Classification Of COPD Research Articles

Impact of observability period on the classification of COPD diagnosis timing among Medicare beneficiaries with lung cancer.

Investigators often use claims data to estimate the diagnosis timing of chronic conditions. However, misclassification of chronic conditions is common due to variability in healthcare utilization and in claims history across patients. We aimed to quantify the effect of various Medicare fee-for-service continuous enrollment period and lookback period (LBP) on misclassification of COPD and sample size. A stepwise tutorial to classify COPD, based on its diagnosis timing relative to lung cancer diagnosis using the Surveillance Epidemiology and End Results cancer registry linked to Medicare insurance claims. We used 3 approaches varying the LBP and required continuous enrollment (i.e., observability) period between 1 to 5 years. Patients with lung cancer were classified based on their COPD related healthcare utilization into 3 groups: pre-existing COPD (diagnosis at least 3 months before lung cancer diagnosis), concurrent COPD (diagnosis during the -/+ 3months of lung cancer diagnosis), and non-COPD. Among those with 5 years of continuous enrollment, we estimated the sensitivity of the LBP to ascertain COPD diagnosis as the number of patients with pre-existing COPD using a shorter LBP divided by the number of patients with pre-existing COPD using a longer LBP. Extending the LBP from 1 to 5 years increased prevalence of pre-existing COPD from ~ 36% to 51%, decreased both concurrent COPD from ~ 34% to 23% and non-COPD from ~ 29% to 25%. There was minimal effect of extending the required continuous enrollment period beyond one year across various LBPs. In those with 5 years of continuous enrollment, sensitivity of COPD classification (95% CI) increased with longer LBP from 70.1% (69.7% to 70.4%) for one-year LBP to 100% for 5-years LBP. The length of optimum LBP and continuous enrollment period depends on the context of the research question and the data generating mechanisms. Among Medicare beneficiaries, the best approach to identify diagnosis timing of COPD relative to lung cancer diagnosis is to use all available LBP with at least one year of required continuous enrollment.

COPDVD: Automated classification of chronic obstructive pulmonary disease on a new collected and evaluated voice dataset

BackgroundChronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected people. Besides pulmonary function failure, another harmful problem of COPD is the systemic effects, e.g., heart failure or voice distortion. However, the systemic effects of COPD might provide valuable information for early detection. In other words, symptoms caused by systemic effects could be helpful to detect the condition in its early stages. ObjectiveThe proposed study aims to explore whether the voice features extracted from the vowel “a” utterance carry any information that can be predictive of COPD by employing Machine Learning (ML) on a newly collected voice dataset. MethodsForty-eight participants were recruited from the pool of research clinic visitors at Blekinge Institute of Technology (BTH) in Sweden between January 2022 and May 2023. A dataset consisting of 1246 recordings from 48 participants was gathered. The collection of voice recordings containing the vowel “a” utterance commenced following an information and consent meeting with each participant using the VoiceDiagnostic application. The collected voice data was subjected to silence segment removal, feature extraction of baseline acoustic features, and Mel Frequency Cepstrum Coefficients (MFCC). Sociodemographic data was also collected from the participants. Three ML models were investigated for the binary classification of COPD and healthy controls: Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB). A nested k-fold cross-validation approach was employed. Additionally, the hyperparameters were optimized using grid-search on each ML model. For best performance assessment, accuracy, F1-score, precision, and recall metrics were computed. Afterward, we further examined the best classifier by utilizing the Area Under the Curve (AUC), Average Precision (AP), and SHapley Additive exPlanations (SHAP) feature-importance measures. ResultsThe classifiers RF, SVM, and CB achieved a maximum accuracy of 77 %, 69 %, and 78 % on the test set and 93 %, 78 % and 97 % on the validation set, respectively. The CB classifier outperformed RF and SVM. After further investigation of the best-performing classifier, CB demonstrated the highest performance, producing an AUC of 82 % and AP of 76 %. In addition to age and gender, the mean values of baseline acoustic and MFCC features demonstrate high importance and deterministic characteristics for classification performance in both test and validation sets, though in varied order. ConclusionThis study concludes that the utterance of vowel “a” recordings contain information that can be captured by the CatBoost classifier with high accuracy for the classification of COPD. Additionally, baseline acoustic and MFCC features, in conjunction with age and gender information, can be employed for classification purposes and benefit healthcare for decision support in COPD diagnosis. Clinical trial registration numberNCT05897944.

Fourier Decomposition-Based Automated Classification of Healthy, COPD, and Asthma Using Single-Channel Lung Sounds

Fourier Decomposition-Based Automated Classification of Healthy, COPD, and Asthma Using Single-Channel Lung Sounds

Quantitative CT Imaging Features Associated with Stable PRISm using Machine Learning

Rationale and ObjectivesThe structural lung features that characterize individuals with preserved ratio impaired spirometry (PRISm) that remain stable overtime are unknown. The objective of this study was to use machine learning models with computed tomography (CT) imaging to classify stable PRISm from stable controls and stable COPD and identify discriminative features. Materials and MethodsA total of 596 participants that did not transition between control, PRISm and COPD groups at baseline and 3-year follow-up were evaluated: n=274 with normal lung function (stable control), n=22 stable PRISm, and n=300 stable COPD. Investigated features included: quantitative CT (QCT) features (n=34), such as total lung volume (%TLCCT) and percentage of ground glass and reticulation (%GG+Reticulationtexture), as well as Radiomic (n=102) features, including varied intensity zone distribution grainy texture (GLDZMZDV). Logistic regression machine learning models were trained using various feature combinations (Base, Base+QCT, Base+Radiomic, Base+QCT+Radiomic). Model performances were evaluated using area under receiver operator curve (AUC) and comparisons between models were made using DeLong test; feature importance was ranked using Shapley Additive Explanations values. ResultsMachine learning models for all feature combinations achieved AUCs between 0.63-0.84 for stable PRISm vs. stable control, and 0.65-0.92 for stable PRISm vs. stable COPD classification. Models incorporating imaging features outperformed those trained solely on base features (p>0.05). Compared to stable control and COPD, those with stable PRISm exhibited decreased %TLCCT and increased %GG+Reticulationtexture and GLDZMZDV. ConclusionThese findings suggest that reduced lung volumes, and elevated high-density and ground glass/reticulation patterns on CT imaging are associated with stable PRISm.

Application of the Lancet Commission COPD classification to COPD Cohort Population in South Korea

The Lancet Commissions on COPD recommended a new classification based on five main risk factors.Patients with COPD were prospectively enrolled in a Korean COPD subgroup study cohort between April 2012 and June 2022. Patients were classified according to the etiologies (Type 1: Genetically determined (COPD-G), Type 2: Abnormal lung development (COPD-D), Type 3: Infections (COPD-I), Type 4: Cigarette smoking (COPD-C), Type 5: Biomass and pollution (COPD-P)).The database enrolled 3476 patients. Among 3392 patients, 52 (2 %), 1339 (39 %), 2930 (86 %), and 2221 (65 %) were compatible with type 2 (COPD-D), 3 (COPD-I), 4 (COPD-C), and 5 (COPD-P), respectively. Most patients (71 %, 2405) had multiple risk factors contributing to their COPD. However, 93, 712, and 182 patients had only type 3 (COPD-I), 4 (COPD-C), and 5 (COPD-P), respectively. Type 3 (COPD-I) only patients were significantly younger, more often female, and had lower lung function. Both the rate and frequency of severe exacerbations were significantly higher in type 3 (COPD-I) only patients (p = 0.038 and p = 0.048, respectively). Compared with type 5 (COPD-P) only, type 3 (COPD-I) only was significantly associated with the risk of severe exacerbation (Odds ratio, 5.7 [95 % CI, 1.0–32.4]; P = 0.049, incident rate ratio, 8.7 [95 % CI, 1.7–44.0]; P = 0.009).Many patients were affected by multiple factors. Therefore, it is important to consider not only smoking history, but also other potential risk factors when evaluating patients with COPD. Further research is needed to explore the implications of this new COPD classification system for clinical practice and treatment strategies.

Enhancing COPD classification using combined quantitative computed tomography and texture-based radiomics: a CanCOLD cohort study.

Recent advances in texture-based computed tomography (CT) radiomics have demonstrated its potential for classifying COPD. Participants from the Canadian Cohort Obstructive Lung Disease (CanCOLD) study were evaluated. A total of 108 features were included: eight quantitative CT (qCT), 95 texture-based radiomic and five demographic features. Machine-learning models included demographics along with texture-based radiomics and/or qCT. Combinations of five feature selection and five classification methods were evaluated; a training dataset was used for feature selection and to train the models, and a testing dataset was used for model evaluation. Models for classifying COPD status and severity were evaluated using the area under the receiver operating characteristic curve (AUC) with DeLong's test for comparison. SHapely Additive exPlanations (SHAP) analysis was used to investigate the features selected. A total of 1204 participants were evaluated (n=602 no COPD; n=602 COPD). There were no differences between the groups for sex (p=0.77) or body mass index (p=0.21). For classifying COPD status, the combination of demographics, texture-based radiomics and qCT performed better (AUC=0.87) than the combination of demographics and texture-based radiomics (AUC=0.81, p<0.05) or qCT alone (AUC=0.84, p<0.05). Similarly, for classifying COPD severity, the combination of demographics, texture-based radiomics and qCT performed better (AUC=0.81) than demographics and texture-based radiomics (AUC=0.72, p<0.05) or qCT alone (AUC=0.79, p<0.05). Texture-based radiomics and qCT features were among the top five features selected (15th percentile of the CT density histogram, CT total airway count, pack-years, CT grey-level distance zone matrix zone distance entropy, CT low-attenuation clusters) for classifying COPD status. Texture-based radiomics and conventional qCT features in combination improve machine‑learning models for classification of COPD status and severity.

Computed tomography‐based artificial intelligence in lung disease—Chronic obstructive pulmonary disease

AbstractChronic obstructive pulmonary disease (COPD) stands as a global health crisis, responsible for substantial morbidity and mortality on a worldwide scale. Its insidious nature underscores the importance of early detection and accurate diagnosis. While spirometry has been the cornerstone for COPD diagnosis, the role of computed tomography (CT) imaging has evolved, offering a valuable avenue for early detection and subtype classification. Recently, the advent of artificial intelligence (AI) has brought forth the potential to revolutionize the accuracy and efficiency of COPD diagnosis, with a specific focus on CT images. This intersection of healthcare and technology signifies a paradigm shift in the way we approach COPD management. The transformative capacity of AI positions it as a vital instrument for early detection and precise subtype classification of COPD. Moreover, the synergistic relationship between medical imaging and AI paves the way for more precise and efficient disease management. Therefore, in this perspective, we tend to offer a comprehensive exploration of the latest breakthroughs in the field of CT‐based AI in COPD diagnosis, aiming to demonstrate the promise and potential of AI in refining the accuracy of COPD classification and to illuminate the evolving landscape of AI's impact on COPD management.

Multi Feature fusion for COPD Classification using Deep learning algorithms

Multi Feature fusion for COPD Classification using Deep learning algorithms

A novel multimodal framework for early diagnosis and classification of COPD based on CT scan images and multivariate pulmonary respiratory diseases

Background and objectiveChronic Obstructive Pulmonary Disease (COPD) is one of the world's worst diseases; its early diagnosis using existing methods like statistical machine learning techniques, medical diagnostic tools, conventional medical procedures, and other methods is challenging due to misclassification results of COPD diagnosis and takes a long time to perform accurate prediction. Due to the severe consequences of COPD, detection and accurate diagnosis of COPD at an early stage is essential. This paper aims to design and develop a multimodal framework for early diagnosis and accurate prediction of COPD patients based on prepared Computerized Tomography (CT) scan images and lung sound/cough (audio) samples using machine learning techniques, which are presented in this study. MethodThe proposed multimodal framework extracts texture, histogram intensity, chroma, Mel-Frequency Cepstral Coefficients (MFCCs), and Gaussian scale space from the prepared CT images and lung sound/cough samples. Accurate data from All India Institute Medical Sciences (AIIMS), Raipur, India, and the open respiratory CT images and lung sound/cough (audio) sample dataset validate the proposed framework. The discriminatory features are selected from the extracted feature sets using unsupervised ML techniques, and customized ensemble learning techniques are applied to perform early classification and assess the severity levels of COPD patients. ResultsThe proposed framework provided 97.50%, 98%, and 95.30% accuracy for early diagnosis of COPD patients based on the fusion technique, CT diagnostic model, and cough sample model. ConclusionFinally, we compare the performance of the proposed framework with existing methods, current approaches, and conventional benchmark techniques for early diagnosis.

The Changing Definition and Perception of COPD.

COPD remains one of the leading causes of death worldwide. The impact of smoking and air pollution remain important causative factors. Traditional classification of COPD includes the overlap of emphysema, chronic bronchitis and asthma. More recently the COPDGene definition includes the terms possible, probable and definite COPD. These are supported by findings from exposures, symptoms, spirometry and radiologic imaging. This new approach can lead to earlier diagnosis and modification of risk factors, earlier therapeutic intervention and improved treatments of established disease.

Embedded system design for classification of COPD and pneumonia patients by lung sound analysis.

Chronic obstructive pulmonary disease (COPD) and pneumonia are lethal pulmonary illnesses with equivocal nature of abnormal pulmonic acoustics. Using lung sound signals, the classification of pulmonary abnormalities is a difficult task. A standalone system was conceived for screening COPD and Pneumonia patients through signal processing and machine learning methodologies. The proposed system will assist practitioners and pulmonologists in the accurate classification of disease. In this research work, ICBHI's and self-collected lung sound (LS) databases are used to investigate COPD and pneumonia patient. In this scheme, empirical mode decomposition (EMD), discrete wavelet transform (DWT), and analysis of variance (ANOVA) techniques are employed for segmentation, noise elimination, and feature selection, respectively. To overcome the inherent limitation of ICBHI's LS database, the adaptive synthetic (ADASYN) sampling technique is used to eradicate class imbalance. Lung sound features are used to train fine Gaussian support vector machine (FG-SVM) for classification of COPD, pneumonia, and heathy healthy subjects. This machine learning scheme is implemented on low cost and portable Raspberry pi 3 model B+ (Cortex-A53 (ARMv8) 64-bit SoC @ 1.4GHz through hardware-supported language. Resultant hardware is capable of screening COPD and pneumonia patients accurately and assist health professionals.

Respiratory symptoms (COPD Assessment Test and modified Medical Research Council dyspnea scores) and GOLD-ABCD COPD classification: the LASSYC study.

To assess the frequency and severity of 24-hour respiratory symptoms according to COPD GOLD-ABCD classification (2017-version), the distribution of the patients with COPD into GOLD categories using mMRC (≥2) or CAT (≥10) scores, and agreement between these cut-off points. In this cross-sectional study (LASSYC study), 24-hour day respiratory symptoms were assessed by the Evaluating Respiratory Symptoms in COPD (E-RS) questionnaire, Nighttime Symptoms of COPD Instrument (NiSCI), Early Morning Symptoms of COPD Instrument (EMSCI), CAT and mMRC scores. Among the 734 patients with COPD, 61% were male, age 69.6±8.7 years, FEV1% post-BD 49.1±17.5%, mMRC 1.8±1.0 and CAT 15.3±.8.1. By mMRC 33.7% were group-A, 29.2% group-B, 10.2% group-C and 26.9% group-D. By CAT 22.3% were group-A, 41% group-B, 4.8% group-C and 31.9% group-D. Using the mMRC the severity of E-RS, NiSCI and EMSCI scores increased from group A to D. Using the CAT, the groups B and D had the higher scores. Agreement between mMRC and CAT was 89.5% (Kappa statistics=75.7%). For mMRC score of 2, CAT score of ≥11 showed the maximum Youden's index (1.34). For mMRC score of 1, CAT score of ≥9 and ≥10 showed the maximum Youden's index (1.48). GOLD COPD classification by CAT seems to better discriminate 24-hour symptoms. Results do not support the equivalent use of CAT≥10 and mMRC≥2 for assessing symptoms.

Correlation of FEV1/FEV6 with BODE Index, CAT Score and DLCO in COPD Patients

Chronic Obstructive Pulmonary Disease is associated with progressive airflow limitation. Spirometry is one of the most important non-invasive tests that help in diagnosing airway obstruction. A Post bronchodilator FEV1/FVC ratio &lt; 0.7 according to GOLD guidelines is considered to be diagnostic of COPD, wherein FEV1 is the forced expiratory volume in the first second, and FVC is the forced vital capacity which requires complete emptying of lungs that may require longer expiratory time and a more significant expiratory effort. The value of FEV1/FEV6 ratio is similar to the FEV1/FVC rate in diagnosing obstruction. FEV1/FEV6 is one such spirometric measurement which reduces the effort of expiration and helps in detecting the airway obstruction at the end of the sixth second during forceful exhalation thus serving as a natural, cost-effective bedside investigation that can be used in every health care facility for prompt diagnosis and management of the disease. FEV1/FEV6 can also be used to assess the prognosis of patients with COPD. It can even independently predict the reduction of lung function, rate of death and risk of carcinoma. The vitalograph is a smooth handheld device which helps in early detection of COPD in a quick, simple and reproducible manner. It not only displays the FEV1, FEV6 ratio and FEV1 per cent predicted but also provided information regarding the severity of COPD classification and lung age estimation. It can be used as a tool to avoid over diagnosis of COPD, especially in elderly patients. It has the advantage of requiring minimal instruction for use by non-respiratory specialists as well. This work concludes that FEV1/FEV6 can be used as a simple, cost-effective bedside investigation to diagnose COPD as an alternative to the conventional spirometry to detect undiagnosed airway obstruction in individuals with a low expiratory effort, especially in poor resource settings.

A COMPREHENSIVE COMPARISON OF MACHINE LEARNING ALGORITHMS ON DIAGNOSING ASTHMA DISEASE AND COPD

Ponte Academic JournalMar 2020, Volume 76, Issue 3 A COMPREHENSIVE COMPARISON OF MACHINE LEARNING ALGORITHMS ON DIAGNOSING ASTHMA DISEASE AND COPDAuthor(s): Erkut Bolat ,Hasan Yildirim, Sedat Altin, Eray YurtsevenJ. Ponte - Mar 2020 - Volume 76 - Issue 3 doi: 10.21506/j.ponte.2020.3.17 Abstract:The diagnosis and classification of asthma and COPD, which are the most common respiratory diseases, have been vital importance. Recently, machine learning has been great interest for processing health data due to this importance. The focus of previous studies has been to obtain the most predictive algorithm for diagnosing aforementioned diseases. However, variable importance has received little attention. The principal objective of this study is to investigate machine learning algorithms both predictivity and variable importance evaluation abilities. A comprehensive comparison has been carried out by considering different performance criteria including accuracy, precision, recall, Kappa statistics, F-measure, ROC curve and AUC value. The findings have been indicated that Random forest (with CART learner), C5.0 for asthma and SVM (with non-linear kernel) and GBM for COPD are the best algorithms. While the most important variable for asthma is FEV3, MEF50 for COPD. FEV1 and FVC have been common variables among the top five variables. The rank of variables has been supported by statistical significance tests. Additionally, ROC curves and AUC values have provided similar and supportive results as visually. Consequently, this study should be of value to practitioners and researchers studying on expert systems on health sciences and machine learning applications. Download full text:Check if you have access through your login credentials or your institution Username Password

CORRELAÇÃO DA FUNÇÃO PULMONAR, QUALIDADE DE VIDA E GRAU DE DISPNÉIA EM PACIENTES COM DOENÇA PULMONAR OBSTRUTIVA CRÔNICA.

Introdução: A DPOC é caracterizada pela presença de obstrução ao fluxo aéreo, não totalmente reversível, sendo essa limitação geralmente progressiva. Objetivo: O estudo visou correlacionar a função pulmonar, qualidade de vida e grau de dispneia em pacientes com DPOC. Metodologia: A amostra foi composta por 30 pacientes. A prova de função pulmonar foi realizada através da espirometria. Para avaliar a QV foi utilizado o questionário AQ20 e para avaliar o grau de dispneia foi utilizado a MRC. Resultados: Houve diferença estatisticamente significativa, entre a média do questionário AQ20 e a escala que avalia o grau de dispneia MCR (p= 0,092). Não observamos diferença estatisticamente significativa entre a classificação da DPOC no que se referem às variáveis, escore do questionário de AQ20 e escala MCR. Conclusão: Em nosso estudo, ao analisar o grau de obstrução em pacientes DPOC com o AQ20, concluímos que, à medida que a intensidade de dispneia aumenta, a QV encontra-se mais prejudicada. A validação e a utilização de questionários de QV e escalas que medem a intensidade de dispneia são de fundamental importância para se avaliar a relação saúde-qualidade de vida nos pacientes portadores da DPOC.

The COPD multi-dimensional phenotype: A new classification from the STORICO Italian observational study.

BackgroundThis paper is aimed to (i) develop an innovative classification of COPD, multi-dimensional phenotype, based on a multidimensional assessment; (ii) describe the identified multi-dimensional phenotypes.MethodsAn exploratory factor analysis to identify the main classificatory variables and, then, a cluster analysis based on these variables were run to classify the COPD-diagnosed 514 patients enrolled in the STORICO (trial registration number: NCT03105999) study into multi-dimensional phenotypes.ResultsThe circadian rhythm of symptoms and health-related quality of life, but neither comorbidity nor respiratory function, qualified as primary classificatory variables. Five multidimensional phenotypes were identified: the MILD COPD characterized by no night-time symptoms and the best health status in terms of quality of life, quality of sleep, level of depression and anxiety, the MILD EMPHYSEMATOUS with prevalent dyspnea in the early-morning and day-time, the SEVERE BRONCHITIC with nocturnal and diurnal cough and phlegm, the SEVERE EMPHYSEMATOUS with nocturnal and diurnal dyspnea and the SEVERE MIXED COPD distinguished by higher frequency of symptoms during 24h and worst quality of life, of sleep and highest levels of depression and anxiety.ConclusionsOur results showed that properly collected respiratory symptoms play a primary classificatory role of COPD patients. The longitudinal observation will disclose the discriminative and prognostic potential of the proposed multidimensional phenotype.Trial registrationTrial registration number: NCT03105999, date of registration: 10th April 2017.

COPD classification models and mortality prediction capacity.

ObjectiveOur aim was to assess the impact of comorbidities on existing COPD prognosis scores.Patients and methodsA total of 543 patients with COPD (FEV1 <80% and FEV1/FVC <70%) were included between January 2003 and January 2004. Patients were stable for at least 6 weeks before inclusion and were followed for 5 years without any intervention by the research team. Comorbidities and causes of death were established from medical reports or information from primary care medical records. The GOLD system and the body mass index, obstruction, dyspnea and exercise (BODE) index were used for COPD classification. Patients were also classified into four clusters depending on the respiratory disease and comorbidities. Cluster analysis was performed by combining multiple correspondence analyses and automatic classification. Receiver operating characteristic curves and the area under the curve (AUC) were calculated for each model, and the DeLong test was used to evaluate differences between AUCs. Improvement in prediction ability was analyzed by the DeLong test, category-free net reclassification improvement and the integrated discrimination index.ResultsAmong the 543 patients enrolled, 521 (96%) were male, with a mean age of 68 years, mean body mass index 28.3 and mean FEV1% 55%. A total of 167 patients died during the study follow-up. Comorbidities were prevalent in our cohort, with a mean Charlson index of 2.4. The most prevalent comorbidities were hypertension, diabetes mellitus and cardiovascular diseases. On comparing the BODE index, GOLDABCD, GOLD2017 and cluster analysis for predicting mortality, cluster system was found to be superior compared with GOLD2017 (0.654 vs 0.722, P=0.006), without significant differences between other classification models. When cardiovascular comorbidities and chronic renal failure were added to the existing scores, their prognostic capacity was statistically superior (P<0.001).ConclusionComorbidities should be taken into account in COPD management scores due to their prevalence and impact on mortality.

Prognostic Accuracy of Three COPD Classification Systems in Relation to Long-Term Mortality of COPD Patients: A Prospective Multicenter Study

Recent research showed group B patients express higher mortality compared to group C patients when GOLD A-D grouping is used. We aimed to compare the prognostic accuracy of three GOLD classification systems, I-IV ("pre-2011"), A-D ("2011-2016") and A-D ("2017-present") in relation to mortality, exacerbation risk, quality of life (QoL) assessment and specific treatments use in a real-life COPD cohort. We used the data of 720 patients from the Czech Multicenter Research Database of COPD. Four-year mortality and time-to-exacerbation using the GOLD "pre-2011", "2011-2016" and "2017-present" classification schemes were assessed. Moreover, distribution of specific treatments use and QoL measures were analyzed. The GOLD I-IV classification system showed gradual increase in 4-year mortality across the stages (GOLD II 18.8%, III 28.5%, IV 38.7%) (p = 0.001). Using the A-D "2011-2016" classification scheme, group C patients had lower mortality (16.7%) than group B (18.7%) (p = 0.009). The A-D "2017-present" classification showed higher mortality in group B (25.5%) compared to group C (20%) (p = 0.05). For additional outcomes, the GOLD I-IV scheme showed highest match between the calculated 4-year exacerbation risk and QoL measures and GOLD stage/grouping. In terms of specific treatment distributions, various patterns for each GOLD classification system were observed with best match of GOLD "2017-present" system to the layout of GOLD groups and categories. We conclude the GOLD I-IV classification system had the highest accuracy related to mortality, QoL measures and exacerbation risk prediction, while the A-D "2017-present" scheme was most accurate within severity of symptoms prediction reflected also by more frequent specific treatments use.

New combined assessment of chronic obstructive pulmonary disease: Utilization, pitfalls, and association with spirometry.

Introduction:Classification of chronic obstructive pulmonary (COPD) disease has changed from being solely based on spirometric variables to combined assessment including symptom scores and history of exacerbations/ hospitalizations. There is both lack of awareness regarding change in its assessment as well as underutilization due to time constraints and seeming complexity. Moreover, treatment of COPD needs to be tailored according to the new combined assessment.Aims:Current study was planned to look at current stratification of patients according to new revised combined assessment (Global Initiative for Chronic Obstructive Lung Disease GOLD 2017) in comparison to old(GOLD 2011) as well as its incorporation in clinical practice. Co-relation between revised combined assessment and spirometric staging was also assessed.Methods:418 consecutive COPD patients were enrolled, their dyspnea scores in terms of modified medical research council scale (mMRC), preceding history of hospitalization/ exacerbation over preceding one year and spirometric variables were recorded. Their stratification according to old and new classification recorded. Their past treatment records were reviewed and combined assessment if done recorded.Results:Substantial shift of categories is seen from C and D respectively to stage A and B on applying the new classification compared to old i.e more severe to less severe. Secondly, revised combined assessment is still highly underutilized. Revised combined assessment has positive co-relation with spirometry and post bronchodilator forced expiratory volume in 1 second(FEV1).Conclusions:Management of substantial number of stable COPD patients may need to be stepped down in accordance with revised combined assessment. There is a need to disseminate information regarding change in COPD classification and stress on its incorporation in our day-to day clinical practice. Revised combined assessment has positive co-relation with spirometry, stressing its utility even in peripheral centers without spirometry facilities.

Old Friends, Old Wine, and Old COPD Classification Are Best

Old Friends, Old Wine, and Old COPD Classification Are Best