Jason Y. Adams

The Patient with Asthma in the Emergency Department

Asthma is a highly prevalent disease that presents commonly to the emergency department (ED) in acute exacerbation. Recent asthma treatment guidelines have added content dedicated to the management of acute exacerbations. Effective management of an exacerbation requires rapid assessment of severity through physical examination, measurement of peak expiratory flow rate, and response to initial treatment. Most therapies are directed at alleviating bronchospasm and decreasing airway inflammation. While inhaled short-acting beta-agonists, systemic corticosteroids, and supplemental oxygen are the initial and often only therapies required for patients with mild moderate exacerbations, high-dose beta agonists and inhaled anti-cholinergics should also be given to patients with severe exacerbations. Adjunctive therapy with intravenous magnesium and Heliox-driven nebulization of bronchodilators should be considered for patients presenting with severe and very severe exacerbations. Early recognition and appropriate management of respiratory failure are required to mitigate the risk of complications including death. Disposition should be determined based on serial assessments of the response to therapy over the first 4 h in the ED. Patients stable for discharge should receive medications, asthma education including a written asthma action plan, and should have follow-up scheduled for them by ED staff. Rapid implementation of evidence-based, multi-disciplinary care is required to ensure the best possible outcomes for this potentially treatable disease.

Development and Validation of a Multi-Algorithm Analytic Platform to Detect Off-Target Mechanical Ventilation

Healthcare-specific analytic software is needed to process the large volumes of streaming physiologic waveform data increasingly available from life support devices such as mechanical ventilators. Detection of clinically relevant events from these data streams will advance understanding of critical illness, enable real-time clinical decision support, and improve both clinical outcomes and patient experience. We used mechanical ventilation waveform data (VWD) as a use case to address broader issues of data access and analysis including discrimination between true events and waveform artifacts. We developed an open source data acquisition platform to acquire VWD, and a modular, multi-algorithm analytic platform (ventMAP) to enable automated detection of off-target ventilation (OTV) delivery in critically-ill patients. We tested the hypothesis that use of artifact correction logic would improve the specificity of clinical event detection without compromising sensitivity. We showed that ventMAP could accurately detect harmful forms of OTV including excessive tidal volumes and common forms of patient-ventilator asynchrony, and that artifact correction significantly improved the specificity of event detection without decreasing sensitivity. Our multi-disciplinary approach has enabled automated analysis of high-volume streaming patient waveform data for clinical and translational research, and will advance the study and management of critically ill patients requiring mechanical ventilation.

Management of Mechanical Ventilation in Decompensated Heart Failure

Mechanical ventilation (MV) is a life-saving intervention for respiratory failure, including decompensated congestive heart failure. MV can reduce ventricular preload and afterload, decrease extra-vascular lung water, and decrease the work of breathing in heart failure. The advantages of positive pressure ventilation must be balanced with potential harm from MV: volutrauma, hyperoxia-induced injury, and difficulty assessing readiness for liberation. In this review, we will focus on cardiac, pulmonary, and broader effects of MV on patients with decompensated HF, focusing on practical considerations for management and supporting evidence.

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

BACKGROUND As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. OBJECTIVE We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA). METHODS Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. RESULTS We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. CONCLUSIONS Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

Effects of positive end-expiratory pressure and recruitment maneuvers in a ventilator-induced injury mouse model

Background Positive-pressure mechanical ventilation is an essential therapeutic intervention, yet it causes the clinical syndrome known as ventilator-induced lung injury. Various lung protective mechanical ventilation strategies have attempted to reduce or prevent ventilator-induced lung injury but few modalities have proven effective. A model that isolates the contribution of mechanical ventilation on the development of acute lung injury is needed to better understand biologic mechanisms that lead to ventilator-induced lung injury. Objectives To evaluate the effects of positive end-expiratory pressure and recruitment maneuvers in reducing lung injury in a ventilator-induced lung injury murine model in short- and longer-term ventilation. Methods 5–12 week-old female BALB/c mice (n = 85) were anesthetized, placed on mechanical ventilation for either 2 hrs or 4 hrs with either low tidal volume (8 ml/kg) or high tidal volume (15 ml/kg) with or without positive end-expiratory pressure and recruitment maneuvers. Results Alteration of the alveolar-capillary barrier was noted at 2 hrs of high tidal volume ventilation. Standardized histology scores, influx of bronchoalveolar lavage albumin, proinflammatory cytokines, and absolute neutrophils were significantly higher in the high-tidal volume ventilation group at 4 hours of ventilation. Application of positive end-expiratory pressure resulted in significantly decreased standardized histology scores and bronchoalveolar absolute neutrophil counts at low- and high-tidal volume ventilation, respectively. Recruitment maneuvers were essential to maintain pulmonary compliance at both 2 and 4 hrs of ventilation. Conclusions Signs of ventilator-induced lung injury are evident soon after high tidal volume ventilation (as early as 2 hours) and lung injury worsens with longer-term ventilation (4 hrs). Application of positive end-expiratory pressure and recruitment maneuvers are protective against worsening VILI across all time points. Dynamic compliance can be used guide the frequency of recruitment maneuvers to help ameloriate ventilator-induced lung injury.

Cosmetic Talc–Related Pulmonary Granulomatosis

Inhalation of cosmetic talc can lead to pulmonary foreign-body granulomatosis, though fewer than 10 cases of inhaled cosmetic talc–related pulmonary granulomatosis have been reported in adults. We report the case of a 64-year-old man with diffuse, bilateral pulmonary nodules and ground glass opacities associated with chronic inhalation of cosmetic talc. Transbronchial biopsy showed peribronchiolar foreign-body granulomas. After cessation of talc exposure, the patient demonstrated clinical and radiographic improvement without the use of corticosteroids. This case demonstrates that a conservative approach with cessation of exposure alone, without the use of corticosteroids, can be an effective therapy in cosmetic talc–related pulmonary granulomatosis.

Muscarinic antagonists in early stage clinical development for the treatment of asthma

ABSTRACT Introduction: Parasympathetic neurons utilize the neurotransmitter acetylcholine to modulate and constrict airway smooth muscles at the muscarinic acetylcholine receptor. Inhaled agents that antagonize the muscarinic (M) acetylcholine receptor, particularly airway M3 receptors, have increasing data supporting use in persistent asthma. Areas covered: Use of inhaled long-acting muscarinic antagonists (LAMA) in asthma is explored. The LAMA tiotropium is approved for maintenance in symptomatic asthma patients despite the use of inhaled corticosteroids (ICS), leukotriene receptor antagonists (LTRA) and/or long-acting beta2 agonists (LABA). LAMA agents currently approved for chronic obstructive pulmonary disease (COPD) include tiotropium, glycopyrrolate/glycopyrronium, umeclidinium and aclidinium. These agents are reviewed for their pharmacological differences and clinical trials in asthma. Expert opinion: Current guidelines place inhaled LAMAs as adjunctive maintenance therapy in symptomatic asthma not controlled by an ICS and/or a LTRA. LAMA agents will play an increasing role in moderate to severe symptomatic asthma patients. Additional LAMA agents are likely to seek a maintenance indication perhaps as a combined inhaler with an ICS or with an ICS and a LABA. These fixed-dose combination inhalers are being tested in COPD and asthma patients. Once-a-day dosing of inhaled LAMA agents in severe asthma patients will likely become the future standard.

Additional Draft Genome Sequences of Escherichia coli Strains Isolated from Septic Patients

ABSTRACT We present the draft genome sequences of eight uropathogenic strains of Escherichia coli isolated from blood cultures collected from patients with sepsis, an extension of previous sequencing work from the same cohort.

Draft genome sequence of Klebsiella pneumoniae UCD-JA29 isolated from a patient with sepsis

ABSTRACT Here, we present the 6,155,188-bp draft genome sequence of Klebsiella pneumoniae UCD-JA29, isolated from blood cultures from a patient with sepsis at the University of California, Davis Medical Center in Sacramento, California, USA.