Derivation and validation of a composite index of severity in chronic obstructive pulmonary disease: the DOSE Index

Using machine learning to predict deterioration of symptoms in COPD patients within a telemonitoring program

COPD exacerbations have a profound clinical impact on patients. Accurately predicting these events could help healthcare professionals take proactive measures to mitigate their impact. For over a decade, telEPOC, a telehealthcare program, has collected data that can be utilized to train machine learning models to anticipate COPD exacerbations. The objective of this study is to develop a machine learning model that, based on a patient's history, predicts the probability of an exacerbation event within the next 3 days. After cleaning and harmonizing the different subsets of data, we split the data along the temporal axis: one subset for model training, another for model selection, and another for model evaluation. We then trained a gradient tree boosting approach as well as neural network-based approaches. After conducting our analysis, we found that the CatBoost algorithm yielded the best results, with an area under the precision-recall curve of 0.53 and an area under the ROC curve of 0.91. Additionally, we assessed the significance of the input variables and discovered that breathing rate, heart rate, and SpO2 were the most informative. The resulting model can operate in a 50% recall and 50% precision regime, which we consider has the potential to be useful in daily practice.

The DOSE index in chronic obstructive pulmonary disease: evaluating healthcare costs

BACKGROUND AND OBJECTIVES

The DOSE index, which incorporates Dyspnea, Obstruction, Smoking, and Exacerbations, is a widely used tool for assessing the severity and prognosis of Chronic Obstructive Pulmonary Disease (COPD). In addition to risk assessment, it has potential clinical utility in predicting healthcare costs, which are primarily driven by exacerbations. While several indices, such as the BODE (Body-mass index, Obstruction, Dyspnea, Exercise) and ADO (Age, Dyspnea, Obstruction) indices, exist for risk prediction, there is a lack of dedicated tools for forecasting healthcare costs. This study explores the potential of the DOSE index compared to other indices, including BODE, ADO, and the Charlson Comorbidity Index (CCI), for this purpose.

MATERIALS AND METHODS

This cross-sectional retrospective study analyzed data from 396 COPD cases. We examined associations between the DOSE index, BODE index, ADO index, CCI, and healthcare costs, including hospitalizations and emergency room treatments. Healthcare costs were categorized as direct medical expenses.

RESULTS

Significant associations were observed between the DOSE index and various healthcare parameters. DOSE quartiles showed strong correlations with outpatient visits (p = 0.013) and outpatient medical expenses (p = 0.011). In addition, hospitalization frequency, duration, and associated costs were significantly correlated with higher DOSE quartiles (p < 0.001). A significant difference was found when comparing DOSE quartiles between patients with high (CCI ≥ 3) and low (CCI < 3) comorbidity scores (p = 0.018). The DOSE index outperformed other indices, likely due to its inclusion of exacerbations, a key driver of healthcare costs.

CONCLUSION

The DOSE index demonstrates potential in predicting healthcare costs, particularly due to its inclusion of exacerbation frequency. This study highlights the importance of considering exacerbations alongside traditional risk factors for more accurate cost forecasting in COPD management. Our findings suggest that the DOSE index may be a valuable tool in both clinical and economic assessments of COPD patients, though further research is warranted to validate these findings in larger datasets.

Development and validation of a mortality risk prediction model for chronic obstructive pulmonary disease: a cross-sectional study using probabilistic graphical modelling

Background

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality. Predicting mortality risk in patients with COPD can be important for disease management strategies. Although all-cause mortality predictors have been developed previously, limited research exists on factors directly affecting COPD-specific mortality.

Methods

In a retrospective study, we used probabilistic graphs to analyse clinical cross-sectional data (COPDGene cohort), including demographics, spirometry, quantitative chest imaging, and symptom features, as well as gene expression data. COPDGene recruited current and former smokers, aged 45-80 years with >10 pack-years smoking history, from across the USA (Phase 1, 11/2007-4/2011) and invited them for a follow-up visit (Phase 2, 7/2013-7/2017). ECLIPSE cohort recruited current and former smokers (COPD patients and controls from USA and Europe), aged 45-80 with smoking history >10 pack-years (12/2005-11/2007). We applied graphical models on multi-modal data COPDGene Phase 1 participants to identify factors directly affecting all-cause and COPD-specific mortality (primary outcomes); and on Phase 2 follow-up cohort to identify additional molecular and social factors affecting mortality. We used penalized Cox regression with features selected by the causal graph to build VAPORED, a mortality risk prediction model. VAPORED was compared to existing scores (BODE: BMI, airflow obstruction, dyspnoea, exercise capacity; ADO: age, dyspnoea, airflow obstruction) on the ability to rank individuals by mortality risk, using four evaluation metrics (concordance, concordance probability estimate (CPE), cumulative/dynamic (C/D) area under the receiver operating characteristic curve (AUC), and integrated C/D AUC). The results were validated in ECLIPSE.

Findings

Graphical models, applied on the COPDGene Phase 1 samples (n = 8610), identified 11 and 7 variables directly linked to all-cause and COPD-specific mortality, respectively. Although many appear in both models, non-lung comorbidities appear only in the all-cause model, while forced vital capacity (FVC %predicted) appears in COPD-specific mortality model only. Additionally, the graph model of Phase 2 data (n = 3182) identified internet access, CD4 T cells and platelets to be linked to lower mortality risk. Furthermore, using the 7 variables linked to COPD-specific mortality (forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC) ration, FVC %predicted, age, history of pneumonia, oxygen saturation, 6-min walk distance, dyspnoea) we developed VAPORED mortality risk score, which we validated on the ECLIPSE cohort (3-yr all-cause mortality data, n = 2312). VAPORED performed significantly better than ADO, BODE, and updated BODE indices in predicting all-cause mortality in ECLIPSE in terms of concordance (VAPORED [0.719] vs ADO [0.693; FDR p-value 0.014], BODE [0.695; FDR p-value 0.020], and updated BODE [0.694; FDR p-value 0.021]); CPE (VAPORED [0.714] vs ADO [0.673; FDR p-value <0.0001], BODE [0.662; FDR p-value <0.0001], and updated BODE [0.646; FDR p-value <0.0001]); 3-year C/D AUC (VAPORED [0.728] vs ADO [0.702; FDR p-value 0.017], BODE [0.704; FDR p-value 0.021], and updated BODE [0.703; FDR p-value 0.024]); integrated C/D AUC (VAPORED [0.723] vs ADO [0.698; FDR p-value 0.047], BODE [0.695; FDR p-value 0.024], and updated BODE [0.690; FDR p-value 0.021]). Finally, we developed a web tool to help clinicians calculate VAPORED mortality risk and compare it to ADO and BODE predictions.

Interpretation

Our work is an important step towards improving our identification of high-risk patients and generating hypotheses of potential biological mechanisms and social factors driving mortality in patients with COPD at the population level. The main limitation of our study is the fact that the analysed datasets consist of older people with extensive smoking history and limited racial diversity. Thus, the results are relevant to high-risk individuals or those diagnosed with COPD and the VAPORED score is validated for them.

Funding

This research was supported by NIH [NHLBI, NLM]. The COPDGene study is supported by the COPD Foundation, through grants from AstraZeneca, Bayer Pharmaceuticals, Boehringer Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer and Sunovion.

Smoking, respiratory symptoms, lung function and life expectancy: A longitudinal study of ageing

BACKGROUND AND OBJECTIVE

Prognostic indices have been developed to predict various outcomes, including mortality. These indices and hazard ratios may be difficult for patients to understand. We investigated the association between smoking, respiratory symptoms and lung function with remaining life expectancy (LE) in older adults.

METHODS

Data were from the 2004/05 English Longitudinal Study of Ageing (ELSA) (n = 8930), participants aged ≥50-years, with mortality data until 2012. Respiratory symptoms included were chronic phlegm and shortness of breath (SOB). The association between smoking, respiratory symptoms and FEV1/FVC, and remaining LE was estimated using a parametric survival function and adjusted for covariates including age at baseline and sex.

RESULTS

The extent to which symptoms and FEV1/FVC predicted differences in remaining LE varied by smoking. Compared to asymptomatic never smokers with normal lung function (the reference group), in never smokers, only those with SOB had a significant reduction in remaining LE. In former and current smokers, those with respiratory symptoms had significantly lower remaining LE compared to the reference group if they had FEV1/FVC <0.70 compared to those with FEV1/FVC ≥0.70. Males aged 50-years, current smokers with SOB and FEV1/FVC <0.70, had a remaining LE of 19.2 (95%CI: 16.5-22.2) years, a decrease of 8.1 (5.3-10.8) years, compared to the reference group.

CONCLUSION

Smoking, respiratory symptoms and FEV1/FVC are strongly associated with remaining LE in older people. The use of remaining LE to communicate mortality risk to patients needs further investigation.

COPD Self-Management: A Patient-Physician Perspective

This article is co-authored by five patients living with chronic obstructive pulmonary disease (COPD), and a primary care physician who has over 30 years of clinical experience and is involved in educating healthcare professionals. The first section of this article is authored by the patients, who describe their experiences of living with COPD. The section that follows is authored by the physician, who discusses the management of COPD in the context of the patients' experiences.

The contribution of estimated dead space fraction to mortality prediction in patients with chronic obstructive pulmonary disease-a new proposal

Background

Mortality due to chronic obstructive pulmonary disease (COPD) is increasing. However, dead space fractions at rest (VD/VTrest) and peak exercise (VD/VTpeak) and variables affecting survival have not been evaluated. This study aimed to investigate these issues.

Methods

This retrospective observational cohort study was conducted from 2010-2020. Patients with COPD who smoked, met the Global Initiatives for Chronic Lung Diseases (GOLD) criteria, had available demographic, complete lung function test (CLFT), medication, acute exacerbation of COPD (AECOPD), Charlson Comorbidity Index, and survival data were enrolled. VD/VTrest and VD/VTpeak were estimated (estVD/VTrest and estVD/VTpeak). Univariate and multivariable Cox regression with stepwise variable selection were performed to estimate hazard ratios of all-cause mortality.

Results

Overall, 14,910 patients with COPD were obtained from the hospital database, and 456 were analyzed after excluding those without CLFT or meeting the lung function criteria during the follow-up period (median (IQR) 597 (331-934.5) days). Of the 456 subjects, 81% had GOLD stages 2 and 3, highly elevated dead space fractions, mild air-trapping and diffusion impairment. The hospitalized AECOPD rate was 0.60 ± 2.84/person/year. Forty-eight subjects (10.5%) died, including 30 with advanced cancer. The incidence density of death was 6.03 per 100 person-years. The crude risk factors for mortality were elevated estVD/VTrest, estVD/VTpeak, ≥2 hospitalizations for AECOPD, advanced age, body mass index (BMI) <18.5 kg/m2, and cancer (hazard ratios (95% C.I.) from 1.03 [1.00-1.06] to 5.45 [3.04-9.79]). The protective factors were high peak expiratory flow%, adjusted diffusing capacity%, alveolar volume%, and BMI 24-26.9 kg/m2. In stepwise Cox regression analysis, after adjusting for all selected factors except cancer, estVD/VTrest and BMI <18.5 kg/m2 were risk factors, whereas BMI 24-26.9 kg/m2 was protective. Cancer was the main cause of all-cause mortality in this study; however, estVD/VTrest and BMI were independent prognostic factors for COPD after excluding cancer.

Conclusions

The predictive formula for dead space fraction enables the estimation of VD/VTrest, and the mortality probability formula facilitates the estimation of COPD mortality. However, the clinical implications should be approached with caution until these formulas have been validated.

Development and validation of a nomogram model for mortality prediction in stable chronic obstructive pulmonary disease patients: A prospective observational study in the RealDTC cohort

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. There is no nomogram model available for mortality prediction of stable COPD. We intended to develop and validate a nomogram model to predict mortality risk in stable COPD patients for personalised prognostic assessment.

METHODS

A prospective observational study was made of COPD outpatients registered in the RealDTC study between December 2016 and December 2019. Patients were randomly assigned to the training cohort and validation cohort in a ratio of 7:3. We used Lasso regression to screen predicted variables. Further, we evaluated the prognostic performance using the area under the time-dependent receiver operating characteristic curve (AUC) and calibration curve. We used the AUC, concordance index, and decision curve analysis to evaluate the net benefits and utility of the nomogram compared with three earlier prediction models.

RESULTS

Of 2499 patients, the median follow-up was 38 months. The characteristics of the patients between the training cohort (n = 1743) and the validation cohort (n = 756) were similar. ABEODS nomogram model, combining age, body mass index, educational level, airflow obstruction, dyspnoea, and severe exacerbation in the first year, was constructed to predict mortality in stable COPD patients. In the integrative analysis of training and validation cohorts of the nomogram model, the three-year mortality prediction achieved AUC = 0.84; 95% confidence interval (CI) = 0.81, 0.88 and AUC = 0.80; 95% CI = 0.74, 0.86, respectively. The ABEODS nomogram model preserved excellent calibration in both the training cohort and validation cohort. The time-dependent AUC, concordance index, and net benefit of the nomogram model were higher than those of BODEx, updated ADO, and DOSE, respectively.

CONCLUSIONS

We developed and validated a prognostic nomogram model that accurately predicts mortality across the COPD severity spectrum. The proposed ABEODS nomogram model performed better than earlier models, including BODEx, updated ADO, and DOSE in Chinese patients with COPD.

REGISTRATION

ChiCTR-POC-17010431.

Mortality and associated factors among people living with HIV admitted at a tertiary-care hospital in Uganda: a cross-sectional study

BACKGROUND

Hospital admission outcomes for people living with HIV (PLHIV) in resource-limited settings are understudied. We describe in-hospital mortality and associated clinical-demographic factors among PLHIV admitted at a tertiary-level public hospital in Uganda.

METHODS

We performed a cross-sectional analysis of routinely collected data for PLHIV admitted at Kiruddu National Referral Hospital between March 2020 and March 2023. We estimated the proportion of PLHIV who had died during hospitalization and performed logistic regression modelling to identify predictors of mortality.

RESULTS

Of the 5,827 hospitalized PLHIV, the median age was 39 years (interquartile range [IQR] 31-49) and 3,293 (56.51%) were female. The median CD4 + cell count was 109 cells/µL (IQR 25-343). At admission, 3,710 (63.67%) were active on antiretroviral therapy (ART); 1,144 (19.63%) had interrupted ART > 3 months and 973 (16.70%) were ART naïve. In-hospital mortality was 26% (1,524) with a median time-to-death of 3 days (IQR 1-7). Factors associated with mortality (with adjusted odds ratios) included ART interruption, 1.33, 95% confidence intervals (CI) 1.13-1.57, p 0.001; CD4 + counts ≤ 200 cells/µL 1.59, 95%CI 1.33-1.91, p < 0.001; undocumented CD4 + cell count status 2.08, 95%CI 1.73-2.50, p < 0.001; impaired function status 7.35, 95%CI 6.42-8.41, p < 0.001; COVID-19 1.70, 95%CI 1.22-2.37, p 0.002; liver disease 1.77, 95%CI 1.36-2.30, p < 0.001; co-infections 1.53, 95%CI 1.32-1.78, p < 0.001; home address > 20 km from hospital 1.23, 95%CI 1.04-1.46, p 0.014; hospital readmission 0.7, 95%CI 0.56-0.88, p 0.002; chronic lung disease 0.62, 95%CI 0.41-0.92, p 0.019; and neurologic disease 0.46, 95%CI 0.32-0.68, p < 0.001.

CONCLUSION

One in four admitted PLHIV die during hospitalization. Identification of risk factors (such as ART interruption, function impairment, low/undocumented CD4 + cell count), early diagnosis and treatment of co-infections and liver disease could improve outcomes.

Chest MRI and CT Predictors of 10-Year All-Cause Mortality in COPD

Pulmonary imaging measurements using magnetic resonance imaging (MRI) and computed tomography (CT) have the potential to deepen our understanding of chronic obstructive pulmonary disease (COPD) by measuring airway and parenchymal pathologic information that cannot be provided by spirometry. Currently, MRI and CT measurements are not included in mortality risk predictions, diagnosis, or COPD staging. We evaluated baseline pulmonary function, MRI and CT measurements alongside imaging texture-features to predict 10-year all-cause mortality in ex-smokers with (n = 93; 31 females; 70 ± 9years) and without (n = 69; 29 females, 69 ± 9years) COPD. CT airway and vessel measurements, helium-3 (3He) MRI ventilation defect percent (VDP) and apparent diffusion coefficients (ADC) were quantified. MRI and CT texture-features were extracted using PyRadiomics (version2.2.0). Associations between 10-year all-cause mortality and all clinical and imaging measurements were evaluated using multivariable regression model odds-ratios. Machine-learning predictive models for 10-year all-cause mortality were evaluated using area-under-receiver-operator-characteristic-curve (AUC), sensitivity and specificity analyses. DLCO (%pred) (HR = 0.955, 95%CI: 0.934-0.976, p < 0.001), MRI ADC (HR = 1.843, 95%CI: 1.260-2.871, p < 0.001), and CT informational-measure-of-correlation (HR = 3.546, 95% CI: 1.660-7.573, p = 0.001) were the strongest predictors of 10-year mortality. A machine-learning model trained on clinical, imaging, and imaging textures was the best predictive model (AUC = 0.82, sensitivity = 83%, specificity = 84%) and outperformed the solely clinical model (AUC = 0.76, sensitivity = 77%, specificity = 79%). In ex-smokers, regardless of COPD status, addition of CT and MR imaging texture measurements to clinical models provided unique prognostic information of mortality risk that can allow for better clinical management.Clinical Trial Registration: www.clinicaltrials.gov NCT02279329.

Risk Factors for Recurrent Exacerbations in the General-Practitioner-Based Swiss Chronic Obstructive Pulmonary Disease (COPD) Cohort

BACKGROUND

Patients with chronic obstructive pulmonary disease (COPD) often suffer from acute exacerbations. Our objective was to describe recurrent exacerbations in a GP-based Swiss COPD cohort and develop a statistical model for predicting exacerbation.

METHODS

COPD cohort demographic and medical data were recorded for 24 months, by means of a questionnaire-based COPD cohort. The data were split into training (75%) and validation (25%) datasets. A negative binomial regression model was developed using the training dataset to predict the exacerbation rate within 1 year. An exacerbation prediction model was developed, and its overall performance was validated. A nomogram was created to facilitate the clinical use of the model.

RESULTS

Of the 229 COPD patients analyzed, 77% of the patients did not experience exacerbation during the follow-up. The best subset in the training dataset revealed that lower forced expiratory volume, high scores on the MRC dyspnea scale, exacerbation history, and being on a combination therapy of LABA + ICS (long-acting beta-agonists + Inhaled Corticosteroids) or LAMA + LABA (Long-acting muscarinic receptor antagonists + long-acting beta-agonists) at baseline were associated with a higher rate of exacerbation. When validated, the area-under-curve (AUC) value was 0.75 for one or more exacerbations. The calibration was accurate (0.34 predicted exacerbations vs 0.28 observed exacerbations).

CONCLUSION

Nomograms built from these models can assist clinicians in the decision-making process of COPD care.

An Update on Outcomes for COPD Pharmacological Trials: A COPD Investigators Report - Reassessment of the 2008 American Thoracic Society/European Respiratory Society Statement on Outcomes for COPD Pharmacological Trials

Background: In 2008, a dedicated American Thoracic Society/European Respiratory Society task force published a paper on the possible use and limitations of clinical outcomes and biomarkers to evaluate the impact of pharmacological therapy in patients with chronic obstructive pulmonary disease. Since then, our scientific understanding of chronic obstructive pulmonary disease has increased considerably; there has been a progressive shift from a one-size-fits-all diagnostic and therapeutic approach to a personalized approach; and many new treatments currently in development will require new endpoints to evaluate their efficacy adequately. Objectives: The emergence of several new relevant outcome measures motivated the authors to review advances in the field and highlight the need to update the content of the original report. Methods: The authors separately created search strategies for the literature, primarily based on their opinions and assessments supported by carefully chosen references. No centralized examination of the literature or uniform criteria for including or excluding evidence were used. Measurements and Main Results: Endpoints, outcomes, and biomarkers have been revisited. The limitations of some of those reported in the American Thoracic Society/European Respiratory Society task force document have been highlighted. In addition, new tools that may be useful, especially in evaluating personalized therapy, have been described. Conclusions: Because the "label-free" treatable traits approach is becoming an important step toward precision medicine, future clinical trials should focus on highly prevalent treatable traits, and this will influence the choice of outcomes and markers to be considered. The use of the new tools, particularly combination endpoints, could help better identify the right patients to be treated with the new drugs.

Validation of Clinical COPD Phenotypes for Prognosis of Long-Term Mortality in Swedish and Dutch Cohorts

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease with variable mortality risk. The aim of our investigation was to validate a simple clinical algorithm for long-term mortality previously proposed by Burgel et al. in 2017. Subjects with COPD from two cohorts, the Swedish PRAXIS study (n = 784, mean age (standard deviation (SD)) 64.0 years (7.5), 42% males) and the Rotterdam Study (n = 735, mean age (SD) 72 years (9.2), 57% males), were included. Five clinical clusters were derived from baseline data on age, body mass index, dyspnoea grade, pulmonary function and comorbidity (cardiovascular disease/diabetes). Cox models were used to study associations with 9-year mortality. The distribution of clinical clusters (1-5) was 29%/45%/8%/6%/12% in the PRAXIS study and 23%/26%/36%/0%/15% in the Rotterdam Study. The cumulative proportion of deaths at the 9-year follow-up was highest in clusters 1 (65%) and 4 (72%), and lowest in cluster 5 (10%) in the PRAXIS study. In the Rotterdam Study, cluster 1 (44%) had the highest cumulative mortality and cluster 5 (5%) the lowest. Compared with cluster 5, the meta-analysed age- and sex-adjusted hazard ratio (95% confidence interval) for cluster 1 was 6.37 (3.94-10.32) and those for clusters 2 and 3 were 2.61 (1.58-4.32) and 3.06 (1.82-5.13), respectively. Burgel's clinical clusters can be used to predict long-term mortality risk. Clusters 1 and 4 are associated with the poorest prognosis, cluster 5 with the best prognosis and clusters 2 and 3 with intermediate prognosis in two independent cohorts from Sweden and the Netherlands.

Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommendations: strengths and concerns for future needs

Chronic Obstructive Pulmonary Disease (COPD) is already the third leading cause of death worldwide and simultaneously a major cause of morbidity and mortality. Global initiative for Chronic Obstructive Lung Disease (also known as GOLD) committee, has been created in 1997 to increase the awareness regarding the burden of COPD. GOLD recommendations have been contributing to diagnosis, management and therapy of COPD since 2001. Through these years, by reviewing published articles, GOLD aimed to provide state-of-the-art information not only for pulmonologists, but also for non-respiratory physicians, and to encourage research on COPD. From 2011, GOLD annual reports have changed the way of COPD evaluation from based entirely on spirometric parameters to more clinical indices, such as the assessment of symptoms and dyspnea alongside with exacerbations. Moreover, according to recent developments in pathophysiology of COPD, there is a trend in identifying new pre-clinical stages, contributing to prevention and early COPD treatment. In the field of therapeutic algorithms, changes turn to a more personalized approach. However, it is not clear in what extent this personalized disease management would be feasible and the real challenge for current recommendations is to include more patient characteristics such as co-morbidities and multidimensional scores in disease evaluation.

Improving the Reliability Between the BODE Index and the BODS Index in Which the 6-Min Walk Test Was Replaced with the Five-Repetition Sit-to-Stand Test

Purpose

The BODS index has been confirmed to have predictive properties similar to the original BODE index for mortality in COPD. We evaluated the agreement between the BODS index and the BODE and explored with an updated BODS how this agreement could be improved and its ability to correctly discriminate individual participants’ mortality in a prospective cohort study.

Patients and Methods

We included prospectively a consecutive sample of 137 patients with COPD, between 40 and 80 years, during 2014 and followed for 5 years (2014–2019) in the Pneumology section of a public university hospital in Spain. They participated in the baseline data collection, which included BODE- and BODS-related measurements and prognostic factors, and were followed up for 5-year mortality. We used Bland–Altman plots and the kappa coefficient to analyze the agreement between both the original and updated BODS and the BODE index, and we used the areas under ROC curves (AUC) to compare their discriminative abilities for 5-year all-cause mortality.

Results

The original BODS index scores and quartiles had weak agreement with the BODE index, and our updated BODS strengthened these agreements (a small, statistically nonsignificant mean bias [<0.03] with LoAs<2 points, and a substantial Kappa coefficient [k =0.63; IC 95%: 0.53–0.73]). In addition, the updated BODS index scores had better summarized ability than the BODS index in discriminating participants’ mortality during the following 5 years (AUC: 0.768 versus 0.736; p=0.04).

Conclusion

The updated BODS index scores and quartiles may provide prognostic information similar to that provided by the BODE index in COPD. Future research should focus on index improvement through external validation, as well as the assessment of safety and effectiveness in clinical practice by means of impact studies.

Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Background

Chronic obstructive pulmonary disease (COPD) is a major cause of death and places a heavy burden on health care. To optimize the allocation of precious preventive care management resources and improve the outcomes for high-risk patients with COPD, we recently built the most accurate model to date to predict severe COPD exacerbations, which need inpatient stays or emergency department visits, in the following 12 months. Our model is a machine learning model. As is the case with most machine learning models, our model does not explain its predictions, forming a barrier for clinical use. Previously, we designed a method to automatically provide rule-type explanations for machine learning predictions and suggest tailored interventions with no loss of model performance. This method has been tested before for asthma outcome prediction but not for COPD outcome prediction.

Objective

This study aims to assess the generalizability of our automatic explanation method for predicting severe COPD exacerbations.

Methods

The patient cohort included all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019. In a secondary analysis of 43,576 data instances, we used our formerly developed automatic explanation method to automatically explain our model’s predictions and suggest tailored interventions.

Results

Our method explained the predictions for 97.1% (100/103) of the patients with COPD whom our model correctly predicted to have severe COPD exacerbations in the following 12 months and the predictions for 73.6% (134/182) of the patients with COPD who had ≥1 severe COPD exacerbation in the following 12 months.

Conclusions

Our automatic explanation method worked well for predicting severe COPD exacerbations. After further improving our method, we hope to use it to facilitate future clinical use of our model.

International Registered Report Identifier (IRRID)

RR2-10.2196/13783

Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Background

Chronic obstructive pulmonary disease (COPD) poses a large burden on health care. Severe COPD exacerbations require emergency department visits or inpatient stays, often cause an irreversible decline in lung function and health status, and account for 90.3% of the total medical cost related to COPD. Many severe COPD exacerbations are deemed preventable with appropriate outpatient care. Current models for predicting severe COPD exacerbations lack accuracy, making it difficult to effectively target patients at high risk for preventive care management to reduce severe COPD exacerbations and improve outcomes.

Objective

The aim of this study is to develop a more accurate model to predict severe COPD exacerbations.

Methods

We examined all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019 and identified 278 candidate features. By performing secondary analysis on 43,576 University of Washington Medicine data instances from 2011 to 2019, we created a machine learning model to predict severe COPD exacerbations in the next year for patients with COPD.

Results

The final model had an area under the receiver operating characteristic curve of 0.866. When using the top 9.99% (752/7529) of the patients with the largest predicted risk to set the cutoff threshold for binary classification, the model gained an accuracy of 90.33% (6801/7529), a sensitivity of 56.6% (103/182), and a specificity of 91.17% (6698/7347).

Conclusions

Our model provided a more accurate prediction of severe COPD exacerbations in the next year compared with prior published models. After further improvement of its performance measures (eg, by adding features extracted from clinical notes), our model could be used in a decision support tool to guide the identification of patients with COPD and at high risk for care management to improve outcomes.

International Registered Report Identifier (IRRID)

RR2-10.2196/13783

Value of Cardiopulmonary Exercise Testing in the Prognosis Assessment of Chronic Obstructive Pulmonary Disease Patients: A Retrospective, Multicentre Cohort Study

INTRODUCTION

Chronic obstructive pulmonary disease (COPD) is one of the most common chronic diseases associated with high mortality. Previous studies suggested a prognostic role for peak oxygen uptake (VO2peak) assessed during cardiopulmonary exercise testing (CPET) in patients with COPD. However, most of these studies had small sample sizes or short follow-up periods, and despite their relevance, CPET parameters are not included in the Global Initiative for Chronic Obstructive Lung Disease (GOLD) tool for assessment of severity.

OBJECTIVES

We therefore aimed to assess the prognostic value of CPET parameters in a large cohort of outpatients with COPD.

METHODS

In this retrospective, multicentre cohort study, medical records of patients with COPD who underwent CPET during 2004-2017 were reviewed and demographics, smoking habits, GOLD grade and category, exacerbation frequency, dyspnoea score, lung function measurements, and CPET parameters were documented. Relationships with survival were evaluated using Kaplan-Meier analysis, Cox regression, and receiver operating characteristic (ROC) curves.

RESULTS

Of a total of 347 patients, 312 patients were included. Five-year and 10-year survival probability was 75% and 57%, respectively. VO2peak significantly predicted survival (hazard ratio: 0.886 [95% confidence interval: 0.830; 0.946]). The optimal VO2peak threshold for discrimination of 5-year survival was 14.6 mL/kg/min (area under ROC curve: 0.713). Five-year survival in patients with VO2peak <14.6 mL/kg/min versus ≥ 14.6 mL/kg/min was 60% versus 86% in GOLD categories A/B and 64% versus 90% in GOLD categories C/D.

CONCLUSIONS

We confirm that VO2peak is a highly significant predictor of survival in COPD patients and recommend the incorporation of VO2peak into the assessment of COPD severity.

Case-finding and improving patient outcomes for chronic obstructive pulmonary disease in primary care: the BLISS research programme including cluster RCT

Background

Chronic obstructive pulmonary disease is a major contributor to morbidity, mortality and health service costs but is vastly underdiagnosed. Evidence on screening and how best to approach this is not clear. There are also uncertainties around the natural history (prognosis) of chronic obstructive pulmonary disease and how it impacts on work performance.

Objectives

Work package 1: to evaluate alternative methods of screening for undiagnosed chronic obstructive pulmonary disease in primary care, with clinical effectiveness and cost-effectiveness analyses and an economic model of a routine screening programme. Work package 2: to recruit a primary care chronic obstructive pulmonary disease cohort, develop a prognostic model [Birmingham Lung Improvement StudieS (BLISS)] to predict risk of respiratory hospital admissions, validate an existing model to predict mortality risk, address some uncertainties about natural history and explore the potential for a home exercise intervention. Work package 3: to identify which factors are associated with employment, absenteeism, presenteeism (working while unwell) and evaluate the feasibility of offering formal occupational health assessment to improve work performance.

Design

Work package 1: a cluster randomised controlled trial with household-level randomised comparison of two alternative case-finding approaches in the intervention arm. Work package 2: cohort study – focus groups. Work package 3: subcohort – feasibility study.

Setting

Primary care settings in West Midlands, UK.

Participants

Work package 1: 74,818 people who have smoked aged 40–79 years without a previous chronic obstructive pulmonary disease diagnosis from 54 general practices. Work package 2: 741 patients with previously diagnosed chronic obstructive pulmonary disease from 71 practices and participants from the work package 1 randomised controlled trial. Twenty-six patients took part in focus groups. Work package 3: occupational subcohort with 248 patients in paid employment at baseline. Thirty-five patients took part in an occupational health intervention feasibility study.

Interventions

Work package 1: targeted case-finding – symptom screening questionnaire, administered opportunistically or additionally by post, followed by diagnostic post-bronchodilator spirometry. The comparator was routine care. Work package 2: twenty-three candidate variables selected from literature and expert reviews. Work package 3: sociodemographic, clinical and occupational characteristics; occupational health assessment and recommendations.

Main outcome measures

Work package 1: yield (screen-detected chronic obstructive pulmonary disease) and cost-effectiveness of case-finding; effectiveness of screening on respiratory hospitalisation and mortality after approximately 4 years. Work package 2: respiratory hospitalisation within 2 years, and barriers to and facilitators of physical activity. Work package 3: work performance – feasibility and acceptability of the occupational health intervention and study processes.

Results

Work package 1: targeted case-finding resulted in greater yield of previously undiagnosed chronic obstructive pulmonary disease than routine care at 1 year [n = 1278 (4%) vs. n = 337 (1%), respectively; adjusted odds ratio 7.45, 95% confidence interval 4.80 to 11.55], and a model-based estimate of a regular screening programme suggested an incremental cost-effectiveness ratio of £16,596 per additional quality-adjusted life-year gained. However, long-term follow-up of the trial showed that at ≈4 years there was no clear evidence that case-finding, compared with routine practice, was effective in reducing respiratory admissions (adjusted hazard ratio 1.04, 95% confidence interval 0.73 to1.47) or mortality (hazard ratio 1.15, 95% confidence interval 0.82 to 1.61). Work package 2: 2305 patients, comprising 1564 with previously diagnosed chronic obstructive pulmonary disease and 741 work package 1 participants (330 with and 411 without obstruction), were recruited. The BLISS prognostic model among cohort participants with confirmed airflow obstruction (n = 1894) included 6 of 23 candidate variables (i.e. age, Chronic Obstructive Pulmonary Disease Assessment Test score, 12-month respiratory admissions, body mass index, diabetes and forced expiratory volume in 1 second percentage predicted). After internal validation and adjustment (uniform shrinkage factor 0.87, 95% confidence interval 0.72 to 1.02), the model discriminated well in predicting 2-year respiratory hospital admissions (c-statistic 0.75, 95% confidence interval 0.72 to 0.79). In focus groups, physical activity engagement was related to self-efficacy and symptom severity. Work package 3: in the occupational subcohort, increasing dyspnoea and exposure to inhaled irritants were associated with lower work productivity at baseline. Longitudinally, increasing exacerbations and worsening symptoms, but not a decline in airflow obstruction, were associated with absenteeism and presenteeism. The acceptability of the occupational health intervention was low, leading to low uptake and low implementation of recommendations and making a full trial unfeasible.

Limitations

Work package 1: even with the most intensive approach, only 38% of patients responded to the case-finding invitation. Management of case-found patients with chronic obstructive pulmonary disease in primary care was generally poor, limiting interpretation of the long-term effectiveness of case-finding on clinical outcomes. Work package 2: the components of the BLISS model may not always be routinely available and calculation of the score requires a computerised system. Work package 3: relatively few cohort participants were in paid employment at baseline, limiting the interpretation of predictors of lower work productivity.

Conclusions

This programme has addressed some of the major uncertainties around screening for undiagnosed chronic obstructive pulmonary disease and has resulted in the development of a novel, accurate model for predicting respiratory hospitalisation in people with chronic obstructive pulmonary disease and the inception of a primary care chronic obstructive pulmonary disease cohort for longer-term follow-up. We have also identified factors that may affect work productivity in people with chronic obstructive pulmonary disease as potential targets for future intervention.

Future work

We plan to obtain data for longer-term follow-up of trial participants at 10 years. The BLISS model needs to be externally validated. Our primary care chronic obstructive pulmonary disease cohort is a unique resource for addressing further questions to better understand the prognosis of chronic obstructive pulmonary disease.

Trial registration

Current Controlled Trials ISRCTN14930255.

Funding

This project was funded by the National Institute for Health Research (NIHR) Programme Grants for Applied Research programme and will be published in full in Programme Grants for Applied Research; Vol. 9, No. 13. See the NIHR Journals Library website for further project information.

Multidimensional indices in the assessment of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD), a very common disease, is the third leading cause of death worldwide. Due to the significant heterogeneity of clinical phenotypes of COPD there is no single method suitable for predicting patients' health status and outcomes, and therefore multidimensional indices, assessing different components of the disease, were developed and are recommended for clinical practice by international guidelines. Several indices have been widely accepted: BODE and its modifications, ADO, DOSE, CODEX, COTE. They differ in their composition and aim, while they are more accurate and better validated in specific settings and populations. We review the characteristics, strengths and limitations of these indices, and we discuss their role in routine management of patients with COPD, as well as in specific clinical scenarios, such as resuscitation and ceiling of care, or decisions to offer more invasive treatments. This analysis may help clinicians to use those indexes in a more practical and appropriate way.

Predicting Future Health Risk in COPD: Differential Impact of Disease-Specific and Multi-Morbidity-Based Risk Stratification

Objective

Multi-morbidity contributes to mortality and hospitalisation in COPD, but it is uncertain how this interacts with disease severity in risk prediction. We compared contributions of multi-morbidity and disease severity factors in modelling future health risk using UK primary care healthcare data.

Methods

Health records from 103,955 patients with COPD identified from the Clinical Practice Research Datalink were analysed. We compared area under the curve (AUC) statistics for logistic regression (LR) models incorporating disease indices with models incorporating categorised comorbidities. We also compared these models with performance of The John Hopkins Adjusted Clinical Groups^® System (ACG) risk prediction algorithm.

Results

LR models predicting all-cause mortality outperformed models predicting hospitalisation. Mortality was best predicted by disease severity (AUC & 95% CI: 0.816 (0.805–0.827)) and prediction was enhanced only marginally by the addition of multi-morbidity indices (AUC & 95% CI: 0.829 (0.818–0.839)). The model combining disease severity and multi-morbidity indices was a better predictor of hospitalisation (AUC & 95% CI: 0.679 (0.672–0.686)). ACG-derived LR models outperformed conventional regression models for hospitalisation (AUC & 95% CI: 0.697 (0.690–0.704)) but not for mortality (AUC & 95% CI: 0.816 (0.805–0.827)).

Conclusion

Stratification of future health risk in COPD can be undertaken using clinical and demographic data recorded in primary care, but the impact of disease severity and multi-morbidity varies depending on the choice of health outcome. A more comprehensive risk modelling algorithm such as ACG offers enhanced prediction for hospitalisation by incorporating a wider range of coded diagnoses.

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

Background

Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well.

Objective

To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions.

Methods

We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation.

Results

We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years.

Conclusions

Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources.

International Registered Report Identifier (IRRID)

PRR1-10.2196/27065

Relationship of Red Cell Index with the Severity of Chronic Obstructive Pulmonary Disease

Background

We aimed to investigate the association between red cell index (RCI) and the severity of Chronic Obstructive Pulmonary Disease (COPD), and compare predictive value of RCI, neutrophil–lymphocyte ratio (NLR) and platelet–lymphocyte ratio (PLR) for the severity of COPD.

Methods

A total of 207 participants were recruited (100 COPD patients and 107 healthy controls). COPD patients were divided into two groups according to the optimal cut-off value of RCI determined by the receiver operating characteristic (ROC) curve. Pearson’s correlation test, logistic regression analysis and other tests were performed.

Results

Compared with low RCI group, the forced expiration volume in 1 second (FEV₁) and FEV₁ in percent of the predicted value (FEV₁%) in high RCI group were lower (p = 0.016, p = 0.001). There was a negative correlation between RCI and FEV₁% (r = −0.302, p = 0.004), while no correlation between FEV₁% and NLR as well as PLR were found. RCI showed higher predictive value than NLR and PLR for predicting Global Initiative for Chronic Obstructive Lung Disease classification (GOLD), with a cut-off value of 1.75 and area under the curve (AUC) of 0.729 (p = 0.001). Multivariate logistic regression analysis proved that RCI was an independent factor for lung function in COPD patients (odds ratio [OR] = 4.27, 95% CI: 1.57–11.63, p = 0.004).

Conclusion

RCI is a novel biomarker that can better assess pulmonary function and severity of COPD than NLR and PLR. Higher RCI is related to deterioration of pulmonary function.

Prediction of Mortality Using Different COPD Risk Assessments - A 12-Year Follow-Up

Purpose

A multidimensional approach in the risk assessment of chronic obstructive pulmonary disease (COPD) is preferable. The aim of this study is to compare the prognostic ability for mortality by different COPD assessment systems; spirometric staging, classification by GOLD 2011, GOLD 2017, the age, dyspnea, obstruction (ADO) and the dyspnea, obstruction, smoking, exacerbation (DOSE) indices.

Patients and Methods

A total of 490 patients diagnosed with COPD were recruited from primary and secondary care in central Sweden in 2005. The cohort was followed until 2017. Data for categorization using the different assessment systems were obtained through questionnaire data from 2005 and medical record reviews between 2000 and 2003. Kaplan-Meier survival analyses and Cox proportional hazard models were used to assess mortality risk. Receiver operating characteristic curves estimated areas under the curve (AUC) to evaluate each assessment systems´ ability to predict mortality.

Results

By the end of follow-up, 49% of the patients were deceased. The mortality rate was higher for patients categorized as stage 3–4, GOLD D in both GOLD classifications and those with a DOSE score above 4 and ADO score above 8. The ADO index was most accurate for predicting mortality, AUC 0.79 (95% CI 0.75–0.83) for all-cause mortality and 0.80 (95% CI 0.75–0.85) for respiratory mortality. The AUC values for stages 1–4, GOLD 2011, GOLD 2017 and DOSE index were 0.73, 0.66, 0.63 and 0.69, respectively, for all-cause mortality.

Conclusion

All of the risk assessment systems predict mortality. The ADO index was in this study the best predictor and could be a helpful tool in COPD risk assessment.

Validation of the Summit Lab Score in Predicting Exacerbations of Chronic Obstructive Pulmonary Disease Among Individuals with High Arterial Stiffness

INTRODUCTION

The presence of cardiovascular (CV) risk factors and CV disease in patients with chronic obstructive pulmonary disease (COPD) leads to worse outcomes. A number of tools are currently available to stratify the risk of adverse outcomes in these patients with COPD. This post hoc analysis evaluated the Summit Lab Score for validation as a predictor of the first episode of moderate-to-severe acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and other outcomes, in patients with COPD and high arterial pulse wave velocity (aPWV).

METHODS

Data from a multicenter, randomized, placebo-controlled, double-blind study were retrospectively analyzed to evaluate treatment effects of once-daily fluticasone furoate/vilanterol 100/25 μg in patients with COPD and an elevated CV risk (aPWV≥11m/s) over 24 weeks. The previously derived Summit Lab Score and, secondarily, the Intermountain Risk Score (IMRS) were computed for each patient, with patients then stratified into tertiles for each score. Risk of moderate-to-severe AECOPD was analyzed across tertiles using Kaplan-Meier survival curve and Cox regression analyses.

RESULTS

In 430 patients with COPD, Kaplan-Meier probabilities of no moderate-to-severe AECOPD for Summit Lab Score tertiles 1, 2, and 3 were 92.3%, 95.5%, and 85.1%, respectively (P trend = 0.015), over 24 weeks. Grouped by IMRS tertiles, the respective probabilities were 92.9%, 91.2%, and 88.3%, respectively (P trend = 0.141). Length of stay in the hospital (P = 0.034) and the hospital ward (P = 0.042) were also significantly different between Summit Lab Score tertiles but not for intensive care (P = 0.191).

CONCLUSION

The Summit Lab Score was associated with the 24-week risk of moderate-to-severe AECOPD in COPD patients with elevated CV risk. Secondarily, IMRS showed a trend towards differences in the risk of AECOPD, which was not statistically significant.

Lipid profile and atherogenic indices in patients with stable chronic obstructive pulmonary disease

BACKGROUND AND AIMS

Limited number of studies investigated lipid profile in chronic obstructive pulmonary disease (COPD) with inconsistent results. This study aimed to investigate lipid parameters in sera of patients with stable COPD and their associations with disease severity, smoking, comorbidities and therapy.

METHODS AND RESULTS

The study included 137 COPD patients and 95 controls. Triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were assessed. Non-HDL-C (NHC), atherogenic coefficient (AC), TG/HDL-C, atherogenic index of plasma (AIP), Castelli's risk index I and II (CRI-I, CRI-II), and monocyte to HDL ratio (MHR) were calculated. HDL-C and MHR were increased, while other lipid parameters and indices were decreased in COPD patients compared to healthy individuals. Smoking did not influence lipid parameters. However, lipid profile was altered only in more severe disease stages. AC, CRI-I and CRI-II showed positive association with lung function parameters in COPD patients, and negative with COPD multicomponent indices (ADO, BODCAT, BODEx, CODEx and DOSE). Combined model that included CRI-II, C-reactive protein, fibrinogen and white blood cells showed great diagnostic performances, and correctly classified 72% of study participants with an AUC of 0.800 (0.742-0.849), P < 0.001. Bronchodilator monotherapy and statins have opposite impact on TC, LDL-C and NHC, while TG, TG/HDL-C and AIP were increased in COPD patients with cardiovascular diseases.

CONCLUSION

Lipid disbalance is present in COPD, and it seems to occur later as the disease progresses. Further studies are needed to illuminate the underlying mechanism of dyslipidaemia.

Tiotropium/Olodaterol Delays Clinically Important Deterioration Compared with Tiotropium Monotherapy in Patients with Early COPD: a Post Hoc Analysis of the TONADO® Trials

Introduction

Since chronic obstructive pulmonary disease (COPD) is a heterogeneous condition, a composite endpoint of clinically important deterioration (CID) may provide a more holistic assessment of treatment efficacy. We compared long-acting muscarinic antagonist/long-acting β₂-agonist combination therapy with tiotropium/olodaterol versus tiotropium alone using a composite endpoint for CID. CID was evaluated overall and in patients with low exacerbation history (at most one moderate exacerbation in the past year [not leading to hospitalisation]), Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2 patients and maintenance-naïve patients with COPD. We assessed whether early treatment optimisation is more effective with tiotropium/olodaterol versus tiotropium in delaying and reducing the risk of CID.

Methods

Data were analysed from 2055 patients treated with either tiotropium/olodaterol 5/5 μg or tiotropium 5 μg (delivered via Respimat^®) in two replicate, 52-week, parallel-group, double-blind studies (TONADO^® 1/2). CID was defined as a decline of at least 0.1 L from baseline in trough forced expiratory volume in 1 s, increase from baseline of at least 4 units in St. George’s Respiratory Questionnaire score, or moderate/severe exacerbation. Time to first occurrence of one of these events was recorded as time to first CID.

Results

Overall, treatment with tiotropium/olodaterol significantly increased the time to, and reduced the risk of, CID versus tiotropium (median time to CID 226 versus 169 days; hazard ratio [HR] 0.76 [95% confidence interval 0.68, 0.85]; P < 0.0001). Significant reductions were also observed in patients with low exacerbation history (241 versus 170; HR 0.73 [0.64, 0.83]; P < 0.0001), GOLD 2 patients (241 versus 169; 0.72 [0.61, 0.84]; P < 0.0001) and maintenance-naïve patients (233 versus 171; 0.75 [0.62, 0.91]; P = 0.0030).

Conclusion

In patients with COPD, including patients with low exacerbation history, GOLD 2 patients and maintenance-naïve patients, tiotropium/olodaterol reduced the risk of CID versus tiotropium. These results demonstrate the advantages of treatment optimisation with tiotropium/olodaterol over tiotropium monotherapy.

Trial Registration

ClinicalTrials.gov identifier: TONADO^® 1 and 2 (NCT01431274 and NCT01431287, registered 8 September 2011).

Graphic Abstract

Electronic supplementary material

The online version of this article (10.1007/s12325-020-01528-2) contains supplementary material, which is available to authorized users.

COPD is a complicated disease that deteriorates over time. Worsening of COPD is associated with the lungs working less effectively, a fall in quality of life and a rise in sudden flare-ups of the disease. In this study, we looked at lung function, quality of life and flare-ups together using a measure called “clinically important deterioration” (CID). We looked at 2055 people with COPD to compare the effects of taking two bronchodilators (tiotropium and olodaterol) against taking one bronchodilator (tiotropium alone). Bronchodilators are a type of inhaled medication that relax the muscles in the lungs and widen airways, making it easier to breathe. They have also been shown to reduce sudden flare-ups of COPD. Across a wide range of people with COPD, we found that treatment with tiotropium/olodaterol reduced the risk of a CID compared with tiotropium alone. This includes in those patients at an early stage of disease, who may benefit from finding the best treatment option for them as early as possible.

Assessing Symptom Burden

Evaluating symptoms is a central part of the chronic obstructive pulmonary disease (COPD) assessment system as suggested by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). Considering the pros and cons of all currently available tests, GOLD suggests using primarily the modified Medical Research Council dyspnea scale or the COPD Assessment Test. Based on the test results, patients are categorized as having a low or high level of symptoms. This level then becomes one of the 2 dimensions of the ABCD grading system, which was designed to match the best initial treatment option to the individual patient's needs.

A Risk Prediction Model for Mortality Among Smokers in the COPDGene® Study

BACKGROUND

Risk factor identification is a proven strategy in advancing treatments and preventive therapy for many chronic conditions. Quantifying the impact of those risk factors on health outcomes can consolidate and focus efforts on individuals with specific high-risk profiles. Using multiple risk factors and longitudinal outcomes in 2 independent cohorts, we developed and validated a risk score model to predict mortality in current and former cigarette smokers.

METHODS

We obtained extensive data on current and former smokers from the COPD Genetic Epidemiology (COPDGene®) study at enrollment. Based on physician input and model goodness-of-fit measures, a subset of variables was selected to fit final Weibull survival models separately for men and women. Coefficients and predictors were translated into a point system, allowing for easy computation of mortality risk scores and probabilities. We then used the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) cohort for external validation of our model.

RESULTS

Of 9867 COPDGene participants with standard baseline data, 17.6% died over 10 years of follow-up, and 9074 of these participants had the full set of baseline predictors (standard plus 6-minute walk distance and computed tomography variables) available for full model fits. The average age of participants in the cohort was 60 for both men and women, and the average predicted 10-year mortality risk was 18% for women and 25% for men. Model time-integrated area under the receiver operating characteristic curve statistics demonstrated good predictive model accuracy (0.797 average), validated in the external cohort (0.756 average). Risk of mortality was impacted most by 6-minute walk distance, forced expiratory volume in 1 second and age, for both men and women.

CONCLUSIONS

Current and former smokers exhibited a wide range of mortality risk over a 10- year period. Our models can identify higher risk individuals who can be targeted for interventions to reduce risk of mortality, for participants with or without chronic obstructive pulmonary disease (COPD) using current Global initiative for obstructive Lung Disease (GOLD) criteria.

Machine Learning and Prediction of All-Cause Mortality in COPD

BACKGROUND

COPD is a leading cause of mortality.

RESEARCH QUESTION

We hypothesized that applying machine learning to clinical and quantitative CT imaging features would improve mortality prediction in COPD.

STUDY DESIGN AND METHODS

We selected 30 clinical, spirometric, and imaging features as inputs for a random survival forest. We used top features in a Cox regression to create a machine learning mortality prediction (MLMP) in COPD model and also assessed the performance of other statistical and machine learning models. We trained the models in subjects with moderate to severe COPD from a subset of subjects in Genetic Epidemiology of COPD (COPDGene) and tested prediction performance in the remainder of individuals with moderate to severe COPD in COPDGene and Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE). We compared our model with the BMI, airflow obstruction, dyspnea, exercise capacity (BODE) index; BODE modifications; and the age, dyspnea, and airflow obstruction index.

RESULTS

We included 2,632 participants from COPDGene and 1,268 participants from ECLIPSE. The top predictors of mortality were 6-min walk distance, FEV1 % predicted, and age. The top imaging predictor was pulmonary artery-to-aorta ratio. The MLMP-COPD model resulted in a C index ≥ 0.7 in both COPDGene and ECLIPSE (6.4- and 7.2-year median follow-ups, respectively), significantly better than all tested mortality indexes (P < .05). The MLMP-COPD model had fewer predictors but similar performance to that of other models. The group with the highest BODE scores (7-10) had 64% mortality, whereas the highest mortality group defined by the MLMP-COPD model had 77% mortality (P = .012).

INTERPRETATION

An MLMP-COPD model outperformed four existing models for predicting all-cause mortality across two COPD cohorts. Performance of machine learning was similar to that of traditional statistical methods. The model is available online at: https://cdnm.shinyapps.io/cgmortalityapp/.

The Summit Score Stratifies Mortality and Morbidity in Chronic Obstructive Pulmonary Disease

Introduction

Tobacco use and other cardiovascular risk factors often accompany chronic obstructive pulmonary disease (COPD). This study derived and validated the Summit Score to predict mortality in people with COPD and cardiovascular risks.

Methods

SUMMIT trial subjects (N=16,485) ages 40–80 years with COPD were randomly assigned 50%/50% to derivation (N=8181) and internal validation (N=8304). Three external COPD validations from Intermountain Healthcare included outpatients with cardiovascular risks (N=9251), outpatients without cardiovascular risks (N=8551), and inpatients (N=26,170). Cox regression evaluated 40 predictors of all-cause mortality. SUMMIT treatments including combined fluticasone furoate (FF) 100μg/vilanterol 25μg (VI) were not included in the score.

Results

Mortality predictors were FEV₁, heart rate, systolic blood pressure, body mass index, age, smoking pack-years, prior COPD hospitalizations, myocardial infarction, heart failure, diabetes, anti-thrombotics, anti-arrhythmics, and xanthines. Combined in the Summit Score (derivation: c=0.668), quartile 4 vs 1 had HR=4.43 in SUMMIT validation (p<0.001, 95% CI=3.27, 6.01, c=0.662) and HR=8.15 in Intermountain cardiovascular risk COPD outpatients (p<0.001, 95% CI=5.86, 11.34, c=0.736), and strongly predicted mortality in the other Intermountain COPD populations. Among all SUMMIT subjects with scores 14–19, FF 100μg/VI 25μg vs placebo had HR=0.76 (p=0.0158, 95% CI=0.61, 0.95), but FF 100μg/VI 25μg was not different from placebo for scores <14 or >19.

Conclusion

In this post hoc analysis of SUMMIT trial data, the Summit Score was derived and validated in multiple Intermountain COPD populations. The score was used to identify a subpopulation in which mortality risk was lower for FF 100μg/VI 25μg treatment.

Trial Registration

The SUMMIT trial is registered at ClinicalTrials.gov as number NCT01313676.

Changes in symptoms and health-related quality of life in patients with exacerbated chronic obstructive pulmonary disease

AIMS

To describe changes in symptoms and identify distinct subgroups of symptoms, to determine whether the sample's characteristics predicted changes in symptoms, and to examine how changes in symptoms predicted changes in health-related quality of life (HRQOL) over 6 months in patients with exacerbated chronic obstructive pulmonary disease (COPD).

BACKGROUND

Different patterns of changes in symptoms and their relationship to changes in HRQOL in patients with exacerbated COPD over long periods of time have been understudied.

METHODS

In this longitudinal study, participants with COPD (N = 42) had been admitted to a medical ward or had visited a pulmonary medicine clinic for treatment of exacerbation. Descriptive and inferential statistics were used to analyze data from questionnaires that assessed symptoms and HRQOL at baseline, daily symptoms over 6 months, and HRQOL at 6 months after exacerbation.

RESULTS

Not all participants experienced improved symptoms over time. Two sets of subgroups (improving &worsening; constantly better &constantly worse) emerged, based on 6-month changes in symptoms. Sample characteristics of the improving and worsening subgroups were similar, whereas usual dyspnea and HRQOL were significantly different in the constantly better and constantly worse subgroups. Little change in HRQOL was found in the total sample, but HRQOL deteriorated in the worsening subgroup, although deterioration was not meaningful. Changes in symptoms significantly predicted changes in HRQOL over 6 months.

CONCLUSION

Assessing patients' symptoms after exacerbations of COPD may enable health care providers to identify those at risk of future exacerbations and poorer HRQOL.0.

Measuring disease activity in COPD: is clinically important deterioration the answer?

Given the heterogeneity of chronic obstructive pulmonary disease (COPD), personalized clinical management is key to optimizing patient outcomes. Important treatment goals include minimizing disease activity and preventing disease progression; however, quantification of these components remains a challenge. Growing evidence suggests that decline over time in forced expiratory volume in 1 s (FEV1), traditionally the key marker of disease progression, may not be sufficient to fully determine deterioration across COPD populations. In addition, there is a lack of evidence showing that currently available multidimensional COPD indexes improve clinical decision-making, treatment, or patient outcomes. The composite clinically important deterioration (CID) endpoint was developed to assess disease worsening by detecting early deteriorations in lung function (measured by FEV1), health status (assessed by the St George's Respiratory Questionnaire), and the presence of exacerbations. Post hoc and prospective analyses of clinical trial data have confirmed that the multidimensional composite CID endpoint better predicts poorer medium-term outcomes compared with any single CID component alone, and that it can demonstrate differences in treatment efficacy in short-term trials. Given the widely acknowledged need for an individualized holistic approach to COPD management, monitoring short-term CID has the potential to facilitate early identification of suboptimal treatment responses and patients at risk of increased disease progression. CID monitoring may lead to better-informed clinical management decisions and potentially improved prognosis.

Predicting and preventing hospital readmission for exacerbations of COPD

More than a third of patients hospitalised for acute exacerbation of COPD are readmitted to hospital within 90 days. Healthcare professionals and service providers are expected to collaboratively drive efforts to improve hospital readmission rates, which can be challenging due to the lack of clear consensus and guidelines on how best to predict and prevent readmissions. This review identifies these risk factors, highlighting the contribution of multimorbidity, frailty and poor socioeconomic status. Predictive models of readmission that address the multifactorial nature of readmissions and heterogeneity of the disease are reviewed, recognising that in an era of precision medicine, in-depth understanding of the intricate biological mechanisms that heighten the risk of COPD exacerbation and re-exacerbation is needed to derive modifiable biomarkers that can stratify accurately the highest risk groups for targeted treatment. We evaluate conventional and emerging strategies to reduce these potentially preventable readmissions. Here, early recognition of exacerbation symptoms and the delivery of prompt treatment can reduce risk of hospital admissions, while patient education can improve treatment adherence as a key component of self-management strategies. Care bundles are recommended to ensure high-quality care is provided consistently, but evidence for their benefit is limited to date. The search continues for interventions which are effective, sustainable and applicable to a diverse population of patients with COPD exacerbations. Further research into mechanisms that drive exacerbation and affect recovery is crucial to improve our understanding of this complex, highly prevalent disease and to advance the development of more effective treatments.

Patient outcomes associated with post-tuberculosis lung damage in Malawi: a prospective cohort study

BACKGROUND

Post-tuberculosis lung damage (PTLD) is a recognised consequence of pulmonary TB (pTB). However, little is known about its prevalence, patterns and associated outcomes, especially in sub-Saharan Africa and HIV-positive adults.

METHODS

Adult (≥15 years) survivors of a first episode of pTB in Blantyre, Malawi, completed the St George's Respiratory Questionnaire, 6-minute walk test, spirometry and high-resolution CT (HRCT) chest imaging at TB treatment completion. Symptom, spirometry, health seeking, TB-retreatment and mortality data were collected prospectively to 1 year. Risk factors for persistent symptoms, pulmonary function decline and respiratory-related health-seeking were identified through multivariable regression modelling.

RESULTS

Between February 2016 and April 2017, 405 participants were recruited. Median age was 35 years (IQR: 28 to 41), 77.3% (313/405) had had microbiologically proven pTB, and 60.3% (244/403) were HIV-positive. At pTB treatment completion, 60.7% (246/405) reported respiratory symptoms, 34.2% (125/365) had abnormal spirometry, 44.2% (170/385) had bronchiectasis ≥1 lobe and 9.4% (36/385) had ≥1 destroyed lobe on HRCT imaging. At 1 year, 30.7% (113/368) reported respiratory symptoms, 19.3% (59/305) and 14.1% (43/305) of patients had experienced declines in FEV₁ or FVC of ≥100 mL, 16.3% (62/380) had reported ≥1 acute respiratory event and 12.2% (45/368) had symptoms affecting their ability to work.

CONCLUSIONS

PTLD is a common and under-recognised consequence of pTB that is disabling for patients and associated with adverse outcomes beyond pTB treatment completion. Increased efforts to prevent PTLD and guidelines for management of established disease are urgently needed. Low-cost clinical interventions to improve patient outcomes must be evaluated.

A Nomogram for Predicting Severe Exacerbations in Stable COPD Patients

Objective

To develop a practicable nomogram aimed at predicting the risk of severe exacerbations in COPD patients at three and five years.

Methods

COPD patients with prospective follow-up data were extracted from Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) obtained from National Heart, Lung and Blood Institute (NHLBI) Biologic Specimen and Data Repository Information Coordinating Center. We comprehensively considered the demographic characteristics, clinical data and inflammation marker of disease severity. Cox proportional hazard regression was performed to identify the best combination of predictors on the basis of the smallest Akaike Information Criterion. A nomogram was developed and evaluated on discrimination, calibration, and clinical efficacy by the concordance index (C-index), calibration plot and decision curve analysis, respectively. Internal validation of the nomogram was assessed by the calibration plot with 1000 bootstrapped resamples.

Results

Among 1711 COPD patients, 523 (30.6%) suffered from at least one severe exacerbation during follow-up. After stepwise regression analysis, six variables were determined including BMI, severe exacerbations in the prior year, comorbidity index, post-bronchodilator FEV₁% predicted, and white blood cells. Nomogram to estimate patients' likelihood of severe exacerbations at three and five years was established. The C-index of the nomogram was 0.74 (95%CI: 0.71-0.76), outperforming ADO, BODE and DOSE risk score. Besides, the calibration plot of three and five years showed great agreement between nomogram predicted possibility and actual risk. Decision curve analysis indicated that implementation of the nomogram in clinical practice would be beneficial and better than aforementioned risk scores.

Conclusion

Our new nomogram was a useful tool to assess the probability of severe exacerbations at three and five years for COPD patients and could facilitate clinicians in stratifying patients and providing optimal therapies.

Prognostic ability of the distance-saturation product in the 6-minute walk test in patients with chronic obstructive pulmonary disease

INTRODUCTION

The product (DSP) of the distance walked (meters) and minimum oxygen saturation obtained during the 6-minute walk test (6MWT) has been proposed as a predictor of mortality in idiopathic pulmonary fibrosis and in bronchiectasis not related to cystic fibrosis.

OBJECTIVE

The aim of this study was to determine the DSP's ability to predict mortality in patients with chronic obstructive pulmonary disease (COPD) at the outpatient level and compare it to the BODE index and meters walked in the 6MWT.

MATERIAL AND METHODS

Descriptive observational study in a cohort of patients with COPD being treated at outpatient pulmonology clinics. Each of the patients completed the 6MWT following ATS/ERS protocols and their BODE index and DSP were calculated.

RESULTS

About 103 patients were included. The average length of follow-up was 36 months. Patients who died showed a lower number of meters walked in the 6MWT (P < 0.001), as well as a lower DSP (P < 0.001). A 6MWT < 334 m, a DSP < 290 and a BODE ≥ 4 showed good prognostic ability at 3 years (AUC 71%, 69% and 70.4%, respectively). The 6MWT was superior to the BODE index in predicting mortality during the first year of follow-up (P = 0.023). We did not find any differences between DSP and meters walked in the 6MWT.

CONCLUSIONS

The DSP is a good predictor of mortality, although it does not offer a better prognostic ability than that of meters walked in the 6MWT.

Primary care risk stratification in COPD using routinely collected data: a secondary data analysis

Most clinical contacts with chronic obstructive pulmonary disease (COPD) patients take place in primary care, presenting opportunity for proactive clinical management. Electronic health records could be used to risk stratify diagnosed patients in this setting, but may be limited by poor data quality or completeness. We developed a risk stratification database algorithm using the DOSE index (Dyspnoea, Obstruction, Smoking and Exacerbation) with routinely collected primary care data, aiming to calculate up to three repeated risk scores per patient over five years, each separated by at least one year. Among 10,393 patients with diagnosed COPD, sufficient primary care data were present to calculate at least one risk score for 77.4%, and the maximum of three risk scores for 50.6%. Linked secondary care data revealed primary care under-recording of hospital exacerbations, which translated to a slight, non-significant cohort average risk score reduction, and an understated risk group allocation for less than 1% of patients. Algorithmic calculation of the DOSE index is possible using primary care data, and appears robust to the absence of linked secondary care data, if unavailable. The DOSE index appears a simple and practical means of incorporating risk stratification into the routine primary care of COPD patients, but further research is needed to evaluate its clinical utility in this setting. Although secondary analysis of routinely collected primary care data could benefit clinicians, patients and the health system, standardised data collection and improved data quality and completeness are also needed.

Principal Component Weighted Index for Wastewater Quality Monitoring

The quality of raw and treated wastewater was evaluated using the principal component weighted index (PCWI) which was defined as a sum of principal component scores weighted according to their eigenvalues. For this purpose, five principal components (PCs) explaining 88% and 83% of the total variability of raw and treated wastewater samples, respectively, were extracted from 11 original physico-chemical parameters by robust principal component analysis (PCA). The PCWIs of raw and treated wastewater were analyzed in terms of their statistical distributions, temporal changes, mutual correlations, correlations with original parameters, and common water quality indexes (WQI). The PCWI allowed us to monitor temporal wastewater quality by one parameter instead of several. Unlike other weighted indexes, the PCWI is composed of independent variables with minimal information noise and objectively determined weights.

External Validation Of The Updated ADO Score In COPD Patients From The Birmingham COPD Cohort

Background

Reviews suggest that the ADO score is the most discriminatory prognostic score for predicting mortality among chronic obstructive pulmonary disease (COPD) patients, but a full evaluation and external validation within primary care settings is critical before implementation.

Objectives

To validate the ADO score in prevalent and screen-detected primary care COPD cases at 3 years and at shorter time periods.

Patients and methods

One thousand eight hundred and ninety-two COPD cases were recruited between 2012 and 2014 from 71 United Kingdom general practices as part of the Birmingham COPD Cohort study. Cases were either on the practice COPD register or screen-detected. We validated the ADO score for predicting 3-year mortality with 1-year and 2-year mortality as secondary endpoints using discrimination (area-under-the-curve (AUC)) and calibration plots.

Results

One hundred and fifty-four deaths occurred within 3 years. The ADO score was discriminatory for predicting 3-year mortality (AUC= 0.74; 95% CI: 0.69–0.79). Similar performance was found for 1- (AUC= 0.73; 0.66–0.80) and 2-year mortality (0.72; 0.67–0.76). The ADO score showed reasonable calibration for predicting 3-year mortality (calibration slope 0.95; 0.70–1.19) but over-predicted in cases with higher predicted risks of mortality at 1 (0.79; 0.45–1.13) and 2-year (0.79; 0.57–1.01) mortality.

Discussion

The ADO score showed promising discrimination in predicting 3-year mortality in a primary care population including screen-detected cases. It may need to be recalibrated if it is used to provide risk predictions for 1- or 2-year mortality since, in these time-periods, over-prediction was evident, especially in cases with higher predicted mortality risks.

Clinical validation of a risk scale for serious outcomes among patients with chronic obstructive pulmonary disease managed in the emergency department

BACKGROUND

The Ottawa chronic obstructive pulmonary disease (COPD) Risk Scale (OCRS), which consists of 10 criteria, was previously derived to identify patients in the emergency department with COPD who were at high risk for short-term serious outcomes. We sought to validate, prospectively and explicitly, the OCRS when applied by physicians in the emergency department.

METHODS

We conducted this prospective cohort study involving patients in the emergency departments at 6 tertiary care hospitals and enrolled adults with acute exacerbation of COPD from May 2011 to December 2013. Physicians evaluated patients for the OCRS criteria, which were recorded on a data form along with the total risk score. We followed patients for 30 days and the primary outcome, short-term serious outcomes, was defined as any of death, admission to monitored unit, intubation, noninvasive ventilation, myocardial infarction (MI) or relapse with hospital admission.

RESULTS

We enrolled 1415 patients with a mean age of 70.6 (SD 10.6) years and 50.2% were female. Short-term serious outcomes occurred in 135 (9.5%) cases. Incidence of short-term serious outcomes ranged from 4.6% for a total score of 0 to 100% for a score of 10. Compared with current practice, an OCRS score threshold of greater than 1 would increase sensitivity for short-term serious outcomes from 51.9% to 79.3% and increase admissions from 45.0% to 56.6%. A threshold of greater than 2 would improve sensitivity to 71.9% with 47.9% of patients being admitted.

INTERPRETATION

In this clinical validation of a risk-stratification tool for COPD in the emergency department, we found that OCRS showed better sensitivity for short-term serious outcomes compared with current practice. This risk scale can now be used to help emergency department disposition decisions for patients with COPD, which should lead to a decrease in unnecessary admissions and in unsafe discharges.

Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisal

OBJECTIVE

To map and assess prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease (COPD).

DESIGN

Systematic review.

DATA SOURCES

PubMed until November 2018 and hand searched references from eligible articles.

ELIGIBILITY CRITERIA FOR STUDY SELECTION

Studies developing, validating, or updating a prediction model in COPD patients and focusing on any potential clinical outcome.

RESULTS

The systematic search yielded 228 eligible articles, describing the development of 408 prognostic models, the external validation of 38 models, and the validation of 20 prognostic models derived for diseases other than COPD. The 408 prognostic models were developed in three clinical settings: outpatients (n=239; 59%), patients admitted to hospital (n=155; 38%), and patients attending the emergency department (n=14; 3%). Among the 408 prognostic models, the most prevalent endpoints were mortality (n=209; 51%), risk for acute exacerbation of COPD (n=42; 10%), and risk for readmission after the index hospital admission (n=36; 9%). Overall, the most commonly used predictors were age (n=166; 41%), forced expiratory volume in one second (n=85; 21%), sex (n=74; 18%), body mass index (n=66; 16%), and smoking (n=65; 16%). Of the 408 prognostic models, 100 (25%) were internally validated and 91 (23%) examined the calibration of the developed model. For 286 (70%) models a model presentation was not available, and only 56 (14%) models were presented through the full equation. Model discrimination using the C statistic was available for 311 (76%) models. 38 models were externally validated, but in only 12 of these was the validation performed by a fully independent team. Only seven prognostic models with an overall low risk of bias according to PROBAST were identified. These models were ADO, B-AE-D, B-AE-D-C, extended ADO, updated ADO, updated BODE, and a model developed by Bertens et al. A meta-analysis of C statistics was performed for 12 prognostic models, and the summary estimates ranged from 0.611 to 0.769.

CONCLUSIONS

This study constitutes a detailed mapping and assessment of the prognostic models for outcome prediction in COPD patients. The findings indicate several methodological pitfalls in their development and a low rate of external validation. Future research should focus on the improvement of existing models through update and external validation, as well as the assessment of the safety, clinical effectiveness, and cost effectiveness of the application of these prognostic models in clinical practice through impact studies.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42017069247.

Helping people live with chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a long-term condition characterised by persistent respiratory symptoms and airflow limitation. It is preventable and treatable, but still results in high levels of morbidity and mortality. This affects health service costs, but more importantly it affects the person with COPD, and their relatives and carers. If healthcare services continue to focus on managing the disease process rather than the person living with the disease itself, they may continue to produce the same outcomes and fail to substantially reduce the burden of the disease. Helping people live with COPD requires clinicians to communicate effectively with people, families and carers and share multidisciplinary team decisions with patients. Clinicians must consider the physical, psychological, social and spiritual implications of the disease. This article explores how nurses can have a positive effect on the lives of people with COPD, and provides practical strategies and suggestions on giving them effective support.

Identification of relevant variables and construction of a multidimensional index for predicting mortality in COPD patients

Background and objective

The Body mass index, airflow Obstruction, Dyspnea, and Exercise (BODE) index is a well-known metric for chronic obstructive pulmonary disease (COPD), but it is inadequate for predicting mortality. This study proposed a new index that combines inspiratory muscle training with the BODE index and verified its ability to predict mortality in patients with COPD.

Methods

Cox regression identified predictors of mortality, which were then included in the new index. The receiver operating characteristic (ROC) curve verified the ability of the new index to predict mortality. The Kaplan-Meier curves compared the survival rates of patients with different scores on the new index.

Results

Among the 326 patients, 48 died during follow-up (1–59 months). Cox regression showed that the fat-free mass index (FFMI), forced expiratory volume in one second/the predicted value (FEV₁%), modified Medical Research Council (mMRC) score, six-minute–walk test (6MWT) distance, and maximal inspiratory pressure were predictors of mortality (P<0.05); these variables were included in the FODEP index. The AUC of the FODEP index (0.860, 95% CI: 95% CI: 0.817–0.896) was greater than that of the BODE index (0.778, 95% CI: 0.729–0.822). The Kaplan-Meier curves suggested that as the FODEP score increased, so did the risk of morality in patients with COPD. The cumulative survival in the group with the highest FODEP-value was significantly lower than that in the other groups (P<0.01).

Conclusion

The FODEP index was more effective than the BODE index at predicting the risk of mortality in patients with COPD.

Predicting COPD 1-year mortality using prognostic predictors routinely measured in primary care

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a major cause of mortality. Patients with advanced disease often have a poor quality of life, such that guidelines recommend providing palliative care in their last year of life. Uptake and use of palliative care in advanced COPD is low; difficulty in predicting 1-year mortality is thought to be a major contributing factor.

METHODS

We identified two primary care COPD cohorts using UK electronic healthcare records (Clinical Practice Research Datalink). The first cohort was randomised equally into training and test sets. An external dataset was drawn from a second cohort. A risk model to predict mortality within 12 months was derived from the training set using backwards elimination Cox regression. The model was given the acronym BARC based on putative prognostic factors including body mass index and blood results (B), age (A), respiratory variables (airflow obstruction, exacerbations, smoking) (R) and comorbidities (C). The BARC index predictive performance was validated in the test set and external dataset by assessing calibration and discrimination. The observed and expected probabilities of death were assessed for increasing quartiles of mortality risk (very low risk, low risk, moderate risk, high risk). The BARC index was compared to the established index scores body mass index, obstructive, dyspnoea and exacerbations (BODEx), dyspnoea, obstruction, smoking and exacerbations (DOSE) and age, dyspnoea and obstruction (ADO).

RESULTS

Fifty-four thousand nine hundred ninety patients were eligible from the first cohort and 4931 from the second cohort. Eighteen variables were included in the BARC, including age, airflow obstruction, body mass index, smoking, exacerbations and comorbidities. The risk model had acceptable predictive performance (test set: C-index = 0.79, 95% CI 0.78-0.81, D-statistic = 1.87, 95% CI 1.77-1.96, calibration slope = 0.95, 95% CI 0.9-0.99; external dataset: C-index = 0.67, 95% CI 0.65-0.7, D-statistic = 0.98, 95% CI 0.8-1.2, calibration slope = 0.54, 95% CI 0.45-0.64) and acceptable accuracy predicting the probability of death (probability of death in 1 year, n high-risk group, test set: expected = 0.31, observed = 0.30; external dataset: expected = 0.22, observed = 0.27). The BARC compared favourably to existing index scores that can also be applied without specialist respiratory variables (area under the curve: BARC = 0.78, 95% CI 0.76-0.79; BODEx = 0.48, 95% CI 0.45-0.51; DOSE = 0.60, 95% CI 0.57-0.61; ADO = 0.68, 95% CI 0.66-0.69, external dataset: BARC = 0.70, 95% CI 0.67-0.72; BODEx = 0.41, 95% CI 0.38-0.45; DOSE = 0.52, 95% CI 0.49-0.55; ADO = 0.57, 95% CI 0.54-0.60).

CONCLUSION

The BARC index performed better than existing tools in predicting 1-year mortality. Critically, the risk score only requires routinely collected non-specialist information which, therefore, could help identify patients seen in primary care that may benefit from palliative care.

A clinical prediction model for hospitalized COPD exacerbations based on "treatable traits"

Background

Assessing risk of future exacerbations is an important component in COPD management. History of exacerbation is a strong and independent predictor of future exacerbations, and the criterion of ≥2 nonhospitalized or ≥1 hospitalized exacerbation is often used to identify high-risk patients in whom therapy should be intensified. However, other factors or "treatable traits" also contribute to risk of exacerbation.

Objective

The objective of the study was to develop and externally validate a novel clinical prediction model for risk of hospitalized COPD exacerbations based on both exacerbation history and treatable traits.

Patients and methods

A total of 237 patients from the COPD Registry of Changi General Hospital, Singapore, aged 75±9 years and with mean post-bronchodilator FEV1 60%±20% predicted, formed the derivation cohort. Hospitalized exacerbation rate was modeled using zero-inflated negative binomial regression. Calibration was assessed by graphically comparing the agreement between predicted and observed annual hospitalized exacerbation rates. Predictive (discriminative) accuracy of the model for identifying high-risk patients (defined as experiencing ≥1 hospitalized exacerbations) was assessed with area under the curve (AUC) and receiver operating characteristics analyses, and compared to other existing risk indices. We externally validated the prediction model using a multicenter dataset comprising 419 COPD patients.

Results

The final model included hospitalized exacerbation rate in the previous year, history of acute invasive/noninvasive ventilation, coronary artery disease, bronchiectasis, and sputum nontuberculous mycobacteria isolation. There was excellent agreement between predicted and observed annual hospitalized exacerbation rates. AUC was 0.789 indicating good discriminative accuracy, and was significantly higher than the AUC of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) risk assessment criterion (history of ≥1 hospitalized exacerbation in the previous year) and the age, dyspnea, and obstruction index. When applied to the independent multicenter validation cohort, the model was well-calibrated and discrimination was good.

Conclusion

We have derived and externally validated a novel risk prediction model for COPD hospitalizations which outperforms several other risk indices. Our model incorporates several treatable traits which can be targeted for intervention to reduce risk of future hospitalized exacerbations.

External Validation and Recalculation of the CODEX Index in COPD Patients. A 3CIAplus Cohort Study

The CODEX index was developed and validated in patients hospitalized for COPD exacerbation to predict the risk of death and readmission within one year after discharge. Our study aimed to validate the CODEX index in a large external population of COPD patients with variable durations of follow-up. Additionally, we aimed to recalculate the thresholds of the CODEX index using the cutoffs of variables previously suggested in the 3CIA study (mCODEX). Individual data on 2,755 patients included in the COPD Cohorts Collaborative International Assessment Plus (3CIA+) were explored. A further two cohorts (ESMI AND EGARPOC-2) were added. To validate the CODEX index, the relationship between mortality and the CODEX index was assessed using cumulative/dynamic ROC curves at different follow-up periods, ranging from 3 months up to 10 years. Calibration was performed using univariate and multivariate Cox proportional hazard models and Hosmer-Lemeshow test. A total of 3,321 (87.8% males) patients were included with a mean ± SD age of 66.9 ± 10.5 years, and a median follow-up of 1,064 days (IQR 25-75% 426-1643), totaling 11,190 person-years. The CODEX index was statistically associated with mortality in the short- (≤3 months), medium- (≤1 year) and long-term (10 years), with an area under the curve of 0.72, 0.70 and 0.76, respectively. The mCODEX index performed better in the medium-term (<1 year) than the original CODEX, and similarly in the long-term. In conclusion, CODEX and mCODEX index are good predictors of mortality in patients with COPD, regardless of disease severity or duration of follow-up.

COPD classification models and mortality prediction capacity

Objective

Our aim was to assess the impact of comorbidities on existing COPD prognosis scores.

Patients and methods

A 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.

Results

Among 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, GOLD_ABCD, 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).

Conclusion

Comorbidities should be taken into account in COPD management scores due to their prevalence and impact on mortality.

Real-world use of rescue inhaler sensors, electronic symptom questionnaires and physical activity monitors in COPD

Background

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterised by airflow obstruction and other morbidities such as respiratory symptoms, reduced physical activity and frequent bronchodilator use. Recent advances in personal digital monitoring devices can permit continuous collection of these data in COPD patients, but the relationships among them are not well understood.

Methods

184 individuals from a single centre of the COPDGene cohort agreed to participate in this 3-week observational study. Each participant used a smartphone to complete a daily symptom diary (EXAcerbations of Chronic pulmonary disease Tool, EXACT), wore a wrist-worn accelerometer to record continuously physical activity and completed the Clinical Visit PROactive Physical Activity in COPD questionnaire. 58 users of metered dose inhalers for rescue (albuterol) were provided with an inhaler sensor, which time stamped each inhaler actuation.

Results

Rescue inhaler use was strongly correlated with E-RS:COPD score, while step counts were correlated with neither rescue use nor E-RS:COPD score. Frequent, unpatterned inhaler use pattern was associated with worse respiratory symptoms and less physical activity compared with frequent inhaler use with a regular daily pattern. There was a strong week-by-week correlation among measurements, suggesting that 1 week of monitoring is sufficient to characterise stable patients with COPD.

Discussion

The study highlights the interaction and relevance of personal real-time monitoring of respiratory symptoms, physical activity and rescue medication in patients with COPD. Additionally, visual displays of longitudinal data may be helpful for disease management to help drive conversations between patients and caregivers and for risk-based monitoring in clinical trials.