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Negative-pressure pulmonary edema (NPPE) has become a well-recognized, though uncommon, complication of acute upper airway obstruction. While initially described in the setting of peri-operative endotracheal intubation, NPPE has been increasingly observed in numerous other clinical scenarios. In this report, we describe a case of NPPE that occurred after a scheduled cardioversion and transesophageal echocardiogram (TEE). We suspect the attempt at TEE probe placement inadvertently led to tracheal insertion as suggested by excessive resistance, poor visualization, stridor, and subsequent acute pulmonary edema. While supportive treatment when recognized can lead to rapid improvement, it is important to recognize NPPE as a possible complication of this commonly indicated procedure. LEARNING OBJECTIVE: Excessive resistance is a common challenge during the transesophageal echocardiogram (TEE) procedure and may be the result of incidental tracheal insertion. Negative-pressure pulmonary edema (NPPE) may result from this acute upper airway obstruction. Thus, NPPE should be on the differential for patients who present with unexplained non-cardiogenic pulmonary edema and should be recognized as a possible complication of the TEE procedure.

Early identification of high risk heart failure (HF) patients is key to timely allocation of life-saving therapies. Hemodynamic assessments can facilitate risk stratification and enhance understanding of HF trajectories. However, risk assessment for HF is a complex, multi-faceted decision-making process that can be challenging. Previous risk models for HF do not integrate invasive hemodynamics or support missing data, and use statistical methods prone to bias or machine learning methods that are not interpretable. To address these limitations, this paper presents CARNA, a hemodynamic risk stratification and phenotyping framework for advanced HF that takes advantage of the explainability and expressivity of machine learned Multi-Valued Decision Diagrams (MVDDs). This interpretable framework learns risk scores that predict the probability of patient outcomes, and outputs descriptive patient phenotypes (sets of features and thresholds) that characterize each predicted risk score. CARNA incorporates invasive hemodynamics and can make predictions on missing data. The CARNA models were trained and validated using a total of five advanced HF patient cohorts collected from previous trials, and compared with six established HF risk scores and three traditional ML risk models. CARNA provides robust risk stratification, outperforming all previous benchmarks. Although focused on advanced HF, the CARNA framework is general purpose and can be used to learn risk stratifications for other diseases and medical applications.

Supplemental Material, sj-pdf-1-scv-10.1177_10892532221080382 for A Novel Hemodynamic Index of Post-operative Right Heart Dysfunction Predicts Mortality in Cardiac Surgical Patients by Ziyad O. Knio, Robert H. Thiele, W. Zachary Wright, Sula Mazimba, Bhiken I. Naik and Matthew C. Hulse in Seminars in Cardiothoracic and Vascular Anesthesia

Supplemental Material, sj-pdf-1-scv-10.1177_10892532221080382 for A Novel Hemodynamic Index of Post-operative Right Heart Dysfunction Predicts Mortality in Cardiac Surgical Patients by Ziyad O. Knio, Robert H. Thiele, W. Zachary Wright, Sula Mazimba, Bhiken I. Naik and Matthew C. Hulse in Seminars in Cardiothoracic and Vascular Anesthesia

Supplemental material, sj-pdf-1-pul-10.1177_20458940211020913 for Health disparities and treatment approaches in portopulmonary hypertension and idiopathic pulmonary arterial hypertension: an analysis of the Pulmonary Hypertension Association Registry by Hilary M. DuBrock, Charles D. Burger, Sonja D. Bartolome, Jeremy P. Feldman, D. Dunbar Ivy, Erika B. Rosenzweig, Jeffrey S. Sager, Kenneth W. Presberg, Stephen C. Mathai, Matthew R. Lammi, James R. Klinger, Michael Eggert, Teresa De Marco, Jean M. Elwing, David Badesch, Todd M. Bull, Linda M. Cadaret, Gautam Ramani, Thenappan Thenappan, H. James Ford, Nadine Al-Naamani, Marc A. Simon, Sula Mazimba, James R. Runo, Murali Chakinala, Evelyn M. Horn, John J. Ryan, Robert P. Frantz and Michael J. Krowka; on behalf of the PHAR Investigators in Pulmonary Circulation