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

Safari, Abdollah; Adibi, Amin; Sin, Don D.; Lee, Tae Yoon; Ho, Joseph Khoa; Sadatsafavi, Mohsen

doi:10.1016/j.eclinm.2022.101574

Cited by 13 publications

(7 citation statements)

References 38 publications

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“…In external validation, the latest version of ACCEPT (version 2.0, which will be implemented in this study) had a c-statistic of 0.73 (95% CI : 0.71, 0.76) for detecting the occurrence of any moderate and severe exacerbations. In comparison, exacerbation history alone had an area under the curve (AUC) of 0.67 (95% CI : 0.64, 0.70; p < 0.001) [ 15 ]. In decision curve analysis, ACCEPT provided positive net benefit over the use of exacerbation history alone over a large range of risk thresholds [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

“…In comparison, exacerbation history alone had an area under the curve (AUC) of 0.67 (95% CI : 0.64, 0.70; p < 0.001) [ 15 ]. In decision curve analysis, ACCEPT provided positive net benefit over the use of exacerbation history alone over a large range of risk thresholds [ 15 ]. Several of ACCEPT’s predictor values will be prepopulated from the electronic patient chart, but pulmonologists will be given the option to override these values.…”

Section: Methodsmentioning

confidence: 99%

“…The IM plementing P redictive A nalytics towards efficient C OPD T reatments (IMPACT) study capitalizes on this window of opportunity to integrate a risk prediction model for COPD exacerbations into this EHR system for routine care and evaluate its impact on quality of care and patient outcomes. The risk prediction model of interest, named ACCEPT ( AC ute C OPD E xacerbation P revention T ool) [ 14 , 15 ], was designed specifically to address the shortcoming of previous studies and is considered the first COPD exacerbation clinical prediction model to be ready for clinical practice implementation [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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IMplementing Predictive Analytics towards efficient COPD Treatments (IMPACT): protocol for a stepped-wedge cluster randomized impact study

Michaux

Metcalfe²,

Burns³

et al. 2023

Diagn Progn Res

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Introduction Personalized disease management informed by quantitative risk prediction has the potential to improve patient care and outcomes. The integration of risk prediction into clinical workflow should be informed by the experiences and preferences of stakeholders, and the impact of such integration should be evaluated in prospective comparative studies. The objectives of the IMplementing Predictive Analytics towards efficient chronic obstructive pulmonary disease (COPD) treatments (IMPACT) study are to integrate an exacerbation risk prediction tool into routine care and to determine its impact on prescription appropriateness (primary outcome), medication adherence, quality of life, exacerbation rates, and sex and gender disparities in COPD care (secondary outcomes). Methods IMPACT will be conducted in two phases. Phase 1 will include the systematic and user-centered development of two decision support tools: (1) a decision tool for pulmonologists called the ACCEPT decision intervention (ADI), which combines risk prediction from the previously developed Acute COPD Exacerbation Prediction Tool with treatment algorithms recommended by the Canadian Thoracic Society’s COPD pharmacotherapy guidelines, and (2) an information pamphlet for COPD patients (patient tool), tailored to their prescribed medication, clinical needs, and lung function. In phase 2, we will conduct a stepped-wedge cluster randomized controlled trial in two outpatient respiratory clinics to evaluate the impact of the decision support tools on quality of care and patient outcomes. Clusters will be practicing pulmonologists (n ≥ 24), who will progressively switch to the intervention over 18 months. At the end of the study, a qualitative process evaluation will be carried out to determine the barriers and enablers of uptake of the tools. Discussion The IMPACT study coincides with a planned harmonization of electronic health record systems across tertiary care centers in British Columbia, Canada. The harmonization of these systems combined with IMPACT’s implementation-oriented design and partnership with stakeholders will facilitate integration of the tools into routine care, if the results of the proposed study reveal positive association with improvement in the process and outcomes of clinical care. The process evaluation at the end of the trial will inform subsequent design iterations before largescale implementation. Trial registration NCT05309356.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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IMplementing Predictive Analytics towards efficient COPD Treatments (IMPACT): protocol for a stepped-wedge cluster randomized impact study

Michaux

Metcalfe²,

Burns³

et al. 2023

Diagn Progn Res

Self Cite

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“…Subsequently, the team updated ACCEPT by incorporating individual‐level data from multiple studies and refining the ML methods used. ACCEPT 2.0 can predict the risk and severity of COPD exacerbations with high accuracy, regardless of a patient's history of exacerbations 80 . Use of EHRs in conjunction with ML presents several advantages in risk prediction for a variety of clinical conditions.…”

Section: Digital Health In Copd (Figure 1)mentioning

confidence: 99%

“…ACCEPT 2.0 can predict the risk and severity of COPD exacerbations with high accuracy, regardless of a patient's history of exacerbations. 80 Use of EHRs in conjunction with ML presents several advantages in risk prediction for a variety of clinical conditions. These include the ability to conduct risk assessments remotely, the capacity for complex and sophisticated analyses to enhance prediction accuracy, and the ability to cover a broad spectrum of clinical conditions.…”

Section: Digital Health In Copd (Figure 1 )mentioning

confidence: 99%

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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Generalizability of Risk Stratification Algorithms for Exacerbations in COPD

Safari

Adibi

et al. 2023

Chest

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

Cited by 13 publications

References 38 publications

IMplementing Predictive Analytics towards efficient COPD Treatments (IMPACT): protocol for a stepped-wedge cluster randomized impact study

IMplementing Predictive Analytics towards efficient COPD Treatments (IMPACT): protocol for a stepped-wedge cluster randomized impact study

Digital health in chronic obstructive pulmonary disease

Generalizability of Risk Stratification Algorithms for Exacerbations in COPD

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