Predicting frequent COPD exacerbations using primary care data

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

doi:10.2147/COPD.S94259

Predicting frequent COPD exacerbations using primary care data

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

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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.

Original language	English
Pages (from-to)	2439-2450
Number of pages	12
Journal	International journal of chronic obstructive pulmonary disease
Volume	10
Issue number	1
DOIs	https://doi.org/10.2147/COPD.S94259
Publication status	Published - 9 Nov 2015

Bibliographical note

This study was funded by an unrestricted grant from the Respiratory Effectiveness Group (REG; www.effectivenessevaluation.org). Access to data from the Optimum Patient Care Research Database was co-funded by Research in Real-Life Ltd (RiRL, Cambridge, UK). The authors would like to thank Dr John Bukowski of WordsWorld Consulting for editorial assistance drafting this manuscript. Additional editorial support was provided by Elizabeth V Hillyer, DVM, funded by RiRL.

Keywords

prediction
risk factor
model
validation
FEV1

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10.2147/COPD.S94259Licence: CC BY-NC

Predicting frequent COPD exacerbations using primary care data
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Cite this

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title = "Predicting frequent COPD exacerbations using primary care data",

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. ",

keywords = "prediction, risk factor, model, validation, FEV1",

author = "Marjan Kerkhof and Daryl Freeman and Rupert Jones and Alison Chisholm and Price, {David B}",

note = "This study was funded by an unrestricted grant from the Respiratory Effectiveness Group (REG; www.effectivenessevaluation.org). Access to data from the Optimum Patient Care Research Database was co-funded by Research in Real-Life Ltd (RiRL, Cambridge, UK). The authors would like to thank Dr John Bukowski of WordsWorld Consulting for editorial assistance drafting this manuscript. Additional editorial support was provided by Elizabeth V Hillyer, DVM, funded by RiRL. ",

year = "2015",

month = nov,

day = "9",

doi = "10.2147/COPD.S94259",

language = "English",

volume = "10",

pages = "2439--2450",

journal = "International journal of chronic obstructive pulmonary disease ",

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T1 - Predicting frequent COPD exacerbations using primary care data

AU - Kerkhof, Marjan

AU - Freeman, Daryl

AU - Jones, Rupert

AU - Chisholm, Alison

AU - Price, David B

N1 - This study was funded by an unrestricted grant from the Respiratory Effectiveness Group (REG; www.effectivenessevaluation.org). Access to data from the Optimum Patient Care Research Database was co-funded by Research in Real-Life Ltd (RiRL, Cambridge, UK). The authors would like to thank Dr John Bukowski of WordsWorld Consulting for editorial assistance drafting this manuscript. Additional editorial support was provided by Elizabeth V Hillyer, DVM, funded by RiRL.

PY - 2015/11/9

Y1 - 2015/11/9

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.

AB - 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.

KW - prediction

KW - risk factor

KW - model

KW - validation

KW - FEV1

U2 - 10.2147/COPD.S94259

DO - 10.2147/COPD.S94259

M3 - Article

SN - 1176-9106

VL - 10

SP - 2439

EP - 2450

JO - International journal of chronic obstructive pulmonary disease

JF - International journal of chronic obstructive pulmonary disease

IS - 1

ER -

Predicting frequent COPD exacerbations using primary care data

Abstract

Bibliographical note

Keywords

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