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.

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 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.

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.

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.

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.

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.

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.

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.

Viruses and non-allergen environmental triggers in asthma.

Asthma is a complex inflammatory disease with many triggers. The best understood asthma inflammatory pathways involve signals characterized by peripheral eosinophilia and elevated immunoglobulin E levels (called T2-high or allergic asthma), though other asthma phenotypes exist (eg, T2-low or non-allergic asthma, eosinophilic or neutrophilic-predominant). Common triggers that lead to poor asthma control and exacerbations include respiratory viruses, aeroallergens, house dust, molds, and other organic and inorganic substances. Increasingly recognized non-allergen triggers include tobacco smoke, small particulate matter (eg, PM2.5), and volatile organic compounds. The interaction between respiratory viruses and non-allergen asthma triggers is not well understood, though it is likely a connection exists which may lead to asthma development and/or exacerbations. In this paper we describe common respiratory viruses and non-allergen triggers associated with asthma. In addition, we aim to show the possible interactions, and potential synergy, between viruses and non-allergen triggers. Finally, we introduce a new clinical approach that collects exhaled breath condensates to identify metabolomics associated with viruses and non-allergen triggers that may promote the early management of asthma symptoms.

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.

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.

An Unusual Case of Tertiary Syphilis Behaving Like Tongue Squamous Cell Carcinoma.

Syphilis may present with a myriad of oral manifestations in the primary, secondary, and tertiary stages, and may be confused with malignancy. Despite a rise in the incidence of syphilis, tertiary syphilis is exceedingly rare. Tertiary syphilis gummas usually affect the hard palate, while tongue involvement is very rare. A 55-year-old male with extensive smoking and alcohol use was referred for malignancy evaluation with an ulcerative mass creating a tongue cleft, and a positron emission tomography scan suggestive for malignancy. Biopsy results demonstrated no carcinoma but histology demonstrated granulomatous inflammation. Further laboratory results demonstrated elevated rapid plasma reagin titers with Treponema pallidum immunoglobulin G antibodies present. The patient was diagnosed with tertiary syphilis, received appropriate antibiotic therapy, and had healing of the tongue with a persistent cleft. Syphilis may mimic many disease processes. As such, it is important to include this disease in the differential of an unusual tongue lesion. An oral lesion may be the first sign of infection.

Development of a research-oriented system for collecting mechanical ventilator waveform data.

Lack of access to high-frequency, high-volume patient-derived data, such as mechanical ventilator waveform data, has limited the secondary use of these data for research, quality improvement, and decision support. Existing methods for collecting these data are obtrusive, require high levels of technical expertise, and are often cost-prohibitive, limiting their use and scalability for research applications. We describe here the development of an unobtrusive, open-source, scalable, and user-friendly architecture for collecting, transmitting, and storing mechanical ventilator waveform data that is generalizable to other patient care devices. The system implements a software framework that automates and enforces end-to-end data collection and transmission. A web-based data management application facilitates nontechnical end users' abilities to manage data acquisition devices, mitigates data loss and misattribution, and automates data storage. Using this integrated system, we have been able to collect ventilator waveform data from >450 patients as part of an ongoing clinical study.

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.

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.

Paradigms in chronic obstructive pulmonary disease: phenotypes, immunobiology, and therapy with a focus on vascular disease.

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous syndrome that represents a major global health burden. COPD phenotypes have recently emerged based on large cohort studies addressing the need to better characterize the syndrome. Though comprehensive phenotyping is still at an early stage, factors such as ethnicity and radiographic, serum, and exhaled breath biomarkers have shown promise. COPD is also an immunological disease where innate and adaptive immune responses to the environment and tobacco smoke are altered. The frequent overlap between COPD and other systemic diseases, such as cardiovascular disease, has influenced COPD therapy, and treatments for both conditions may lead to improved patient outcomes. Here, we discuss current paradigms that center on improving the definition of COPD, understanding the immunological overlap between COPD and vascular inflammation, and the treatment of COPD-with a focus on comorbid cardiovascular disease.

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.

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.