New Spirometry Indices for Detecting Mild Airflow Obstruction.

The diagnosis of chronic obstructive pulmonary disease (COPD) relies on demonstration of airflow obstruction. Traditional spirometric indices miss a number of subjects with respiratory symptoms or structural lung disease on imaging. We hypothesized that utilizing all data points on the expiratory spirometry curves to assess their shape will improve detection of mild airflow obstruction and structural lung disease. We analyzed spirometry data of 8307 participants enrolled in the COPDGene study, and derived metrics of airflow obstruction based on the shape on the volume-time (Parameter D), and flow-volume curves (Transition Point and Transition Distance). We tested associations of these parameters with CT measures of lung disease, respiratory morbidity, and mortality using regression analyses. There were significant correlations between FEV1/FVC with Parameter D (r = -0.83; p < 0.001), Transition Point (r = 0.69; p < 0.001), and Transition Distance (r = 0.50; p < 0.001). All metrics had significant associations with emphysema, small airway disease, dyspnea, and respiratory-quality of life (p < 0.001). The highest quartile for Parameter D was independently associated with all-cause mortality (adjusted HR 3.22,95% CI 2.42-4.27; p < 0.001) but a substantial number of participants in the highest quartile were categorized as GOLD 0 and 1 by traditional criteria (1.8% and 33.7%). Parameter D identified an additional 9.5% of participants with mild or non-recognized disease as abnormal with greater burden of structural lung disease compared with controls. The data points on the flow-volume and volume-time curves can be used to derive indices of airflow obstruction that identify additional subjects with disease who are deemed to be normal by traditional criteria

Correction: BCG vaccination and tuberculosis prevention: A forty years cohort study, Monastir, Tunisia.

[This corrects the article DOI: 10.1371/journal.pone.0219991.]

Awake Testing during Deep Brain Stimulation Surgery Predicts Postoperative Stimulation Side Effect Thresholds

Despite substantial experience with deep brain stimulation for movement disorders and recent interest in electrode targeting under general anesthesia, little is known about whether awake macrostimulation during electrode targeting predicts postoperative side effects from stimulation. We hypothesized that intraoperative awake macrostimulation with the newly implanted DBS lead predicts dose-limiting side effects during device activation in clinic. We reviewed 384 electrode implants for movement disorders, characterized the presence or absence of stimulus amplitude thresholds for dose-limiting DBS side effects during surgery, and measured their predictive value for side effects during device activation in clinic with odds ratios ±95% confidence intervals. We also estimated associations between voltage thresholds for side effects within participants. Intraoperative clinical response to macrostimulation led to adjustments in DBS electrode position during surgery in 37.5% of cases (31.0% adjustment of lead depth, 18.2% new trajectory, or 11.7% both). Within and across targets and disease states, dose-limiting stimulation side effects from the final electrode position in surgery predict postoperative side effects, and side effect thresholds in clinic occur at lower stimulus amplitudes versus those encountered in surgery. In conclusion, awake clinical testing during DBS targeting impacts surgical decision-making and predicts dose-limiting side effects during subsequent device activation

The Peak Index: Spirometry Metric for Airflow Obstruction Severity and Heterogeneity

Rationale: Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitation. Spirometry loops are not smooth curves and have undulations and peaks that likely reflect heterogeneity of airflow.Objectives: To assess whether the Peak Index, the number of peaks adjusted for lung size, is associated with clinical outcomes.Methods: We analyzed spirometry data of 9,584 participants enrolled in the COPDGene study and counted the number of peaks in the descending part of the expiratory flow-volume curve from the peak expiratory flow to end-expiration. We adjusted the peaks count for the volume of the lungs from peak expiratory flow to end-expiration to derive the Peak Index. Multivariable regression analyses were performed to test associations between the Peak Index and lung function, respiratory morbidity, structural lung disease on computed tomography (CT), forced expiratory volume in 1 second (FEV1) decline, and mortality.Results: The Peak Index progressively increased from Global Initiative for Chronic Obstructive Lung Disease stage 0 through 4 (P < 0.001). On multivariable analysis, the Peak Index was significantly associated with CT emphysema (adjusted β = 0.906; 95% confidence interval [CI], 0.789 to 1.023; P < 0.001) and small airways disease (adjusted β = 1.367; 95% CI, 1.188 to 1.545; P < 0.001), St. George's Respiratory Questionnaire score (adjusted β = 1.075; 95% CI, 0.807 to 1.342; P < 0.001), 6-minute-walk distance (adjusted β = -1.993; 95% CI, -3.481 to -0.506; P < 0.001), and FEV1 change over time (adjusted β = -1.604; 95% CI, -2.691 to -0.516; P = 0.004), after adjustment for age, sex, race, body mass index, current smoking status, pack-years of smoking, and FEV1. The Peak Index was also associated with the BODE (body mass index, airflow obstruction, dyspnea, and exercise capacity) index and mortality (P < 0.001).Conclusions: The Peak Index is a spirometry metric that is associated with CT measures of lung disease, respiratory morbidity, lung function decline, and mortality.Clinical trial registered with www.clinicaltrials.gov (NCT00608764)

Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease

BACKGROUNDCurrently recommended traditional spirometry outputs do not reflect the relative contributions of emphysema and airway disease to airflow obstruction. We hypothesized that machine-learning algorithms can be trained on spirometry data to identify these structural phenotypes.METHODSParticipants enrolled in a large multicenter study (COPDGene) were included. The data points from expiratory flow-volume curves were trained using a deep-learning model to predict structural phenotypes of chronic obstructive pulmonary disease (COPD) on CT, and results were compared with traditional spirometry metrics and an optimized random forest classifier. Area under the receiver operating characteristic curve (AUC) and weighted F-score were used to measure the discriminative accuracy of a fully convolutional neural network, random forest, and traditional spirometry metrics to phenotype CT as normal, emphysema-predominant (>5% emphysema), airway-predominant (Pi10 > median), and mixed phenotypes. Similar comparisons were made for the detection of functional small airway disease phenotype (>20% on parametric response mapping).RESULTSAmong 8980 individuals, the neural network was more accurate in discriminating predominant emphysema/airway phenotypes (AUC 0.80, 95%CI 0.79-0.81) compared with traditional measures of spirometry, FEV1/FVC (AUC 0.71, 95%CI 0.69-0.71), FEV1% predicted (AUC 0.70, 95%CI 0.68-0.71), and random forest classifier (AUC 0.78, 95%CI 0.77-0.79). The neural network was also more accurate in discriminating predominant emphysema/small airway phenotypes (AUC 0.91, 95%CI 0.90-0.92) compared with FEV1/FVC (AUC 0.80, 95%CI 0.78-0.82), FEV1% predicted (AUC 0.83, 95%CI 0.80-0.84), and with comparable accuracy with random forest classifier (AUC 0.90, 95%CI 0.88-0.91).CONCLUSIONSStructural phenotypes of COPD can be identified from spirometry using deep-learning and machine-learning approaches, demonstrating their potential to identify individuals for targeted therapies.TRIAL REGISTRATIONClinicalTrials.gov NCT00608764.FUNDINGThis study was supported by NIH grants K23 HL133438 and R21EB027891 and an American Thoracic Foundation 2018 Unrestricted Research Grant. The COPDGene study is supported by NIH grants NHLBI U01 HL089897 and U01 HL089856. The COPDGene study (NCT00608764) is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprising AstraZeneca, Boehringer-Ingelheim, GlaxoSmithKline, Novartis, and Sunovion