Development and Validation of a Predictive Model to Identify Individuals Likely to Have Undiagnosed Chronic Obstructive Pulmonary Disease Using an Administrative Claims Database

Moretz, Chad; Zhou, Yunping; Dhamane, Amol; Burslem, Kate; Saverno, Kim; Jain, Gaurav; Devercelli, Giovanna; Kaila, Shuchita; Ellis, Jeffrey J.; Hernandez, Gemzel; Renda, Andrew

doi:10.18553/jmcp.2015.21.12.1149

Cited by 9 publications

(14 citation statements)

References 28 publications

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“…Some studies have developed predictive models to identify patients with undiagnosed COPD using administrative claims. 12 , 13 A COPD-predictive model described by Mapel et al 12 had a PPV of 23% and NPV of 95.4%, while a model developed by Moretz et al 13 had a PPV of 73% and NPV of 66%. The current model had a PPV of 48.1%, between values reported by Mapel et al and Moretz et al, suggesting that approximately one in two patients identified will have a severe COPD exacerbation.…”

Section: Discussionmentioning

confidence: 99%

“…1 Predictive models to identify individuals likely to have COPD have been developed previously. 12 , 13 Similarly, observational and retrospective claim-based studies have attempted to identify factors associated with a risk of future exacerbations. These studies suggest that patients with a history of one or more exacerbations leading to hospitalizations have a high risk of future exacerbations.…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of a predictive model to identify patients at risk of severe COPD exacerbations using administrative claims data

Annavarapu¹,

Goldfarb²,

Gelb³

et al. 2018

COPD

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BackgroundPatients with COPD often experience severe exacerbations involving hospitalization, which accelerate lung function decline and reduce quality of life. This study aimed to develop and validate a predictive model to identify patients at risk of developing severe COPD exacerbations using administrative claims data, to facilitate appropriate disease management programs.MethodsA predictive model was developed using a retrospective cohort of COPD patients aged 55–89 years identified between July 1, 2010 and June 30, 2013 using Humana’s claims data. The baseline period was 12 months postdiagnosis, and the prediction period covered months 12–24. Patients with and without severe exacerbations in the prediction period were compared to identify characteristics associated with severe COPD exacerbations. Models were developed using stepwise logistic regression, and a final model was chosen to optimize sensitivity, specificity, positive predictive value (PPV), and negative PV (NPV).ResultsOf 45,722 patients, 5,317 had severe exacerbations in the prediction period. Patients with severe exacerbations had significantly higher comorbidity burden, use of respiratory medications, and tobacco-cessation counseling compared to those without severe exacerbations in the baseline period. The predictive model included 29 variables that were significantly associated with severe exacerbations. The strongest predictors were prior severe exacerbations and higher Deyo–Charlson comorbidity score (OR 1.50 and 1.47, respectively). The best-performing predictive model had an area under the curve of 0.77. A receiver operating characteristic cutoff of 0.4 was chosen to optimize PPV, and the model had sensitivity of 17%, specificity of 98%, PPV of 48%, and NPV of 90%.ConclusionThis study found that of every two patients identified by the predictive model to be at risk of severe exacerbation, one patient may have a severe exacerbation. Once at-risk patients are identified, appropriate maintenance medication, implementation of disease-management programs, and education may prevent future exacerbations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and validation of a predictive model to identify patients at risk of severe COPD exacerbations using administrative claims data

Annavarapu¹,

Goldfarb²,

Gelb³

et al. 2018

COPD

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“…A predictive model was developed by Moretz et al using Humana’s administrative claims database for identifying patients likely to have undiagnosed COPD 10. This claims database includes members enrolled in commercial or Medicare Advantage plans with prescription drug benefit (MAPD) plans.…”

Section: Introductionmentioning

confidence: 99%

<p>Spirometry evaluation to assess performance of a claims-based predictive model identifying patients with undiagnosed COPD</p>

Moretz¹,

Annavarapu

Luthra

et al. 2019

COPD

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Background A claims-based model to predict patients likely to have undiagnosed COPD was developed by Moretz et al in 2015. This study aims to assess the performance of the aforementioned model using prospectively collected spirometry data. Methods A study population aged 40–89 years enrolled in a Medicare Advantage plan with prescription drug coverage or commercial health plan and without a claim for COPD diagnosis was identified from April 1, 2012 to March 31, 2016 in the Humana claims database. This population was stratified into subjects likely or unlikely to have undiagnosed COPD using the claims-based predictive model. Subjects were randomly selected for spirometry evaluation of FEV 1 and FVC. The predictive model was validated using airflow limitation ratio (FEV 1 /FVC <0.70). Results A total of 218 subjects classified by the predictive model as likely and 331 not likely to have undiagnosed COPD completed spirometry evaluation. Those predicted to have undiagnosed COPD had a higher mean age (70.2 vs 67.9 years, P =0.0012) and a lower mean FEV 1 /FVC ratio (0.724 vs 0.753, P =0.0002) compared to those predicted not to have undiagnosed COPD. Performance metrics for the predictive model were: area under the curve =0.61, sensitivity =52.5%, specificity =64.6%, positive predictive value =33.5%, and negative predictive value =80.1%. Conclusion The claims-based predictive model identifies those not at risk of having COPD eight out of ten times, and those who are likely to have COPD one out of three times.

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“…Abnormal breathing sounds (such as crackles, rhonchus, and wheezing sounds) are usually considered as indicator symptoms in chronic respiratory diseases [ 1 ], such as chronic obstructive pulmonary disease (COPD), chronic bronchitis, and bronchial asthma, etc. For these diseases, unnecessary secretions (such as sputum) would be produced in the respiratory tract and then cause chronic inflammation leading to airway obstruction.…”

Section: Introductionmentioning

confidence: 99%

Design of Wearable Breathing Sound Monitoring System for Real-Time Wheeze Detection

Lin

Tsai

et al. 2017

Sensors

104

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In the clinic, the wheezing sound is usually considered as an indicator symptom to reflect the degree of airway obstruction. The auscultation approach is the most common way to diagnose wheezing sounds, but it subjectively depends on the experience of the physician. Several previous studies attempted to extract the features of breathing sounds to detect wheezing sounds automatically. However, there is still a lack of suitable monitoring systems for real-time wheeze detection in daily life. In this study, a wearable and wireless breathing sound monitoring system for real-time wheeze detection was proposed. Moreover, a breathing sounds analysis algorithm was designed to continuously extract and analyze the features of breathing sounds to provide the objectively quantitative information of breathing sounds to professional physicians. Here, normalized spectral integration (NSI) was also designed and applied in wheeze detection. The proposed algorithm required only short-term data of breathing sounds and lower computational complexity to perform real-time wheeze detection, and is suitable to be implemented in a commercial portable device, which contains relatively low computing power and memory. From the experimental results, the proposed system could provide good performance on wheeze detection exactly and might be a useful assisting tool for analysis of breathing sounds in clinical diagnosis.

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Development and Validation of a Predictive Model to Identify Individuals Likely to Have Undiagnosed Chronic Obstructive Pulmonary Disease Using an Administrative Claims Database

Cited by 9 publications

References 28 publications

Development and validation of a predictive model to identify patients at risk of severe COPD exacerbations using administrative claims data

Development and validation of a predictive model to identify patients at risk of severe COPD exacerbations using administrative claims data

<p>Spirometry evaluation to assess performance of a claims-based predictive model identifying patients with undiagnosed COPD</p>

Design of Wearable Breathing Sound Monitoring System for Real-Time Wheeze Detection

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