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The effect of a post-bronchodilator FEV1/FVC < 0.7 on COPD diagnosis and treatment: a regression discontinuity design

Abstract

Background

Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines recommend the diagnosis of chronic obstructive pulmonary disease (COPD) only in patients with a post-bronchodilator forced expiratory volume in 1 s to forced vital capacity ratio (FEV1/FVC) less than 0.7. However the impact of this recommendation on clinical practice is unknown.

Objective

To estimate the effect of a documented post-bronchodilator FEV1/FVC < 0.7 on the diagnosis and treatment of COPD.

Design

We used a regression discontinuity design to measure the effect of a post-bronchodilator FEV1/FVC < 0.7 on COPD diagnosis and treatment.

Participants

Patients included in a national electronic health record database who were 18 years of age and older and had a clinical encounter between 2007 and 2022 in which a post-bronchodilator FEV1/FVC value was documented.

Main measures

An encounter was associated with a COPD diagnosis if an international classification of disease code for COPD was assigned, and was associated with COPD treatment if a prescription for a medication commonly used to treat COPD was filled within 90 days.

Results

Among 27,817 clinical encounters, involving 18,991 patients, a post-bronchodilator FEV1/FVC < 0.7 was present in 14,876 (53.4%). The presence of a documented post-bronchodilator FEV1/FVC < 0.7 increased the probability of a COPD diagnosis by 6.0% (95% confidence interval [CI] 1.1–10.9%) from 38.0% just above the 0.7 cutoff to 44.0% just below this cutoff. The presence of a documented post-bronchodilator FEV1/FVC < 0.7 had no effect on the probability of COPD treatment (−2.1%, 95% CI −7.2 to 3.0%).

Conclusions

The presence of a documented post-bronchodilator FEV1/FVC < 0.7 had only a small effect on the diagnosis of COPD and no effect on corresponding treatment decisions.

Introduction

Chronic obstructive pulmonary disease (COPD) is defined by the presence of obstruction on spirometry [1, 2]. According to Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines, obstruction is present if the post-bronchodilator forced expiratory volume in 1 s to forced vital capacity ratio (FEV1/FVC) is less than 0.7, with the diagnosis of COPD recommended only in patients with obstruction [3]. Despite this, studies comparing the performance of spirometry with the prior clinical diagnosis of COPD have found that the presence of obstruction correlates only loosely with this diagnosis [4, 5]. Between 30 and 60% of patients diagnosed with COPD do not have obstruction on spirometry [6,7,8,9,10], while between 60 and 80% of patients with obstruction have not been diagnosed with COPD [11,12,13,14,15,16].

This contrast between the presence of obstruction and the diagnosis of COPD has been attributed to the underuse of spirometry [17,18,19,20,21], with the assumption that physicians would diagnose COPD in accordance with GOLD guidelines if they had access to the results of spirometry. However, while such access would position physicians to arrive at an accurate diagnosis of COPD, diagnostic accuracy depends further on the proper interpretation of these results. Studies comparing the prior performance of spirometry with the clinical diagnosis of COPD have found that even after spirometry has been performed, the diagnosis of COPD still often fails to align with GOLD guidelines [22,23,24].

To better understand the role of spirometry in medical decision making, we sought to estimate effect of the presence of a post-bronchodilator FEV1/FVC < 0.7 on COPD diagnosis and treatment. We hypothesized that if physicians applied GOLD guidelines to spirometry to diagnose COPD, this would yield a substantial discontinuity in the probability of a COPD diagnosis at the 0.7 cutoff; COPD would generally not be diagnosed in patients with an FEV1/FVC ≥ 0.7 and would generally be diagnosed in patients with an FEV1/FVC < 0.7. Because current GOLD guidelines do not recommend that spirometry directly inform treatment decisions, though treatment decisions likely follow the establishment of a COPD diagnosis, we hypothesized that a post-bronchodilator FEV1/FVC < 0.7 would affect COPD treatment but to a lesser extent than it would affect diagnosis.

Methods

Data source

We used data from the Optum Labs Data Warehouse (OLDW), a database composed of de-identified administrative claims and electronic health record (EHR) data from across the United States [25]. EHR data included in the OLDW were derived from provider notes using a proprietary natural language processing (NLP) system [26]. This use of NLP made it possible to link clinical data to specific clinical encounters in which physicians demonstrated access to these data by including them in their clinic notes, allowing us to study the effect of these data on diagnostic and therapeutic decision making.

Study population

We included clinical encounters from 2007 to 2022 in the OLDW that involved patients 18 years of age and older and that documented a post-bronchodilator FEV1/FVC measurement in the associated clinic note.

Exposure and outcomes

The exposure was the documented presence of a post-bronchodilator FEV1/FVC < 0.7. The primary outcome was whether an encounter was associated with the diagnosis of COPD. We defined an encounter as associated with a diagnosis of COPD if the encounter was assigned any ICD code for COPD, including chronic bronchitis and emphysema (e-Table 1). In addition to estimating the effect of the exposure on COPD diagnosis, we evaluated, as a secondary outcome, the effect of the same exposure on COPD treatment. We defined an encounter as associated with COPD treatment if a medication commonly used to treat COPD was filled within 90 days of the encounter (e-Table 2).

Study design

We used a regression discontinuity design (RDD) to estimate the effect of the exposure on the primary and secondary outcomes. An RDD is method of causal inference that allows one to estimate the effect of an exposure on an outcome when the presence of the exposure is a function of whether the value of a continuous variable falls above or below a discrete cutoff [27, 28]. Given a sufficient amount of data, one can define a small enough bandwidth around the cutoff that observations within the bandwidth that fall on one side or the other of the cutoff will, on average, differ only by the presence or absence of the exposure of interest [29]. The effect of the exposure can then be estimated as the discontinuity across this cutoff. In this way, an RDD provides an effect estimate from observational data that is free from unmeasured confounding.

The continuous variable in our RDD was the post-bronchodilator FEV1/FVC while the cutoff was the 0.7 value used by GOLD guidelines to define the presence of obstruction. The bandwidth around the 0.7 cutoff was selected for minimal coverage error with robust bias-corrected inference [30, 31]. We used local linear regression to construct the point-estimator and local quadratic regression to construct the bias correction. We followed the convention of using a triangular kernel in our RDD, increasing the relative weight assigned to clinical encounters with post-bronchodilator FEV1/FVC values within the bandwidth that were closer to the 0.7 cutoff [30].

In addition to estimating the effect of a documented post-bronchodilator FEV1/FVC < 0.7 on the diagnosis and treatment of COPD, we also estimated its effect on specific types of COPD diagnosis and specific classes of COPD treatment.

In our primary analysis we used a sharp RDD, as the exposure of interest was present in all encounters in which the value of the continuous variable was less than the cutoff and was absent in all encounters in which the value of the continuous variable was equal to or greater than the cutoff. In a secondary analysis we used a fuzzy RDD to assess the effect of COPD diagnosis on COPD treatment, allowing for the facts that not all patients with an FEV1/FVC < 0.7 were diagnosed with COPD and that this diagnosis could be present in patients with an FEV1/FVC ≥ 0.7 [32].

Subgroups

In exploratory analyses we estimated the effect of a post-bronchodilator FEV1/FVC < 0.7 on COPD diagnosis and treatment in pre-specified subgroups defined by age ≥ 65, gender, race, history of tobacco use, history of COPD, encounter type, and physician specialty.

Validity assessment

We performed multiple tests to assess the validity of the RDD. First, we tested for differences in the density of the post-bronchodilator FEV1/FVC values around the 0.7 cutoff to assess for potential manipulation of the continuous variable. Manipulation can occur when the value of the continuous variable is changed in response to whether it falls above or below the cutoff, with the presence of manipulation invalidating the assumption that observations within the bandwidth falling above or below the cutoff will be otherwise identical. Second, we performed placebo tests to test the effect of a post-bronchodilator FEV1/FVC < 0.7 on age, sex, race, tobacco use, history of COPD, and clinical encounter type, to assess covariate balance. Third, we compared the effect estimates using a 0.7 cutoff to estimates produced using all cutoff values at a 0.01 interval between 0.5 and 0.9. Fourth, we compared effect estimates using pre- and post-bronchodilator FEV1/FVC values, with the expectation that an effect would be greater in the latter.

Sensitivity analysis

We assessed the sensitivity of our results to different modeling assumptions. First, we compared the use of bias-corrected estimates and robust standard errors to the use of conventional estimates and conventional standard errors. Second, we compared an unadjusted RDD to an RDD adjusted for age, sex, race, ethnicity, tobacco use, history of COPD diagnosis, encounter type, and physician specialty. Third, we compared the use of different kernel shapes in our RDD. Fourth, we compared the use of different orders for the polynomials used to construct the point estimator and bias-correction estimator in our RDD. Fifth, we compared the use of different methods for bandwidth selection. Sixth, we compared the use of different bandwidth values to construct the point estimator and bias-correction estimator. Seventh, we compared the use of different procedures to compute the variance–covariance matrix estimator. Eighth, we compared the use of different cutoff dates for the association of COPD diagnosis and treatment with clinical encounters, ranging from the day of the encounter to 365 days after the encounter.

Statistical analysis

All statistical tests were two sided and a P value < 0.05 was interpreted as statistically significant. R version 4.2.1 was used for data analysis [33]. The rdrobust package was used to perform the RDD [34]. We followed the STROBE checklist for reporting observational studies in epidemiology (e-Table 3) [35].

Results

We identified 27,817 clinical encounters in which a post-bronchodilator FEV1/FVC measurement was documented, involving 18,991 different patients and more than 2038 different physicians (Table 1). The encounters most often involved women (N = 14,517, 52%), non-Hispanic White patients (N = 25,083, 90%), patients at least 65 years old (N = 14,586, 52%), and patients with a history of tobacco use (N = 14,909, 54%). A total of 6674 (24.0%) encounters involved patients with a prior COPD diagnosis. The primary physician specialty was documented for 11,755 encounters, with 1932 (6.9%) encounters with pulmonologists, 6705 (24.1%) with internists or non-pulmonologist internal medicine subspecialists, 643 (2.3%) with family medicine or general practitioners, 1527 (5.5%) with emergency medicine physicians, and 948 (3.4%) with surgeons.

Table 1 Encounter characteristics

COPD was diagnosed in 12,697 (45.6%) encounters, including 3219 (25.3%) encounters in which a post-bronchodilator FEV1/FVC was ≥ 0.7. A total of 16,515 (59.4%) encounters were associated with COPD treatment, including 6608 (40.0%) in which a post-bronchodilator FEV1/FVC was ≥ 0.7.

The presence of a documented post-bronchodilator FEV1/FVC < 0.7 had a small but statistically significant effect on the diagnosis of COPD (Fig. 1). The probability of a COPD diagnosis increased from 38.0% just above the 0.7 post-bronchodilator FEV1/FVC cutoff to 44.0% just below this cutoff, a discontinuity of 6.0% (95% CI 1.1–10.9%, P value = 0.016) at the cutoff (Table 2). This effect was not seen with pre-bronchodilator spirometry (e-Table 4) and was seen only in the diagnosis of chronic obstruction (5.4% 95% CI 0.9–9.8%) and not in the diagnosis of chronic bronchitis (1.3%, 95% CI −2.2 to 4.8%) or emphysema (−0.2%, 95% CI −2.0 to 1.7%) (e-Table 5).

Fig. 1
figure 1

Association between post-bronchodilator FEV1/FVC and the diagnosis and treatment of COPD. Binscatter plots depict the association between post-bronchodilator FEV1/FVC values and the probability that COPD is A diagnosed and B treated. Post-bronchodilator FEV1/FVC values are binned at the 0.01 interval. The vertical dashed line represents the post-bronchodilator FEV1/FVC cutoff of 0.7 recommended by GOLD guidelines. COPD = chronic obstructive pulmonary disease; FEV1 = forced expiratory volume in 1 s; FVC = forced vital capacity; GOLD = Global Initiative for Chronic Obstructive Lung Disease

Table 2 Effect of a post-bronchodilator FEV1/FVC < 0.7 on COPD diagnosis and treatment

The presence of a post-bronchodilator FEV1/FVC < 0.7 did not affect COPD treatment (Fig. 1). The probability of treatment was 60.7% just above the 0.7 cutoff and 58.7% just below this cutoff, with a discontinuity of −2.1% (95% CI −7.2 to 3.0%) at the cutoff (Table 2). A significant effect was seen by treatment type only in the case of roflumilast (0.9%, 95% CI 0.1–1.7%) (e-Table 6). In our secondary analysis applying a fuzzy RDD to assess the effect of a COPD diagnosis on COPD treatment, the diagnosis of COPD did not have a significant effect on COPD treatment (48.1%, 95% CI −55.7 to 152.0%).

Subgroup analysis

Our exploratory subgroup analysis suggested that physician specialty and the history of a COPD diagnosis may both impact the role of spirometry in COPD diagnosis (Table 3). While the presence of a post-bronchodilator FEV1/FVC < 0.7 did not have a significant effect on COPD diagnosis by pulmonologists (9.8%, 95% CI −4.9 to 24.4%) or other internal medicine physicians (4.5%, 95% CI −3.8 to 12.7%), it did increase the probability of a COPD diagnosis in encounters with emergency medicine physicians (29.3%, 95% CI 7.3–51.3%). Notably, in patients with a prior diagnosis of COPD, the presence of a post-bronchodilator FEV1/FVC < 0.7 had no effect on the diagnosis of COPD (0.9%, 95% CI −6.1 to 7.9%), with a probability of diagnosis of 84.8% above the cutoff and a probability of 85.8% below the cutoff.

Table 3 Effect of a post-bronchodilator FEV1/FVC < 0.7 on COPD diagnosis and treatment by subgroup

Validity assessment

The validity of the RDD was supported by the absence of evidence of manipulation of the continuous variable (P = 0.186, e-Figure 2) and by the negative results of all placebo tests (e-Figure 3) [29, 36]. Assessing the effect of different post-bronchodilator FEV1/FVC cutoff values on COPD diagnosis we found that cutoffs of 0.58 and 0.67 were associated with a significant increase in the probability of diagnosis, while cutoffs of 0.60, 0.66, and 0.67 were associated with a significant decrease in the probability of diagnosis (e-Figure 4). These results likely reflect Type I errors as these cutoffs are not expected to have an effect on COPD diagnosis and the directionality of these effects were roughly even.

Sensitivity analysis

Our results were generally robust to the adoption of different modeling assumptions. The presence of a post-bronchodilator FEV1/FVC < 0.7 was found to have an effect on COPD diagnosis but not treatment with conventional estimates and conventional standard errors (e-Table 7), with an adjusted analysis (e-Table 8), with a rectangular kernel (e-Table 9), with polynomial orders for the point estimator of less than 2 and for the bias-correction estimator of less than 4 (e-Table 10), with most approaches to bandwidth selection (e-Table 11), with different procedures to compute the variance–covariance matrix estimator (e-Table 12), with different main (e-Figure 5) and bias (e-Figure 6) bandwidths, and with different time periods used to associate diagnosis (e-Figure 7) and treatment (e-Figure 8) with an encounter.

Discussion

We applied an RDD to a national EHR database and found that the presence of a documented post-bronchodilator FEV1/FVC < 0.7 only slightly increased the probability of a diagnosis of COPD. In the same RDD, the cutoff had no effect on COPD treatment even across a range of clinical contexts and encounter types. These findings suggest that GOLD guidelines have less of an effect on clinical decision making than has been assumed and that the performance of spirometry is insufficient to guarantee the diagnosis of COPD in accordance with these guidelines.

There are multiple potential etiologies for the observed discrepancy between the recommendations of GOLD guidelines and the diagnosis of COPD in this study. Though the OLDW database uses NLP to extract data from clinic notes, spirometry data may have been auto-populated and some physicians who included these data in their documentation may nonetheless have been unaware of them. Other physicians may have been aware of the results of spirometry, but unaware of their implications. As European Respiratory Society and American Thoracic Society (ERS/ATS) guidelines for spirometry interpretation do not provide physicians with recommendations regarding the diagnostic implications of spirometry, physicians must decide if test results are consistent with a diagnosis of COPD [37]. Other physicians may have relied on different spirometric criteria to determine the presence of obstruction. There is a lack of consensus regarding the use of the fixed 0.7 cutoff recommended by GOLD to define the presence of obstruction on spirometry and some physicians may have instead used the FEV1/FVC lower limit of normal to identify obstruction [37]. Finally, some physicians may have been aware of the diagnostic implications of spirometry and yet set aside the 0.7 cutoff as too simple a tool to apply to a clinically heterogeneous disease such as COPD [38, 39].

If physicians were largely unaware of the diagnostic implications of a post-bronchodilator FEV1/FVC < 0.7, our study suggests a role for the use of clinical decision support to help physicians diagnose COPD in a manner concordant with GOLD guidelines [40]. Indeed, our study suggests that if this is the case, in the absence of such decision support, COPD misdiagnosis may persist with some frequency even after spirometry has been performed. Attempts to improve COPD diagnosis simply by increasing the performance of spirometry, as with recent proposals to screen for COPD with spirometry, will be less successful than imagined if spirometry interpretation—rather than just spirometry performance—represents an important limiting factor in COPD diagnosis [41].

On the other hand, if the discrepancy between the recommendations of GOLD guidelines and the clinical diagnosis of COPD stems from the decision on the part of clinicians to depart from these guidelines, then simply alerting physicians to the fact that the post-bronchodilator FEV1/FVC is less than 0.7 will have little effect on clinical practice. If this is the case, COPD diagnosis might be advanced by replacing the simple 0.7 cutoff with a more robust model of airway obstruction [42, 43].

We found that the presence of a post-bronchodilator FEV1/FVC < 0.7 had no effect on COPD treatment. While GOLD had previously recommended the use of spirometry to directly inform treatment decisions, more recent guidelines recommend instead that treatment decisions be informed by exacerbation history and respiratory symptoms [3]. Nonetheless, as GOLD guidelines have consistently recommended at minimum the prescription of a short-acting bronchodilator for patients with COPD, the presence of a post-bronchodilator FEV1/FVC < 0.7 would be expected to have at least some effect on COPD treatment [44].

The results of our exploratory subgroup analysis suggest that physicians with different medical specialties may use spirometry in different ways to diagnose COPD. While the presence of a post-bronchodilator FEV1/FVC < 0.7 had a significant effect on the diagnosis of COPD by emergency medicine physicians, a similar effect was not seen among other type of physicians. This finding suggests that physicians who have a longitudinal relationship with their patients may rely more on history and symptoms to diagnose COPD while physicians without this type of clinical relationship may rely more on objective data in the form of spirometry. Likewise, while the presence of a post-bronchodilator FEV1/FVC < 0.7 had a significant effect on the diagnosis of COPD in patients who had not been previously diagnosed, it had no effect on the diagnosis of patients who already carried this diagnosis. Diagnostic momentum appears to play a significant role in COPD diagnosis and once a diagnosis of COPD has been made, spirometry has little effect upon it.

Finally, this study provides the first estimate of the effect of spirometry interpretation on diagnostic and therapeutic decision making in clinical practice. While several recent studies have speculated as to the downstream clinical consequences that follow from the recommended adoption of race-neutral reference equations, these consequences have yet to be studied empirically [45,46,47,48,49]. Our finding that the presence of a post-bronchodilator FEV1/FVC < 0.7 has only a minimal effect on COPD diagnosis and no effect on COPD treatment challenges the assumption that whether a spirometric parameter falls above or below a lower limit of normal—the effect of adopting one set of reference equations or another—results in corresponding changes in clinical decision making. The relationship between spirometry interpretation and such clinical decisions is not as straightforward as has been assumed and empirical studies are needed to estimate the clinical consequences that follow from the adoption of novel reference equations.

Limitations and strengths

This study has several strengths. First, we used a national EHR database and included encounters involving tens of thousands of patients and thousands of physicians. Second, the use of NLP to extract spirometry data from clinic notes allowed us to link these data to specific clinical encounters and conclude not only that spirometry had been performed but that the results of such performance were documented and thus accessible to physicians. Third, our use of an RDD mitigated the impact of unmeasured confounding on our effect estimates. Fourth, we performed an extensive sensitivity analysis and found that our results were generally insensitive to different modeling assumptions.

This study also has several limitations. First, we were unable to identify the FEV1 percent predicted values or FEV1/FVC lower limit of normal values to which the physicians in our cohort had access, and we were thus unable to assess the effect these values may have had on diagnostic and therapeutic decision making. Second, though our data were drawn from a national EHR database, the relative paucity of post-bronchodilator spirometry data, given the large number of patients represented in the database, suggests that spirometry data were likely included for only a subset of those patients for whom spirometry was performed. The selection effects mediating the inclusion of these data are unknown and may limit the external validity of our findings. Third, our use of ICD codes to associate COPD diagnoses with clinical encounters likely underestimates the prevalence of COPD diagnosis in our cohort as ICD codes for COPD are unlikely to be assigned to all clinical encounters with a patient diagnosed with COPD, as some encounters will involve medical issues unrelated to this diagnosis. Fourth, our use of prescription data to associate COPD treatment with a clinical encounter likely overestimates the prevalence of COPD treatment as we were unable to identify the specific rationale for each prescription and many medications can also be used to treat other diseases. Fifth, an RDD provides a local estimate of an effect at a cutoff. We were thus unable to estimate the effect of a post-bronchodilator FEV1/FVC < 0.7 for clinical encounters in which the FEV1/FVC value was far from the 0.7 cutoff [50].

Conclusion

In conclusion, we found that the presence of a documented post-bronchodilator FEV1/FVC < 0.7 had only a small effect on the diagnosis of COPD and had no effect on COPD treatment, suggesting that the prevailing, guideline-recommended diagnostic cutoff for COPD may not meaningfully affect clinical decision making.

Availability of data and materials

Electronic health record data that support the findings of this study are available from the Optum Labs Data Warehouse.

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Funding

ATM reports funding from NHLBI F32 HL167456.

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Moffett, A.T., Halpern, S.D. & Weissman, G.E. The effect of a post-bronchodilator FEV1/FVC < 0.7 on COPD diagnosis and treatment: a regression discontinuity design. Respir Res 26, 122 (2025). https://doi.org/10.1186/s12931-025-03198-6

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