Abstract
Rationale: Chronic obstructive pulmonary disease (COPD) is increasingly recognized as a multicomponent disease with systemic consequences and effects on quality of life. Single measures such as lung function provide a limited reflection of how the disease affects patients. Composite measures have the potential to account for many of the facets of COPD.
Objectives: To derive and validate a multicomponent assessment tool of COPD severity that is applicable to all patients and health care settings.
Methods: The index was derived using data from 375 patients with COPD in primary care. Regression analysis led to a model explaining 48% of the variance in health status as measured by the Clinical COPD Questionnaire with four components: dyspnea (D), airflow obstruction (O), smoking status (S), and exacerbation frequency (E). The DOSE Index was validated in cross-sectional and longitudinal samples in various health care settings in Holland, Japan, and the United Kingdom.
Measurements and Main Results: The DOSE Index correlated with health status in all data sets. A high DOSE Index score (≥4) was associated with a greater risk of hospital admission (odds ratio, 8.3 [4.1–17]) or respiratory failure (odds ratio, 7.8 [3.4–18.3]). The index predicted exacerbations in the subsequent year (P ≤ 0.014).
Conclusions: The DOSE Index is a simple, valid tool for assessing the severity of COPD. The index is related to a range of clinically important outcomes such as health care consumption and predicts future events.
Chronic obstructive pulmonary disease (COPD) is a complex disease, but current guideline recommendations for assessment of severity are based on airflow obstruction. Multicomponent indices have been developed but are not widely used in clinical practice.
The DOSE Index (MRC Dyspnea Scale, airflow obstruction, smoking status, and exacerbation frequency) was derived and validated in international data sets. Unlike other severity indices, this index is intended for use in routine clinical settings, not just as a measure of disease severity but also as a quick guide to management.
Traditionally, the FEV1 has been the main measure of COPD severity for clinicians and still has a prominent place in guidelines. Although patients are concerned mainly with symptoms, exacerbations, and functional capacity (6), airflow obstruction is important to clinicians to measure lung damage and to determine treatment. A composite measure could account for various dimensions of the disease, and take into account both the patient's and the physician's perspectives.
One highly regarded composite measure is the BODE [body mass index (B), degree of airflow obstruction (O), dyspnea (D), and exercise capacity (E)] Index (7), which was originally designed to predict mortality in COPD. However, the BODE Index involves a 6-minute walk test (6MWT), which limits its use in routine clinical settings as it takes time, supervision, and space. Another validated prognostic index, the COPD Prognostic Index (8), is also cumbersome to use in routine clinical settings as it includes seven items, one of which is a health status questionnaire.
The aim of this study was to derive a multicomponent assessment index of current COPD severity. The index was intended to include items that are clinically important, applicable to all grades of disease severity and all health care settings, and simple and clear to use.
Once derived, we aimed to validate the index against other markers of current disease severity such as health care consumption. We also aimed to test the index in relation to predicting future events, such as hospital admissions, exacerbations, and courses of treatment.
The DOSE Index was derived from the Devon Primary Care COPD audit data set. This consisted of 375 patients in primary care with confirmed COPD, of whom 197 (53%) had GOLD (Global Initiative for Chronic Obstructive Lung Disease) stage II disease, 144 (38%) had GOLD stage III disease, and 34 (9%) had GOLD stage IV disease. Patients underwent a comprehensive, guideline-based assessment of their disease, performed by a respiratory specialist. The process of the audit has been described elsewhere (9). Exacerbations in this cohort were recorded by patient self-report with confirmation from primary care medical records; the definition of an exacerbation was that stated in the National Institute for Health and Clinical Excellence (NICE, London, UK) guidelines (10). Unscheduled visits refer to emergency consultations at the patient's home, or in an emergency treatment center outside normal office hours, for example, at night or at the weekend.
On the basis of theoretical clinical considerations a number of possible predictors of health status were selected (Table 1). The number of exacerbations per year is known to predict outcomes (11), is needed for determining therapy, and is recognized as a clinically important marker in current guidelines (10). Smoking status and pack-years are known to affect prognosis, as are measures of airflow obstruction, for example, FEV1% predicted and exercise capacity (1).
Variable | n | ρ | P Value |
|---|---|---|---|
| MRC Dyspnea Scale score | 329 | 0.67 | <0.001 |
| FEV1% predicted | 329 | 0.31 | <0.001 |
| Obstruction grade | 329 | 0.31 | <0.001 |
| Steroid courses | 320 | 0.31 | <0.001 |
| Out-of-hours visits | 328 | 0.21 | <0.001 |
| A&E attendances | 328 | 0.19 | <0.01 |
| Exacerbations | 329 | 0.26 | <0.001 |
| Current smoking status | 329 | 0.18 | <0.001 |
| Pack-years | 321 | 0.06 | 0.32 |
| BMI | 329 | 0.05 | 0.37 |
Health status was measured using the Clinical COPD Questionnaire (CCQ) (12). Evaluation of the CCQ, including treatment of missing data, was in line with instructions given by the original authors. For 12% of patients (n = 43) no CCQ total score could be calculated. In a first step, the relationships of the possible predictor variables with health status were quantified through bivariate correlations. Subsequently, those variables showing strongly significant relationships (i.e., P ≤ 0.01) with health status were entered as predictors into a multiple regression analysis with health status as the dependent variable. The variance in health status explained by the model was assessed. Finally, a scoring system was designed to weigh the components according to their clinical and statistical strength.
Further details of the weighting process are provided in the online supplement. Details of validation of the DOSE Index in other data sets are also given in the online supplement.
To assess external validity of the DOSE Index as a marker of current health status, Spearman's rank correlations between the DOSE Index and the St. George's Respiratory Questionnaire (SGRQ) (13) in the Holland, London, and Tokyo data sets were calculated.
Further, the DOSE Index was tested against other markers of disease severity. The relationship between the DOSE Index score and health care consumption such as bed days in hospital was analyzed using scatterplots to establish a cutoff point into two categories: low and high DOSE Index score. Using the DOSE Index categories as the predictor, linear regression analyses were performed on markers of disease severity, such as bed days or current resting hypoxia in the Devon validation data set. Respiratory failure was defined as resting oxygen saturation (SaO2) of less than 92% (14).
In the Tokyo data set, the relationships between the DOSE Index score and the BODE Index, the body mass index (BMI), and the 6MWT were examined using Spearman's rank correlations.
Details of methods used in assessing longitudinal changes in the DOSE Index score are given in the online supplement.
The index was derived in one sample of people with COPD and validated in four different samples; the five samples investigated in this study are characterized in Table 2.
Devon Derivation | Devon Validation | Tokyo Validation | Holland Validation | London Validation | |
|---|---|---|---|---|---|
| Number | 375 | 460 | 81 | 152 | 133 |
| Sex, males | 224 (60%) | 265 (58%) | 77 (95%) | 96 (63%) | 94 (71%) |
| Age, years | 69.2 (8.6) | 69.5 (8.7) | 73.2 (6.9) | 63.1 (11.0) | 67 (8.0) |
| Mean FEV1% predicted | 50 (14) | 49 (15) | 49.4 (14.8) | 66.9 (18.4) | 41.8 (15.8) |
| GOLD I, II, III, IV, % | 0/52/39/9 | 0/51/37/12 | 0/49/40/11 | 10/61/26/3 | 1/29/39/31 |
| Current smokers | 120 (32%) | 144 (31%) | 2 (3%) | 62 (41%) | 49 (37%) |
| Exacerbations per year | 1.3 (1.6) | 1.5 (1.9) | 1.01 (1.7) | 0.47 (0.5) | 1.04 |
| BMI | 26.7 (5.6) | 26.7 (5.7) | 22.2 (3.1) | 26.7 (5.0) | 25.7 |
| MRC Dyspnea Scale | 1.66 (1.0) | 1.79 (1.1) | 1.21 (0.8) | 2.26 (1.0) | 3.14 (1.0) |
| SGRQ, total | — | — | 33.2 (13.2)* | 30.8 (20.3) | 53.6 (17.0) |
| CCQ, total | 2.0 (1.1) | —† | — | 1.4 (0.9) | — |
The correlations of all variables initially considered to be potentially related to health status are shown in Table 1. Four correlations were significant: The Medical Research Council (MRC, London, UK) Dyspnea Scale score, the obstruction grade based on FEV1% predicted, the number of exacerbations, and the current smoking status. Colinearity between these predictors of health status was not significant as correlations among the predictors ranged from r = −0.37 to r = 0.22; colinearity is judged as significant if it exceeds r = ±0.80. Multiple regression analysis showed that these four variables explained 48% of the variance in health status (Table 3). Thus, the DOSE Index consists of the MRC Dyspnea Scale score (D), the airflow obstruction grade (O), the current smoking status (S), and the number of exacerbations (E).
b | SE Coefficient | β | |
|---|---|---|---|
| MRC Dyspnea Scale score | 0.62 | 0.05 | 0.58* |
| Number of exacerbations (12 mo) | 0.11 | 0.03 | 0.15* |
| Current smoking status | 0.35 | 0.10 | 0.15* |
| FEV1% predicted | −0.01 | 0.01 | −0.09† |
| (Constant) | 0.42 | 0.25 | 0.09 |
To design the index scoring system, a number ranging between 0 and 3 was assigned to each factor. Weighting was given according to both clinical and statistical considerations. Further details are provided in the online supplement.
For each component of the scoring system, cutoff values were chosen. FEV1% predicted cutoffs were selected using the obstruction grading based on the GOLD and NICE guidelines (1, 10). For exacerbations the cutoff values related to the frequency of exacerbations, which predicts outcome: values above 2.9 exacerbations per year were associated with faster decline in lung function (15). Two or more exacerbations per year are considered to be an indication for certain drug treatments (1, 10). The DOSE Index score is built from the sum of its components and the total score ranges from 0 to 8; the higher the score, the more severe the disease status (Table 4).
DOSE Index Points | |||||||
|---|---|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | ||||
| MRC Dyspnea Scale score | 0–1 | 2 | 3 | 4 | |||
| Obstruction FEV1% predicted | >50 | 30–49 | <30 | ||||
| Smoking status | Nonsmoker | Smoker | |||||
| Exacerbations per year | 0–1 | 2–3 | >3 | ||||
In the Devon validation cohort, the distribution of the DOSE Index was analyzed (Figure 1). The median (interquartile range) was 2 (1–3). The distribution was skewed toward lower DOSE Index scores, which corresponded to a high proportion of mild to moderate COPD cases in this cohort.
Figure 1. Distribution of DOSE Index scores in the Devon validation cohort, presented for various airflow obstruction grades. Solid = severe disease; gray = moderate disease; dashed = mild disease.
[More] [Minimize]The associations of the DOSE Index with health status were found to correlate significantly with health status in the London, Japan, and Holland samples (Table 5).
Visualization of the DOSE Index against the number of bed days in the previous year indicated that above a DOSE Index score of 4, bed days increased rapidly (Figure 2).
Figure 2. Mean number of days spent in hospital as a function of the DOSE Index score. Note: DOSE Index scores higher than 5 are not displayed because of the small number of cases in these categories (8 and 2, respectively).
[More] [Minimize]Linear regression analysis confirmed the validity of a DOSE Index score cutoff of 4 to predict increased health care consumption (Table 6). On average, patients with a DOSE Index score above 4 were eight times more likely to be admitted and had spent 5.1 more bed days in hospital than patients with a DOSE Index score of 4 or less. There were few emergency department attendances in the Devon data set, but the DOSE Index cutoff of 4 predicted emergency department attendances at a near-significant level (β = 0.10, P = 0.06, R2 = 0.01).
DOSE Index Score ≤4 | DOSE Index Score >4 | Effect Size | P Value | |
|---|---|---|---|---|
| Whether admitted in last 12 mo | 6% | 34% | Odds ratio, 8.30 (95% CI, 4.10 to 17.00) | <0.0001 (χ2) |
| Out-of-hours visits in last 12 mo (mean) | 0.08 | 0.58 | Mean difference, 0.51 (0.34 to 0.69) | <0.0001 (t test) |
| Emergency department attendances (mean) | 0.10 | 0.44 | Mean difference, 0.34 (−0.01 to 0.69) | 0.06 (t test) |
| Bed days (mean) | 0.30 | 5.40 | Mean difference, 5.10 (3.90 to 6.40) | <0.0001 (t test) |
Respiratory failure was defined as an oxygen saturation level of less than 93% on air at rest, using pulse oximetry. Using the cutoff of 4, the DOSE Index score was a highly significant predictor of desaturation. In those with a lower DOSE Index score, 9% were desaturated compared with 45% in the higher DOSE Index score group (odds ratio, 7.8 [3.4–18.3], χ2 P < 0.0001).
Exercise capacity (as measured by the distance walked in the 6MWT) showed a strong negative correlation (r = −0.54, P ≤ 0.01) with the DOSE Index in the Tokyo data set. The BMI was also negatively associated with DOSE Index (r = −0.30, P ≤ 0.01). The BODE Index was highly correlated with the DOSE Index (r = 0.78, P ≤ 0.01).
In the London exacerbation cohort, 338 DOSE Index scores were available on 175 patients. Over a 9-year period the DOSE Index score increased at a rate of 0.18 unit/year (z = 5.26; P < 0.001; 95% confidence interval [CI], 0.11 to 0.25). In Figure 3, raw mean DOSE Index score (with standard error bars) was plotted against year of follow-up. No allowance has been made for patients withdrawing from the study or missing DOSE Index scores in any year. Year 0 refers to the first year of the study.
Figure 3. Changes in the DOSE Index over time. Note: DOSE Index scores higher than 5 are not displayed because of the small number of cases in these categories.
[More] [Minimize]In the London study, there were a total of 338 annual DOSE Index scores on 175 patients, during which there were 50 hospital admissions for an acute exacerbation. The DOSE Index was related by random-effects Poisson regression to the number of hospitalizations in the current year (exponentiated coefficient [which has the interpretation of incidence rate ratios], 1.44; P < 0.001; 95% CI, 1.20–1.72). Fewer data were available for the subsequent year, as patients withdrew or died. For the data available (217 annual DOSE Index scores on 107 patients) there was no relationship between DOSE Index score and hospital admission (coefficient, 1.03; P = 0.77; 95% CI, 0.82–1.30).
Using Pearson correlations, DOSE Index score correlations with number of bed days were stronger than FEV1% predicted and the MRC Dyspnea Scale in the Devon validation cohort. DOSE Index score correlations were r = 0.334 and P = 0.001; for FEV1% predicted they were r = 0.027 and P = 0.698; and for the MRC Dyspnea Scale they were r = 0.288 and P = 0.001.
In the London Cohort, based on whether patients were admitted or not, DOSE Index score was a stronger predictor of future admissions than the FEV1% predicted. The receiver operator characteristics analysis gave an area under the curve (AUC) of 0.755 for the DOSE Index, against FEV1% predicted (AUC = 0.254) the difference being significant at χ2 = 12.16 (P = 0.0005), and slightly but not significantly better than the MRC Dyspnea Scale (AUC = 0.6278, χ2 = 3.54, P = 0.060).
There were 149 patients and 284 annual DOSE Index scores for years when treatment with antibiotics and oral steroids were recorded in the London cohort. The DOSE Index score was related to the annual number of exacerbations in that year (random-effects Poisson regression coefficient = 1.30; P ≤ 0.001; 95% CI, 1.23–1.39). The DOSE Index score was also related to the number of exacerbations in the subsequent year although there were fewer patients (n = 128) and observations (n = 245) (coefficient, 1.07; P = 0.01; 95% CI, 1.02–1.14). A similar relationship was seen between the DOSE Index score and the number of exacerbations treated with antibiotics in the subsequent year (coefficient, 1.08; P = 0.009; 95% CI, 1.02–1.15), and with exacerbations treated with oral corticosteroids in the subsequent year (coefficient, 1.10; P = 0.007; 95% CI, 1.03–1.18).
Associations between the DOSE Index scores and various markers of disease severity indicate that the DOSE Index score reflects these markers better than its components (Table 7). One exception is the MRC Dyspnea Scale; the DOSE Index has similar predictive strength compared with the MRC alone in relation to many variables examined in cross-sectional and longitudinal data.
SGRQ (Tokyo) | CCQ (Devon Derivation) | Emergency Visits (Devon Derivation) | Bed Days (Devon Derivation) | 6MWT (Holland) | |
|---|---|---|---|---|---|
| DOSE | 0.49* | 0.64* | 0.19* | 0.17* | 0.43* |
| MRC Dyspnea Scale | 0.48* | 0.67* | 0.18* | 0.18* | 0.39* |
| Obstruction FEV1% predicted | 0.45* | 0.34* | 0.09 | 0.06 | 0.30* |
| Smoking status | 0.10 | 0.18* | 0.01 | 0.02 | 0.10 |
| Exacerbations per year | 0.02 | 0.26* | 0.21* | 0.21* | 0.17 |
In this article we describe the derivation and validation of a simple index that could be conveniently performed in a routine clinical setting and that includes items that are clinically important in their own right. In a large data set from primary care we have shown that the MRC Dyspnea Scale, airflow obstruction (FEV1% predicted), current smoking status, and number of exacerbations per year were predictors of health status as measured by the CCQ total score. A weighted index was derived and validated in a range of patient populations with varying nationality, disease status, and clinical settings.
The selection of components for a multicomponent index can be performed in various ways. In this study we used a combination of statistical techniques coupled with the opinion of clinicians and evidence-based guideline recommendations. The MRC Dyspnea Scale was a strong predictor of health status. Arguably, it is a health status measure itself and therefore not independent of the CCQ. However, given the strong predictive power of the MRC Dyspnea Scale, and its simplicity and proven use in other settings, it was included. Both the MRC Dyspnea Scale and the FEV1% predicted are strongly recommended as measures of severity in international guidelines. The FEV1% predicted is needed for diagnosis and staging and is therefore widely recorded. In our data set the FEV1% predicted was found to be a predictor of health status. Current smoking status showed a stronger association with health status than pack-years, to the surprise of some clinicians. As smoking status is also an important predictive marker of outcome in COPD, it was included in the index.
Acute exacerbations of COPD are key events in determining the health status of patients with COPD (15), and the current guidelines recommend recording exacerbation frequency as a marker of disease severity (1, 10). Exacerbation frequency was significantly correlated to health status, as were oral steroid courses and out-of-hours visits. We opted to include exacerbation frequency rather than oral steroid courses or unscheduled care as exacerbation frequency was clinically important and not dependent on the vagaries of the local health care service. Exacerbations are clinically important as early effective treatment reduces their impact (16), and thus it is important to encourage patients to obtain early treatment.
The BMI was not included as it was not statistically correlated to health status in the derivation index. The benefit of the BMI is in detecting those patients with very severe disease in whom weight loss was caused by depletion of muscle bulk. The BMI is of lesser relevance in early disease and severe nutritional problems may exist in the presence of a normal BMI. Only in the Tokyo data set did BMI correlate to the DOSE Index. Despite not being included in the DOSE Index, the BMI remains an important measure of prognosis, especially in the later stages of the disease. In summary, the components of the DOSE Index explain 48% of health status variance in the derivation data set, which no other combination has achieved.
To be useful, any multicomponent index of disease severity needs to reflect more than just health status. We have shown that the DOSE Index score correlates with exercise tolerance, respiratory failure, and health care consumption. It can identify patients with more severe disease who are at higher risk of hospital admission. Such patients can then be given appropriate care and support; for instance, by case management, which would address their needs in terms of optimal therapy; psychosocial support; and self-management education, with the aim of reducing their chance of being admitted to hospital. In this way, health care resources can be used appropriately to the benefit of patients and the health service.
The DOSE Index may be used as a quick guide to managing patients of all grades of severity as the items are relevant to therapy. Current smokers should be offered smoking cessation therapy and, according to the NICE guidelines, “patients who consider themselves functionally disabled (usually MRC Dyspnea Scale score of three or more) should be offered pulmonary rehabilitation” (10). For patients with recurrent exacerbations, long-acting bronchodilators are indicated, and if their FEV1% predicted is less than 50% of predicted inhaled steroids are indicated (1, 10). Those with frequent exacerbations may benefit from self-management education (17).
Thus the four components of the DOSE Index may provide a checklist for smoking cessation, pulmonary rehabilitation, long-acting bronchodilators, and inhaled steroids.
The BODE Index was established primarily as a predictor of survival (7). Although of undoubted value, the inclusion of the 6MWT limits its use in routine clinical care. In this study we found that in the Tokyo data set, the DOSE Index correlates well with the 6MWT, the BMI, and the BODE Index. However, the DOSE Index is intended to complement the BODE Index as it is simpler to perform and the DOSE Index has the additional benefit of providing a checklist of management options. The DOSE Index may be used in initial assessment and those in need of further assessment, such as those with a DOSE Index score of 4, may proceed to exercise testing and the BODE Index score calculated.
The DOSE Index was derived primarily as an index of health status and one of its components, the MRC Dyspnea Scale, is itself a measure of health status. Compared with the index, the MRC Dyspnea Scale was found to be a similar predictor of health status and exercise capacity in the data sets examined. However, the MRC Dyspnea Scale does not reflect other important components of disease severity such as smoking and exacerbations, which also affect prognosis and management.
One criticism that could be made concerns the way we went about developing this index. We did not start with qualitative research to explore patients' and clinicians' perceptions of COPD severity and the need for such an index and how it should best be produced. It was not possible to compare the prognostic value of the BODE Index with that the DOSE Index as we were unable to locate a data set that contained all the relevant items over a period of years.
In summary, the DOSE Index provides a simple four-component index that reflects health status, exercise tolerance health care consumption, and respiratory failure. The DOSE Index also provides clinicians with a wider perspective on COPD severity than is encompassed by any of its component items. The four components also provide a guide to disease management. Further research is needed to establish the use of the index as a predictor of future events such as hospital admissions and mortality.
The authors thank Bryanie Shackell for help in preparing the article, Prof. John Campbell for advice on the paper, and Dr. Rod Taylor for statistical expert advice.
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