Predicting frequent COPD exacerbations using primary care data

Marjan Kerkhof, Daryl Freeman, Rupert Jones, Alison Chisholm, David B Price

Research output: Contribution to journalArticle

35 Citations (Scopus)
4 Downloads (Pure)

Abstract

Purpose: Acute COPD exacerbations account for much of the rising disability and costs associated with COPD, but data on predictive risk factors are limited. The goal of the current study was to develop a robust, clinically based model to predict frequent exacerbation risk. Patients and methods: Patients identified from the Optimum Patient Care Research Database (OPCRD) with a diagnostic code for COPD and a forced expiratory volume in 1 second/forced vital capacity ratio <0.7 were included in this historical follow-up study if they were ≥40 years old and had data encompassing the year before (predictor year) and year after (outcome year) study index date. The data set contained potential risk factors including demographic, clinical, and comorbid variables. Following univariable analysis, predictors of two or more exacerbations were fed into a stepwise multivariable logistic regression. Sensitivity analyses were conducted for subpopulations of patients without any asthma diagnosis ever and those with questionnaire data on symptoms and smoking pack-years. The full predictive model was validated against 1 year of prospective OPCRD data. Results: The full data set contained 16,565 patients (53% male, median age 70 years), including 9,393 patients without any recorded asthma and 3,713 patients with questionnaire data. The full model retained eleven variables that significantly predicted two or more exacerbations, of which the number of exacerbations in the preceding year had the strongest association; others included height, age, forced expiratory volume in 1 second, and several comorbid conditions. Significant predictors not previously identified included eosinophilia and COPD Assessment Test score. The predictive ability of the full model (C statistic 0.751) changed little when applied to the validation data set (n=2,713; C statistic 0.735). Results of the sensitivity analyses supported the main findings. Conclusion: Patients at risk of exacerbation can be identified from routinely available, computerized primary care data. Further study is needed to validate the model in other patient populations.
Original languageEnglish
Pages (from-to)2439-2450
Number of pages12
JournalInternational journal of chronic obstructive pulmonary disease
Volume10
Issue number1
DOIs
Publication statusPublished - 9 Nov 2015

Fingerprint

Chronic Obstructive Pulmonary Disease
Primary Health Care
Forced Expiratory Volume
Patient Care
Asthma
Databases
Aptitude
Vital Capacity
Eosinophilia
Research
Logistic Models
Smoking
Demography
Outcome Assessment (Health Care)
Costs and Cost Analysis
Population
Datasets

Keywords

  • prediction
  • risk factor
  • model
  • validation
  • FEV1

Cite this

Predicting frequent COPD exacerbations using primary care data. / Kerkhof, Marjan; Freeman, Daryl; Jones, Rupert; Chisholm, Alison; Price, David B.

In: International journal of chronic obstructive pulmonary disease , Vol. 10, No. 1, 09.11.2015, p. 2439-2450.

Research output: Contribution to journalArticle

Kerkhof, Marjan ; Freeman, Daryl ; Jones, Rupert ; Chisholm, Alison ; Price, David B. / Predicting frequent COPD exacerbations using primary care data. In: International journal of chronic obstructive pulmonary disease . 2015 ; Vol. 10, No. 1. pp. 2439-2450.
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abstract = "Purpose: Acute COPD exacerbations account for much of the rising disability and costs associated with COPD, but data on predictive risk factors are limited. The goal of the current study was to develop a robust, clinically based model to predict frequent exacerbation risk. Patients and methods: Patients identified from the Optimum Patient Care Research Database (OPCRD) with a diagnostic code for COPD and a forced expiratory volume in 1 second/forced vital capacity ratio <0.7 were included in this historical follow-up study if they were ≥40 years old and had data encompassing the year before (predictor year) and year after (outcome year) study index date. The data set contained potential risk factors including demographic, clinical, and comorbid variables. Following univariable analysis, predictors of two or more exacerbations were fed into a stepwise multivariable logistic regression. Sensitivity analyses were conducted for subpopulations of patients without any asthma diagnosis ever and those with questionnaire data on symptoms and smoking pack-years. The full predictive model was validated against 1 year of prospective OPCRD data. Results: The full data set contained 16,565 patients (53{\%} male, median age 70 years), including 9,393 patients without any recorded asthma and 3,713 patients with questionnaire data. The full model retained eleven variables that significantly predicted two or more exacerbations, of which the number of exacerbations in the preceding year had the strongest association; others included height, age, forced expiratory volume in 1 second, and several comorbid conditions. Significant predictors not previously identified included eosinophilia and COPD Assessment Test score. The predictive ability of the full model (C statistic 0.751) changed little when applied to the validation data set (n=2,713; C statistic 0.735). Results of the sensitivity analyses supported the main findings. Conclusion: Patients at risk of exacerbation can be identified from routinely available, computerized primary care data. Further study is needed to validate the model in other patient populations.",
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N2 - Purpose: Acute COPD exacerbations account for much of the rising disability and costs associated with COPD, but data on predictive risk factors are limited. The goal of the current study was to develop a robust, clinically based model to predict frequent exacerbation risk. Patients and methods: Patients identified from the Optimum Patient Care Research Database (OPCRD) with a diagnostic code for COPD and a forced expiratory volume in 1 second/forced vital capacity ratio <0.7 were included in this historical follow-up study if they were ≥40 years old and had data encompassing the year before (predictor year) and year after (outcome year) study index date. The data set contained potential risk factors including demographic, clinical, and comorbid variables. Following univariable analysis, predictors of two or more exacerbations were fed into a stepwise multivariable logistic regression. Sensitivity analyses were conducted for subpopulations of patients without any asthma diagnosis ever and those with questionnaire data on symptoms and smoking pack-years. The full predictive model was validated against 1 year of prospective OPCRD data. Results: The full data set contained 16,565 patients (53% male, median age 70 years), including 9,393 patients without any recorded asthma and 3,713 patients with questionnaire data. The full model retained eleven variables that significantly predicted two or more exacerbations, of which the number of exacerbations in the preceding year had the strongest association; others included height, age, forced expiratory volume in 1 second, and several comorbid conditions. Significant predictors not previously identified included eosinophilia and COPD Assessment Test score. The predictive ability of the full model (C statistic 0.751) changed little when applied to the validation data set (n=2,713; C statistic 0.735). Results of the sensitivity analyses supported the main findings. Conclusion: Patients at risk of exacerbation can be identified from routinely available, computerized primary care data. Further study is needed to validate the model in other patient populations.

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