- Research Article
- Open Access
Development of ATAQ-LAM: a tool to assess quality of life in Lymphangioleiomyomatosis
© Walker et al. 2015
- Received: 18 November 2014
- Accepted: 26 June 2015
- Published: 30 July 2015
Lymphangioleiomyomatosis (LAM) is a progressive lung disease that impairs health-related quality of life (HRQL).
To develop and conduct initial testing of ATAQ-LAM (A Tool to Assess Quality of Life in LAM).
A pilot version of the questionnaire was administered to respondents with LAM. We used a deletion algorithm to retain items and then applied multi-trait scaling to place retained items into appropriate domains, thus generating the ATAQ-LAM. Rasch analysis was used to assess item fit to a unidimensional model of HRQL. We determined internal consistency (IC) and floor and ceiling effects of ATAQ-LAM scores and conducted analyses aimed at supporting the validity of ATAQ-LAM.
Sixty-nine LAM patients provided response data. Thirty-two items survived the deletion algorithm. Scaling suggested ATAQ-LAM should have a four-domain structure (Exertional dyspnea, IC = 0.94; Cough, IC = 0.91; Fatigue, IC = 0.91; Emotional Well-Being, IC = 0.89). All items fit the Rasch model. Among 17 respondents with spirometry within three months of questionnaire completion, three of five ATAQ-LAM scores correlated with FEV1% (Exertional Dyspnea: r = −0.72, p = 0.001; Fatigue: r = −0.62, p = 0.007 and total: r = −0.53, p = 0.02). Compared with those in the highest tertile of FEV1%, subjects in the lowest tertile had greater ATAQ-LAM total (121.8 ± 14.3 vs. 79.8 ± 13.1, p = 0.04), Exertional Dyspnea (54.4 ± 6.3 vs. 25.5 ± 5.8, p = 0.005) and Fatigue (2.8 ± 2.4 vs. 14.8 ± 2.3, p = 0.03) scores, indicating greater impairment in HRQL.
ATAQ-LAM is a disease-specific instrument designed to assess HRQL in LAM patients. Additional studies are needed to generate data in support of its validity as an instrument capable of assessing HRQL over time in LAM patients.
- Tuberous Sclerosis Complex
- Supplemental Oxygen
- Exertional Dyspnea
- Scientific Advisory Committee
- HRQL Instrument
Lymphangioleiomyomatosis (LAM) is an incurable, low-grade malignancy that occurs either sporadically (S-LAM) or as a result of tuberous sclerosis complex (TSC-LAM). LAM affects predominantly women of child-bearing age, and although its rarity creates challenges in generating epidemiological estimates, LAM has been reported to occur in about 5 persons per 100,000 in the U.S [1–3]. In LAM, viable lung tissue is progressively replaced by thin-walled cysts, leading to an obstructive ventilatory defect and increasingly severe dyspnea .
Besides dyspnea, other intrusive symptoms include cough and fatigue. Given the symptoms, need for supplemental oxygen and the anxiety of living with a potentially life-shortening illness, it stands to reason that health-related quality of life (HRQL) may be impaired in patients living with LAM . Recent advances in understanding the pathogenesis of LAM, including the identification of promising therapeutic targets, have paved the way for testing novel or existing drugs . A trial (called MILES and designed to assess the efficacy and safety of rapamycin in LAM) showed that the drug halted LAM progression (as defined by decline in the one-second forced expiratory volume [FEV1]) and was associated with improvements in scores from one generic HRQL measure (the EuroQOL visual analogue scale for quality of life) but not other generic (the General Well-Being Questionnaire or the SF-36) or a chronic obstructive pulmonary disease specific instrument (the St. George’s Respiratory Questionnaire or SGRQ) . Whether a LAM-specific HRQL questionnaire would have performed similarly (or better) is unknown, because one was not available for use at the time MILES was conducted; however, it is believed that carefully developed, patient-tailored, and disease-specific instruments are likely to be more sensitive to underlying change than generic instruments .
We previously conducted seven focus groups with 37 TSC- or S-LAM patients to better understand how LAM affects patients’ lives  and to gather data that we used to develop content (domains and items) for a preliminary version of a LAM specific HRQL instrument. Shortness of breath, fatigue and cough were the symptoms mentioned in all seven groups, but shortness of breath was by far the most bothersome. The psychological experiences of living with LAM included frustration with the physical limitations LAM imposes; worry over living with an unpredictable, incurable, life-threatening illness; and myriad issues associated with assuming the “patient role”. Such issues included a loss of identity, the perception of being viewed as weak when yielding to physical limitations, embarrassment when using supplemental oxygen, and the loss of control that came with needing to take medication to fight the disease.
Here, we describe the development and initial validity analyses of ATAQ-LAM (A Tool to Assess Quality of Life in LAM), a LAM-specific, multi-dimensional instrument to assess HRQL.
Phase I: Item pool development and evaluation
Guided by qualitative content analysis of the transcripts from the focus groups  and clinical experience caring for LAM patients, we developed a pool of 56 items comprising a preliminary version of ATAQ-LAM. The qualitative work revealed themes including physical manifestations of shortness of breath, fatigue, cough, chest sensations, difficulty sleeping, and GI issues along with the psychological manifestations mentioned above.
In developing the items, we elected to focus on the most “proximal” effects—those that can be most directly linked back to living with LAM. Thus, the pilot version of ATAQ-LAM included the generated items grouped into 10 hypothesized domains: Exertional dyspnea (14 items), Effects of dyspnea (4 items), Chest pain (2 items), Cough and wheeze (13), Fatigue (7 items), Emotional well-being (7 items), Relationships (2 items), Sexuality (2 items), Symptom-specific HRQL (4 items), and Global HRQL (1 item). The recall period was 48 h. Each item was structured identically: between two contrasting statements were six numerical response options (1, 2, 3, 4, 5, 6)—a response of “1” indicates the strongest agreement with the statement on the left, and a response of “6” indicates the strongest agreement with the statement on the right. Summation scoring was used for the total and each domain, with higher scores connoting greater impairment.
Phase II: Item reduction and validation
Subjects and general overview
A convenience sample of subjects was recruited at the 2014 LAMposium in Chicago, Illinois. LAMposium is an annual, three-day conference that offers patients the opportunity to become educated about LAM and to connect with one another. After providing written, informed consent, each subject completed the pilot version once. Demographic and clinical data (including most recent percent predicted one-second forced expiratory volume [FEV1%] results, oxygen- and medication-use) were collected from patient records. After LAMposium, 11 LAM patients participated in debriefing interviews (Please see the Supplement for their baseline characteristics). All themes and items were deemed relevant by interviewees, and no other new topics or items were suggested, despite extensive probing.
Ethics, consent and permissions
All participants gave written, informed consent to participate. The study protocol was approved by the National Jewish Health Institutional Review Board (protocol #HS-2774).
Summary statistics were generated for baseline characteristics of the study sample.
We began the analysis by passing items through an item-deletion algorithm. Please see the Supplement for full details. First, items for which fewer than five response options were used were deleted (N = 3). Next, items missing responses from greater than 20 % of the sample were deleted (N = 2). Surviving items with greater than 49 % of respondents scoring at the floor (N = 13) or ceiling (N = 0) were considered for deletion, one of which was retained. Other items (N = 7) were deleted for item-item correlations > 0.7. Lastly, after thoughtful consideration, two “relationship” items were deleted because of concerns over lack of specificity to the experience of living with LAM. Ultimately, 32 items in 8 domains were retained: Exertional dyspnea (8 items), Effects of dyspnea (4 items), Chest pain (1 item), Cough (5 items), Fatigue (4 items), Emotional well-being (5 items), Symptom-specific HRQL (4 items) and global HRQL (1 item). The lone Chest pain item was moved to the Symptom-specific HRQL domain (thus yielding 7 domains).
We began the examination of the 32 retained items with a multi-trait scaling analysis, which is a method to address internal consistency reliability, item convergence, and item discrimination . This analysis generates a correlation matrix, with each row representing an item and each column a hypothesized domain. This matrix allows for evaluation of the extent to which an item correlates with its hypothesized domain. An item that correlates at a level of 0.3 or greater with its hypothesized domain possesses convergent validity, and an item whose correlation with its hypothesized domain is significantly greater than its correlation with any other domain possesses discriminant validity . Meng and colleagues’ method  was used to test whether the difference between these correlation coefficients was statistically significant. With results from this analysis, items can be rescaled (moved from one domain to another, better-fitting, domain), thus yielding the most systematically-structured domains. At the end of this analysis, redistribution yielded four domains (Exertional dyspnea, Cough, Fatigue, and Emotional well-being) and three other items that contribute to a total score but do not belong to any domain. Please see the Additional file 1 for full details.
Next, we subjected all items in aggregate to Rasch analysis (Winsteps, Version 18.104.22.168, www.winsteps.com). The mathematics of the Rasch model locates items and patients on the same scale according to—for an item—the level of HRQL impairment it signifies and—for a patient—her level of HRQL impairment (as determined by her responses to all items) . Rasch uses the difference between item and patient location—many terms have been used, but here we refer to these locations as item difficulty and patient severity, respectively—to model the probability of responses to each item. Because Rasch analysis is based on Guttman scaling, a patient’s endorsement of any item implies her endorsement of less difficult items (i.e., items of lesser difficulty or lower on the domain). Rasch analysis allows determination of whether a dataset adheres to fundamental measurement properties, an important one being unidimensionality—items function together to assess a singular construct (in the case of ATAQ-LAM, it would be LAM-specific HRQL). We assessed item fit by using the infit mean square statistic; values from 0.5-1.5 are considered useful for measurement, and values greater than 2.0 degrade measurement .
Performance characteristics and validity
Summation scoring was used for each domain and total. For each domain, we calculated mean scores, internal consistency reliability using Cronbach’s coefficient alpha,  and the percentage of respondents at their floor and ceiling values. In the “validity analyses,” we used the Pearson product–moment method to examine correlations between ATAQ-LAM scores and FEV1% (among patients who performed spirometry within three months of completing ATAQ-LAM). We hypothesized that scores from all but the cough domain would be significantly correlated with FEV1%. We also hypothesized that ATAQ-LAM scores would be greater (connoting more impaired HRQL) among patients with more severe LAM; we tested this hypothesis by conducting two analyses: 1) we used ANOVA to compare ATAQ-LAM scores among three subgroups defined by tertiles of FEV1%; and 2) we used t tests to compare ATAQ-LAM scores between subjects using—versus not using—supplemental oxygen.
Analyses other than Rasch were performed using SAS Version 9.3 software (SAS, Inc.; Cary, NC). We considered p < 0.05 to represent statistical significance.
Baseline characteristics of study participants
Age in years
49.1 ± 12.0 (range 24 – 73)
Trade school graduate
LAM duration, years
8.2 ± 7.9 (range 0.03 – 40)
1.70 ± 0.6 (range 0.79 – 2.87)
65.1 ± 22.5 (range 32 – 95)
Sleep + exertion, not at rest
All the time
Properties of four scales of ATAQ-LAM
Mean correlation among items within the domain
Mean ± SDa
% at floor
% at ceiling
33.9 ± 15.1
13.8 ± 7.1
15.4 ± 7.1
14.3 ± 7.2
84.6 ± 34.1
Comparison of ATAQ-LAM scores between subjects using any—versus no—supplemental oxygen
N = 29
N = 35
43.1 ± 14.0
27.7 ± 12.5
15.4 ± 13.1, 1.01
15.6 ± 6.7
12.5 ± 7.1
3.1 ± 6.9, 0.44
19.2 ± 7.2
12.7 ± 5.6
6.5 ± 6.3, 0.92
17.1 ± 8.5
12.3 ± 5.6
4.8 ± 6.9, 0.67
104.1 ± 34.1
71.3 ± 27.3
32.7 ± 30.2, 0.96
We developed ATAQ-LAM (“A Tool to Assess Quality of Life in LAM”), a 32-item questionnaire to assess HRQL in patients with LAM—a cystic lung disease with intrusive symptoms that affect how patients feel and function. Importantly, and in accordance with the movement to more-meaningfully include patients in the research enterprise, ATAQ-LAM was built by incorporating LAM patients’ perspectives at the ground level (in item generation). Through item deletion, we were left with items that cover the most prominent, important and proximal effects of LAM-related HRQL.
In 2002, the Scientific Advisory Committee (SAC) of the Medical Outcomes Trust  described standards for eight attributes that HRQL instruments should possess, including: 1) conceptual and measurement model; 2) reliability; 3) validity; 4) responsiveness; 5) interpretability; 6) respondent and administrative burden; 7) alternative forms; and 8) cultural and language adaptations. Although there is much more work to be done, the development strategy and analyses performed here confirm that certain psychometric properties—and by extension, certain SAC standards—of ATAQ-LAM are acceptable. The multi-trait analysis indicated that the final scaling should yield domains responsive to change over time and with the ability to discriminate between LAM patients with different degrees of HRQL impairment. Rasch analysis confirmed that overall, the items targeted the study sample well: there was good matching of items to the severity of HRQL impairment. Internal consistency reliability was excellent; the percent of subjects scoring at the minimum or maximum for any score suggests a very low possibility for significant floor or ceiling effects. Missingness was minimal. To calculate domain scores when responses are missing, we would suggest imputing responses for missing items using the mean from the non-missing items in a domain (assuming at least half the items in that domain have responses).
Although “validation” is never truly finished—rather, it is an ongoing process of testing hypotheses and gathering data—validity of ATAQ-LAM was supported by three findings: 1) significant correlation, in the expected direction, between ATAQ-LAM scores and FEV1%; 2) subjects requiring supplemental oxygen had higher ATAQ-LAM scores than subjects not requiring supplemental oxygen; and 3) ATAQ-LAM scores (total, Exertional Dyspnea and Fatigue) were significantly higher (more impaired HRQL) for subjects with the lowest FEV1% compared with subjects with the highest FEV1% values. The latter reveal that ATAQ-LAM can discriminate between patients with differing levels of LAM severity—and by extension, differing levels of HRQL impairment.
In a prior study, the SGRQ was observed to possess validity in LAM . One can presume that by including items most relevant to patients with LAM (and excluding those that are not, including several items on the SGRQ), ATAQ-LAM would be more sensitive to change than SGRQ; whether this is true remains a question that requires further study. In the MILES Trial, rapamycin halted physiological decline in patients with LAM, but did not lead to significant improvements in EuroQOL dyspnea or fatigue scores. Although ATAQ-LAM covers dyspnea and fatigue, the items are distinct from the EuroQOL, and there is no way to know whether, if they indeed existed in MILES, true differences between treatment and placebo arms in these two domains would have been detected by ATAQ-LAM.
The fact that the Cough domain and Emotional Well-being scores did not differ between subjects with the lowest—versus highest—FEV1% values is not terribly surprising: with only 17 subjects with useable FEV1 data (spirometry performed within three months of completing the questionnaire), we may simply have lacked power to detect such a relationship between cough and FEV1 if, indeed, one truly exists. More likely is that cough is independent of FEV1% in LAM. The same may be true for emotional well-being—there are a number of factors besides FEV1 that could contribute to a person’s sense of well-being.
There are limitations to this work that is but the first of many more needed to build confidence in ATAQ-LAM. The number of respondents was low; however, for a rare disease (LAM affects maybe 5000-10,000 people in the U.S.), 69 respondents is a relatively large proportion of patients. Even so, there were not enough subjects to allow us to perform a reliable factor analysis. Respondents completed the questionnaire at LAMposium which may have affected responses—traveling to Chicago for the meeting could have been tiring and/or stressful; seeing friends (other patients) for the first time in a long time could have “artificially” increased patients’ moods (and thus HRQL). For the FEV1% analyses, only 17 subjects had spirometry within three months of completing the questionnaire. In future studies, it will be helpful to have subjects complete the questionnaire prior to completing spirometry on the same day—or at least within the same week. Moving forward, it will also be interesting and informative to administer ATAQ-LAM alongside other patient-reported outcome measures (e.g., SF-36) to compare and contrast scores and their implications.
Despite these limitations, we have generated a multi-dimensional instrument whose items have convergent and discriminant validity. The instrument possesses excellent face validity, appears to have acceptable content validity, and is able to differentiate patients hypothesized to have differing degrees of HRQL impairment. Through future work, the validation process will continue (e.g., by determining the ability of ATAQ-LAM scores to capture change in HRQL over time) and estimates of the minimal important difference for its scores can be triangulated.
This study was supported in part by an unrestricted, investigator-initiated grant from the LAM Foundation. During the time this work was conducted, Dr. Swigris was supported in part by a Career Development Award from the NIH (K23 HL092227).
- Corrin B, Liebow AA, Friedman PJ. Pulmonary lymphangiomyomatosis. A review. Am J Pathol. 1975;79:348–82.PubMed CentralPubMedGoogle Scholar
- McCormack FX, Travis WD, Colby TV, Henske EP, Moss J. Lymphangioleiomyomatosis: calling it what it is: a low-grade, destructive, metastasizing neoplasm. Am J Respir Crit Care Med. 2012;186:1210–2.PubMed CentralPubMedView ArticleGoogle Scholar
- Taylor JR, Ryu J, Colby TV, Raffin TA. Lymphangioleiomyomatosis. Clinical course in 32 patients. N Engl J Med. 1990;323:1254–60.PubMedView ArticleGoogle Scholar
- Ryu JH, Moss J, Beck GJ, Lee JC, Brown KK, Chapman JT, et al. The NHLBI lymphangioleiomyomatosis registry: characteristics of 230 patients at enrollment. Am J Respir Crit Care Med. 2006;173:105–11.PubMed CentralPubMedView ArticleGoogle Scholar
- Belkin A, Albright K, Fier K, Desserich J, Swigris J. “Getting stuck with LAM”: patients perspectives on living with Lymphangioleiomyomatosis. Health and Quality of Life Outcomes. 2014;12:79.PubMed CentralPubMedView ArticleGoogle Scholar
- McCormack FX, Inoue Y, Moss J, Singer LG, Strange C, Nakata K, et al. Efficacy and safety of sirolimus in lymphangioleiomyomatosis. N Engl J Med. 2011;364:1595–606.PubMed CentralPubMedView ArticleGoogle Scholar
- Guyatt G, Feeny D, Patrick D. Measuring health-related quality of life. Ann Intern Med. 1993;118:622–9.PubMedView ArticleGoogle Scholar
- Hays R, Fayers P. Evaluating multi-item scales. In: Fayers P, Hays R, editors. Assessing quality of life in clinical trials: Methods and Practice. Secondth ed. Oxford: Oxford University Press; 2005. p. 41–53.Google Scholar
- Meng X, Rosenthal R, Rubin D. Comparing correlated correlation coefficients. Psychol Bull. 1992;111:172–5.View ArticleGoogle Scholar
- Bond T, Fox C. Applying the Rasch Model: Fundamental Measurement in the Human Sciences. Mahway, New Jersey: Lawrence Erlbaum Associates; 2007.Google Scholar
- Linacre J. What do Infit and Outfit, Mean-square and Standardized mean? Rasch Measurement Transactions. 2002;16:878.Google Scholar
- Cronbach L. Coefficient alpha and the internal structure of tests. Psychometrika. 1951;22:293–6.Google Scholar
- Aaronson N, Alonso J, Burnam A, Lohr KN, Patrick DL, Perrin E, et al. Assessing health status and quality-of-life instruments: attributes and review criteria. Qual Life Res. 2002;11:193–205.PubMedView ArticleGoogle Scholar
- Swigris JJ, Lee HS, Cohen M, Inoue Y, Moss J, Singer LG, et al. St. George’s Respiratory Questionnaire Has Longitudinal Construct Validity in Lymphangioleiomyomatosis. Chest. 2013;143:1671–8.PubMed CentralPubMedView ArticleGoogle Scholar
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