RESPIRATORY IMPAIRMENT AND COPD HOSPITALISATION IN OLDER PERSONS: A COMPETING RISK ANALYSIS

Abstract

Objective

To evaluate, among older persons, the association between respiratory impairment and hospitalization for chronic obstructive pulmonary disease (COPD), based on spirometric Z-scores (Lambda-Mu-Sigma [LMS]) and a competing risk approach.

Methods

Using data on 3,563 white participants aged 65–80 years (Cardiovascular Health Study), we evaluated the association of LMS-defined respiratory impairment with incident COPD hospitalization and the competing outcome of death without COPD hospitalization, over a 5-year period. Respiratory impairment included airflow limitation (mild, moderate, and severe) and restrictive-pattern.

Results

Over a 5-year period, 276 (7.7%) participants had incident COPD hospitalization, whereas 296 (8.3%) died without COPD hospitalization. The risk of COPD hospitalization was elevated more than 2-fold in LMS-defined mild and moderate airflow limitation and restrictive-pattern (adjusted hazard ratio [HR]: 2.25 [1.25, 4.05], 2.54 [1.53, 4.22], and 2.65 [1.82, 3.86], respectively), and more than 8-fold in LMS-defined severe airflow limitation (adjusted HR: 8.33 [6.24, 11.12]). Conversely, only LMS-defined restrictive-pattern was associated with the competing outcome of death without COPD hospitalization (adjusted HR: 1.68 [1.22, 2.32]).

Conclusion

In white older persons, LMS-defined respiratory impairment is strongly associated with an increased risk of COPD hospitalization. These results support the LMS method as a basis for defining respiratory impairment in older persons.

INTRODUCTION

Hospitalization for chronic obstructive pulmonary disease (COPD) has substantial clinical and societal importance, given the high rates of associated morbidity, mortality, and healthcare costs, particularly in older persons.^1,2 Prior work, based on data from the Cardiovascular Health Study (CHS), has shown that spirometric respiratory impairment (airflow limitation and restrictive-pattern), defined on the basis of criteria published by the Global Initiative for Obstructive Lung Disease (GOLD), is strongly associated with the outcome of COPD hospitalization.³ The validity of these results is uncertain, however, for the three reasons discussed below.

First, the GOLD threshold for distinguishing normal spirometry from respiratory impairment is based on a fixed-ratio of forced expiratory volume in 1-second to forced vital capacity (FEV1/FVC) of 0.70.^3,4 Because aging is associated with increasing rigidity of the chest wall and decreasing elastic recoil of the lung, an FEV1/FVC <0.70 frequently occurs in otherwise healthy never-smokers who are 65-years or older.^5–10

Second, to establish restrictive-pattern and to stage airflow limitation, respectively, GOLD expresses the FVC and FEV1 as percent predicted (%Pred).^3,4 Because aging is also associated with increased variability in spirometric performance, increased disparity between %Pred values and the lower limit of normal (LLN) is found among older persons. ^6–8,11,12

Third, Kaplan-Meier survival curves and standard Cox models were used exclusively to evaluate the association between GOLD-defined respiratory impairment and COPD hospitalization.³ Because the competing risk of death was not explicitly considered, traditional analytical methods, such as Kaplan-Meier and Cox methods, may introduce bias in an analysis of time to COPD hospitalization, as older persons frequently die without experiencing the outcome of interest, especially during an extended period of follow-up.^13–15

To address the above concerns, we set out in the current study to evaluate the association between a novel strategy for establishing spirometric respiratory impairment, termed Lambda-Mu-Sigma (LMS),^6,7 and incident COPD hospitalization in older persons over a follow-up period of 5-years, using a competing risk analysis.^13–15 The LMS method calculates spirometric Z-scores that account for age-related changes in pulmonary function, including variability in spirometric performance and skewness of reference data.^6,7 Importantly, in the more informative competing risk analysis, we also included the outcome of death without COPD hospitalization and used both Cox cause-specific and subdistribution hazards models.^13–15 Whereas cause-specific models evaluate relative risk, subdistribution models evaluate the cumulative burden of an outcome.^13–15 Lastly, as a secondary aim, we compared the 5-year cumulative incidence probabilities of COPD hospitalization among participants who had discordant designations for respiratory impairment on the basis of the LMS and GOLD criteria.

METHODS

Study Population

We used data from the Cardiovascular Health Study (CHS), with institutional review board approvals obtained at our respective institutions. CHS is a population-based, longitudinal study of 5,888 older Americans, with an age range of 65–100 years.¹⁶ The study population was assembled in 1989–1990, as a random sample of Medicare beneficiaries in four U.S. communities (participation rate of 57.3%).¹⁷

For the present study, eligible participants were white, aged 65–80 years, and had completed at least two ATS-acceptable spirometric maneuvers at baseline. Our analyses were limited to whites aged 65–80 years, because reference values for the LMS-method are currently unavailable for non-whites and those aged >80 years.^6,7 To focus on “irreversible” pathology as the principal comparison group for airflow limitation, participants with self-reported asthma were excluded. As per current convention, we did not exclude participants based on spirometric reproducibility criteria.¹⁸ The final study sample included 3,563 participants.

Spirometry

In CHS, participants underwent spirometry during the baseline examination, according to contemporary ATS protocols.¹⁹ For each participant, the measured FEV1/FVC was calculated from the largest set of FEV1 and FVC values that were recorded in any of the spirometric maneuvers meeting ATS-acceptability criteria.^18–20

Investigators have previously recommended that spirometric measures should be expressed as a Z-score, which converts a raw measurement on a test to a standardized score in units of standard deviations.^11,12 More recently, a method for calculating spirometric Z-scores, termed Lambda-Mu-Sigma (LMS), has been proposed.^6,7 This strategy uses specific elements of a distribution, including the median (Mu)—representing how spirometric measures change based on predictor variables; the coefficient-of-variation (Sigma)—representing the spread of reference values and adjusting for non-uniform dispersion; and skewness (Lambda)—representing the departure from normality.^6,7 Clinically, Z-scores are used routinely to diagnose osteoporosis based on bone density testing, and the LMS method is applied widely to growth charts.^6,21

Based on measured values for each participant, LMS-derived Z-scores for FEV1, FVC, and FEV1/FVC were calculated as:^6,7 [(measured ÷ predicted median)^Lambda minus 1] ÷ (Lambda x Sigma). Predicted values for the median, lambda, and skewness were calculated from LMS equations.^6,7 A Z-score of −1.64 then defined the lower limit of normal as the 5^th percentile of distribution (5 LMS-tile).^6,7 participants were Using the 5 LMS-tile as a diagnostic threshold, classified as having normal spirometry—FEV1/FVC and FVC ≥5 LMS-tile, airflow limitation—FEV1/FVC<5 LMS-tile, or restrictive-pattern—FEV1/FVC≥5 LMS-tile but FVC<5 LMS-tile.^6,7,22–26

The severity of LMS-defined airflow limitation was staged according to FEV1, based on prior work and expressed as a percentile distribution of LMS-derived Z-scores (LMS-tiles).²⁵ Specifically, a 3-level severity was applied, with mild, moderate, and severe airflow limitation, staged by FEV1 ≥5, 0.5-to-4.9, and <0.5 LMS-tile, respectively.²⁵ Prior work had shown that these FEV1 staging criteria are associated with respiratory symptoms and mortality.²⁵

Participants were also classified on the basis of GOLD criteria: normal spirometry was defined by FEV1/FVC≥0.70 and FVC≥80%Pred, airflow limitation by FEV1/FVC<0.70, and restrictive-pattern by FEV1/FVC≥0.70 but FVC<80%Pred.⁴ The severity of GOLD-defined airflow limitation was staged according to FEV1 %Pred.⁴ A 3-level range of severity was applied (mild, moderate, and severe), corresponding to published cut-points of 80 and 50 %Pred.⁴ The %Pred for FVC and FEV1 were calculated as ([measured ÷ predicted mean] x 100), with predicted values derived from published multiple regression equations.²⁷

Clinical Measures

Baseline characteristics included age, sex, body mass index (BMI, kg/m²), smoking history, chronic conditions, and health status.¹⁶ The primary outcome of interest was incident COPD hospitalization over a follow-up of 5-years, defined as the first hospitalization for which COPD was listed as a discharge diagnosis. To ascertain hospitalization, medical records were obtained for all reported hospitalizations; Medicare Utilization files were also evaluated to determine potentially missed hospitalizations.¹⁶ Up to ten discharge codes from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) were abstracted for each hospitalization.¹⁶ A COPD hospitalization was then identified by the ICD-9-CM codes of 491 (chronic bronchitis), 492 (emphysema), or 496 (chronic airway obstruction).^16,28 The competing outcome of death without COPD hospitalization was based on reviews of obituaries, medical records, and death certificates.¹⁶ Vital status and (where appropriate) hospitalization diagnoses were available on all study participants.

Statistical Analysis

Baseline characteristics were summarized, overall and by LMS-defined spirometric categories, as mean values accompanied by standard deviations or as counts accompanied by percentages. For significance testing of differences between the respiratory impairment groups and the normal spirometry group, p-values were adjusted for multiple comparisons using the false discovery rate procedure.²⁹

The associations of LMS-defined respiratory impairment with incident COPD hospitalization were evaluated using competing risk analysis, with the competing outcome being death without COPD hospitalization.^13–15 Frequency distributions for incident COPD hospitalization and death without COPD hospitalization were calculated over the course of 5-years, with results stratified by baseline spirometric category. As a measure of risk over time, Cox cause-specific hazards models were used to evaluate the associations of LMS-defined airflow limitation (mild, moderate, and severe) and restrictive-pattern with the outcomes of incident COPD hospitalization and death without COPD hospitalization. For each outcome, the reference group was defined by normal spirometry.

In the model for COPD hospitalization, participants were censored if they died or had completed 5-years of follow-up without experiencing a COPD hospitalization. For the model of death without COPD hospitalization, participants were censored if they were hospitalized for COPD or were alive at the completion of 5-years of follow-up. Noninformative censoring assumes that participants who experience a competing outcome had the same probability of the primary outcome as did others who were at risk at the time of the competing outcome and had the same profile of covariates.¹³

The three LMS stages of airflow limitation and the LMS-defined restrictive-pattern were treated as nominal categories. For each Cox cause-specific regression model, goodness-of-fit was assessed by model-fitting procedures and the analysis of residuals. The proportional hazards assumption was tested by using interaction terms crossing the time-to-event outcome and each variable in the multivariable model; the terms were retained if p was <0.05 after adjusting for the multiplicity of comparisons. Higher-order effects were tested for the continuous covariates and included in the final model if they met a forward selection criterion of p <0.20.³⁰

As a cumulative measure of absolute risk, Fine and Gray models of subdistribution hazards^13–15 were used to estimate the cumulative incident probabilities of COPD hospitalization and death without COPD hospitalization over 5-years, according to the 3-level LMS staging of airflow limitation and LMS-defined restrictive-pattern. These models were stratified to allow each spirometric category to have its own baseline subdistribution hazard function.

Lastly, we compared the 5-year cumulative incident probability of COPD hospitalization among participants who had discordant designations for respiratory impairment on the basis of the LMS and GOLD criteria. Because all participants who had GOLD-defined normal spirometry also had LMS-defined normal spirometry, but not vice-versa, the discordant designations occurred exclusively as GOLD-defined respiratory impairment but LMS-defined normal spirometry. We could not meaningfully evaluate hazard ratios for these discordant designations, however, because the reference groups defining normal spirometry for LMS and GOLD differ, and the reference groups serve as the basis for calculating hazard ratios. Accordingly, inferences made from a direct comparison of such hazard ratios would be flawed.

In both the Cox cause-specific and subdistribution hazards models, adjustments were made for age, height, sex, smoking history, BMI, number of chronic conditions, and health status. Because only 20 participants had missing values for these covariates, complete case analyses were conducted for all regression modeling. SAS 9.2 was used in the analyses (SAS Institute, Inc., Cary, NC), with a p<0.05 (two-sided) denoting statistical significance.

RESULTS

Table 1 provides the baseline characteristics of the study population. Among all participants, the mean age was 71.5 years; 57.6% (2,054/3,563) were female and 55.9% (1,991/3,563) were former or current smokers. The mean BMI was 26.3 kg/m², and the mean number of chronic conditions was 1.0; fair-to-poor health status was reported by 20.4% (728/3,563). LMS-defined respiratory impairment was present in 23.2% (825/3,563), including 13.8% (492/3,563) with airflow limitation and 9.3% (333/3,563) with restrictive-pattern. Smoking prevalence, particularly current smoking, and the number of chronic conditions were highest among participants with LMS-defined respiratory impairment (airflow limitation and restrictive-pattern) and lowest among those with normal spirometry.

Table 1.

Baseline characteristics of study participants

Characteristic	ALL (N=3,563)	LMS-Defined Spirometric Category *
		Normal (N=2,738)	Airflow Limitation			Restrictive-Pattern (N=333)
			Mild (N=107)	Moderate (N=132)	Severe (N=253)
Age (years), mean (SD)	71.5 (4.1)	71.5 (4.1)	71.4 (4.6)	71.3 (4.3)	71.4 (4.0)	71.6 (4.3)
Females, No. (%)	2,054 (57.6)	1,623 (59.3)	54 (50.5)	66 (50.0) †	116 (45.9) †	195 (58.6)
Smoking status, No. (%) ‡
Never	1,572 (44.1)	1,366 (49.9)	22 (20.6)	19 (14.4)	40 (15.8)	125 (37.5)
Former	1,566 (44.0)	1,146 (41.9)	54 (50.5)	72 (54.5)	145 (57.3)	149 (44.7)
Current	425 (11.9)	226 (8.3)	31 (29.0)	41 (31.1)	68 (26.9)	59 (17.7)
BMI (kg/m2), mean (SD)	26.3 (3.9)	26.3 (3.8)	24.5 (3.7) †	26.0 (4.1)	25.7 (3.9) †	27.6 (4.3) †
Chronic conditions, mean (SD) §	1.0 (0.9)	0.9 (0.9)	0.8 (0.9)	1.1 (0.9) †	1.3 (1.1) †	1.3 (1.1) †
Self-reported COPD, No. (%) §	260 (7.3)	130 (4.8)	11 (10.3) †	10 (7.8)	80 (31.8) †	29 (8.9) †
Fair to Poor Health status, No. (%)	728 (20.4)	488 (17.8)	25 (23.4)	34 (25.8) †	86 (34.0) †	95 (28.5) †

Abbreviations: BMI, body mass index; LMS, Lambda-Mu-Sigma method; LMS-tile, percentile distribution of LMS derived Z-scores; SD, standard deviation.

^*LMS-defined normal spirometry was established by FEV1/FVC and FVC, both ≥5 LMS-tile; airflow limitation by FEV1/FVC<5 LMS-tile, with mild as FEV1 ≥5 LMS-tile, moderate as FEV1 0.5-to-4.9 LMS-tile, and severe as FEV1 <0.5 LMS-tile; and restrictive-pattern by FEV1/FVC ≥5 LMS-tile but FVC <5 LMS-tile.

^† False discovery rate adjusted P < 0.05, comparing each subgroup of respiratory impairment (airflow limitation and restrictive-pattern) to normal spirometry.

^‡ False discovery rate adjusted P < 0.05, comparing smoking status for each subgroup of respiratory impairment (airflow limitation and restrictive-pattern) to normal spirometry.

^§ Included self-reported chronic obstructive pulmonary disease (chronic bronchitis or emphysema).

Table 2 shows the frequency distributions of incident COPD hospitalization and the competing outcome of death without COPD hospitalization, over 5-years and according to LMS-defined spirometric category. The frequency of COPD hospitalization ranged from 3.8% (104/2,738) in the normal spirometry group to 40.3% (102/253) in the severe airflow limitation group. The frequency of death without COPD hospitalization ranged from 7.7% (210/2,738) in the normal spirometry group to 14.4% (48/333) in the restrictive-pattern group.

Table 2.

Frequency distributions of the primary outcome of incident COPD hospitalization and the competing outcome of death without COPD hospitalization over the course of 5-years and according to baseline LMS-defined spirometry category (N=3,563)

LMS-Defined Spirometry Category *	No. (%)†	COPD Hospitalization	Death Without COPD Hospitalization
		No. (%)‡
Normal	2,738 (76.8)	104 (3.8)	210 (7.7)
Airflow limitation
Mild	107 (3.0)	13 (12.1)	9 (8.4)
Moderate	132 (3.7)	18 (13.6)	12 (9.1)
Severe	253 (7.1)	102 (40.3)	17 (6.7)
Restrictive-pattern	333 (9.3)	39 (11.7)	48 (14.4)
Total	3,563 (100)	276 (7.8)	296 (8.3)

Abbreviations: FEV1, forced expiratory volume in 1-second; FVC, forced vital capacity; LMS, Lambda-Mu-Sigma method; LMS-tile, percentile distribution of LMS derived Z-scores.

^*LMS-defined normal spirometry was established by FEV1/FVC and FVC, both ≥5 LMS-tile; airflow limitation by FEV1/FVC<5 LMS-tile, with mild as FEV1 ≥5 LMS-tile, moderate as FEV1 0.5-to-4.9 LMS-tile, and severe as FEV1 <0.5 LMS-tile; and restrictive-pattern by FEV1/FVC ≥5 LMS-tile but FVC <5 LMS-tile.

^† Percent of study population (N=3,563).

^‡ Row percent.

Table 3 shows Cox cause-specific hazard ratios for incident COPD hospitalization and the competing outcome of death without COPD hospitalization, over 5-years and according to LMS-defined spirometric category. Relative to normal spirometry, the risk of COPD hospitalization was elevated more than 2-fold in mild and moderate airflow limitation and restrictive-pattern, with adjusted HR (95% confidence interval [CI]) of 2.25 (1.25, 4.05), 2.54 (1.53, 4.23), and 2.65 (1.82, 3.86), respectively, and more than 8-fold in severe airflow limitation, with an adjusted HR of 8.33 (6.24, 11.12). For the outcome of death without COPD hospitalization, restrictive-pattern, relative to normal spirometry, showed an increased risk, with an adjusted HR of 1.68 (1.22, 2.32). In contrast, airflow limitation was not associated with an increased risk of death without COPD hospitalization.

Table 3.

Cox cause-specific hazard ratios for the primary outcome of incident COPD hospitalization and the competing outcome of death without COPD hospitalization over the course of 5-years and according to baseline LMS-defined spirometry category (N=3563)

LMS-Defined Spirometry Category *	Cox Cause-Specific Hazards Model
	COPD Hospitalization †		Death Without COPD hospitalization ‡
	HR	95%CI	HR	95% CI
Normal	1.00
Airflow limitation
Mild	2.25	1.25, 4.05	1.00	0.51, 1.97
Moderate	2.54	1.53, 4.23	1.06	0.59, 1.91
Severe	8.33	6.24, 11.1	0.90	0.54, 1.49
Restrictive-pattern	2.65	1.82, 3.86	1.68	1.22, 2.32

Abbreviations: CI, confidence interval; FEV1, forced expiratory volume in 1-second; FVC, forced vital capacity; LMS, Lambda-Mu-Sigma method; LMS-tile, percentile distribution of LMS derived Z-scores.

^*LMS-defined normal spirometry was established by FEV1/FVC and FVC, both ≥5 LMS-tile; airflow limitation by FEV1/FVC<5 LMS-tile, with mild as FEV1 ≥5 LMS-tile, moderate as FEV1 0.5-to-4.9 LMS-tile, and severe as FEV1 <0.5 LMS-tile; and restrictive-pattern by FEV1/FVC ≥5 LMS-tile but FVC <5 LMS-tile.

^† Adjusted for age, height, sex, smoking, BMI, BMI ², # of chronic conditions, perceived health status and perceived health status by time interaction. In the model of COPD hospitalization, 38 (7.7%) of the 492 participants who had airflow limitation and 48 (14.4%) of the 333 participants who had restrictive-pattern were censored because they died without having a COPD hospitalization; the remaining cases were censored at the end of follow-up, as described in the Methods.

^‡ Adjusted for age, age², height, sex, smoking, BMI, # of chronic conditions, and perceived health status. In the model of death without COPD hospitalization, 133 (27.0%) of the 492 participants who had airflow limitation and 39 (11.7%) of the 333 participants who had restrictive-pattern were censored because they had a COPD hospitalization; the remaining cases were censored at the end of follow-up (see Methods).

The Figure shows the 5-year cumulative incidence probabilities of COPD hospitalization and death without COPD hospitalization, according to LMS-defined spirometric category. For the two competing outcomes, the baseline spirometric categories yielded a distinct set of cumulative incidence probabilities. By year five, the severe airflow limitation group showed the highest probability of COPD hospitalization at 0.27 (0.22, 0.31), but the lowest probability of death without COPD hospitalization at 0.05 (0.03, 0.08). Restrictive-pattern yielded similar probabilities for COPD hospitalization and death without COPD hospitalization at 0.10 (0.07, 0.13) and 0.12 (0.09, 0.16), respectively.

Figure.

Cumulative incidence probability of COPD hospitalization* and death without COPD hospitalization^† over 5-years, according to baseline LMS-defined spirometry category^‡

Abbreviations: BMI, body mass index; COPD, Chronic Obstructive Pulmonary Disease; LMS, Lambda-Mu-Sigma method; LMS-tile, percentile distribution of LMS derived Z-scores.

* Adjusted for age, height, sex, smoking history, BMI, BMI², number of chronic conditions, and health status.

† Adjusted for age, age², height, sex, smoking history, BMI, number of chronic conditions, and health status.

‡ LMS-defined normal spirometry was established by FEV1/FVC and FVC, both ≥5 LMS-tile; airflow limitation by FEV1/FVC <5 LMS-tile, with mild as FEV1 ≥5 LMS-tile, moderate as FEV1 0.5-to-4.9 LMS-tile, and severe as FEV1 <0.5 LMS-tile; and restrictive-pattern by FEV1/FVC ≥5 LMS-tile but FVC <5 LMS-tile.

Table 4 shows frequency distributions and 5-year cumulative incidence probabilities of COPD hospitalization for the discordant designations of GOLD-defined respiratory impairment but LMS-defined normal spirometry. Of the 2,738 participants who had normal spirometry by LMS, GOLD defined 946 (34.6%) as having respiratory impairment, including 541 (19.8%) and 240 (8.8%) as mild and moderate airflow limitation, respectively, and 165 (6.0%) as restrictive-pattern. The discordant designations of respiratory impairment by GOLD relative to LMS yielded low probabilities of COPD hospitalization, ranging from only 0.04 to 0.06. These values are comparable to those among participants who had normal spirometry by GOLD (0.03) or LMS (0.04, see Figure).

Table 4.

Frequency distributions and 5-year cumulative incidence probabilities of COPD hospitalization for the discordant designation of GOLD-defined respiratory impairment but LMS-defined normal spirometry ^*

GOLD-Defined Spirometric Category †	LMS–Defined Normal Spirometry ‡ (N = 2,738)
	No. (%)	Cumulative incidence probability (95% CI) of COPD hospitalization §
Normal	1,792 (65.5) ¶	0.03 (0.02, 0.04)
Airflow limitation
Mild	541 (19.8)	0.06 (0.04, 0.07)
Moderate	240 (8.8)	0.05 (0.03, 0.08)
Severe	0 (0)	--------------
Restrictive-pattern	165 (6.0)	0.04 (0.01, 0.08)

Abbreviations: BMI, body mass index; FEV1, forced expiratory volume in 1-second; FVC, forced vital capacity; GOLD, Global Initiative for Obstructive Lung Disease; LMS, Lambda-Mu-Sigma method; LMS-tile, percentile distribution of LMS derived Z-scores; %Pred, percent predicted ([measured ÷ predicted mean] x 100).

^*The discordant designations for respiratory impairment occurred exclusively as GOLD-abnormal but LMS-normal, because all participants who had GOLD-defined normal spirometry also had LMS-defined normal spirometry, but not vice-versa.

^† GOLD-defined normal spirometry was established by FEV1/FVC ≥0.70 and FVC ≥80%Pred; airflow limitation by FEV1/FVC <0.70, with mild as FEV1 ≥80%Pred, moderate as FEV1 50–79%Pred, and severe as FEV1 <50%Pred; and restrictive-pattern by FEV1/FVC ≥0.70 but FVC <80%Pred.

^‡ LMS-defined normal spirometry was established by FEV1/FVC and FVC, both ≥5 LMS-tile.

^§ Adjusted for age, height, sex, smoking history, BMI, number of chronic conditions, and health status.

^¶ The combined LMS and GOLD normal spirometry group was entirely determined by GOLD criteria, because all participants who had GOLD-defined normal spirometry also had LMS-defined normal spirometry, but not vice-versa.

DISCUSSION

In a large sample of community-living white older persons, we found that LMS-defined respiratory impairment was present in nearly one-quarter of participants and conferred an increased relative risk and 5-year cumulative incidence probability of COPD hospitalization. Participants who had LMS-defined severe airflow limitation were especially vulnerable, having an 8-fold elevation in the risk of COPD hospitalization and the highest 5-year cumulative incidence probability of COPD hospitalization (0.27). We also found that LMS-defined restrictive-pattern, but not airflow limitation, increased the risk for the competing outcome of death without COPD hospitalization. Lastly, in direct comparisons between LMS and GOLD criteria, we found that more than one-third of participants who had normal spirometry by LMS had respiratory impairment by GOLD, and that these participants had a low cumulative incidence probability of COPD hospitalization over 5 years, ranging from only 0.04 to 0.06, which was comparable to that among participants who had normal spirometry by GOLD alone (0.03). These results support the LMS method as a basis for defining spirometric respiratory impairment, particularly regarding the diagnosis and staging of COPD.

In the present study, the strong association between LMS-defined respiratory impairment and COPD hospitalization can be explained by three factors. First, both LMS-defined airflow limitation and restrictive-pattern were associated with a higher prevalence of cigarette smoking, especially current smoking. Smoking is the most potent risk factor for developing COPD,¹ which is most often characterized by airflow limitation but may also present as restrictive-pattern (air trapping, respiratory muscle weakness, or smoking-related interstitial lung disease).^31–36 In addition, current smoking substantially increases the risk of COPD hospitalization.^1,37,38 Second, because study participants who had self-reported asthma were excluded, the LMS-defined airflow limitation likely represented irreversible pathology, a defining feature of COPD.¹ Third, because LMS-derived Z-scores account for the three elements of the distribution—median, coefficient-of-variation, and skewness—the respiratory impairment defined by LMS is more likely to represent clinically significant pathology, rather than age-related reductions in pulmonary function.⁶ This supposition is supported by the strong and independent associations between LMS-defined respiratory impairment and other health-outcomes, including respiratory symptoms, frailty, and all-cause mortality, as shown in prior studies.^22–26

The differences observed in the present study regarding the association of LMS-defined airflow limitation and restrictive-pattern with the competing outcome of death without COPD hospitalization suggest potential differences in underlying pathophysiology. Because a reduced FVC—a diagnostic criterion for restrictive-pattern—is a risk factor for coronary heart disease and sudden cardiac death, the strong association between LMS-defined restrictive-pattern and death without COPD hospitalization suggests the additional contribution of a cardiovascular process.^39,40 In particular, heart failure may have mediated the association between LMS-defined restrictive pattern and death without COPD hospitalization in this population of older persons.⁴¹ The lack of an association between LMS-defined airflow limitation and death without COPD hospitalization is consistent with a primary respiratory process. Of note and as shown in Table 3, when death without COPD hospitalization was evaluated, 27.0% of participants who had airflow limitation were censored because they had a COPD hospitalization. Otherwise, if competing risks are not considered, we have shown previously that LMS-defined airflow limitation confers a comparable risk of death to that of restrictive-pattern.²⁴ Future work should evaluate the factors that lead to death, including intervening illnesses resulting in hospitalization,⁴² among older persons who have LMS-defined restrictive pattern and airflow limitation.

We found that more than one-third of participants who had normal spirometry by LMS had respiratory impairment by GOLD, and that these participants had low probabilities of COPD hospitalization, which were similar to that of participants who had normal spirometry by GOLD. These results support the view that respiratory impairment is overdiagnosed by GOLD criteria. Defining respiratory impairment based on the LMS approach, rather than the GOLD approach, has a strong mathematical and clinical justification. Using a single diagnostic threshold set at a Z-score of −1.64 (LLN), the present study and prior work have shown that LMS spirometric criteria yield plausible rates of respiratory impairment in aging populations, and are associated with multiple adverse outcomes.^22–26 This has implications for clinical practice. Specifically, because it is less likely to misidentify normal aging as respiratory impairment, the LMS approach may avoid inappropriate and harmful pharmacotherapy, as well as delays in the consideration of other diagnoses.^{22–26,43–46}

Although the present study avoids specific problems found in prior reports, several potential limitations should be noted. First, LMS prediction equations have not yet been published for non-whites. Because racial differences exist in pulmonary function, our results may not be generalizable to groups other than whites.^6,47 Second, we cannot confirm the specific pathophysiology that led to respiratory impairment. In addition to COPD, airflow limitation could have been due to bronchiectasis, given its increased prevalence in COPD, or to asthma, given that spirometry in CHS was not specifically obtained after a bronchodilator, that self-reported asthma (a key exclusion criterion) may have been underreported by participants, and that longstanding asthma may similarly lead to irreversible airflow limitation.^48–52 In addition to COPD and heart failure, other underlying causes for restrictive-pattern may have also included thoracic kyphosis, non-COPD related respiratory muscle weakness, and non-smoking related interstitial lung disease (usual interstitial pneumonia).^53–56 Nonetheless, because LMS-defined airflow limitation and restrictive-pattern were associated with an increased prevalence of smoking and risk of COPD hospitalization, the predominant contributor to respiratory impairment was likely COPD.

To address these unanswered questions, the association of LMS-defined respiratory impairment and COPD hospitalizations should be confirmed in subsequent studies that include participants from other racial and ethnic groups and that use postbronchodilator spirometry. [We note that LMS prediction equations will soon be available for non-whites.]⁷ Furthermore, to better evaluate the disease processes that underlie the association between respiratory impairment and COPD hospitalizations, future studies may need to include echocardiography, chest imaging, and more comprehensive tests of pulmonary function, including body plethysmography (e.g. to confirm restrictive lung disease),^57,58 respiratory muscle pressures, and diffusion capacity.

In summary, among community-living white older persons and in an analysis that explicitly addressed the competing risk of death, LMS-defined respiratory impairment significantly increased the relative risk and cumulative incidence probability of COPD hospitalization. These results, together with previously established associations with respiratory symptoms, frailty, and all-cause mortality, provide strong support for the LMS method as a basis for defining respiratory impairment in aging populations.^{6,7, 22–26}