, CI 1.03-1.33), ferritin (OR 1.05, CI 1.005-1.11), LDH (OR 1.57, 95% CI 1.13-2.17) and CRP (OR 1.13, CI 1.06-1.21), were associated with IMV by univariate analysis. Conclusions: Obesity, smoking history, and elevated inflammatory markers were associated with increased need for IMV in patients with COVID-19.

Study Objectives: Home sleep apnea testing (HSAT) is an efficient and cost-effective method of diagnosing obstructive sleep apnea (OSA). However, nondiagnostic HSAT necessitates additional tests that erode these benefits, delaying diagnoses and increasing costs. Our objective was to optimize this diagnostic pathway by using predictive modeling to identify patients who should be referred directly to polysomnography (PSG) due to their high probability of nondiagnostic HSAT. Methods: HSAT performed as the initial test for suspected OSA within the Veterans Administration Greater Los Angeles Healthcare System was analyzed retrospectively. Data were extracted from pre-HSAT questionnaires and the medical record. Tests were diagnostic if there was a respiratory event index (REI) ≥ 5 events/h. Tests with REI < 5 events/h or technical inadequacy-two outcomes requiring additional testing with PSG-were considered nondiagnostic. Standard logistic regression models were compared with models trained using machine learning techniques. Results: Models were trained using 80% of available data and validated on the remaining 20%. Performance was evaluated using partial area under the precision-recall curve (pAUPRC). Machine learning techniques consistently yielded higher pAUPRC than standard logistic regression, which had pAUPRC of 0.574. The random forest model outperformed all other models (pAUPRC 0.862). Preferred calibration of this model yielded the following: sensitivity 0.46, specificity 0.95, positive predictive value 0.81, negative predictive value 0.80. Conclusions: Compared with standard logistic regression models, machine learning models improve prediction of patients requiring in-laboratory PSG. These models could be implemented into a clinical decision support tool to help clinicians select the optimal test to diagnose OSA.

BackgroundDatathons facilitate collaboration between clinicians, statisticians, and data scientists in order to answer important clinical questions. Previous datathons have resulted in numerous publications of interest to the critical care community and serve as a viable model for interdisciplinary collaboration.ObjectiveWe report on an open-source software called Chatto that was created by members of our group, in the context of the second international Critical Care Datathon, held in September 2015.MethodsDatathon participants formed teams to discuss potential research questions and the methods required to address them. They were provided with the Chatto suite of tools to facilitate their teamwork. Each multidisciplinary team spent the next 2 days with clinicians working alongside data scientists to write code, extract and analyze data, and reformulate their queries in real time as needed. All projects were then presented on the last day of the datathon to a panel of judges that consisted of clinicians and scientists.ResultsUse of Chatto was particularly effective in the datathon setting, enabling teams to reduce the time spent configuring their research environments to just a few minutes—a process that would normally take hours to days. Chatto continued to serve as a useful research tool after the conclusion of the datathon.ConclusionsThis suite of tools fulfills two purposes: (1) facilitation of interdisciplinary teamwork through archiving and version control of datasets, analytical code, and team discussions, and (2) advancement of research reproducibility by functioning postpublication as an online environment in which independent investigators can rerun or modify analyses with relative ease. With the introduction of Chatto, we hope to solve a variety of challenges presented by collaborative data mining projects while improving research reproducibility.

Pulmonary complications of hematopoietic stem cell transplantation (HSCT) are responsible for significant morbidity and mortality. There is no literature to date on the use of extracorporeal membranous oxygenation (ECMO) in patients with pulmonary manifestations of chronic graft-versus-host disease after HSCT. We describe the successful use of ECMO for refractory respiratory failure in such a patient.

Mortality increased with boarding of critically ill patients. Further research is needed to identify safer practices for managing patients during periods of high ICU occupancy.

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