Commonly Used Patient-Reported Outcomes Do Not Improve Prediction of COPD Exacerbations

Towards precision in defining COPD exacerbations

COPD is the most prevalent chronic respiratory disease worldwide and a major cause of disability and death. Acute exacerbations of COPD remain a key feature of the disease in many patients and research assessing interventions to prevent and treat them requires a robust definition with high sensitivity and specificity. To date, no such definition exists, and multiple different definitions are used in clinical studies depending on the research question. The strengths and weaknesses of current definitions are discussed in the context of evolving knowledge and different settings in which studies are undertaken. Whether identification and recording of exacerbations remains essentially clinical, or can be identified with a dependable biomarker, it should be sensitive and adaptable to context while retaining clarity and facilitating data collection. This is essential to progress a better understanding of the pathophysiology and phenotypic expression of exacerbations to reduce their impact and personal burden for patients.

COPD exacerbations carry high risk for long-term disability and death. As the search for a standardised measure continues, study investigators must ensure definitions are explicit and justified to better understand how to prevent and manage these episodes.https://bit.ly/2UNqScy

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.

Machine Learning Algorithms Utilizing Functional Respiratory Imaging May Predict COPD Exacerbations

RATIONALE AND OBJECTIVES

Acute chronic obstructive pulmonary disease exacerbations (AECOPD) have a significant negative impact on the quality of life and accelerate progression of the disease. Functional respiratory imaging (FRI) has the potential to better characterize this disease. The purpose of this study was to identify FRI parameters specific to AECOPD and assess their ability to predict future AECOPD, by use of machine learning algorithms, enabling a better understanding and quantification of disease manifestation and progression.

MATERIALS AND METHODS

A multicenter cohort of 62 patients with COPD was analyzed. FRI obtained from baseline high resolution CT data (unenhanced and volume gated), clinical, and pulmonary function test were analyzed and incorporated into machine learning algorithms.

RESULTS

A total of 11 baseline FRI parameters could significantly distinguish ( p < 0.05) the development of AECOPD from a stable period. In contrast, no baseline clinical or pulmonary function test parameters allowed significant classification. Furthermore, using Support Vector Machines, an accuracy of 80.65% and positive predictive value of 82.35% could be obtained by combining baseline FRI features such as total specific image-based airway volume and total specific image-based airway resistance, measured at functional residual capacity. Patients who developed an AECOPD, showed significantly smaller airway volumes and (hence) significantly higher airway resistances at baseline.

CONCLUSION

This study indicates that FRI is a sensitive tool (PPV 82.35%) for predicting future AECOPD on a patient specific level in contrast to classical clinical parameters.

Validation of ACCESS: an automated tool to support self-management of COPD exacerbations

Background

To support patients with COPD in their self-management of symptom worsening, we developed Adaptive Computerized COPD Exacerbation Self-management Support (ACCESS), an innovative software application that provides automated treatment advice without the interference of a health care professional. Exacerbation detection is based on 12 symptom-related yes-or-no questions and the measurement of peripheral capillary oxygen saturation (SpO₂), forced expiratory volume in one second (FEV₁), and body temperature. Automated treatment advice is based on a decision model built by clinical expert panel opinion and Bayesian network modeling. The current paper describes the validity of ACCESS.

Methods

We performed secondary analyses on data from a 3-month prospective observational study in which patients with COPD registered respiratory symptoms daily on diary cards and measured SpO₂, FEV₁, and body temperature. We examined the validity of the most important treatment advice of ACCESS, ie, to contact the health care professional, against symptom- and event-based exacerbations.

Results

Fifty-four patients completed 2,928 diary cards. One or more of the different pieces of ACCESS advice were provided in 71.7% of all cases. We identified 115 symptom-based exacerbations. Cross-tabulation showed a sensitivity of 97.4% (95% CI 92.0-99.3), specificity of 65.6% (95% CI 63.5-67.6), and positive and negative predictive value of 13.4% (95% CI 11.2-15.9) and 99.8% (95% CI 99.3-99.9), respectively, for ACCESS' advice to contact a health care professional in case of an exacerbation.

Conclusion

In many cases (71.7%), ACCESS gave at least one self-management advice to lower symptom burden, showing that ACCES provides self-management support for both day-to-day symptom variations and exacerbations. High sensitivity shows that if there is an exacerbation, ACCESS will advise patients to contact a health care professional. The high negative predictive value leads us to conclude that when ACCES does not provide the advice to contact a health care professional, the risk of an exacerbation is very low. Thus, ACCESS can safely be used in patients with COPD to support self-management in case of an exacerbation.

Life-Space mobility and clinical outcomes in COPD

Background

Social isolation is a common experience in patients with COPD but is not captured by existing patient-reported outcomes, and its association with clinical outcomes is unknown.

Methods

We prospectively enrolled adults with stable COPD who completed the University of Alabama at Birmingham Life Space Assessment (LSA) (range: 0–120, restricted Life-Space mobility: ≤60 and a marker of social isolation in older adults); six-minute walk test (6MWT), and the University of California at San Diego Shortness of Breath Questionnaire, COPD Assessment Test, and Hospital Anxiety and Depression Scale. The occurrence of severe exacerbations (emergency room visit or hospitalization) was recorded by review of the electronic record up to 1 year after enrollment. We determined associations between Life-Space mobility and clinical outcomes using regression analyses.

Results

Fifty subjects had a mean ± SD %-predicted FEV₁ of 42.9±15.5, and 23 (46%) had restricted Life-Space mobility. After adjusting for age, gender, %-predicted FEV₁, comorbidity count, inhaled corticosteroid/long-acting beta₂-agonist use, and prior cardiopulmonary rehabilitation, subjects with restricted Life-Space had an increased risk for severe exacerbations (adjusted incidence rate ratio 4.65, 95% CI 1.19–18.23, P=0.03). LSA scores were associated with 6MWD (R=0.50, P<0.001), dyspnea (R=−0.58, P<0.001), quality of life (R=−0.34, P=0.02), and depressive symptoms (R=−0.39, P=0.005).

Conclusion

Restricted Life-Space mobility predicts severe exacerbations and is associated with reduced exercise tolerance, more severe dyspnea, reduced quality of life, and greater depressive symptoms.

The dyspnoea-inactivity vicious circle in COPD: development and external validation of a conceptual model

The vicious circle of dyspnoea-inactivity has been proposed, but never validated empirically, to explain the clinical course of chronic obstructive pulmonary disease (COPD). We aimed to develop and validate externally a comprehensive vicious circle model.We utilised two methods. 1) Identification and validation of all published vicious circle models by a systematic literature search and fitting structural equation models to longitudinal data from the Spanish PAC-COPD (Phenotype and Course of COPD) cohort (n=210, mean age 68 years, mean forced expiratory volume in 1 s (FEV1) 54% predicted), testing both the hypothesised relationships between variables in the model ("paths") and model fit. 2) Development of a new model and external validation using longitudinal data from the Swiss and Dutch ICE COLD ERIC (International Collaborative Effort on Chronic Obstructive Lung Disease: Exacerbation Risk Index Cohorts) cohort (n=226, mean age 66 years, mean FEV1 57% predicted).We identified nine vicious circle models for which structural equation models confirmed most hypothesised paths but showed inappropriate fit. In the new model, airflow limitation, hyperinflation, dyspnoea, physical activity, exercise capacity and COPD exacerbations remained related to other variables and model fit was appropriate. Fitting it to ICE COLD ERIC, all paths were replicated and model fit was appropriate.Previously published vicious circle models do not fully explain the vicious circle concept. We developed and externally validated a new comprehensive model that gives a more relevant role to exercise capacity and COPD exacerbations.

Including changes in dyspnea after inpatient rehabilitation improves prediction models of exacerbations in COPD

BACKGROUND

Reducing the probability of future exacerbations is one of the main goals of pulmonary rehabilitation (PR) in COPD. Recent studies identified predictors of future exacerbations. However, PR might alter both predictors and number of exacerbations.

OBJECTIVES

This secondary analysis examined which predictors assessed at both the beginning and the end of PR predict the risk of moderate (i.e. use of cortisone and/or antibiotics) and severe (hospitalization) exacerbations in the year after PR.

METHODS

A total of n = 383 COPD patients (34.7% female, mean age = 57.8 years (SD = 7.1), mean FEV1%pred = 51.0 (SD = 14.9)) who attended a 3-week inpatient PR were included. Number of moderate and severe exacerbations were assessed one year after PR (T2) via questionnaires. Potential predictors were assessed at the beginning (T0) and the end (T1) of PR. Negative binomial regression models were used.

RESULTS

The mean numbers of severe (M_s)/moderate (M_m) exacerbations in the year after PR (M_s,t2 = 0.19; M_{m, t2} = 1.07) was reduced compared to the numbers of exacerbations in the year before PR (M_s,t1 = 0.50, p < 0.001; M_m,t1 = 1.21, p = 0.051). Previous exacerbations, retirement, change in dyspnea (for severe exacerbations) and dyspnea at T1 (for moderate exacerbations) were identified as significant predictors.

CONCLUSIONS

PR might alter associations between predictors and future exacerbations. Dyspnea at the end of PR or change in dyspnea are better predictors than dyspnea at the beginning of PR.

Lung volume indices predict morbidity in smokers with preserved spirometry

BACKGROUND

Abnormal lung volumes that reflect air trapping are common in COPD. However, their significance in smokers with preserved spirometry (normal FEV₁ to FVC ratio) is unclear.

METHODS

Using the Veterans Administration Informatics and Computing Infrastructure database, we identified 7479 patients at risk for COPD (ever smokers >40 years of age without restrictive lung disease) who had preserved spirometry and concomitant lung volume measurements, and examined their subsequent health records for clinical diagnoses of COPD, healthcare utilisation, follow-up spirometry and mortality.

RESULTS

Air trapping was prevalent, with 31% of patients having residual volume to total lung capacity ratio (RV:TLC) greater than the upper limit of normal (ULN). RV:TLC varied widely from 14% to 77% (51% to 204% of predicted) across the normal ranges of FEV₁:FVC and FEV₁. Patients with RV:TLC greater than the ULN were more likely to receive subsequent clinical diagnoses of COPD (HR (95% CI)=1.55 (1.42 to 1.70), p<0.001) and had higher all-cause mortality (HR (95% CI)=1.41 (1.29 to 1.54), p<0.001). They had higher rates of respiratory medication prescriptions and hospital and intensive care unit admissions. Other air trapping and static hyperinflation indices showed similar associations with health outcomes. Additionally, high-normal RV:TLC was associated with intermediate adverse health outcomes compared with low-normal and abnormal RV:TLC. Abnormal RV:TLC predicted higher likelihood of progression to spirometric COPD (OR (95% CI)=1.30 (1.03 to 1.65), p=0.027).

CONCLUSION

In this study of the Veterans Affairs electronic health records, air trapping was common in smokers with preserved spirometry and predicted adverse respiratory outcomes and progression to overt COPD.