Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning.

INTRODUCTION: Spirometry is the gold standard for COPD diagnosis and severity determination, but is technique-dependent, nonspecific, and requires administration by a trained healthcare professional. There is a need for a fast, reliable, and precise alternative diagnostic test. This study's aim was to use interpretable machine learning to diagnose COPD and assess severity using 75-second carbon dioxide (CO2) breath records captured with TidalSense's N-TidalTM capnometer. METHOD: For COPD diagnosis, machine learning algorithms were trained and evaluated on 294 COPD (including GOLD stages 1-4) and 705 non-COPD participants. A logistic regression model was also trained to distinguish GOLD 1 from GOLD 4 COPD with the output probability used as an index of severity. RESULTS: The best diagnostic model achieved an AUROC of 0.890, sensitivity of 0.771, specificity of 0.850 and positive predictive value (PPV) of 0.834. Evaluating performance on all test capnograms that were confidently ruled in or out yielded PPV of 0.930 and NPV of 0.890. The severity determination model yielded an AUROC of 0.980, sensitivity of 0.958, specificity of 0.961 and PPV of 0.958 in distinguishing GOLD 1 from GOLD 4. Output probabilities from the severity determination model produced a correlation of 0.71 with percentage predicted FEV1. CONCLUSION: The N-TidalTM device could be used alongside interpretable machine learning as an accurate, point-of-care diagnostic test for COPD, particularly in primary care as a rapid rule-in or rule-out test. N-TidalTM also could be effective in monitoring disease progression, providing a possible alternative to spirometry for disease monitoring.

Clinical severity classes in COVID-19 pneumonia have distinct immunological profiles, facilitating risk stratification by machine learning.

Objective: Clinical triage in coronavirus disease 2019 (COVID-19) places a heavy burden on senior clinicians during a pandemic situation. However, risk stratification based on serum biomarker bioprofiling could be implemented by a larger, nonspecialist workforce. Method: Measures of Complement Activation and inflammation in patientS with CoronAvirus DisEase 2019 (CASCADE) patients (n = 72), (clinicaltrials.gov: NCT04453527), classified as mild, moderate, or severe (by support needed to maintain SpO2 > 93%), and healthy controls (HC, n = 20), were bioprofiled using 76 immunological biomarkers and compared using ANOVA. Spearman correlation analysis on biomarker pairs was visualised via heatmaps. Linear Discriminant Analysis (LDA) models were generated to identify patients likely to deteriorate. An X-Gradient-boost (XGB) model trained on CASCADE data to triage patients as mild, moderate, and severe was retrospectively employed to classify COROnavirus Nomacopan Emergency Treatment for covid 19 infected patients with early signs of respiratory distress (CORONET) patients (n = 7) treated with nomacopan. Results: The LDA models distinctly discriminated between deteriorators, nondeteriorators, and HC, with IL-27, IP-10, MDC, ferritin, C5, and sC5b-9 among the key predictor variables during deterioration. C3a and C5 were elevated in all severity classes vs. HC (p < 0.05). sC5b-9 was elevated in the "moderate" and "severe" categories vs. HC (p < 0.001). Heatmap analysis shows a pairwise increase of negatively correlated pairs with IL-27. The XGB model indicated sC5b-9, IL-8, MCP1, and prothrombin F1 and F2 were key discriminators in nomacopan-treated patients (CORONET study). Conclusion: Distinct immunological fingerprints from serum biomarkers exist within different severity classes of COVID-19, and harnessing them using machine learning enabled the development of clinically useful triage and prognostic tools. Complement-mediated lung injury plays a key role in COVID-19 pneumonia, and preliminary results hint at the usefulness of a C5 inhibitor in COVID-19 recovery.

Capability, Opportunity, and Motivation Model for Behavior Change in People With Asthma: Protocol for a Cross-Sectional Study.

BACKGROUND: Asthma is a common lung condition that cannot be cured, but it can usually be effectively managed using available treatments. Despite this, it is widely acknowledged that 70% of patients do not adhere to their asthma treatment. Personalizing treatment by providing the most appropriate interventions based on the patient's psychological or behavioral needs produces successful behavior change. However, health care providers have limited available resources to deliver a patient-centered approach for their psychological or behavioral needs, resulting in a current one-size-fits-all strategy due to the nonfeasible nature of existing surveys. The solution would be to provide health care professionals with a clinically feasible questionnaire that identifies the patient's personal psychological and behavioral factors related to adherence. OBJECTIVE: We aim to apply the capability, opportunity, and motivation model of behavior change (COM-B) questionnaire to detect a patient's perceived psychological and behavioral barriers to adherence. Additionally, we aim to explore the key psychological and behavioral barriers indicated by the COM-B questionnaire and adherence to treatment in patients with confirmed asthma with heterogeneous severity. Exploratory objectives will include a focus on the associations between the COM-B questionnaire responses and asthma phenotype, including clinical, biological, psychosocial, and behavioral components. METHODS: In a single visit, participants visiting Portsmouth Hospital's asthma clinic with a diagnosis of asthma will be asked to complete a 20-minute questionnaire on an iPad about their psychological and behavioral barriers following the theoretical domains framework and capability, opportunity, and motivation model. Participants' data are routinely collected, including demographics, asthma characteristics, asthma control, asthma quality of life, and medication regime, which will be recorded on an electronic data capture form. RESULTS: The study is already underway, and it is anticipated that the results will be available by early 2023. CONCLUSIONS: The COM-B asthma study will investigate an easily accessible theory-based tool (a questionnaire) for identifying psychological and behavioral barriers in patients with asthma who are not adhering to their treatment. This will provide useful information on the behavioral barriers to asthma adherence and whether or not a questionnaire can be used to identify these needs. The highlighted barriers will improve health care professionals' knowledge of this important subject, and participants will benefit from the study by removing their barriers. Overall, this will enable health care professionals to use effective individualized interventions to support improved medication adherence while also recognizing and meeting the psychological needs of patients with asthma. TRIAL REGISTRATION: ClinicalTrials.gov NCT05643924; https://clinicaltrials.gov/ct2/show/NCT05643924. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44710.

Machine diagnosis of chronic obstructive pulmonary disease using a novel fast-response capnometer.

BACKGROUND: Although currently most widely used in mechanical ventilation and cardiopulmonary resuscitation, features of the carbon dioxide (CO2) waveform produced through capnometry have been shown to correlate with V/Q mismatch, dead space volume, type of breathing pattern, and small airway obstruction. This study applied feature engineering and machine learning techniques to capnography data collected by the N-Tidal™ device across four clinical studies to build a classifier that could distinguish CO2 recordings (capnograms) of patients with COPD from those without COPD. METHODS: Capnography data from four longitudinal observational studies (CBRS, GBRS, CBRS2 and ABRS) was analysed from 295 patients, generating a total of 88,186 capnograms. CO2 sensor data was processed using TidalSense's regulated cloud platform, performing real-time geometric analysis on CO2 waveforms to generate 82 physiologic features per capnogram. These features were used to train machine learning classifiers to discriminate COPD from 'non-COPD' (a group that included healthy participants and those with other cardiorespiratory conditions); model performance was validated on independent test sets. RESULTS: The best machine learning model (XGBoost) performance provided a class-balanced AUROC of 0.985 ± 0.013, positive predictive value (PPV) of 0.914 ± 0.039 and sensitivity of 0.915 ± 0.066 for a diagnosis of COPD. The waveform features that are most important for driving classification are related to the alpha angle and expiratory plateau regions. These features correlated with spirometry readings, supporting their proposed properties as markers of COPD. CONCLUSION: The N-Tidal™ device can be used to accurately diagnose COPD in near-real-time, lending support to future use in a clinical setting. TRIAL REGISTRATION: Please see NCT03615365, NCT02814253, NCT04504838 and NCT03356288.

Measurement of Vital Signs by Lifelight Software in Comparison to Standard of Care Multisite Development (VISION-MD): Protocol for an Observational Study.

BACKGROUND: Measuring vital signs (VS) is an important aspect of clinical care but is time-consuming and requires multiple pieces of equipment and trained staff. Interest in the contactless measurement of VS has grown since the COVID-19 pandemic, including in nonclinical situations. Lifelight is an app being developed as a medical device for the contactless measurement of VS using remote photoplethysmography (rPPG) via the camera on smart devices. The VISION-D (Measurement of Vital Signs by Lifelight Software in Comparison to the Standard of Care-Development) and VISION-V (Validation) studies demonstrated the accuracy of Lifelight compared with standard-of-care measurement of blood pressure, pulse rate, and respiratory rate, supporting the certification of Lifelight as a class I Conformité Européenne (CE) medical device. OBJECTIVE: To support further development of the Lifelight app, the observational VISION Multisite Development (VISION-MD) study is collecting high-quality data from a broad range of patients, including those with VS measurements outside the normal healthy range and patients who are critically ill. METHODS: The study is recruiting adults (aged ≥16 years) who are inpatients (some critically ill), outpatients, and healthy volunteers, aiming to cover a broad range of normal and clinically relevant VS values; there are no exclusion criteria. High-resolution 60-second videos of the face are recorded by the Lifelight app while simultaneously measuring VS using standard-of-care methods (automated sphygmomanometer for blood pressure; finger clip sensor for pulse rate and oxygen saturation; manual counting of respiratory rate). Feedback from patients and nurses who use Lifelight is collected via a questionnaire. Data to estimate the cost-effectiveness of Lifelight compared with standard-of-care VS measurement are also being collected. A new method for rPPG signal processing is currently being developed, based on the identification of small areas of high-quality signals in each individual. Anticipated recruitment is 1950 participants, with the expectation that data from approximately 1700 will be used for software development. Data from 250 participants will be retained to test the performance of Lifelight against predefined performance targets. RESULTS: Recruitment began in May 2021 but was hindered by the restrictions instigated during the COVID-19 pandemic. The development of data processing methodology is in progress. The data for analysis will become available from September 2022, and the algorithms will be refined continuously to improve clinical accuracy. The performance of Lifelight compared with that of the standard-of-care measurement of VS will then be tested. Recruitment will resume if further data are required. The analyses are expected to be completed in early 2023. CONCLUSIONS: This study will support the refinement of data collection and processing toward the development of a robust app that is suitable for routine clinical use. TRIAL REGISTRATION: ClinicalTrials.gov NCT04763746; https://clinicaltrials.gov/ct2/show/NCT04763746. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41533.

Measurement of Vital Signs Using Lifelight Remote Photoplethysmography: Results of the VISION-D and VISION-V Observational Studies.

BACKGROUND: The detection of early changes in vital signs (VSs) enables timely intervention; however, the measurement of VSs requires hands-on technical expertise and is often time-consuming. The contactless measurement of VSs is beneficial to prevent infection, such as during the COVID-19 pandemic. Lifelight is a novel software being developed to measure VSs by remote photoplethysmography based on video captures of the face via the integral camera on mobile phones and tablets. We report two early studies in the development of Lifelight. OBJECTIVE: The objective of the Vital Sign Comparison Between Lifelight and Standard of Care: Development (VISION-D) study (NCT04763746) was to measure respiratory rate (RR), pulse rate (PR), and blood pressure (BP) simultaneously by using the current standard of care manual methods and the Lifelight software to iteratively refine the software algorithms. The objective of the Vital Sign Comparison Between Lifelight and Standard of Care: Validation (VISION-V) study (NCT03998098) was to validate the use of Lifelight software to accurately measure VSs. METHODS: BP, PR, and RR were measured simultaneously using Lifelight, a sphygmomanometer (BP and PR), and the manual counting of RR. Accuracy performance targets for each VS were defined from a systematic literature review of the performance of state-of-the-art VSs technologies. RESULTS: The VISION-D data set (17,233 measurements from 8585 participants) met the accuracy targets for RR (mean error 0.3, SD 3.6 vs target mean error 2.3, SD 5.0; n=7462), PR (mean error 0.3, SD 4.0 vs mean error 2.2, SD 9.2; n=10,214), and diastolic BP (mean error -0.4, SD 8.5 vs mean error 5.5, SD 8.9; n=8951); for systolic BP, the mean error target was met but not the SD (mean error 3.5, SD 16.8 vs mean error 6.7, SD 15.3; n=9233). Fitzpatrick skin type did not affect accuracy. The VISION-V data set (679 measurements from 127 participants) met all the standards: mean error -0.1, SD 3.4 for RR; mean error 1.4, SD 3.8 for PR; mean error 2.8, SD 14.5 for systolic BP; and mean error -0.3, SD 7.0 for diastolic BP. CONCLUSIONS: At this early stage in development, Lifelight demonstrates sufficient accuracy in the measurement of VSs to support certification for a Level 1 Conformité Européenne mark. As the use of Lifelight does not require specific training or equipment, the software is potentially useful for the contactless measurement of VSs by nonclinical staff in residential and home care settings. Work is continuing to enhance data collection and processing to achieve the robustness and accuracy required for routine clinical use. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/14326.

A Study to Investigate the Prevalence of Device-Specific Errors in Inhaler Technique in Adults With Airway Disease (The SCORES Study): Protocol for a Single Visit Prevalence Study.

BACKGROUND: It is a recurring theme in clinical practice that patients using inhaled medications via an inhaler do not use their device to a standard that allows for optimum therapeutic effect, and some studies have shown that up to 90% of people do not use their inhalers properly. Observation and correction of the inhaler technique by health care professionals is advised by both national and international guidelines and should be performed at every opportunity to ensure that the optimum inhaler technique is achieved by the user. This study will provide a greater understanding of the most frequent technique errors made by people using 13 different inhaler types. OBJECTIVE: This study aims to identify and compare inhaler technique errors and their prevalence in adults, using device-specific checklists in accordance with manufacturers' guidelines, for 13 specific inhaler types across all lung conditions and to correlate these errors with possible determinants of poor technique. It also aims to assess the error frequency at each step in the device-specific questionnaires and compare the error rates among device types. METHODS: In a single visit, participants using an inhaler included in the inclusion criteria will have their inhaler technique observed using an identical placebo device, which will be recorded using device-specific checklists, and technique-optimized, or switched to a suitable inhaler. RESULTS: The study is already underway, and it is anticipated that the results will be available by 2022. CONCLUSIONS: The SCORES (Study to Investigate the Prevalence of Device-Specific Errors in Inhaler Technique in Adults With Airway Disease) study will ascertain the prevalence of device-specific inhaler technique errors at each step in the device-specific checklists, compare error rates among 13 device types, and correlate these errors with possible determinants of poor technique. Future work will involve the clarification and classification of these errors into critical and noncritical categories. TRIAL REGISTRATION: ClinicalTrials.gov NCT04262271; https://clinicaltrials.gov/ct2/show/NCT04262271. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/26350.

The VICTORY (Investigation of Inflammacheck to Measure Exhaled Breath Condensate Hydrogen Peroxide in Respiratory Conditions) Study: Protocol for a Cross-sectional Observational Study.

BACKGROUND: More than 7% of the world's population is living with a chronic respiratory condition. In the United Kingdom, lung disease affects approximately 1 in 5 people, resulting in over 700,000 hospital admissions each year. People with respiratory conditions have several symptoms and can require multiple health care visits and investigations before a diagnosis is made. The tests available can be difficult to perform, especially if a person is symptomatic, leading to poor quality results. A new, easy-to-perform, point-of-care test that can be performed in any health care setting and that can differentiate between various respiratory conditions would have a significant, beneficial impact on the ability to diagnose respiratory diseases. OBJECTIVE: The objective of this study is to use a new handheld device (Inflammacheck) in different respiratory conditions to measure the exhaled breath condensate hydrogen peroxide (EBC H2O2) and compare these results with those of healthy controls and with each other. This study also aims to determine whether the device can measure other parameters, including breath humidity, breath temperature, breath flow dynamics, and end tidal carbon dioxide. METHODS: We will perform a single-visit, cross-sectional observational study of EBC H2O2 levels, as measured by Inflammacheck, in people with respiratory disease and volunteers with no known lung disease. Participants with a confirmed diagnosis of asthma, chronic obstructive pulmonary disease, lung cancer, bronchiectasis, pneumonia, breathing pattern disorder, and interstitial lung disease as well as volunteers with no history of lung disease will be asked to breathe into the Inflammacheck device to record their breath sample. RESULTS: The results from this study will be available in 2022, in anticipation of COVID-19-related delays. CONCLUSIONS: This study will investigate the EBC H2O2, as well as other exhaled breath parameters, for use as a future diagnostic tool.

COVID-19: A collision of complement, coagulation and inflammatory pathways.

COVID-19 is frequently accompanied by a hypercoagulable inflammatory state with microangiopathic pulmonary changes that can precede the diffuse alveolar damage characteristic of typical acute respiratory distress syndrome (ARDS) seen in other severe pathogenic infections. Parallels with systemic inflammatory disorders such as atypical hemolytic uremic syndrome (aHUS) have implicated the complement pathway in the pathogenesis of COVID-19, and particularly the anaphylatoxins C3a and C5a released from cleavage of C3 and C5, respectively. C5a is a potent cell signalling protein that activates a cytokine storm-a hyper-inflammatory phenomenon-within hours of infection and the innate immune response. However, excess C5a can result in a pro-inflammatory environment orchestrated through a plethora of mechanisms that propagate lung injury, lymphocyte exhaustion, and an immune paresis. Furthermore, disruption of the homeostatic interactions between complement and extrinsic and intrinsic coagulation pathways contributes to a net pro-coagulant state in the microvasculature of critical organs. Fatal COVID-19 has been associated with a systemic inflammatory response accompanied by a pro-coagulant state and organ damage, particularly microvascular thrombi in the lungs and kidneys. Pathologic studies report strong evidence of complement activation. C5 blockade reduces inflammatory cytokines and their manifestations in animal studies, and has shown benefits in patients with aHUS, prompting investigation of this approach in the treatment of COVID-19. This review describes the role of the complement pathway and particularly C5a and its aberrations in highly pathogenic virus infections, and therefore its potential as a therapeutic target in COVID-19.

Protocadherin-1 Localization and Cell-Adhesion Function in Airway Epithelial Cells in Asthma.

BACKGROUND: The asthma gene PCDH1 encodes Protocadherin-1, a putative adhesion molecule of unknown function expressed in the airway epithelium. Here, we characterize the localization, differential expression, homotypic adhesion specificity and function of PCDH1 in airway epithelial cells in asthma. METHODS: We performed confocal fluorescence microscopy to determine subcellular localization of PCDH1 in 16HBE cells and primary bronchial epithelial cells (PBECs) grown at air-liquid interface. Next, to compare PCDH1 expression and localization in asthma and controls we performed qRT-PCR and fluorescence microscopy in PBECs and immunohistochemistry on airway wall biopsies. We examined homotypic adhesion specificity of HEK293T clones overexpressing fluorescently tagged-PCDH1 isoforms. Finally, to evaluate the role for PCDH1 in epithelial barrier formation and repair, we performed siRNA knockdown-studies and measured epithelial resistance. RESULTS: PCDH1 localized to the cell membrane at cell-cell contact sites, baso-lateral to adherens junctions, with increasing expression during epithelial differentiation. No differences in gene expression or localization of PCDH1 isoforms expressing the extracellular domain were observed in either PBECs or airway wall biopsies between asthma patients and controls. Overexpression of PCDH1 mediated homotypic interaction, whereas downregulation of PCDH1 reduced epithelial barrier formation, and impaired repair after wounding. CONCLUSIONS: In conclusion, PCDH1 is localized to the cell membrane of bronchial epithelial cells baso-lateral to the adherens junction. Expression of PCDH1 is not reduced nor delocalized in asthma even though PCDH1 contributes to homotypic adhesion, epithelial barrier formation and repair.

The use of collagen injections in the treatment of metatarsalgia: a case report.

Metatarsalgia is an increasingly common condition seen in the outpatient setting. A number of conservative and surgical management options are available for this condition. We present an interesting case that was unsuccessfully treated with'dermal filler.' To our knowledge, we have not found any published material in peer-reviewed journals documenting the use of collagen injections in the treatment of metatarsalgia. The authors feel that the lesson learned from this case should be highlighted for other foot and ankle surgeons.