Fazirsiran for Adults with Alpha-1 Antitrypsin Deficiency Liver Disease: A Phase 2 Placebo Controlled Trial (SEQUOIA).

BACKGROUND & AIMS: Homozygous ZZ alpha-1 antitrypsin (AAT) deficiency produces mutant AAT (Z-AAT) proteins in hepatocytes, leading to progressive liver fibrosis. We evaluated the safety and efficacy of an investigational RNA interference therapeutic, fazirsiran, that degrades Z-AAT mRNA, reducing deleterious protein synthesis. METHODS: This ongoing, phase 2 study randomized 40 patients to subcutaneous placebo or fazirsiran 25/100/200 mg. The primary endpoint was percentage change in serum Z-AAT concentration from baseline to Week 16. Patients with fibrosis on baseline liver biopsy received treatment on Day 1, Week 4, and then every 12 weeks, and had a second liver biopsy at or after Weeks 48, 72, or 96. Patients without fibrosis received two doses on Day 1 and Week 4. RESULTS: At Week 16, least-squares mean percent declines in serum Z-AAT concentration were -61%, -83% and -94% with fazirsiran 25/100/200 mg, respectively, versus placebo (all P< .0001). Efficacy was sustained through Week 52. At post-dose liver biopsy, fazirsiran reduced median liver Z-AAT concentration by 93% compared with an increase of 26% with placebo. All fazirsiran-treated patients had histological reduction from baseline in hepatic globule burden. Portal inflammation improved in 5/12 and 0/8 patients with baseline score >0 in the fazirsiran and placebo groups, respectively. Histological METAVIR score improved by >1 point in 7/14 and 3/8 patients with fibrosis >F0 at baseline in the fazirsiran and placebo groups, respectively. No adverse events led to discontinuation and pulmonary function tests remained stable. CONCLUSIONS: Fazirsiran reduced serum and liver concentrations of Z-AAT in a dose dependent manner and reduced hepatic globule burden (NCT03945292).

Leveraging IoTs and Machine Learning for Patient Diagnosis and Ventilation Management in the Intensive Care Unit.

Future healthcare systems will rely heavily on clinical decision support systems (CDSS) to improve the decision-making processes of clinicians. To explore the design of future CDSS, we developed a research-focused CDSS for the management of patients in the intensive care unit that leverages Internet of Things (IoT) devices capable of collecting streaming physiologic data from ventilators and other medical devices. We then created machine learning (ML) models that could analyze the collected physiologic data to determine if the ventilator was delivering potentially harmful therapy and if a deadly respiratory condition, acute respiratory distress syndrome (ARDS), was present. We also present work to aggregate these models into a mobile application that can provide responsive, real-time alerts of changes in ventilation to providers. As illustrated in the recent COVID-19 pandemic, being able to accurately predict ARDS in newly infected patients can assist in prioritizing care. We show that CDSS may be used to analyze physiologic data for clinical event recognition and automated diagnosis, and we also highlight future research avenues for hospital CDSS.

How much do hospitalized adults move? A systematic review and meta-analysis.

AIM: To quantify the type and duration of physical activity performed by hospitalized adults. BACKGROUND: Inactivity is pervasive among hospitalized patients and is associated with increased mortality, functional decline, and cognitive impairment. Objective measurement of activity is necessary to examine associations with clinical outcomes and quantify optimal inpatient mobility interventions. METHODS: We used PRISMA guidelines to search three databases in December 2017 to retrieve original research evaluating activity type and duration among adult acute-care inpatients. We abstracted data on inpatient population, measurement method, monitoring time, activity duration, and study quality. RESULTS: Thirty-eight articles were included in the review and 7 articles were included in the meta-analysis. Study populations included geriatric (n = 5), surgical (n = 5), medical (n = 12), post-stroke (n = 10), psychiatric (n = 2), and critical care inpatients (n = 4). To measure activity, 29% of studies used human observation and 71% used activity monitors. Among inpatient populations, 87-100% of time was spent sitting or lying in-bed. Among medical inpatients monitored over a continuous 24-hour period (n = 7), 70 min per day was spent standing/walking (95% CI 57-83 min). CONCLUSIONS: This review provides a baseline assessment and benchmark of inpatient activity, which can be used to compare inpatient mobility practices. While there is substantial heterogeneity in how researchers measure and define how much inpatients move, there is consistent evidence that patients are mostly inactive and in-bed during hospitalization. Future research is needed to establish standardized methods to accurately and consistently measure inpatient mobility over time.

Balloon dilation atrial septostomy for advanced pulmonary hypertension in patients on prostanoid therapy.

BACKGROUND: Prostanoid therapy improves quality of life and may increase survival in patients with advanced pulmonary hypertension (PH). Balloon dilated atrial septostomy (BDAS) can palliate or bridge to transplantation for patients resistant to medical therapy. The safety and efficacy of BDAS in the prostanoid era has not previously been reported. METHODS: All patients had progressive symptoms despite prostanoid therapy at the time of their first BDAS. Sixteen patients who underwent a total of 23 septostomies between 2004 and 2014 were included in this retrospective case series. RESULTS: Patients were aged 47.6 years ± 11.3 with 12/16 women. Etiologies included idiopathic (7), methamphetamine (6), scleroderma (1), and anorexigen (2). One patient died within 24 hr post-procedure. Thirty-day and 1-year survival were 75% and 64%, respectively. Six of the septostomies were revisions, including two which were ultimately stented. Three subjects were successfully bridged to transplant. Pulmonary capillary wedge pressure (PCWP) increased from a mean of 13 to 17 mm Hg, cardiac index increased from 2.1 to 2.4 L/min/m(2) , and arterial saturation decreased from 90.7 ± 4.3 to 82.5 ± 5.6%. All non-survivors at 30 days were male and had higher baseline serum creatinine, mean RAP, right ventricular end diastolic pressure (RVEDP), and left ventricle (LV) filling pressures, and lower right ventricle (RV) ejection fraction. Mortality was associated with unchanged post-septostomy cardiac output despite an increase in left ventricular end diastolic pressure (LVEDP). CONCLUSIONS: BDAS may be an alternate therapy for select PH patients who have symptomatic progression despite prostanoid therapy. Survival is comparable to prior reports of BDAS in the pre-prostanoid era.

Improving Wildfire Readiness Among Patients With Chronic Obstructive Pulmonary Disease and Asthma: Applying a Population Health Approach to Climate Change.

As a result of climate change, wildfire frequency, duration, and severity are increasing in the United States. Exposure to wildfire-related air pollutants can lead to negative health outcomes, particularly among patients with pre-existing respiratory diseases (eg, asthma and chronic obstructive pulmonary disease) and those who are at higher risk for developing these conditions. Underserved communities are disproportionately affected for multiple reasons, including lack of financial and social resources, increased exposure to air pollutants at home and at work, and impaired access to healthcare. To best serve clinically high-risk and underserved populations, health systems must leverage community public health data, develop and mobilize a wildfire preparedness action plan to identify populations at high risk, and implement interventions to mitigate the consequences of poor air quality. University of California, Davis Health, located at the epicenter of the largest wildfires in California's history, has developed the 5 pillar Wildfire Population Health Approach: (1) identify clinically at-risk and underserved patient populations using well-validated, condition-targeted registries; (2) assemble multidisciplinary care teams to understand the needs of these communities and patients; (3) create custom analytics and wildfire-risk stratification; (4) develop care pathways based on wildfire-risk tiers by disease, risk of exposure, and healthcare access; and (5) identify outcome measures tailored to interventions with a commitment to continuous, iterative improvement efforts. The Wildfire Population Health Approach provides an action plan for health systems and care teams to meet the needs of clinically at-risk and underserved patients affected by the increasing health threat posed by climate change-related wildfires.

COPD with elevated sputum group 2 innate lymphoid cells is characterized by severe disease.

Rationale: Pulmonary innate immune cells play a central role in the initiation and perpetuation of chronic obstructive pulmonary disease (COPD), however the precise mechanisms that orchestrate the development and severity of COPD are poorly understood. Objectives: We hypothesized that the recently described family of innate lymphoid cells (ILCs) play an important role in COPD. Methods: Subjects with COPD and healthy controls were clinically evaluated, and their sputum samples were assessed by flow cytometry. A mouse model of spontaneous COPD [genetically deficient in surfactant protein-D (SP-D -/- )] and ozone (O 3 ) exposure were used to examine the mechanism by which lack of functional SP-D may skew ILC2s to produce IL-17A in combination with IL-5 and IL-13, leading to a mixed inflammatory profile and more severe disease. Measurements and Main Results: COPD was characterized by poor spirometry, sputum inflammation, and the emergence of sputum GATA3 + ILCs (ILC2s), but not T-bet + ILCs (ILC1s) nor RORγt + ILCs (ILC3s). COPD subjects with elevated sputum ILC2s (the ILC2 high group) had worse spirometry and sputum neutrophilia and eosinophilia than healthy and ILC2 low subjects. This was associated with the presence of dual-positive IL-5 + IL-17A + and IL-13 + IL-17A + ILCs and nonfunctional SP-D in the sputum in ILC2 high subjects. SP-D -/- mice showed spontaneous airway neutrophilia. Lack of SP-D in the mouse lung licensed ILC2s to produce IL-17A, which was dose-dependently inhibited by recombinant SP-D. SP-D -/- mice showed enhanced susceptibility to O 3 -induced airway neutrophilia, which was associated with the emergence of inflammatory IL-13 + IL-17A + ILCs. Conclusions: We report that the presence of sputum ILC2s predicts the severity of COPD, and unravel a novel pathway of IL-17A plasticity in lung ILC2s, prevented by the immunomodulatory protein SP-D.

Using the ROX Index to Predict Treatment Outcome for High-Flow Nasal Cannula and/or Noninvasive Ventilation in Patients With COPD Exacerbations.

BACKGROUND: The ratio of oxygen saturation index (ROX index; or SpO2 /FIO2 /breathing frequency) has been shown to predict risk of intubation after high-flow nasal cannula (HFNC) support among adults with acute hypoxemic respiratory failure primarily due to pneumonia. However, its predictive value for other subtypes of respiratory failure is unknown. This study investigated whether the ROX index predicts liberation from HFNC or noninvasive ventilation (NIV), intubation with mechanical ventilation, or death in adults admitted for respiratory failure due to an exacerbation of COPD. METHODS: We performed a retrospective study of 260 adults hospitalized with a COPD exacerbation and treated with HFNC and/or NIV (continuous or bi-level). ROX index scores were collected at treatment initiation and predefined time intervals throughout HFNC and/or NIV treatment or until the subject was intubated or died. A ROX index score of ≥ 4.88 was applied to the cohort to determine if the same score would perform similarly in this different cohort. Accuracy of the ROX index was determined by calculating the area under the receiver operator curve. RESULTS: A total of 47 subjects (18%) required invasive mechanical ventilation or died while on HFNC/NIV. The ROX index at treatment initiation, 1 h, and 6 h demonstrated the best prediction accuracy for avoidance of invasive mechanical ventilation or death (area under the receiver operator curve 0.73 [95% CI 0.66-0.80], 0.72 [95% CI 0.65-0.79], and 0.72 [95% CI 0.63-0.82], respectively). The optimal cutoff value for sensitivity (Sn) and specificity (Sp) was a ROX index score > 6.88 (sensitivity 62%, specificity 57%). CONCLUSIONS: The ROX index applied to adults with COPD exacerbations treated with HFNC and/or NIV required higher scores to achieve similar prediction of low risk of treatment failure when compared to subjects with hypoxemic respiratory failure/pneumonia. ROX scores < 4.88 did not accurately predict intubation or death.

Recombinant Alpha-1 Antitrypsin-Fc Fusion Protein INBRX-101 in Adults With Alpha-1 Antitrypsin Deficiency: A Phase 1 Study.

Background: Alpha-1 antitrypsin deficiency (AATD) is characterized by low alpha-1 antitrypsin (AAT) levels, predisposing individuals to lung disease. The standard of care, plasma-derived AAT (pdAAT), is delivered as weekly infusions to maintain serum AAT concentrations ≥11µM (≈50% of those in healthy individuals). INBRX-101, a recombinant human AAT-Fc fusion protein, was designed to have a longer half-life and achieve higher AAT levels than pdAAT. Methods: In this phase 1 dose-escalation study (N=31), adults with AATD received 1 dose (part 1) or 3 doses (part 2) of 10 (part 1), 40, 80, or 120mg/kg INBRX-101 every 3 weeks (Q3W) via intravenous infusion. The primary endpoint was safety and tolerability. Secondary endpoints were pharmacokinetics (PK), pharmacodynamics (PD), and immunogenicity of INBRX-101. Results: INBRX-101 was well tolerated. Most treatment-emergent adverse events were grade ≤2. In part 2 (n=18; each dose, n=6), dose-related increases in serum functional AAT (fAAT) were observed; mean fAAT levels remained above the 21 µM target for up to 4 weeks after the final dose in the 120-mg/kg cohort. Antidrug antibodies had no meaningful impact on PK or PD. INBRX-101 was detected in pulmonary epithelial lining fluid (PELF) from all patients assessed (n=11), and PELF fAAT increased after dosing. PK/PD modeling projected steady-state serum fAAT ≥21µM at 120 mg/kg Q3W (average concentration ≈43µM; trough concentration ≈28µM) and Q4W (≈34µM; ≈21µM). Conclusion: The favorable safety profile and ability to maintain serum fAAT levels >21µM with extended-interval dosing, support a phase 2 trial evaluating Q3W and Q4W dosing of INBRX-101.

Work of Breathing During Proportional Assist Ventilation as a Predictor of Extubation Failure.

BACKGROUND: Despite decades of research on predictors of extubation success, use of ventilatory support after extubation is common and 10-20% of patients require re-intubation. Proportional assist ventilation (PAV) mode automatically calculates estimated total work of breathing (total WOB). Here, we assessed the performance of total WOB to predict extubation failure in invasively ventilated subjects. METHODS: This prospective observational study was conducted in 6 adult ICUs at an academic medical center. We enrolled intubated subjects who successfully completed a spontaneous breathing trial, had a rapid shallow breathing index < 105 breaths/min/L, and were deemed ready for extubation by the primary team. Total WOB values were recorded at the end of a 30-min PAV trial. Extubation failure was defined as any respiratory support and/or re-intubation within 72 h of extubation. We compared total WOB scores between groups and performance of total WOB for predicting extubation failure with receiver operating characteristic curves. RESULTS: Of 61 subjects enrolled, 9.8% (n = 6) required re-intubation, and 50.8% (n = 31) required any respiratory support within 72 h of extubation. Median total WOB at 30 min on PAV was 0.9 J/L (interquartile range 0.7-1.3 J/L). Total WOB was significantly different between subjects who failed or were successfully extubated (median 1.1 J/L vs 0.7 J/L, P = .004). The area under the curve was 0.71 [95% CI 0.58-0.85] for predicting any requirement of respiratory support and 0.85 [95% CI 0.69-1.00] for predicting re-intubation alone within 72 h of extubation. Total WOB cutoff values maximizing sensitivity and specificity equally were 1.0 J/L for any respiratory support (positive predictive value [PPV] 70.0%, negative predictive value [NPV] 67.7%) and 1.3 J/L for re-intubation (PPV 26.3%, NPV 97.6%). CONCLUSIONS: The discriminative performance of a PAV-derived total WOB value to predict extubation failure was good, indicating total WOB may represent an adjunctive tool for assessing extubation readiness. However, these results should be interpreted as preliminary, with specific thresholds of PAV-derived total WOB requiring further investigation in a large multi-center study.

Remote patient monitoring in the management of chronic obstructive pulmonary disease.

Remote patient monitoring allows monitoring high-risk patients through implementation of an expanding number of technologies in coordination with a healthcare team to augment care, with the potential to provide early detection of exacerbation, prompt access to therapy and clinical services, and ultimately improved patient outcomes and decreased healthcare utilization.In this review, we describe the application of remote patient monitoring in chronic obstructive pulmonary disease including the potential benefits and possible barriers to implementation both for the individual and the healthcare system.

A US Time and Motion Pilot Study of Nebulized COPD Therapy in an Inpatient and a Long-Term Care Setting.

INTRODUCTION: There is a lack of quantitative data on healthcare professionals' (HCPs) time dedicated to nebulized chronic obstructive pulmonary disease (COPD) therapy in inpatient and long-term care (LTC) settings. Using time and motion methodology, we quantified HCP time and opportunity costs (time and materials) associated with nebulized COPD therapy in inpatient and LTC settings and estimated efficiencies of changing to once-daily therapy. METHODS: A case report form was built to reflect local nebulization workflow. Primary outcomes were mean active HCP time per predefined task and mean total active HCP time associated with short-acting beta agonist (SABA) and SABA/short-acting muscarinic antagonist (SAMA) combination nebulization processes. RESULTS: Twenty observations occurred within each setting. Inpatient observations included three SABA and 17 SABA/SAMA (from 18 different patients), and LTC observations included five SABA and 15 SABA/SAMA (from eight different patients). Mean total process time was 16.12 min in the inpatient setting (95% CI 14.48-17.76) and 21.0 min in the LTC setting (95% CI 18.8-23.2), with the actual nebulization comprising over 50% of process time for both. In LTC, CIs suggest a difference by cognitive impairment status: mean 24.1 min (95% CI 21.3-26.9) if cognitively impaired versus 19.0 min (95% CI 16.1-21.8) if not. In the inpatient setting, the estimated process time/admission was 7.8 h; a once-daily nebulized drug would require only 2.3 h. In LTC, the estimated process time was 32.1 h/month; a once-daily nebulized drug would require only 13.7 h/month. Estimated nebulization cost was $243/admission for current versus $72 for once-daily dosing in inpatient, and $1177/month versus $504 in LTC. CONCLUSIONS: The nebulization process for COPD patients in both inpatient and LTC settings consumes considerable HCP time. A switch from SABA or SABA/SAMA to a drug with a once-daily nebulization frequency could generate substantial time savings depending on the setting and site characteristics.

A 66-Year-Old Man With Subacute Cough and Worsening Dyspnea Previously Diagnosed With COVID-19 Pneumonia.

A 66-year-old man presented with subacute cough and worsening dyspnea. Labs were notable for moderate peripheral eosinophilia, and computed tomography (CT) scan demonstrated extensive crazy-paving throughout bilateral upper lung fields. Bronchoalveolar lavage (BAL) revealed macrophages with lipid-filled vacuoles and negative periodic acid-Schiff (PAS) stain. Further history obtained from the patient and family was notable for daily application of commercially available vapor rub to nares and intentional deep inhalation of nebulized fluids containing scented oils. The patient was diagnosed with exogenous lipoid pneumonia through an unusual route of lipid administration.

A 56-Year-Old Male With Weight Loss, Night Sweats, Dyspnea, and Bladder Cancer.

A 56-year-old man presented with subacute night sweats, fever, and weight loss with worsening dyspnea. Computed tomography (CT) scan demonstrated miliary pattern of nodules evenly distributed throughout all lung fields. Given the patient's CT findings and temporal association with Bacille Calmette-Guerin (BCG) immunotherapy for bladder cancer, the patient was diagnosed with disseminated Mycobacterium bovis secondary to BCG bladder instillations.

Comparative Effectiveness of Roflumilast and Azithromycin for the Treatment of Chronic Obstructive Pulmonary Disease.

BACKGROUND: In chronic obstructive pulmonary disease (COPD) patients with exacerbations despite optimized bronchodilator therapy, roflumilast and chronic azithromycin are recommended options. Roflumilast is recommended in severe COPD patients with chronic bronchitis, whereas chronic azithromycin is more broadly indicated. The comparative effectiveness between these 2 treatments to reduce exacerbation rate remains unclear. OBJECTIVES: Our objective was analysis of the Veterans Health Administration (VHA) database (medication and claims data without lung function or presence of chronic bronchitis or tobacco use) to compare the effectiveness of roflumilast and azithromycin on hospitalizations and mortality. METHODS: The primary outcome of the study was cumulative incidences of first COPD-related and all-cause hospitalization. Sensitivity analysis on hospitalizations was conducted for VHA patients who also had Medicare. RESULTS: In 1302 roflumilast and 2573 azithromycin patients, the all-cause mortality rates at 1 year were 19% and 15%, respectively. The median times-to-all-cause death were 47 months (interquartile range [IQR] 16-81) for the roflumilast and 48 months (IQR 20-83) for the azithromycin groups. Roflumilast was associated with higher mortality (hazard ratio [HR] 1.16; 95% confidence interval [CI], 1.04-1.29). Roflumilast showed no significant association for COPD-related hospitalization (subdistribution HR [SHR]=1.14, 95% CI, 1.00-1.29) and all-cause hospitalization (HR 1.07, 95% CI, 0.97-1.18). For patients with Medicare (N=2030), roflumilast was associated with higher COPD-related (SHR 1.21; 95% CI, 1.05-1.41) and all-cause (SHR 1.23; 95% CI, 1.09-1.38) hospitalizations. CONCLUSIONS: Roflumilast was associated with higher hazard ratios for death, COPD-related hospitalizations, and all-cause hospitalizations in COPD patients only after adjustment for VHA and external Medicare events. Prospective clinical trials are needed to directly compare the relative efficacy of these therapies.

Alpha-1 antitrypsin deficiency and recombinant protein sources with focus on plant sources: Updates, challenges and perspectives.

Alpha-1 antitrypsin deficiency (A1ATD) is an autosomal recessive disease characterized by low plasma levels of A1AT, a serine protease inhibitor representing the most abundant circulating antiprotease normally present at plasma levels of 1-2 g/L. The dominant clinical manifestations include predispositions to early onset emphysema due to protease/antiprotease imbalance in distal lung parenchyma and liver disease largely due to unsecreted polymerized accumulations of misfolded mutant A1AT within the endoplasmic reticulum of hepatocytes. Since 1987, the only FDA licensed specific therapy for the emphysema component has been infusions of A1AT purified from pooled human plasma at the 2020 cost of up to US $200,000/year with the risk of intermittent shortages. In the past three decades various, potentially less expensive, recombinant forms of human A1AT have reached early stages of development, one of which is just reaching the stage of human clinical trials. The focus of this review is to update strategies for the treatment of the pulmonary component of A1ATD with some focus on perspectives for therapeutic production and regulatory approval of a recombinant product from plants. We review other competitive technologies for treating the lung disease manifestations of A1ATD, highlight strategies for the generation of data potentially helpful for securing FDA Investigational New Drug (IND) approval and present challenges in the selection of clinical trial strategies required for FDA licensing of a New Drug Approval (NDA) for this disease.

Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data.

To develop and characterize a machine learning algorithm to discriminate acute respiratory distress syndrome from other causes of respiratory failure using only ventilator waveform data. DESIGN: Retrospective, observational cohort study. SETTING: Academic medical center ICU. PATIENTS: Adults admitted to the ICU requiring invasive mechanical ventilation, including 50 patients with acute respiratory distress syndrome and 50 patients with primary indications for mechanical ventilation other than hypoxemic respiratory failure. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Pressure and flow time series data from mechanical ventilation during the first 24-hours after meeting acute respiratory distress syndrome criteria (or first 24-hr of mechanical ventilation for non-acute respiratory distress syndrome patients) were processed to extract nine physiologic features. A random forest machine learning algorithm was trained to discriminate between the patients with and without acute respiratory distress syndrome. Model performance was assessed using the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Analyses examined performance when the model was trained using data from the first 24 hours and tested using withheld data from either the first 24 hours (24/24 model) or 6 hours (24/6 model). Area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.88, 0.90, 0.71, 0.77, and 0.90 (24/24); and 0.89, 0.90, 0.75, 0.83, and 0.83 (24/6). CONCLUSIONS: Use of machine learning and physiologic information derived from raw ventilator waveform data may enable acute respiratory distress syndrome screening at early time points after intubation. This approach, combined with traditional diagnostic criteria, could improve timely acute respiratory distress syndrome recognition and enable automated clinical decision support, especially in settings with limited availability of conventional diagnostic tests and electronic health records.

A 51-Year-Old Man With Unresolved Pulmonary Infiltrates Following Streptococcus pneumoniae Pneumonia.

CASE PRESENTATION: A 51-year-old man presented to the clinic 8 weeks after a 6-day hospital admission for severe multilobar pneumonia caused by Streptococcus pneumoniae. His productive cough resolved after antibiotics, but he reported persistent dyspnea. He recounted a lifelong history of recurrent sinusitis but no previous episodes of pneumonia. The patient denied fever, weight loss, or tobacco, alcohol, or drug use. He worked as an upholstery craftsman with no work-related exposures. He had no bird or exotic animal exposures, and no history of travel outside Sacramento, California, where he lived. Aside from the recently completed 2-week course of levofloxacin, he was not taking any medications.

Improving Mechanical Ventilator Clinical Decision Support Systems with a Machine Learning Classifier for Determining Ventilator Mode.

Clinical decision support systems (CDSS) will play increasing role in improving quality of medical care for critically ill patients. However, due to limitations in current informatics infrastructure, CDSS do not always have complete information on state of supporting physiologic monitoring devices, which can limit input data available to CDSS. This is especially true in use case of mechanical ventilation (MV), where current CDSS have no knowledge of critical ventilation settings, such as ventilation mode. To enable MV CDSS make accurate recommendations related to ventilator mode, we developed a highly performant machine learning model that is able to perform per-breath classification of five of most widely used ventilation modes in USA with average F1-score of 97.52%. We also show how our approach makes methodologic improvements over previous work and is highly robust to missing data caused by software/sensor error.