Prediction models for exacerbations in patients with COPD

Guerra, Beniamino; Gaveikaite, Violeta; Bianchi, Carlo; Puhan, Milo A.

doi:10.1183/16000617.0061-2016

Cited by 86 publications

(65 citation statements)

References 70 publications

(75 reference statements)

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“…When the availability of predictors and practical applicability were also taken into account, none of the models were deemed ready for clinical implementation. 6 We are aware of only two additional prediction models published after this review -by Kerkhof 20 Annavarapu did not report calibration at all, and overall, both models suffered from a lack of generalisability given the local nature of the data that were available to the investigators.…”

Section: Discussionmentioning

confidence: 99%

“…This is in contrast to the logistic regression models used in a majority of previous clinical prediction models, which allow prediction probabilities of having at least one exacerbation in a single time window. 6 Further, this framework can potentially be used for prognostic enrichment of randomised trials by identifying patients who are more likely to exacerbate. Similar to asthma trials, the required sample size and consequently the cost of large trials can be substantially reduced by using prediction models to recruit patients above a certain threshold of expected exacerbation rate.…”

Section: Discussionmentioning

confidence: 99%

“…5 A 2017 systematic review by Guerra and colleagues identified 27 prediction tools for COPD exacerbations. 6 Among these, only two reported on the validation of the model and none were deemed ready for personalised COPD management in the clinic. 6 Here, we describe a new model, the Acute COPD Exacerbation Prediction Tool (ACCEPT), to predict, at an individual level, the rate and severity of COPD exacerbation and report on its performance in an independent external cohort, and explain, using case studies, its potential clinical application.…”

Section: Background and Objectivesmentioning

confidence: 99%

“…6 Among these, only two reported on the validation of the model and none were deemed ready for personalised COPD management in the clinic. 6 Here, we describe a new model, the Acute COPD Exacerbation Prediction Tool (ACCEPT), to predict, at an individual level, the rate and severity of COPD exacerbation and report on its performance in an independent external cohort, and explain, using case studies, its potential clinical application. As a decision tool, ACCEPT provides a personalised risk profile that allows clinicians to tailor treatment regimens to the individual needs of the patients.…”

Section: Background and Objectivesmentioning

confidence: 99%

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The Acute COPD Exacerbation Prediction Tool (ACCEPT): development and external validation study of a personalised prediction model

Adibi

Sin

Safari

et al. 2019

Preprint

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Background: Accurate prediction of exacerbation risk enables personalised chronic obstructive pulmonary disease (COPD) care. We developed and validated a generalisable model to predict the individualised rate and severity of COPD exacerbations. Methods:We pooled data from three COPD trials on patients with a history of exacerbations.We developed a mixed-effect model to predict exacerbations over one-year. Severe exacerbations were those requiring inpatient care. Predictors were a history of exacerbations, age, sex, body mass index, smoking status, domiciliary oxygen therapy, lung function, symptom burden, and current medication use. ECLIPSE, a multicentre cohort study, was used for external validation. Results: The development dataset included 2,380 patients (mean 64·7 years, 1373 [57·7%] men, mean exacerbation rate 1·42/year, 0·29/year [20·5%] severe). When validated against all COPD patients in ECLIPSE (n=1819, mean 63·3 years, 1186 [65·2%] men, mean exacerbation rate 1·20/year, 0·27/year [22·2%] severe), the area-under-curve was 0·81 (95%CI 0·79-0·83) for ≥2 exacerbations and 0·77 (95%CI 0·74-0·80) for ≥1 severe exacerbation. Predicted rates were 0·25/year for severe and 1·31/year for all exacerbations, close to the observed rates (0·27/year and 1·20/year, respectively). In ECLIPSE patients with a prior exacerbation history (n=996, mean 63·6 years, 611 (61·3%) men, mean exacerbation rate 1·82/year, 0·40/year [22·0%] severe), the area-under-curve was 0·73 (95%CI 0·70-0·76) for ≥2 exacerbations and 0·74 (95%CI 0·70-0·78) for ≥1 severe exacerbation. Calibration was accurate for severe exacerbations (predicted=0·37/year, observed=0·40/year) and all exacerbations (predicted=1·80/year, observed=1·82/year). The model is accessible at http://resp.core.ubc.ca/ipress/accept. Interpretation: This model can be used as a decision tool to personalise COPD treatment and prevent exacerbations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Background and Objectivesmentioning

confidence: 99%

Section: Background and Objectivesmentioning

confidence: 99%

See 2 more Smart Citations

The Acute COPD Exacerbation Prediction Tool (ACCEPT): development and external validation study of a personalised prediction model

Adibi

Sin

Safari

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…It was not externally validated in other independent populations, therefore, its clinical applicability was uncertain. A meta-analysis was also accomplished to appraise existing prediction models predicting exacerbations in COPD patients, 20 which disappointingly drew the conclusion that most of the prediction models were at high risk of bias and none could be employed in clinical application currently.…”

Section: Introductionmentioning

confidence: 99%

<p>A Nomogram for Predicting Severe Exacerbations in Stable COPD Patients</p>

Chen¹,

Wang²,

Hu³

et al. 2020

COPD

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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, postbronchodilator FEV 1 % 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.

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Digital health in chronic obstructive pulmonary disease

Long

Chen

2023

Chronic Diseases and Translational Medicine

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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. K E Y W O R D SCOPD management, chronic obstructive pulmonary disease, digital health Highlights• Digital health can support the early diagnosis and management of Chronic obstructive pulmonary disease, but there are still important challenges and opportunities to be addressed.

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Prediction models for exacerbations in patients with COPD

Cited by 86 publications

References 70 publications

The Acute COPD Exacerbation Prediction Tool (ACCEPT): development and external validation study of a personalised prediction model

The Acute COPD Exacerbation Prediction Tool (ACCEPT): development and external validation study of a personalised prediction model

<p>A Nomogram for Predicting Severe Exacerbations in Stable COPD Patients</p>

Digital health in chronic obstructive pulmonary disease

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