Prediction models for exacerbations in patients with COPD

Developing and validating prediction models for severe exacerbations and readmissions in patients hospitalised for COPD exacerbation (SERCO) in China: a prospective observational study

BACKGROUND

There is a lack of individualised prediction models for patients hospitalised with chronic obstructive pulmonary disease (COPD) for clinical practice. We developed and validated prediction models of severe exacerbations and readmissions in patients hospitalised for COPD exacerbation (SERCO).

METHODS

Data were obtained from the Acute Exacerbations of Chronic Obstructive Pulmonary Disease Inpatient Registry study (NCT02657525) in China. Cause-specific hazard models were used to estimate coefficients. C-statistic was used to evaluate the discrimination. Slope and intercept were used to evaluate the calibration and used for model adjustment. Models were validated internally by 10-fold cross-validation and externally using data from different regions. Risk-stratified scoring scales and nomograms were provided. The discrimination ability of the SERCO model was compared with the exacerbation history in the previous year.

RESULTS

Two sets with 2196 and 1869 patients from different geographical regions were used for model development and external validation. The 12-month severe exacerbations cumulative incidence rates were 11.55% (95% CI 10.06% to 13.16%) in development cohorts and 12.30% (95% CI 10.67% to 14.05%) in validation cohorts. The COPD-specific readmission incidence rates were 11.31% (95% CI 9.83% to 12.91%) and 12.26% (95% CI 10.63% to 14.02%), respectively. Demographic characteristics, medical history, comorbidities, drug usage, Global Initiative for Chronic Obstructive Lung Disease stage and interactions were included as predictors. C-indexes for severe exacerbations were 77.3 (95% CI 70.7 to 83.9), 76.5 (95% CI 72.6 to 80.4) and 74.7 (95% CI 71.2 to 78.2) at 1, 6 and 12 months. The corresponding values for readmissions were 77.1 (95% CI 70.1 to 84.0), 76.3 (95% CI 72.3 to 80.4) and 74.5 (95% CI 71.0 to 78.0). The SERCO model was consistently discriminative and accurate with C-indexes in the derivation and internal validation groups. In external validation, the C-indexes were relatively lower at 60-70 levels. The SERCO model discriminated outcomes better than prior severe exacerbation history. The slope and intercept after adjustment showed close agreement between predicted and observed risks. However, in external validation, the models may overestimate the risk in higher-risk groups. The model-driven risk groups showed significant disparities in prognosis.

CONCLUSION

The SERCO model provides individual predictions for severe exacerbation and COPD-specific readmission risk, which enables identifying high-risk patients and implementing personalised preventive intervention for patients with COPD.

Prediction of Morbidity and Mortality After Esophagectomy: A Systematic Review

BACKGROUND

Esophagectomy for esophageal cancer has a complication rate of up to 60%. Prediction models could be helpful to preoperatively estimate which patients are at increased risk of morbidity and mortality. The objective of this study was to determine the best prediction models for morbidity and mortality after esophagectomy and to identify commonalities among the models.

PATIENTS AND METHODS

A systematic review was performed in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and was prospectively registered in PROSPERO ( https://www.crd.york.ac.uk/prospero/ , study ID CRD42022350846). Pubmed, Embase, and Clarivate Analytics/Web of Science Core Collection were searched for studies published between 2010 and August 2022. The Prediction model Risk of Bias Assessment Tool was used to assess the risk of bias. Extracted data were tabulated and a narrative synthesis was performed.

RESULTS

Of the 15,011 articles identified, 22 studies were included using data from tens of thousands of patients. This systematic review included 33 different models, of which 18 models were newly developed. Many studies showed a high risk of bias. The prognostic accuracy of models differed between 0.51 and 0.85. For most models, variables are readily available. Two models for mortality and one model for pulmonary complications have the potential to be developed further.

CONCLUSIONS

The availability of rigorous prediction models is limited. Several models are promising but need to be further developed. Some models provide information about risk factors for the development of complications. Performance status is a potential modifiable risk factor. None are ready for clinical implementation.

Dynamic hyperinflation is a risk factor for mortality and severe exacerbations in COPD patients

OBJECTIVE

To assess if dynamic hyperinflation is an independent risk factor for mortality and severe exacerbations in COPD patients.

METHODS

A cohort of 141 patients with stable COPD and moderate to very severe airflow limitation, treated according to conventional guidelines, was followed for a median of 9 years. Clinical characteristics were recorded and arterial blood gases, pulmonary function tests, 6-min walk and incremental exercise test with measurement of respiratory pattern and operative lung volumes were performed. Endpoints were all-cause mortality and hospitalization for COPD exacerbation.

RESULTS

58 patients died during the follow-up period (1228 patients x year). The mortality rate was higher in patients with dynamic hyperinflation (n = 106) than in those without it (n = 35) (14.6; 95% CI, 14.5-14.8 vs. 7.2; 95% CI, 7.1-7.4 per 1000 patients-year). After adjusting for sex, age, body mass index, pack-years and treatment with inhaled corticosteroids, dynamic hyperinflation was associated with a higher mortality risk (adjusted hazard ratio [aHR], 2.725; 95% CI, 1.010-8.161), and in a multivariate model, comorbidity, peak oxygen uptake and dynamic hyperinflation were retained as independent predictors of mortality. The time until first severe exacerbation was shorter for patients with dynamic hyperinflation (aHR, 3.961; 95% CI, 1.385-11.328), and dynamic hyperinflation, FEV1 and diffusing capacity were retained as independent risk factors for severe exacerbation. Moreover, patients with dynamic hyperinflation had a higher hospitalization risk than those without it (adjusted incidence rate ratio, 1.574; 95% CI, 1.087-2.581).

CONCLUSION

In stable COPD patients, dynamic hyperinflation is an independent prognostic factor for mortality and severe exacerbations.

Development of Machine Learning Models for the Identification of Elevated Ketone Bodies During Hyperglycemia in Patients with Type 1 Diabetes

Aims: Diabetic ketoacidosis (DKA) is a serious life-threatening condition caused by a lack of insulin, which leads to elevated plasma glucose and metabolic acidosis. Early identification of developing DKA is important to start treatment and minimize complications and risk of death. The aim of the present study is to develop and test prediction model(s) that gives an alarm about their risk of developing elevated ketone bodies during hyperglycemia. Methods: We analyzed data from 138 type 1 diabetes patients with measurements of ketone bodies and continuous glucose monitoring (CGM) data from over 30,000 days of wear time. We utilized a supervised binary classification machine learning approach to identify elevated levels of ketone bodies (≥0.6 mmol/L). Data material was randomly divided at patient level in 70%/30% (training/test) dataset. Logistic regression (LR) and random forest (RF) classifier were compared. Results: Among included patients, 913 ketone samples were eligible for modeling, including 273 event samples with ketone levels ≥0.6 mmol/L. An area under the receiver operating characteristic curve from the RF classifier was 0.836 (confidence interval [CI] 90%, 0.783-0.886) and 0.710 (CI 90%, 0.646-0.77) for the LR classifier. Conclusions: The novel approach for identifying elevated ketone levels in patients with type 1 diabetes utilized in this study indicates that CGM could be a valuable resource for the early prediction of patients at risk of developing DKA. Future studies are needed to validate the results.

Diffusing Capacity as a Predictor of Hospitalizations in a Clinical Cohort of Chronic Obstructive Pulmonary Disease

Rationale: Chronic obstructive pulmonary disease (COPD) hospitalizations are a major burden on patients. Diffusing capacity of the lung for carbon monoxide (DlCO) is a potential predictor that has not been studied in large cohorts. Objectives: This study used electronic health record data to evaluate whether clinically obtained DlCO predicts COPD hospitalizations. Methods: We performed time-to-event analyses of individuals with COPD and DlCO measurements from the Johns Hopkins COPD Precision Medicine Center of Excellence. Cox proportional hazard methods were used to model time from DlCO measurement to first COPD hospitalization and composite first hospitalization or death, adjusting for age, sex, race, body mass index, smoking status, forced expiratory volume in 1 second (FEV1), history of prior COPD hospitalization, and comorbidities. To identify the utility of including DlCO in risk models, area under the receiver operating curve (AUC) values were calculated for models with and without DlCO. Results were externally validated in a separate analogous cohort. Results: Of 2,793 participants, 368 (13%) had a COPD hospitalization within 3 years. In adjusted analyses, for every 10% decrease in DlCO% predicted, risk of COPD hospitalization increased by 10% (hazard ratio, 1.1; 95% confidence interval, 1.1-1.2; P < 0.001). Similar associations were observed for COPD hospitalizations or death. The model including demographics, comorbidities, FEV1, DlCO, and prior COPD hospitalizations performed well, with an AUC of 0.85 and an AUC of 0.84 in an external validation cohort. Conclusions: Diffusing capacity is a strong predictor of COPD hospitalizations in a clinical cohort of individuals with COPD, independent of airflow obstruction and prior hospitalizations. These findings support incorporation of DlCO in risk assessment of patients with COPD.

Prediction value of neutrophil and eosinophil count at risk of COPD exacerbation

INTRODUCTION

Predicting acute exacerbations (AEs) in chronic obstructive pulmonary disease (COPD) is crucial. This study aimed to identify blood biomarkers for predicting COPD exacerbations by inflammatory phenotypes.

MATERIALS AND METHODS

We analyzed blood cell counts and clinical outcomes in 340 COPD patients aged 20-90 years. Patients were categorized into eosinophilic inflammation (EOCOPD) and non-eosinophilic inflammation (N-EOCOPD) groups. Blood cell counts, eosinophil-to-lymphocyte ratio (ELR), neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-eosinophil ratio (NER) were calculated. Linear and logistic regression models assessed relationships between health outcomes and blood cell counts.

RESULTS

EOCOPD patients had distinct characteristics compared to N-EOCOPD patients. Increased neutrophil % and decreased lymphocyte % were associated with reduced pulmonary function, worse quality of life and more exacerbations, but they did not show statistical significance after adjusting by age, sex, BMI, smoking status, FEV1% and patient's medication. Subgroup analysis revealed a 1.372-fold increase in the OR of AE for every 1 unit increase in NLR in EOCOPD patients (p < .05). In N-EOCOPD patients, every 1% increase in blood eosinophil decreased the risk of exacerbation by 59.6%.

CONCLUSIONS

Our study indicates that distinct white blood cell profiles in COPD patients, with or without eosinophilic inflammation, can help assess the risk of AE in clinical settings.

Direct Medical Costs of COPD in the USA: An Analysis of the Medical Expenditure Panel Survey 2017-2018

AIM

In this study, we aimed to provide a nationally representative estimate of the economic burden of chronic obstructive pulmonary disease (COPD) by examining direct medical costs among individuals aged 45 years and older in the USA.

METHODS

Medical Expenditure Panel Survey (2017-2018) data were used to estimate the direct medical costs associated with COPD. All-cause (unadjusted) cost and COPD-specific (adjusted) cost were determined for the various service categories using a regression-based approach among patients with COPD. We developed a weighted two-part model and adjusted for various demographic, socioeconomic, and clinical characteristics.

RESULTS

The study sample consisted of 23,590 patients, of which 1073 had COPD. Patients with COPD had a mean age of 67.4 years (standard error (SE): 0.41), and the total all-cause mean medical cost per patient per year (PPPY) was 2018 US $19,449 (SE: US $865), of which US $6145 (SE: US $295) was for prescription drugs. Using the regression approach, the mean total COPD-specific cost was US $4322 (SE: US $577) PPPY, with prescription drugs contributing US $1887 (SE: 216) PPPY. These results represented an annual total COPD-specific cost of US $24.0 billion, with prescription drugs contributing US $10.5 billion. The mean annual out-of-pocket spending accounted for 7.5% (mean: US $325) of the total COPD-specific cost; for COPD-specific prescription drug cost, 11.3% (mean: US $212) was out-of-pocket cost.

CONCLUSION

COPD poses a significant economic burden on healthcare payers and patients 45 years of age and older in the USA. While prescription drugs accounted for almost half of the total cost, more than 10% of the prescription drug cost was out-of-pocket.

Development of a daily predictive model for the exacerbation of chronic obstructive pulmonary disease

Acute exacerbation (AE) of chronic obstructive pulmonary disease (COPD) compromises health status; it increases disease progression and the risk of future exacerbations. We aimed to develop a model to predict COPD exacerbation. We merged the Korean COPD subgroup study (KOCOSS) dataset with nationwide medical claims data, information regarding weather, air pollution, and epidemic respiratory virus data. The Korean National Health and Nutrition Examination Survey (KNHANES) dataset was used for validation. Several machine learning methods were employed to increase the predictive power. The development dataset consisted of 590 COPD patients enrolled in the KOCOSS cohort; these were randomly divided into training and internal validation subsets on the basis of the individual claims data. We selected demographic and spirometry data, medications for COPD and hospital visit for AE, air pollution data and meteorological data, and influenza virus data as contributing factors for the final model. Six machine learning and logistic regression tools were used to evaluate the performance of the model. A light gradient boosted machine (LGBM) afforded the best predictive power with an area under the curve (AUC) of 0.935 and an F1 score of 0.653. Similar favorable predictive performance was observed for the 2151 individuals in the external validation dataset. Daily prediction of the COPD exacerbation risk may help patients to rapidly assess their risk of exacerbation and will guide them to take appropriate intervention in advance. This might lead to reduction of the personal and socioeconomic burdens associated with exacerbation.

Unleashing the Power of Very Small Data to Predict Acute Exacerbations of Chronic Obstructive Pulmonary Disease

Introduction

In this article, we explore to what extent it is possible to leverage on very small data to build machine learning (ML) models that predict acute exacerbations of chronic obstructive pulmonary disease (AECOPD).

Methods

We build ML models using the small data collected during the eHealth Diary telemonitoring study between 2013 and 2017 in Sweden. This data refers to a group of multimorbid patients, namely 18 patients with chronic obstructive pulmonary disease (COPD) as the major reason behind previous hospitalisations. The telemonitoring was supervised by a specialised hospital-based home care (HBHC) unit, which also was responsible for the medical actions needed.

Results

We implement two different ML approaches, one based on time-dependent covariates and the other one based on time-independent covariates. We compare the first approach with standard COX Proportional Hazards (CPH). For the second one, we use different proportions of synthetic data to build models and then evaluate the best model against authentic data.

Discussion

To the best of our knowledge, the present ML study shows for the first time that the most important variable for an increased risk of future AECOPDs is "maintenance medication changes by HBHC". This finding is clinically relevant since a sub-optimal maintenance treatment, requiring medication changes, puts the patient in risk for future AECOPDs.

Conclusion

The experiments return useful insights about the use of small data for ML.

Digital health in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) can be prevented and treated through effective care, reducing exacerbations and hospitalizations. Early identification of individuals at high risk of COPD exacerbation is an opportunity for preventive measures. However, many patients struggle to follow their treatment plans because of a lack of knowledge about the disease, limited access to resources, and insufficient clinical support. The growth of digital health-which encompasses advancements in health information technology, artificial intelligence, telehealth, the Internet of Things, mobile health, wearable technology, and digital therapeutics-offers opportunities for improving the early diagnosis and management of COPD. This study reviewed the field of digital health in terms of COPD. The findings showed that despite significant advances in digital health, there are still obstacles impeding its effectiveness. Finally, we highlighted some of the major challenges and possibilities for developing and integrating digital health in COPD management.

Early versus non-early desaturation during 6MWT in COPD patients: A follow-up study

Introduction

Six-minute walk test (6MWT) has a significant prognostic value in chronic obstructive pulmonary disease (COPD). Those who desaturate early during 6MWT are likely to have frequent exacerbations.

Aims and Objectives

To follow-up and compare exacerbations and hospitalisations of COPD patients having early desaturation versus nonearly desaturation determined during baseline 6MWT.

Methods

It was a longitudinal follow-up study conducted in a tertiary care institute from November 1, 2018 to May 15, 2020 involving 100 COPD patients. A decrease in SpO2 by ≥4% in baseline 6MWT was considered a significant desaturation. If the desaturation occurred within first minute of the 6MWT, the patient was called early desaturator (ED); if it occurred later, the patient was called nonearly desaturator (NED). If the saturation did not fall, then the patient was called nondesaturator. During the follow-up, 12 patients dropped out and 88 remained.

Results

Of 88 patients, 55 (62.5%) were desaturators and 33 were nondesaturator. Of 55 desaturators, 16 were ED and 39 were NED. EDs had significantly higher number of severe exacerbations (P <.05), higher hospitalisation (P <.001), and higher BODE index (P <.01) compared to NEDs. The receptor operating characteristic curve and multiple logistic regression analysis showed that previous exacerbations, presence of early desaturation, and distance saturation product during the 6MWT were significant predictors for predicting hospitalizations.

Conclusion

Early desaturation can be used as a screening tool for assessing the risk of hospitalization in COPD patients.

Development of the ESEx index: a tool for predicting risk of recurrent severe COPD exacerbations

Background

In chronic obstructive pulmonary disease (COPD), multiple recurrent severe exacerbations that require hospitalization can occur. These events are strongly associated with death and other clinical complications.

Objectives

We aimed to develop a prognostic model that could identify patients with COPD that are at risk of multiple recurrent severe exacerbations within 3 years.

Design

Prospective cohort.

Methods

The derivation cohort comprised patients with stable, moderate-to-severe COPD. Multivariable logistic regression analyses were performed to develop the final model. Based on regression coefficients, a simplified index (ESEx) was established. Both, model and index, were assessed for predictive performance by measuring discrimination and calibration.

Results

Over 3 years, 16.4% of patients with COPD experienced at least three severe recurrent exacerbations. The prognostic model showed good discrimination of high-risk patients, based on three characteristics: the number of severe exacerbations in the previous year, performance in the five-repetition sit-to-stand test, and in the 6-minute-walk test. The ESEx index provided good level of discrimination [areas under the receiver operating characteristic curve (AUCs): 0.913].

Conclusions

The ESEx index showed good internal validation for the identification of patients at risk of three recurrent severe COPD exacerbations within 3 years. These tools could be used to identify patients who require early interventions and motivate patients to improve physical performance to prevent recurrent exacerbations.

Predicting severe chronic obstructive pulmonary disease exacerbations using quantitative CT: a retrospective model development and external validation study

BACKGROUND

Quantitative CT is becoming increasingly common for the characterisation of lung disease; however, its added potential as a clinical tool for predicting severe exacerbations remains understudied. We aimed to develop and validate quantitative CT-based models for predicting severe chronic obstructive pulmonary disease (COPD) exacerbations.

METHODS

We analysed the Subpopulations and Intermediate Outcome Measures In COPD Study (SPIROMICS) cohort, a multicentre study done at 12 clinical sites across the USA, of individuals aged 40-80 years from four strata: individuals who never smoked, individuals who smoked but had normal spirometry, individuals who smoked and had mild to moderate COPD, and individuals who smoked and had severe COPD. We used 3-year follow-up data to develop logistic regression classifiers for predicting severe exacerbations. Predictors included age, sex, race, BMI, pulmonary function, exacerbation history, smoking status, respiratory quality of life, and CT-based measures of density gradient texture and airway structure. We externally validated our models in a subset from the Genetic Epidemiology of COPD (COPDGene) cohort. Discriminative model performance was assessed using the area under the receiver operating characteristic curve (AUC), which was also compared with other predictors, including exacerbation history and the BMI, airflow obstruction, dyspnoea, and exercise capacity (BODE) index. We evaluated model calibration using calibration plots and Brier scores.

FINDINGS

Participants in SPIROMICS were enrolled between Nov 12, 2010, and July 31, 2015. Participants in COPDGene were enrolled between Jan 10, 2008, and April 15, 2011. We included 1956 participants from the SPIROMICS cohort who had complete 3-year follow-up data: the mean age of the cohort was 63·1 years (SD 9·2) and 1017 (52%) were men and 939 (48%) were women. Among the 1956 participants, 434 (22%) had a history of at least one severe exacerbation. For the CT-based models, the AUC was 0·854 (95% CI 0·852-0·855) for at least one severe exacerbation within 3 years and 0·931 (0·930-0·933) for consistent exacerbations (defined as ≥1 acute episode in each of the 3 years). Models were well calibrated with low Brier scores (0·121 for at least one severe exacerbation; 0·039 for consistent exacerbations). For the prediction of at least one severe event during 3-year follow-up, AUCs were significantly higher with CT biomarkers (0·854 [0·852-0·855]) than exacerbation history (0·823 [0·822-0·825]) and BODE index 0·812 [0·811-0·814]). 6965 participants were included in the external validation cohort, with a mean age of 60·5 years (SD 8·9). In this cohort, AUC for at least one severe exacerbation was 0·768 (0·767-0·769; Brier score 0·088).

INTERPRETATION

CT-based prediction models can be used for identification of patients with COPD who are at high risk of severe exacerbations. The newly identified CT biomarkers could potentially enable investigation into underlying disease mechanisms responsible for exacerbations.

FUNDING

National Institutes of Health and the National Heart, Lung, and Blood Institute.

Advancing Digital Solutions to Overcome Longstanding Barriers in Asthma and COPD Management

Maintenance therapy delivered via inhaler is central to asthma and chronic obstructive pulmonary disease (COPD) management. Poor adherence to inhaled medication and errors in inhalation technique have long represented major barriers to the optimal management of these chronic conditions. Technological innovations may provide a means of overcoming these barriers. This narrative review examines ongoing advances in digital technologies relevant to asthma and COPD with the potential to inform clinical decision-making and improve patient care. Digital inhaler devices linked to mobile apps can help bring about changes in patients' behaviors and attitudes towards disease management, particularly when they build in elements of interactivity and gamification. They can also support ongoing technique education, empowering patients and helping providers maximize the value of consultations and develop effective action plans informed by insights into the patient's inhaler use patterns and their respiratory health. When combined with innovative techniques such as machine learning, digital devices have the potential to predict exacerbations and prompt pre-emptive intervention. Finally, digital devices may support an advanced precision medicine approach to respiratory disease management and help support shared decision-making. Further work is needed to increase uptake of digital devices and integrate their use into care pathways before their full potential in personalized asthma and COPD management can be realized.

Generalizability of Risk Stratification Algorithms for Exacerbations in COPD

BACKGROUND

Contemporary management of COPD relies on exacerbation history to risk-stratify patients for future exacerbations. Multivariate prediction models can improve the performance of risk stratification. However, the clinical usefulness of risk stratification can vary from one population to another.

RESEARCH QUESTION

How do two validated exacerbation risk prediction models (Acute COPD Exacerbation Prediction Tool [ACCEPT] and the Bertens model) compared with exacerbation history alone perform in different patient populations?

STUDY DESIGN AND METHODS

We used data from three clinical studies representing populations at different levels of moderate to severe exacerbation risk: the Study to Understand Mortality and Morbidity in COPD (SUMMIT; N = 2,421; annual risk, 0.22), the Long-term Oxygen Treatment Trial (LOTT; N = 595; annual risk, 0.38), and Towards a Revolution in COPD Health (TORCH; N = 1,091; annual risk, 0.52). We compared the area under the receiver operating characteristic curve (AUC) and net benefit (measure of clinical usefulness) among three risk stratification algorithms for predicting exacerbations in the next 12 months. We also evaluated the effect of model recalibration on clinical usefulness.

RESULTS

Compared with exacerbation history, ACCEPT showed better performance in all three samples (change in AUC, 0.08, 0.07, and 0.10, respectively; P ≤ .001 for all). The Bertens model showed better performance compared with exacerbation history in SUMMIT and TORCH (change in AUC, 0.10 and 0.05, respectively; P < .001 for both), but not in LOTT. No algorithm was superior in clinical usefulness across all samples. Before recalibration, the Bertens model generally outperformed the other algorithms in low-risk settings, whereas ACCEPT outperformed others in high-risk settings. All three algorithms showed the risk of harm (providing lower net benefit than not using any risk stratification). After recalibration, risk of harm was mitigated substantially for both prediction models.

INTERPRETATION

Exacerbation history alone is unlikely to provide clinical usefulness for predicting COPD exacerbations in all settings and could be associated with a risk of harm. Prediction models have superior predictive performance, but require setting-specific recalibration to confer higher clinical usefulness.

Development and Validation of a Multivariable Prediction Model to Identify Acute Exacerbation of COPD and Its Severity for COPD Management in China (DETECT Study): A Multicenter, Observational, Cross-Sectional Study

Purpose

There is an unmet clinical need for an accurate and objective diagnostic tool for early detection of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). DETECT (NCT03556475) was a multicenter, observational, cross-sectional study aiming to develop and validate multivariable prediction models for AECOPD occurrence and severity in patients with chronic obstructive pulmonary disease (COPD) in China.

Patients and Methods

Patients aged ≥40 years with moderate/severe COPD, AECOPD, or no COPD were consecutively enrolled between April 22, 2020, and January 18, 2021, across seven study sites in China. Multivariable prediction models were constructed to identify AECOPD occurrence (primary outcome) and AECOPD severity (secondary outcome). Candidate variables were selected using a stepwise procedure, and the bootstrap method was used for internal model validation.

Results

Among 299 patients enrolled, 246 were included in the final analysis, of whom 30.1%, 40.7%, and 29.3% had COPD, AECOPD, or no COPD, respectively. Mean age was 64.1 years. Variables significantly associated with AECOPD occurrence (P<0.05) and severity (P<0.05) in the final models included COPD disease-related characteristics, as well as signs and symptoms. Based on cut-off values of 0.374 and 0.405 for primary and secondary models, respectively, the performance of the primary model constructed to identify AECOPD occurrence (AUC: 0.86; sensitivity: 0.84; specificity: 0.77), and of the secondary model for AECOPD severity (AUC: 0.81; sensitivity: 0.90; specificity: 0.73) indicated high diagnostic accuracy and clinical applicability.

Conclusion

By leveraging easy-to-collect patient and disease data, we developed identification tools that can be used for timely detection of AECOPD and its severity. These tools may help physicians diagnose AECOPD in a timely manner, before further disease progression and possible hospitalizations.

ACCEPT 2·0: Recalibrating and externally validating the Acute COPD exacerbation prediction tool (ACCEPT)

BACKGROUND

The Acute Chronic Obstructive Pulmonary Disease (COPD) Exacerbation Prediction Tool (ACCEPT) was developed for individualised prediction of COPD exacerbations. ACCEPT was well calibrated overall and had a high discriminatory power, but overestimated risk among individuals without recent exacerbations. The objectives of this study were to 1) fine-tune ACCEPT to make better predictions for individuals with a negative exacerbation history, 2) develop more parsimonious models, and 3) externally validate the models in a new dataset.

METHODS

We recalibrated ACCEPT using data from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE, a three-year observational study, 1,803 patients, 2,117 exacerbations) study by applying non-parametric regression splines to the predicted rates. We developed three reduced versions of ACCEPT by removing symptom score and/or baseline medications as predictors. We examined the discrimination, calibration, and net benefit of ACCEPT 2·0 in the placebo arm of the Towards a Revolution in COPD Health (TORCH, a three-year randomised clinical trial of inhaled therapies in COPD, 1,091 patients, 1,064 exacerbations) study. The primary outcome for prediction was the occurrence of ≥2 moderate or ≥1 severe exacerbation in the next 12 months; the secondary outcomes were prediction of the occurrence of any moderate/severe exacerbation or any severe exacerbation.

FINDINGS

ACCEPT 2·0 had an area-under-the-curve (AUC) of 0·76 for predicting the primary outcome. Exacerbation history alone (current standard of care) had an AUC of 0·68. The model was well calibrated in patients with positive or negative exacerbation histories. Changes in AUC in reduced versions were minimal for the primary outcome as well as for predicting the occurrence of any moderate/severe exacerbations (ΔAUC<0·011), but more substantial for predicting the occurrence of any severe exacerbations (ΔAUC<0·020). All versions of ACCEPT 2·0 provided positive net benefit over the use of exacerbation history alone for some range of thresholds.

INTERPRETATION

ACCEPT 2·0 showed good calibration regardless of exacerbation history, and predicts exacerbation risk better than current standard of care for a range of thresholds. Future studies need to investigate the utility of exacerbation prediction in various subgroups of patients.

FUNDING

This study was funded by a team grant from the Canadian Institutes of Health Research (PHT 178432).

Health-Screening-Based Chronic Obstructive Pulmonary Disease and Its Effect on Cardiovascular Disease Risk

Chronic obstructive pulmonary disease (COPD) is considered a major cause of death worldwide, and various studies have been conducted for its early diagnosis. Our work developed a scoring system by predicting and validating COPD and performed predictive model implementations. Participants who underwent a health screening between 2017 and 2020 were extracted from the Korea National Health and Nutrition Examination Survey (KNHANES) database. COPD individuals were defined as aged 40 years or older with prebronchodilator forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC < 0.7). The logistic regression model was performed, and the C-index was used for variable selection. Receiver operating characteristic (ROC) curves with area under the curve (AUC) values were generated for evaluation. Age, sex, waist circumference and diastolic blood pressure were used to predict COPD and to develop a COPD score based on a multivariable model. A simplified model for COPD was validated with an AUC value of 0.780 from the ROC curves. In addition, we evaluated the association of the derived score with cardiovascular disease (CVD). COPD scores showed significant performance in COPD prediction. The developed score also showed a good effect on the diagnostic ability for CVD risk. In the future, studies comparing the diagnostic accuracy of the derived scores with standard diagnostic tests are needed.

Identification of Sputum Biomarkers Predictive of Pulmonary Exacerbations in COPD

BACKGROUND

Improved understanding of the pathways associated with airway pathophysiologic features in COPD will identify new predictive biomarkers and novel therapeutic targets.

RESEARCH QUESTION

Which physiologic pathways are altered in the airways of patients with COPD and will predict exacerbations?

STUDY DESIGN AND METHODS

We applied a mass spectrometric panel of metabolomic biomarkers related to mucus hydration and inflammation to sputa from the multicenter Subpopulations and Intermediate Outcome Measures in COPD Study. Biomarkers elevated in sputa from patients with COPD were evaluated for relationships to measures of COPD disease severity and their ability to predict future exacerbations.

RESULTS

Sputum supernatants from 980 patients were analyzed: 77 healthy nonsmokers, 341 smokers with preserved spirometry, and 562 patients with COPD (178 with Global Initiative on Chronic Obstructive Lung Disease [GOLD] stage 1 disease, 303 with GOLD stage 2 disease, and 81 with GOLD stage 3 disease) were analyzed. Biomarkers from multiple pathways were elevated in COPD and correlated with sputum neutrophil counts. Among the most significant analytes (false discovery rate, 0.1) were sialic acid, hypoxanthine, xanthine, methylthioadenosine, adenine, and glutathione. Sialic acid and hypoxanthine were associated strongly with measures of disease severity, and elevation of these biomarkers was associated with shorter time to exacerbation and improved prediction models of future exacerbations.

INTERPRETATION

Biomarker evaluation implicated pathways involved in mucus hydration, adenosine metabolism, methionine salvage, and oxidative stress in COPD airway pathophysiologic characteristics. Therapies that target these pathways may be of benefit in COPD, and a simple model adding sputum-soluble phase biomarkers improves prediction of pulmonary exacerbations.

TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT01969344; URL: www.

CLINICALTRIALS

gov.

Clinical implementation of an algorithm for predicting exacerbations in patients with COPD in telemonitoring: a study protocol for a single-blinded randomized controlled trial

Background

Acute exacerbations have a significant impact on patients with COPD by accelerating the decline in lung function leading to decreased health-related quality of life and survival time. In telehealth, health care professionals exercise clinical judgment over a physical distance. Telehealth has been implemented as a way to monitor patients more closely in daily life with an intention to intervene earlier when physical measurements indicate that health deteriorates. Several studies call for research investigating the ability of telehealth to automatically flag risk of exacerbations by applying the physical measurements that are collected as part of the monitoring routines to support health care professionals. However, more research is needed to further develop, test, and validate prediction algorithms to ensure that these algorithms improve outcomes before they are widely implemented in practice.

Method

This trial tests a COPD prediction algorithm that is integrated into an existing telehealth system, which has been developed from the previous Danish large-scale trial, TeleCare North (NCT: 01984840). The COPD prediction algorithm aims to support clinical decisions by predicting the risk of exacerbations for patients with COPD based on selected physiological parameters. A prospective, parallel two-armed randomized controlled trial with approximately 200 participants with COPD will be conducted. The participants live in Aalborg municipality, which is located in the North Denmark Region. All participants are familiar with the telehealth system in advance. In addition to the participants’ usual weekly monitored measurements, they are asked to measure their oxygen saturation two more times a week during the trial period. The primary outcome is the number of exacerbations defined as an acute hospitalization from baseline to follow-up. Secondary outcomes include changes in health-related quality of life measured by both the 12-Item Short Form Survey version 2 and EuroQol-5 Dimension Questionnaire as well as the incremental cost-effectiveness ratio.

Discussion

This trial seeks to explore whether the COPD prediction algorithm has the potential to support early detection of exacerbations in a telehealth setting. The COPD prediction algorithm may initiate timely treatment, which may decrease the number of hospitalizations.

Trial registration

NCT05218525 (pending at clinicaltrials.gov) (date, month, year)

Evaluating Triple Therapy Treatment Pathways in Chronic Obstructive Pulmonary Disease (COPD): A Machine-Learning Predictive Model

Purpose

Inhaled triple therapy (TT) comprising a long-acting muscarinic antagonist, long-acting β₂ agonist, and inhaled corticosteroid is recommended for symptomatic chronic obstructive pulmonary disease (COPD) patients, or those at risk of exacerbation. However, it is not well understood which patient characteristics contribute most to future exacerbation risk. This study assessed patient predictors associated with future exacerbation time following initiation of TT.

Patients and Methods

This retrospective cohort study used data from the Optum™ Clinformatics™ Data Mart, a large health claims database in the United States. COPD patients who initiated TT between January 2008 and March 2018 (index) were eligible. Patients were required to be aged ≥18 years at index and have continuous enrollment for the 12 months prior to index (baseline) and the 12 months following index (follow-up). Patients who had received TT during baseline were excluded. Data from eligible patients were analyzed using a reverse engineering forward simulation machine learning platform to predict future COPD exacerbation time.

Results

Data from 73,625 patients were included. The model found that prior exacerbation was largely correlated with post-index exacerbation time; patients who had ≥4 exacerbation episodes during baseline had an average increase of 32.4 days post-index exacerbation, compared with patients with no exacerbations during baseline. Likewise, ≥2 inpatient visits (effect size 27.1 days), the use of xanthines (effect size 11.5 days), or rheumatoid arthritis (effect size 6.4 days) during baseline were associated with increased exacerbation time. Conversely, diagnosis of anemia (effect size –5.68 days), or oral corticosteroids in the past month (effect size –3.43 days) were associated with reduced exacerbation time.

Conclusion

Frequent prior exacerbations, healthcare resource utilization, xanthine use, and rheumatoid arthritis were the strongest factors predicting the future increase of exacerbations. These results improve our understanding of exacerbation risk among COPD patients initiating triple therapy.

Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach

Background

Self-reporting digital apps provide a way of remotely monitoring and managing patients with chronic conditions in the community. Leveraging the data collected by these apps in prognostic models could provide increased personalization of care and reduce the burden of care for people who live with chronic conditions. This study evaluated the predictive ability of prognostic models for the prediction of acute exacerbation events in people with chronic obstructive pulmonary disease by using data self-reported to a digital health app.

Objective

The aim of this study was to evaluate if data self-reported to a digital health app can be used to predict acute exacerbation events in the near future.

Methods

This is a retrospective study evaluating the use of symptom and chronic obstructive pulmonary disease assessment test data self-reported to a digital health app (myCOPD) in predicting acute exacerbation events. We include data from 2374 patients who made 68,139 self-reports. We evaluated the degree to which the different variables self-reported to the app are predictive of exacerbation events and developed both heuristic and machine learning models to predict whether the patient will report an exacerbation event within 3 days of self-reporting to the app. The model’s predictive ability was evaluated based on self-reports from an independent set of patients.

Results

Users self-reported symptoms, and standard chronic obstructive pulmonary disease assessment tests displayed correlation with future exacerbation events. Both a baseline model (area under the receiver operating characteristic curve [AUROC] 0.655, 95% CI 0.689-0.676) and a machine learning model (AUROC 0.727, 95% CI 0.720-0.735) showed moderate ability in predicting exacerbation events, occurring within 3 days of a given self-report. Although the baseline model obtained a fixed sensitivity and specificity of 0.551 (95% CI 0.508-0.596) and 0.759 (95% CI 0.752-0.767) respectively, the sensitivity and specificity of the machine learning model can be tuned by dichotomizing the continuous predictions it provides with different thresholds.

Conclusions

Data self-reported to health care apps designed to remotely monitor patients with chronic obstructive pulmonary disease can be used to predict acute exacerbation events with moderate performance. This could increase personalization of care by allowing preemptive action to be taken to mitigate the risk of future exacerbation events.

The association between transfer coefficient of the lung and the risk of exacerbation in asthma-COPD overlap: an observational cohort study

BACKGROUND

Asthma-chronic obstructive pulmonary disease (COPD) overlap (ACO) patients experience exacerbations more frequently than those with asthma or COPD alone. Since low diffusing capacity of the lung for carbon monoxide (DLCO) is known as a strong risk factor for severe exacerbation in COPD, DLCO or a transfer coefficient of the lung for carbon monoxide (KCO) is speculated to also be associated with the risk of exacerbations in ACO.

METHODS

This study was conducted as an observational cohort survey at the National Hospital Organization Fukuoka National Hospital. DLCO and KCO were measured in 94 patients aged ≥ 40 years with a confirmed diagnosis of ACO. Multivariable-adjusted hazard ratios (HRs) for the exacerbation-free rate over one year were estimated and compared across the levels of DLCO and KCO.

RESULTS

Within one year, 33.3% of the cohort experienced exacerbations. After adjustment for potential confounders, low KCO (< 80% per predicted) was positively associated with the incidence of exacerbation (multivariable-adjusted HR = 3.71 (95% confidence interval 1.32-10.4)). The association between low DLCO (< 80% per predicted) and exacerbations showed similar trends, although it failed to reach statistical significance (multivariable-adjusted HR = 1.31 (95% confidence interval 0.55-3.11)).

CONCLUSIONS

Low KCO was a significant risk factor for exacerbations among patients with ACO. Clinicians should be aware that ACO patients with impaired KCO are at increased risk of exacerbations and that careful management in such a population is mandatory.

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

Digital Healthcare for Airway Diseases from Personal Environmental Exposure

Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to well-being. In particular, health products and services have expanded by the use and development of artificial intelligence, mobile health applications, and wearable electronic devices. Such advancements have enabled accurate and updated tracking and modeling of health conditions. For instance, digital health technologies are capable of measuring environmental pollution and predicting its adverse health effects. Several health conditions, including chronic airway diseases such as asthma and chronic obstructive pulmonary disease, can be exacerbated by pollution. These diseases impose substantial health burdens with high morbidity and mortality. Recently, efforts have been made to develop digital technologies to alleviate such conditions. Moreover, the COVID-19 pandemic has facilitated the application of telemedicine and telemonitoring for patients with chronic airway diseases. This article reviews current trends and studies in digital technology utilization for investigating and managing environmental exposure and chronic airway diseases. First, we discussed the recent progression of digital technologies in general environmental healthcare. Then, we summarized the capacity of digital technologies in predicting exacerbation and self-management of airway diseases. Concluding these reviews, we provided suggestions to improve digital health technologies' abilities to reduce the adverse effects of environmental exposure in chronic airway diseases, based on personal exposure-response modeling.

Prediction of Acute COPD Exacerbation in the Swiss Multicenter COPD Cohort Study (TOPDOCS) by Clinical Parameters, Medication Use, and Immunological Biomarkers

BACKGROUND AND OBJECTIVE

Whether immunological biomarkers combined with clinical characteristics measured during an exacerbation-free period are predictive of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) frequency and severity is unknown.

METHOD

We measured immunological biomarkers and clinical characteristics in 271 stable chronic obstructive pulmonary disease (COPD) patients (67% male, mean age 63 years) from "The Obstructive Pulmonary Disease Outcomes Cohort of Switzerland" cohort on a single occasion. One-year follow-up data were available for 178 patients. Variables independently associated with AECOPD frequency and severity were identified by multivariable regression analyses. Receiver operating characteristic analysis was used to obtain optimal cutoff levels and measure the area under the curve (AUC) in order to assess if baseline data can be used to predict future AECOPD.

RESULTS

Higher number of COPD medications (adjusted incident rate ratio [aIRR] 1.17) and platelet count (aIRR 1.03), and lower FEV1% predicted (aIRR 0.84) and IgG2 (aIRR 0.84) were independently associated with AECOPD frequency in the year before baseline. Optimal cutoff levels for experiencing frequent (>1) AECOPD were ≥3 COPD medications (AUC = 0.72), FEV1 ≤40% predicted (AUC = 0.72), and IgG2 ≤2.6 g/L (AUC = 0.64). The performance of a model using clinical and biomarker parameters to predict future, frequent AECOPD events in the same patients was fair (AUC = 0.78) but not superior to a model using only clinical parameters (AUC = 0.79). The IFN-lambda rs8099917GG-genotype was more prevalent in patients who had severe AECOPD.

CONCLUSIONS

Clinical and biomarker parameters assessed at a single point in time correlated with the frequency of AECOPD events during the year before and the year after assessment. However, only clinical parameters had fair discriminatory power in identifying patients likely to experience frequent AECOPD.

COPD - do the right thing

A gap exists between guidelines and real-world clinical practice for the management and treatment of chronic obstructive pulmonary disease (COPD). Although this has narrowed in the last decade, there is room for improvement in detection rates, treatment choices and disease monitoring. In practical terms, primary care practitioners need to become aware of the huge impact of COPD on patients, have non-judgemental views of smoking and of COPD as a chronic disease, use a holistic consultation approach and actively motivate patients to adhere to treatment.This article is based on discussions at a virtual meeting of leading Nordic experts in COPD (the authors) who were developing an educational programme for COPD primary care in the Nordic region. The article aims to describe the diagnosis and lifelong management cycle of COPD, with a strong focus on providing a hands-on, practical approach for medical professionals to optimise patient outcomes in COPD primary care.

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.

Body composition parameters and functional status test in predicting future acute exacerbation risk among hospitalized patients with chronic obstructive pulmonary disease

BACKGROUND & AIMS

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. Nutritional and functional status derangement is a commonly seen in COPD patients, and this is associated with a higher disease severity and mortality. To assess body composition analysis - measured by segmental multi-frequency bioelectrical impedance analysis (BIA)- and functional status and investigate their relationship with the COPD acute exacerbation risk.

METHODS

Eighty COPD patients admitted to hospital for COPD acute exacerbation were prospectively followed-up for one year after discharge, focusing on a new incidence of COPD acute exacerbation. Following discharge, participants' body composition was assessed with the use of segmental multi-frequency BIA, whereas physical function by performing 5-repetitions and 30 s sit-to-stand (STS) tests. Unadjusted and multivariate logistic regression analyses were performed to evaluate the ability of the various measures to predict incidence of future COPD acute exacerbation in one-year period.

RESULTS

Seventy-six out of 80 participants completed the study and were analyzed. Fifty-one [24 male (47.1%)] out of 76 participants (67.1%), mean aged of 69.3 ± 8.9 years, developed at least one new COPD acute exacerbation during the one year follow-up. The probability of COPD acute exacerbation in one year was significantly related to BMI (OR = 0.75, 95% CI; 0.61-0.91, p = 0.004) and Fat Free Mass (OR = 0.88, 95% CI; 0.79-0.97, p = 0.012) after adjustment for sex, age and smoking index (pack × years). Both 5-repetitions and 30 s STS tests had a good predictive ability for the incidence of COPD acute exacerbation in one year (AUC = 0.80, 95% CI; 0.65-0.95, p = 0.009 and AUC = 0.83, 95% CI; 0.70-0.96, p = 0.004, respectively).

CONCLUSION

In an observational study among patients admitted with COPD acute exacerbation, body composition analysis parameters and functional status are related to acute exacerbation risk incidence.

Predicting Severe Chronic Obstructive Pulmonary Disease Exacerbations. Developing a Population Surveillance Approach with Administrative Data

Rationale: Automatic prediction algorithms based on routinely collected health data may be able to identify patients at high risk for hospitalizations related to acute exacerbations of chronic obstructive pulmonary disease (COPD).Objectives: To conduct a proof-of-concept study of a population surveillance approach for identifying individuals at high risk of severe COPD exacerbations.Methods: We used British Columbia's administrative health databases (1997-2016) to identify patients with diagnosed COPD. We used data from the previous 6 months to predict the risk of severe exacerbation in the next 2 months after a randomly selected index date. We applied statistical and machine-learning algorithms for risk prediction (logistic regression, random forest, neural network, and gradient boosting). We used calibration plots and receiver operating characteristic curves to evaluate model performance based on a randomly chosen future date at least 1 year later (temporal validation).Results: There were 108,433 patients in the development dataset and 113,786 in the validation dataset; of these, 1,126 and 1,136, respectively, were hospitalized for COPD within their outcome windows. The best prediction algorithm (gradient boosting) had an area under the receiver operating characteristic curve of 0.82 (95% confidence interval, 0.80-0.83), which was significantly higher than the corresponding value for the model with exacerbation history as the only predictor (current standard of care: 0.68). The predicted risk scores were well calibrated in the validation dataset.Conclusions: Imminent COPD-related hospitalizations can be predicted with good accuracy using administrative health data. This model may be used as a means to target high-risk patients for preventive exacerbation therapies.

Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations

Background

COPDPredict™ is a novel digital application dedicated to providing early warning of imminent COPD (chronic obstructive pulmonary disease) exacerbations for prompt intervention. Exacerbation prediction algorithms are based on a decision tree model constructed from percentage thresholds for disease state changes in patient-reported wellbeing, forced expiratory volume in one second (FEV₁) and C-reactive protein (CRP) levels. Our study determined the validity of COPDPredict™ to identify exacerbations and provide timely notifications to patients and clinicians compared to clinician-defined episodes.

Methods

In a 6-month prospective observational study, 90 patients with COPD and frequent exacerbations registered wellbeing self-assessments daily using COPDPredict™ App and measured FEV₁ using connected spirometers. CRP was measured using finger-prick testing.

Results

Wellbeing self-assessment submissions showed 98% compliance. Ten patients did not experience exacerbations and treatment was unchanged. A total of 112 clinician-defined exacerbations were identified in the remaining 80 patients: 52 experienced 1 exacerbation; 28 had 2.2±0.4 episodes. Sixty-two patients self-managed using prescribed rescue medication. In 14 patients, exacerbations were more severe but responded to timely escalated treatment at home. Four patients attended the emergency room; with 2 hospitalised for <72 hours. Compared to the 6 months pre-COPDPredict™, hospitalisations were reduced by 98% (90 vs 2, p<0.001). COPDPredict™ identified COPD-related exacerbations at 7, 3 days (median, IQR) prior to clinician-defined episodes, sending appropriate alerts to patients and clinicians. Cross-tabulation demonstrated sensitivity of 97.9% (95% CI 95.7-99.2), specificity of 84.0% (95% CI 82.6-85.3), positive and negative predictive value of 38.4% (95% CI 36.4-40.4) and 99.8% (95% CI 99.5-99.9), respectively.

Conclusion

High sensitivity indicates that if there is an exacerbation, COPDPredict™ informs patients and clinicians accurately. The high negative predictive value implies that when an exacerbation is not indicated by COPDPredict™, risk of an exacerbation is low. Thus, COPDPredict™ provides safe, personalised, preventative care for patients with COPD.

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

Unmet needs in the management of exacerbations of chronic obstructive pulmonary disease

Exacerbations of chronic obstructive pulmonary disease (COPD) are episodes of acute worsening of respiratory symptoms that require additional therapy. These events play a pivotal role in the natural course of the disease and are associated with a progressive decline in lung function, reduced health status, a low physical activity level, tremendous health care costs, and increased mortality. Although most exacerbations have an infectious origin, the underlying mechanisms are heterogeneous and specific predictors of their occurrence in individual patients are currently unknown. Accurate prediction and early diagnosis of exacerbations is essential to develop novel targets for prevention and personalized treatments to reduce the impact of these events. Several potential biomarkers have previously been studied, these however lack specificity, accuracy and do not add value to the available clinical predictors. At present, microbial composition and host-microbiome interactions in the lung are increasingly recognized for their role in affecting the susceptibility to exacerbations, and may steer towards a novel direction in the management of COPD exacerbations. This narrative review describes the current challenges and unmet needs in the management of acute exacerbations of COPD. Exacerbation triggers, biological clusters, current treatment strategies, and their limitations, previously studied biomarkers and prediction tools, the lung microbiome and its role in COPD exacerbations as well as future directions are discussed.

Predicting Hospitalization Due to COPD Exacerbations in Swedish Primary Care Patients Using Machine Learning - Based on the ARCTIC Study

Purpose

Chronic obstructive pulmonary disease (COPD) exacerbations can negatively impact disease severity, progression, mortality and lead to hospitalizations. We aimed to develop a model that predicts a patient's risk of hospitalization due to severe exacerbations (defined as COPD-related hospitalizations) of COPD, using Swedish patient level data.

Patients and Methods

Patient level data for 7823 Swedish patients with COPD was collected from electronic medical records (EMRs) and national registries covering healthcare contacts, diagnoses, prescriptions, lab tests, hospitalizations and socioeconomic factors between 2000 and 2013. Models were created using machine-learning methods to predict risk of imminent exacerbation causing patient hospitalization due to COPD within the next 10 days. Exacerbations occurring within this period were considered as one event. Model performance was assessed using the Area under the Precision-Recall Curve (AUPRC). To compare performance with previous similar studies, the Area Under Receiver Operating Curve (AUROC) was also reported. The model with the highest mean cross validation AUPRC was selected as the final model and was in a final step trained on the entire training dataset.

Results

The most important factors for predicting severe exacerbations were exacerbations in the previous six months and in whole history, number of COPD-related healthcare contacts and comorbidity burden. Validation on test data yielded an AUROC of 0.86 and AUPRC of 0.08, which was high in comparison to previously published attempts to predict COPD exacerbation.

Conclusion

Our work suggests that clinically available information on patient history collected via automated retrieval from EMRs and national registries or directly during patient consultation can form the basis for future clinical tools to predict risk of severe COPD exacerbations.

Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and the traditional variables extracted from computed tomography (CT) images may not be sufficient to describe all the topological features of lung tissues in COPD patients. We employed an unsupervised three-dimensional (3D) convolutional autoencoder (CAE)-feature constructor (FC) deep learning network to learn from CT data and derive tissue pattern-clusters jointly. We then applied exploratory factor analysis (EFA) to discover the unobserved latent traits (factors) among pattern-clusters. CT images at total lung capacity (TLC) and residual volume (RV) of 541 former smokers and 59 healthy non-smokers from the cohort of the SubPopulations and Intermediate Outcome Measures in the COPD Study (SPIROMICS) were analyzed. TLC and RV images were registered to calculate the Jacobian (determinant) values for all the voxels in TLC images. 3D Regions of interest (ROIs) with two data channels of CT intensity and Jacobian value were randomly extracted from training images and were fed to the 3D CAE-FC model. 80 pattern-clusters and 7 factors were identified. Factor scores computed for individual subjects were able to predict spirometry-measured pulmonary functions. Two factors which correlated with various emphysema subtypes, parametric response mapping (PRM) metrics, airway variants, and airway tree to lung volume ratio were discriminants of patients across all severity stages. Our findings suggest the potential of developing factor-based surrogate markers for new COPD phenotypes.

The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study

BACKGROUND

Accurate prediction of exacerbation risk enables personalised care for patients with chronic obstructive pulmonary disease (COPD). We developed and validated a generalisable model to predict individualised rate and severity of COPD exacerbations.

METHODS

In this risk modelling study, we pooled data from three COPD trials on patients with a history of exacerbations. We developed a mixed-effect model to predict exacerbations over 1 year. Severe exacerbations were those requiring inpatient care. Predictors were history of exacerbations, age, sex, body-mass index, smoking status, domiciliary oxygen therapy, lung function, symptom burden, and current medication use. Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE), a multicentre cohort study, was used for external validation.

RESULTS

The development dataset included 2380 patients, 1373 (58%) of whom were men. Mean age was 64·7 years (SD 8·8). Mean exacerbation rate was 1·42 events per year and 0·29 events per year were severe. When validated against all patients with COPD in ECLIPSE (mean exacerbation rate was 1·20 events per year, 0·27 events per year were severe), the area-under-curve (AUC) was 0·81 (95% CI 0·79-0·83) for at least two exacerbations and 0·77 (95% CI 0·74-0·80) for at least one severe exacerbation. Predicted exacerbation and observed exacerbation rates were similar (1·31 events per year for all exacerbations and 0·25 events per year for severe exacerbations vs 1·20 events per year and 0·27 events per year). In ECLIPSE, in patients with previous exacerbation history (mean exacerbation rate was 1·82 events per year, 0·40 events per year were severe), AUC was 0·73 (95% CI 0·70-0·76) for two or more exacerbations and 0·74 (95% CI 0·70-0·78) for at least one severe exacerbation. Calibration was accurate for severe exacerbations (predicted 0·37 events per year vs observed 0·40 events per year) and all exacerbations (predicted 1·80 events per year vs observed 1·82 events per year).

INTERPRETATION

This model can be used as a decision tool to personalise COPD treatment and prevent exacerbations.

FUNDING

Canadian Institutes of Health Research.

The COPD-readmission (CORE) score: A novel prediction model for one-year chronic obstructive pulmonary disease readmissions

BACKGROUND

Acute exacerbation of chronic obstructive pulmonary disease (COPD) results in deterioration of lung function and mortality. Previous prediction models have been designed for severe exacerbation of COPD, leading to readmission. However, these models lacked newly established predictors such as the eosinophil count. The present study developed a novel CO PD-re admission (CORE) score.

METHODS

We retrospectively reviewed medical records of patients visiting Taipei Tzu Chi Hospital between January 1, 2014, and May 31, 2017. We analyzed all covariates by univariate and then multivariate logistic regressions. Numeric or ordinal variables showing statistical significance were transformed into dichotomous variables by cut-off values determined by the Youden Index. The CORE score was designed to predict one-year readmission rates.

RESULTS

A total of 625 patients were recruited. After analysis, the CORE score included five predictors (eosinophil count, lung function, triple inhaler therapy, previous hospitalization, and neuromuscular disease). We observed a highly linear relationship between the CORE score and COPD readmission (R = 0.981; R ² = 0.963; P < 0.001). The CORE score had a higher predictive accuracy than that for hospitalization in the previous year (area under the curve = 0.703 vs. 0.619; P < 0.001). Patients with higher CORE scores had a shorter time to first COPD readmission (P < 0.001). Using the zero point as a reference, the hazard ratios for each score from 1 to 4 were 1.209, 2.211, 3.359, and 4.510, respectively.

CONCLUSION

The CORE score includes two novel predictors (eosinophil count and triple inhaler therapy). The model has a high predictive power for one-year COPD readmission.

The Construction of Primary Screening Model and Discriminant Model for Chronic Obstructive Pulmonary Disease in Northeast China

Objective

The diagnosis of chronic obstructive pulmonary disease (COPD) is challenging, especially in the primary institution which lacks spirometer. To reduce the rate of COPD missed diagnoses in Northeast China, which has a higher prevalence of COPD, this study aimed to establish efficient primary screening and discriminant models of COPD in this region.

Patients and Methods

Subjects from Northeast China were enrolled from December 2017 to April 2019 from The First Hospital of China Medical University. Pulmonary function tests and questionnaire were given to all participants. Using illness or no illness as the goal for screening models and disease severity as the goal for discriminant models, multivariate linear regression, logical regression, linear discriminant analysis, K-nearest neighbor, decision tree and support vector machine were constructed through R language and Python software. After comparing effectiveness among them, the most optimal primary screening and discriminant models were established.

Results

Enrolled were 232 COPD patients (124 GOLD I–II and 108 GOLD III–IV) and 218 normal controls. Eight primary screening models were established. The optimal model was Y = −1.2562–0.3891X₄ (education level) + 1.7996X₅ (dyspnea) + 0.5102X₆ (cooking fuel grade) + 1.498X₇ (smoking index) + 0.8077X₉ (family history)-0.5552X₁₁ (BMI) + 0.538X₁₃ (cough with sputum) + 2.0328X₁₄ (wheezing) + 1.3378X₁₆ (farmers) + 0.8187X₁₇ (mother’s smoking exposure history during pregnancy)-0.389X₁₈ (kitchen ventilation) + 0.6888X₁₉ (childhood heating). Six discriminant models were established. The optimal model was decision tree (the optimal variables: dyspnea (x₅), cooking fuel grade (x₆), second-hand smoking index (x₈), BMI (x₁₁), cough (x₁₂), cough with sputum (x₁₃), wheezing (x₁₄), farmer (x₁₆), kitchen ventilation (x₁₈), and childhood heating (x₁₉)). The code was established to combine the discriminant model with computer technology.

Conclusion

Many factors were related to COPD in Northeast China. Stepwise logistic regression and decision tree were the optimal screening and discriminant models for COPD in this region.

Risk factors for all-cause hospital readmission following exacerbation of COPD: a systematic review and meta-analysis

BACKGROUND

Readmission rates following hospitalisation for COPD exacerbations are unacceptably high, and the contributing factors are poorly understood. Our objective was to summarise and evaluate the factors associated with 30- and 90-day all-cause readmission following hospitalisation for an exacerbation of COPD.

METHODS

We systematically searched electronic databases from inception to 5 November 2019. Data were extracted by two independent authors in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Study quality was assessed using a modified version of the Newcastle-Ottawa Scale. We synthesised a narrative from eligible studies and conducted a meta-analysis where this was possible using a random-effects model.

RESULTS

In total, 3533 abstracts were screened and 208 full-text manuscripts were reviewed. A total of 32 papers met the inclusion criteria, and 14 studies were included in the meta-analysis. The readmission rate ranged from 8.8-26.0% at 30 days and from 17.5-39.0% at 90 days. Our narrative synthesis showed that comorbidities, previous exacerbations and hospitalisations, and increased length of initial hospital stay were the major risk factors for readmission at 30 and 90 days. Pooled adjusted odds ratios (95% confidence intervals) revealed that heart failure (1.29 (1.22-1.37)), renal failure (1.26 (1.19-1.33)), depression (1.19 (1.05-1.34)) and alcohol use (1.11 (1.07-1.16)) were all associated with an increased risk of 30-day all-cause readmission, whereas being female was a protective factor (0.91 (0.88-0.94)).

CONCLUSIONS

Comorbidities, previous exacerbations and hospitalisation, and increased length of stay were significant risk factors for 30- and 90-day all-cause readmission after an index hospitalisation with an exacerbation of COPD.

Infectious Disease and Primary Care Research-What English General Practitioners Say They Need

Background: Infections are one of the most common reasons for patients attending primary care. Antimicrobial resistance (AMR) is perhaps one of the biggest threats to modern medicine; data show that 81% of antibiotics in the UK are prescribed in primary care. Aim: To identify where the perceived gaps in knowledge, skills, guidance and research around infections and antibiotic use lie from the general practitioner (GP) viewpoint. Design and Setting: An online questionnaire survey. Method: The survey, based on questions asked of Royal College of General Practitioners (RCGP) members in 1999, and covering letter were electronically sent to GPs between May and August 2017 via various primary care dissemination routes. Results: Four hundred and twenty-eight GPs responded. Suspected Infection in the elderly, recurrent urinary tract infection (UTI), surveillance of AMR in the community, leg ulcers, persistent cough and cellulitis all fell into the top six conditions ranked in order of importance that require further research, evidence and guidance. Acute sore throat, otitis media and sinusitis were of lower importance than in 1999. Conclusion: This survey will help the NHS, the UK National Institute for Health and Care Excellence (NICE) and researchers to prioritise for the development of guidance and research for chronic conditions highlighted for which there is little evidence base for diagnostic and management guidelines in primary care. In contrast, 20 years of investment into research, guidance and resources for acute respiratory infections have successfully reduced these as priority areas for GPs.

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.

CODEXS: A New Multidimensional Index to Better Predict Frequent COPD Exacerbators with Inclusion of Depression Score

Purpose

Depression is reported in association with chronic obstructive pulmonary disease (COPD). However, to date, no multidimensional indices have taken depression into consideration to predict COPD patients’ prognosis. This study aimed to determine whether a new multidimensional index named CODEXS, based on comorbidities, airflow obstruction, dyspnea, previous exacerbation and depression assessed by Self-Rating Depression Scale (SDS), could predict 1-year exacerbations.

Methods

This was a prospective study, patients with stable COPD were used to develop CODEXS at the first visit, and followed up in the 3rd, 6th, and 12th months. After the last visit, patients were divided into frequent and infrequent exacerbators. Another cohort of COPD patients was used for validation. The SDS scoring system in the multidimensional indices ranged from 0 to 4 based on the modified SDS value, representing no depression (25–39 [0], 40–49 [1]), mild depression (50–59), moderate depression (60–69), and severe depression (≥70). Comorbidity, dyspnea, airflow obstruction, and severe exacerbations were calculated according to CODEX thresholds.

Results

Two sets of 105 and 107 patients were recruited in the development and validation cohorts, respectively. Depression was demonstrated as an independent risk factor for frequent exacerbators (odds ratio (OR)= 1.14, 95% confidence interval (CI) = 1.06–1.23, P < 0.001). The prevalence of depression in frequent exacerbators (35.09%) was higher than that in infrequent exacerbators. CODEXS was significantly associated with exacerbation (OR =2.91; 95% CI, 1.89–4.48, p<0.001). Receiver operating characteristic (ROC) curve comparison showed that CODEXS was superior to BODEX(BMI, airflow obstruction, dyspnea, previous exacerbation), BODE (BMI, airflow obstruction, dyspnea, exercise), and updated ADO (age, dyspnea, and airflow obstruction) indices, confirmed by the validation cohort with sensitivity at 85.94% and specificity at 76.74%.

Conclusion

Depression is an independent risk factor for COPD exacerbation. CODEXS is a useful predictor for predicting frequent exacerbators within 1 year and is superior to other previously published indices.