Figures
Abstract
Background
Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality and of loss of disability-adjusted life years worldwide. It often is accompanied by the presence of comorbidity.
Objectives
To systematically review the influence of COPD comorbidity on generic health-related quality of life (HRQoL).
Methods
A systematic review approach was used to search the databases Pubmed, Embase and Cochrane Library for studies evaluating the influence of comorbidity on HRQoL in COPD. Identified studies were analyzed according to study characteristics, generic HRQoL measurement instrument, COPD severity and comorbid HRQoL impact. Studies using only non-generic instruments were excluded.
Results
25 studies met the selection criteria. Seven studies utilized the EQ-5D, six studies each used the SF-36 or SF-12. The remaining studies used one of six other instruments each. Utilities were calculated by four EQ-5D studies and one 15D study. Patient populations covered both early and advanced stages of COPD and ranged from populations with mostly stage 1 and 2 to studies with patients classified mainly stage 3 and 4. Evidence was mainly created for cardiovascular disease, depression and anxiety as well as diabetes but also for quantitative comorbid associations. Strong evidence is pointing towards the significant negative association of depression and anxiety on reduced HRQoL in COPD patients. While all studies found the occurrence of specific comorbidities to decrease HRQoL in COPD patients, the orders of magnitude diverged. Due to different patient populations, different measurement tools and different concomitant diseases the study heterogeneity was high.
Citation: Huber MB, Wacker ME, Vogelmeier CF, Leidl R (2015) Comorbid Influences on Generic Health-Related Quality of Life in COPD: A Systematic Review. PLoS ONE 10(7): e0132670. https://doi.org/10.1371/journal.pone.0132670
Editor: Henrik Watz, Pulmonary Research Institute at LungClinic Grosshansdorf, GERMANY
Received: April 10, 2015; Accepted: June 17, 2015; Published: July 13, 2015
Copyright: © 2015 Huber et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Background
Generating over 76 million disability adjusted life-years (DALYs) globally in 2010, chronic obstructive pulmonary disease (COPD) surpassed road-traffic injuries, and when focusing on the US alone, it was the second highest contributor of DALYs after ischemic heart disease [1]. Smoking is the main risk factor for developing COPD [2], while all hallmarks of ageing [3] seem to influence its progression [4]. The pathogenesis of COPD is multidimensional [5, 6]. Inflammation, airway remodeling and fibrosis as well as tissue destruction seem to play constituting roles for the usually progressive nature of the disease [7]. There is currently no cure for COPD and the need for disease modifying treatments is still unmet [8] although possible targets seem promising [9, 10]. Comorbidity is common for COPD [11] and COPD patients use disproportional amounts of health services for the treatment of their comorbid conditions [12–16]. However, comorbidities are often considered as exclusion criteria for participants of COPD studies or are disregarded in respective evaluations [17]. In a study [18] from Italy in 2014, around 80% of COPD patients were treated by protocols derived from randomized clinical trials, for which they would not have been eligible to participate in. By doing so, a reductionist paradigm regarding disease classification [19, 20], largely based on end-stage appearance of symptoms, is compounded by strict study eligibility criteria. Taken together, this likely fails to account for different patho-phenotypes and thus mirrors a partial failure to reflect clinical reality. Health-related quality of life (HRQoL), next to survival and costs, is one important measure for cost-effectiveness of interventions. However, controversies about clinical implementation of HRQoL are still present [21]. The importance of improving clinical management and thus HRQoL of COPD patients afflicted by comorbidity has been object of investigation by several studies [22–25]. Therefore, evaluating comorbid influences on HRQoL in COPD could help to unravel disease constellations of interest for patients, physicians and payers. The aim of this review is to aggregate and summarize evidence for the influence of comorbidities on generic HRQoL in COPD.
Methods
Measuring HRQoL
HRQoL can be measured by different instruments. Some of these instruments are disease specific (e.g. St. George's Respiratory Questionnaire (SGRQ) [26]), while others are generic, meaning they can be compared among different fields of indication. Disease-specific instruments were excluded from this review. The rationale for this decision is rooted in the fact that disease-specific instruments were not designed to evaluate comorbid influences unless the effects are expressed by the index disease and can therefore be measured by the disease-specific instrument. For example, the widely used SGRQ was designed to measure the effects of airflow limitation on HRQoL [26] but it was not intended to measure effects unrelated to airflow limitation. In contrast, generic instruments were designed to measure HRQoL irrespective of multimorbidity and way of expression. Still, it could be interesting to evaluate the effect of different comorbidities on various measures of disease-specific outcome. This is beyond the scope of this review, however. Examples for well accepted and widely used generic instruments include the EuroQol five-dimension questionnaire (EQ-5D) [27], which, inter alia, was used in major COPD studies like the TOwards a Revolution in COPD Health (TORCH)-trial [28]. The EQ-5D consists of 5 descriptive questions (self-classifier) and a valuation by a visual analogue scale (VAS) labelled EQ-5D-VAS score. The results for the 5 dimensions can be transformed into utilities, which are needed for cost-effectiveness analysis. In order to derive population based utilities for different health states the most widely used method is time-trade-off (TTO) [29], typically surveyed in representative samples of the general population. The TTO procedure elicits the time in perfect health which respondents consider equal to a given time in a health state, with the relation of both rendering the health state’s value. Other important generic instruments include but are not limited to the 36-Item Short Form Health Survey (SF-36) [30], the 12-Item Short Form Health Survey (SF-12) [31], two instruments with pre-defined summary scores such as average across the items of one dimension, and the 15D questionnaire (15D) [32]. The SF-36 is made up of 36 items, which are grouped into 8 subdomains. For each subdomain, a score between 0 (worst) and 100 (best) can be reached. The SF-12 is a short version of the SF-36 and contains 12 items. These 12 items reproduce at least 90% variance of the physical component summary score (PCS) and the mental component summary score (MCS) from SF-36 [31]. The 15D is a 15-dimensional self-administered generic instrument which can be used a single and profile index score measure, also rendering utility measurement. Citations for other, less used instruments are provided in this review and can be used to gather more respective information. Beyond clinical assessment, utility measures of HRQoL provide a key effect measure in economic evaluation studies.
Search strategy and exclusion criteria
The respective literature search was performed on May 5th 2015. Studies only using disease-specific instruments were excluded. The publication date was not restricted. Pubmed was searched using the following terms: (((((copd[MeSH Terms]) OR copd) OR obstructive lung disease) OR obstructive pulmonary disease)) AND (((("Quality of Life"[Mesh]) OR quality of life) OR health status) OR "Health Status"[Mesh]) AND (("Comorbidity"[Mesh]) OR comorbid*)
This resulted in 1125 hits. Embase was searched by ((copd OR obstructive lung disease OR obstructive pulmonary disease) AND comorbid* AND (quality of life OR health status)) NOT SU = MEDLINE and was restricted for journal articles. 629 results were found. 3 records [33–35] were identified by hand search. In addition to this, the Cochrane Library was searched for respective reviews (COPD AND comorbid* AND quality of life) but none were found. Combining the results lead to 1757 studies in total. After removing duplicates, 1528 studies remained. The language filter (English, German) was implemented and studies using non-generic HRQoL instruments were removed. Studies that did not deliver comorbid based results, were also excluded. The PRISMA flow diagram [36] was used to depict the study selection process (Fig 1). The PRISMA checklist is annexed as supplementary data (see S1 File.) as well as a list (see S2 File.) with studies and their respective reason for exclusion.
Data extraction
Basic population characteristics, generic HRQoL measurement instrument, severity of COPD, prevalence of comorbidities and the comorbid association regarding generic HRQoL were extracted from all selected studies under review. Valuation methods for index instruments like the EQ-5D and the 15D instrument were actively investigated for unless clearly stated in the respective study. Comorbid generic HRQoL data was the main point of interest. Tabular aggregation of homogenous and sorted data for single comorbidities was attempted but failed due to heterogeneity issues among studies. Thus, study-per-study subsumption based on utilized HRQoL instrument was incorporated. They were separated into instruments valuated by patients (e.g. EQ-5D-VAS only), instruments valuated by population (e.g. EQ-5D-5L and 15D) and measures with pre-defined summary score (e.g. SF-36, SF-12). This improves clarity for different stakeholders. Medical doctors, for example, will prefer patient valuated results due to their relevance for clinical practice. In order to reduce possible bias [37] of this review, non-significant associations of comorbidities and HRQoL were also reported. The discussion part is structured based on comorbidity.
Results
Of the 25 studies, 16 were published in 2010 or later, while 6 were from 2014 alone. Study origin was diverse, including countries from different parts of the world. The mostly used generic instruments for measuring HRQoL were the EQ-5D in seven studies [38, 33, 39–43], the SF-12 in six studies [44–49] and SF-36 in six studies [34, 35, 50–53] as well. Table 1A shows a summary of basic study characteristics, COPD severity and comorbidity impact based on HRQoL valued by patients. Table 1B shows summary measures utilizing population valuation and Table 1C shows respective parameters for studies using pre-defined summary measure instruments.
All but one EQ-5D study reported using the EQ-5D-3L version with a 3-level distinction of problems reported. Miravitlles et al. 2014 [43] did neither report the used version, nor the used value sets. Three studies used patient’s VAS valuation [33, 39, 38]. Three studies used value sets based on time-trade-off valuations by a general population, taken from a UK [60], US [61] and Spanish [62] setting. Instruments used by a single study each are the self-administered 15-dimension 15D [32], the 35-item Multidimensional Index of Life Quality (MILQ) [63], the 16-item Quality of Life Scale (QOLS) [64], the 26-item World Health Organization Quality of Life-BREF (WHOQOL-BREF) [65], the 4-item Health Related Quality of Life-4 (HRQOL-4) [66] and the 38-item Nottingham Health Profile (NHP) [67]. They were utilized by one study [54, 56, 59, 58, 57, 43, 55] each. Valuation of the 15D instrument was done by population-based multiattributive utility theory [32]. Besides EQ-5D and 15D, no index instruments were used. References regarding the validation of the used instrument for COPD as well as respective comorbidity were only given by one study [51]. Sample sizes ranged from 58 [58] to 11,985 [45] COPD patients. The prevailing gender was male in the majority of studies and was even as high as 95% in a study [52] with veterans and 93% in a study [58] from India. The average age was above 60 years in all studies respectively. The severity of COPD was assessed in nearly all studies. Classification of COPD severity was mostly based on GOLD criteria but other cut-off points for predicted forced expiratory volume in 1 second (FEV1) were also used for classification of patients. Some studies only stated average FEV1 values and one study [56] did not state severity classification at all. The majority of patients had an average FEV1 predicted of around and/or above 50% but heterogeneity was high. Studies [42, 55, 49] with mainly severe to very severe cases of COPD were also present. Seven studies [50, 48, 56, 52, 49, 57, 58] assessed the number of comorbidities per patient. The majority of patients seem to be afflicted by around two or more comorbidities. All studies reported some form of association between specific comorbidity and worse HRQoL. However, the comorbid influence as well as its significance differed among studies. The most prevalent evaluated diseases were cardiovascular disease (CVD), which is a far reaching umbrella term for diseases of the heart and/or blood vessels, as well as depression and anxiety. Ten studies [38, 39, 45, 54, 47, 56, 44, 59, 53, 46] looked at the influence of cardiovascular disease on HRQoL. A negative association was stated in seven of the studies, while three studies [54, 59, 53] did not find a significant association. The non-significance was mentioned by the authors but not explained through specific reasons. Regarding depression, only one [56] of the ten studies [33, 39, 34, 40, 48, 56, 42, 43, 57, 54] did not find a significant negative association with HRQoL. The EQ-5D index, based on TTO, was associated with depression in three studies [40, 42, 43]. In another study [54], psychiatric disease had an adjusted odds ratio (OR) of 4.65 for low 15D score. Ng et al. 2009 [48] calculated an adjusted OR of 4.17 for depression and low self-rated health measured by SF-12. Two additional studies [52, 57] report a significant negative association for depression and HRQoL measured by SF-36 and QOLS. A comparable picture emerges for anxiety, which had a non-significant association in only one study [57]. Diabetes was associated with worse HRQoL in all respective studies [38, 45, 54, 35, 46, 56, 44]. Two studies [59, 42], using EQ-5D index and NHP, also found a negative influence of musculoskeletal disease on HRQoL, the former study stating a value of -0.08 (p = 0.006) based on multiple linear regression for the association between presence of musculoskeletal disease and EQ-5D index. The presence of comorbidity, irrespective of type, was also associated with lower HRQoL scores in six [46, 56, 59, 50, 40, 53] out of ten studies and was significantly associated with worse physical functioning in one study [52]. Presence of more than one comorbidity resulted in an adjusted OR of 3.22 for poor HRQoL, measured by NHP [59]. Rutten-van Mölken et al. 2006 [41] stated, that a higher number of comorbidities and higher Charlson Comorbidity Index (CCI) score was not associated with lower EQ-5D-VAS score, while the impact on EQ-5D index was significant but only small. Blindermann et al. 2009 also found CCI not to be associated with worse MILQ scores but considered this to be rooted in the low CCI median of 1 they started with. Three studies [49, 58, 57] did not find any significant association for number of comorbidities and worse generic HRQoL. Other significant negative associations were found for insomnia [53], alcohol abuse [54], arthritis [56], gastroesophageal reflux disease (GERD) [51] and osteoporosis [42].
Discussion
The results clearly show that specific concomitant diseases in COPD were associated with worse generic HRQoL, irrespective of utilized instrument. However, the degree of HRQoL impact varied and some studies delivered contradicting results.
Comorbid CVD
One of the comorbidities with significant influence on HRQoL was CVD. Boros et al. 2012 [38] calculated a standardized linear regression coefficient of -0.313 for the association of the EQ-5D-VAS and presence of heart failure in COPD. This transforms into a 15 point reduction on the VAS scale (according to author correspondence). This standardized coefficient is around 10 times higher than standardized EQ-5D-VAS coefficients for other cardiovascular diseases excluding ischemic heart disease (-0.145), in the same study. Frei et al. 2014 [39] stated a EQ-5D-VAS predictor of -4.6 and -3.8 for cerebrovascular and symptomatic heart disease respectively, while Wacker et al. 2014 [44] found a significant negative association for heart failure as well as stroke and the physical and mental component summary among patients with COPD. This receives additional importance because observational data indicates, that COPD patients are at increased risk for developing CVD [68]. Three [59, 54, 53] out of ten respective studies [38, 53, 39, 54, 45, 47, 44, 56, 59, 46] did not find a significant association between comorbid CVD and HRQoL. Van Manen et al. 2001 [53] explain the lack of significant association for heart disease by pointing to the relative low number of patients with the disease (n = 25) in their study. Koskela et al. 2014 [54] did not state a reason for the lack of association and Wijnhoven et al. 2003 [59] only found a negative association for heart disease and asthma but not for COPD. They point towards differences in disease characteristics as possible explanation. In addition to this, CVD is a far reaching umbrella term for diseases of the heart and circulation and this may explain inconsistencies among results since different patient populations may be affected by different cardiovascular disorders and different severity grades. This general limitation is mentioned by Sundh et al. 2015 [42].
Comorbid depression and anxiety
Other comorbidities with strong association for worse HRQoL were depression and anxiety. 11 studies evaluated its comorbid influence on HRQoL. Cut-off points for being depressed were ≥ 11 for the HADS in three studies. Interestingly, the only study [57] using a lower HADS cut-off point of ≥8 stated a non-significant (p<0.381) HRQoL association for anxiety but not depression. The non-significance may be explained by the lower cut-off point and hence, a lower severity grade of overall anxiety in this patient population. This is confirmed by looking at the HADS-A mean scores. While Bentsen et al. [57] stated a HADS-A mean of 5.9 (SD: 3.9), the other two studies [39] stated higher means of 9 (SD = 4.2) for females, 7.2 (SD: 4) for males or an average of 7 (SD: na). Table 2 shows an overview of respective results among studies using the EQ-5D instrument. It became apparent that depression ranked first among comorbidities with significant association for worse HRQoL in all four studies. Consequentially, in all three studies [33, 39, 40] where a comparison was possible, depression had always a stronger influence on worse HRQoL than anxiety. Naberan et al. 2012 [40] calculated an r value of -0.674 (-0.602) for the correlation of HADS depression (anxiety) and the EQ-5D index score and found this to be the best correlation in their study. In another study [39], depression was associated with a reduction of EQ-5D-VAS score by around 9 points. Interesting from a practical perspective, Cleland et al. 2007 [33] stated the possible use of the EQ-5D-VAS as quick and easy screening tool for patient’s mental health in COPD. This procedure would be supported by the results of Frei et al. 2014 [39], who also found a strong association for depression and low EQ-5D-VAS score. Irrespective of these two studies, other authors point towards usage of health status measures as indicators for depression in COPD as well [69]. A problem regarding evaluation of index based influence of comorbidity on HRQoL in COPD is that depression and anxiety, two disorders which showed a strong influence on HRQoL, are completely missing from indices like the CCI. Therefore exclusively using these indices will likely fail to deliver a complete picture of comorbid associations with HRQoL. In general, we would therefore agree with Frei et al. 2014 [39], who stated, that comorbidity based indices which predict mortality are not designed to evaluate HRQoL status. They may serve as indicator but attention has to be paid when evaluating their results.
Comorbid diabetes
Diabetes was associated with worse HRQoL in all respective studies and among many different instruments including EQ-5D-VAS [38], SF-12 [45, 46, 44], SF-36 [35], 15D [54] and HRQOL-4 [56]. In addition to this finding, comorbid diabetes seems to worsen the prognosis [70] and lengthen the hospital stays of COPD patients with acute exacerbations due to immune dysfunction [71].
Comorbid musculoskeletal disease
Musculoskeletal disease also was significantly associated with worse HRQoL in two studies [42, 59]. The association for presence of the disease and EQ-5D index score was -0.08 (p = 0.006) [42]. Interestingly, muscle wasting in COPD seems also to be a better predictor for mortality, than BMI [72, 73]. Muscle wasting is connected to fatigue and reduced activity [74]. Since mobility, activity and self-care are three dimensions of the EQ-5D a drop of HRQoL scores in patients who suffer from muscle wasting is not very surprising.
Number of comorbidities
Three [57, 49, 58] out of eleven studies [38, 41, 52, 53, 50, 46, 57, 58, 56, 59, 49] did not find a significant association between number of comorbidities and worse generic HRQoL. Shavro et al. 2012 [58] mention this finding to be surprising and possible rooted in the small sample size (n = 58) and/or aspects of Indian culture. A connection between comorbidities and worse scores for the WHOQOL-BREF or SGRQ scores was not found in their study. Xiang et al. 2014 [49] found number of comorbidities to be associated with worse SGRQ results but not with SF-12. Bentsen et al. 2014 [57] found the same relation for QOLS and SF-12. It seems counter-intuitive that disease specific instruments react more sensitive to the presence of comorbidity compared to generic instruments, unless the respective comorbidity has a significant effect on COPD symptoms. Unfortunately, since only number of comorbidities were evaluated by both studies, specific conclusions for individual diseases can’t be drawn and the statistical power is low to begin with. However, advantages of using disease-specific and generic instruments together have been stated before [75]. Conducting a review on the comorbid influences on HRQoL measured by disease-specific instruments, would thus be interesting from a research perspective.
Comparing comorbid costs and HRQoL
When considering the effect of comorbidities on the cost-effectiveness of COPD intervention, next to HRQoL, the cost impact is relevant. A previous systematic review found comorbidities in COPD patients to be associated with significant excess cost [76]. However, the impact of comorbidities on cost of COPD patients cannot directly be compared with that of HRQoL: The instruments used for measurement of HRQoL were diverse, as were statistical measures of comorbidity impact. Furthermore, control groups were lacking in many HRQoL studies. In contrast, most of the HRQoL studies measured stage of COPD while this information was sparse in cost studies. A simultaneous view upon cost and HRQoL impact of comorbidities is thus hindered by possible differences in study patients, and by diverging methods. Simultaneous study of both dimensions in individual patients is needed to provide a comprehensive view of comorbidity impact.
Limitations and strength of this review
Limitations of this review include the non-active search for studies which consider COPD as the comorbidity and other diseases as index disease. However, by doing so, an increase in already high study heterogeneity was likely prevented to some degree. The present heterogeneity is rooted in the evaluation of different patient populations and comorbidities but also in the use of different HRQoL instruments, different value-sets and different outcome measurements, which decrease comparability even further. Moreover, when population-based value sets are being used to aggregate HRQoL, applying an experience-based approach rather than one based on hypothetical health states as in three studies [42, 41, 40] could help to increase physician acceptance of HRQoL results, shifting the focus to actual patient experience [77]. Last but not least the severity of comorbidities was not assessed in the studies under review. Thus, the influence of comorbid severity on HRQoL remains unclear.
Strength of this review is the aggregation of generic evidence on HRQoL pertaining the comorbid influences in COPD and illustrating evidence in aggregated and comprehended form. Over 1700 studies were filtered and screened and to our knowledge this is the first review evaluating comorbid effects on generic HRQoL. Furthermore, using multiplicative methods, which showed superior performance compared to minimum or additive methods when trying to incorporate selective comorbid burden into health state utilities [78–80] could allow creating decision analytical COPD models by the aggregated data, which resemble clinical reality to a higher degree.
Conclusion
Comorbidities in COPD are significantly associated with worse HRQoL among all used instruments. The majority of evidence was generated for CVD, depression and anxiety as well as diabetes but other comorbid conditions like musculoskeletal disease, have a worsening influence on HRQoL in COPD as well. The sole presence of quantitative comorbidity was also connected to lower HRQoL. These results should be considered in clinical practice and in studies evaluating interventions in respective patient populations. Not considering the HRQoL impact of existing comorbidity might lead to inappropriate clinical management and to biases in evaluation studies. It became apparent that facilitating multimorbid intervention guidance, instead of applying a parsimony based single disease paradigm, constitutes an important current and future goal for patient management in COPD.
Author Contributions
Analyzed the data: MBH. Wrote the paper: MBH MEW CFV RL. Drafted the manuscript: MBH.
References
- 1. Murray CJL, Lopez AD. Measuring the Global Burden of Disease. New England Journal of Medicine. 2013;369(5):448–57. pmid:23902484
- 2. Raherison C, Girodet PO. Epidemiology of COPD. Eur Respir Rev. 2009;18(114):213–21. pmid:20956146
- 3. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194–217. pmid:23746838
- 4.
Meiners S, Eickelberg O, Konigshoff M. Hallmarks of the ageing lung. Eur Respir J. 2015. https://doi.org/10.1183/09031936.00186914
- 5. Tuder RM, Petrache I. Pathogenesis of chronic obstructive pulmonary disease. J Clin Invest. 2012;122(8):2749–55. pmid:22850885
- 6. Agusti A, Edwards LD, Rennard SI, MacNee W, Tal-Singer R, Miller BE et al. Persistent systemic inflammation is associated with poor clinical outcomes in COPD: a novel phenotype. PLoS One. 2012;7(5):e37483. pmid:22624038
- 7. Postma DS, Timens W. Remodeling in Asthma and Chronic Obstructive Pulmonary Disease. Proceedings of the American Thoracic Society. 2006;3(5):434–9. pmid:16799088
- 8. Martinez FJ, Donohue JF, Rennard SI. The future of chronic obstructive pulmonary disease treatment—difficulties of and barriers to drug development. Lancet. 2011;378(9795):1027–37. pmid:21907866
- 9. Barnes PJ. New anti-inflammatory targets for chronic obstructive pulmonary disease. Nat Rev Drug Discov. 2013;12(7):543–59. pmid:23977698
- 10.
Comer BS, Ba M, Singer CA, Gerthoffer WT. Epigenetic targets for novel therapies of lung diseases. Pharmacol Ther. 2014. https://doi.org/10.1016/j.pharmthera.2014.11.006
- 11. Fabbri LM, Beghe B, Agusti A. COPD and the solar system: introducing the chronic obstructive pulmonary disease comorbidome. American Journal of Respiratory and Critical Care Medicine. 2012;186(2):117–9. pmid:22798411
- 12. Vanfleteren LEGW, Spruit MA, Groenen M, Gaffron S, van Empel VPM, Bruijnzeel PLB et al. Clusters of Comorbidities Based on Validated Objective Measurements and Systemic Inflammation in Patients with Chronic Obstructive Pulmonary Disease. American Journal of Respiratory and Critical Care Medicine. 2013;187(7):728–35. pmid:23392440
- 13. Gershon AS, Mecredy GC, Guan J, Victor JC, Goldstein R, To T. Quantifying comorbidity in individuals with COPD: a population study. Eur Respir J. 2015;45(1):51–9. pmid:25142481
- 14. Baty F, Putora PM, Isenring B, Blum T, Brutsche M. Comorbidities and Burden of COPD: A Population Based Case-Control Study. PLoS ONE. 2013;8(5).
- 15. Divo M, Cote C, de Torres JP, Casanova C, Marin JM, Pinto-Plata V et al. Comorbidities and risk of mortality in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2012;186(2):155–61. pmid:22561964
- 16. Decramer M, Janssens W. Chronic obstructive pulmonary disease and comorbidities. Lancet Respir Med. 2013;1(1):73–83. pmid:24321806
- 17. Boyd CM, Vollenweider D, Puhan MA. Informing evidence-based decision-making for patients with comorbidity: availability of necessary information in clinical trials for chronic diseases. PLoS One. 2012;7(8):e41601. pmid:22870234
- 18. Scichilone N, Basile M, Battaglia S, Bellia V. What proportion of chronic obstructive pulmonary disease outpatients is eligible for inclusion in randomized clinical trials? Respiration. 2014;87(1):11–7. pmid:24281343
- 19. Loscalzo J, Kohane I, Barabasi AL. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol Syst Biol. 2007;3:124. pmid:17625512
- 20. Loscalzo J, Barabasi AL. Systems biology and the future of medicine. Wiley Interdiscip Rev Syst Biol Med. 2011;3(6):619–27. pmid:21928407
- 21. Kind P, Lafata JE, Matuszewski K, Raisch D. The use of QALYs in clinical and patient decision-making: issues and prospects. Value Health. 2009;12 Suppl 1:S27–30. pmid:19250128
- 22. Hillas G, Perlikos F, Tsiligianni I, Tzanakis N. Managing comorbidities in COPD. Int J Chron Obstruct Pulmon Dis. 2015;10:95–109. pmid:25609943
- 23.
Campo G, Pavasini R, Malagu M, Mascetti S, Biscaglia S, Ceconi C et al. Chronic Obstructive Pulmonary Disease and Ischemic Heart Disease Comorbidity: Overview of Mechanisms and Clinical Management. Cardiovasc Drugs Ther. 2015. https://doi.org/10.1007/s10557-014-6569-y
- 24. Fabbri LM, Boyd C, Boschetto P, Rabe KF, Buist AS, Yawn B et al. How to integrate multiple comorbidities in guideline development: article 10 in Integrating and coordinating efforts in COPD guideline development. An official ATS/ERS workshop report. Proc Am Thorac Soc. 2012;9(5):274–81. pmid:23256171
- 25. Effing TW, Lenferink A, Buckman J, Spicer D, Cafarella PA, Burt MG et al. Development of a self-treatment approach for patients with COPD and comorbidities: an ongoing learning process. J Thorac Dis. 2014;6(11):1597–605. pmid:25478200
- 26. Jones PW, Quirk FH, Baveystock CM, Littlejohns P. A self-complete measure of health status for chronic airflow limitation. The St. George's Respiratory Questionnaire. Am Rev Respir Dis. 1992;145(6):1321–7. pmid:1595997
- 27. EuroQol—a new facility for the measurement of health-related quality of life. Health Policy. 1990;16(3):199–208. pmid:10109801
- 28. Briggs AH, Glick HA, Lozano-Ortega G, Spencer M, Calverley PM, Jones PW et al. Is treatment with ICS and LABA cost-effective for COPD? Multinational economic analysis of the TORCH study. Eur Respir J. 2010;35(3):532–9. pmid:19717476
- 29. Morimoto T, Fukui T. Utilities measured by rating scale, time trade-off, and standard gamble: review and reference for health care professionals. J Epidemiol. 2002;12(2):160–78. pmid:12033527
- 30. Ware JE Jr., Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. 1992;30(6):473–83. pmid:1593914
- 31. Ware J Jr., Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220–33. pmid:8628042
- 32. Sintonen H. The 15D instrument of health-related quality of life: properties and applications. Ann Med. 2001;33(5):328–36. pmid:11491191
- 33. Cleland JA, Lee AJ, Hall S. Associations of depression and anxiety with gender, age, health-related quality of life and symptoms in primary care COPD patients. Fam Pract. 2007;24(3):217–23. pmid:17504776
- 34. Kil SY, Oh WO, Koo BJ, Suk MH. Relationship between depression and health-related quality of life in older Korean patients with chronic obstructive pulmonary disease. J Clin Nurs. 2010;19(9–10):1307–14. pmid:20500340
- 35. Krishnan G, Grant BJ, Muti PC, Mishra A, Ochs-Balcom HM, Freudenheim JL et al. Association between anemia and quality of life in a population sample of individuals with chronic obstructive pulmonary disease. BMC Pulm Med. 2006;6:23. pmid:16953872
- 36. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. pmid:19621072
- 37. Ioannidis JP. Why most published research findings are false. PLoS Med. 2005;2(8):e124. pmid:16060722
- 38. Boros PW, Lubinski W. Health state and the quality of life in patients with chronic obstructive pulmonary disease in Poland: A study using the EQ-5D questionnaire. Polskie Archiwum Medycyny Wewnetrznej. 2012;122(3):73–81. pmid:22354456
- 39. Frei A, Muggensturm P, Putcha N, Siebeling L, Zoller M, Boyd CM et al. Five comorbidities reflected the health status in patients with chronic obstructive pulmonary disease: the newly developed COMCOLD index. J Clin Epidemiol. 2014;67(8):904–11. pmid:24786594
- 40. Naberan K, Azpeitia A, Cantoni J, Miravitlles M. Impairment of quality of life in women with chronic obstructive pulmonary disease. Respir Med. 2012;106(3):367–73. pmid:22018505
- 41. Rutten-van Molken MP, Oostenbrink JB, Tashkin DP, Burkhart D, Monz BU. Does quality of life of COPD patients as measured by the generic EuroQol five-dimension questionnaire differentiate between COPD severity stages? Chest. 2006;130(4):1117–28. pmid:17035446
- 42. Sundh J, Johansson G, Larsson K, Linden A, Lofdahl CG, Janson C et al. Comorbidity and health-related quality of life in patients with severe chronic obstructive pulmonary disease attending Swedish secondary care units. Int J Chron Obstruct Pulmon Dis. 2015;10:173–83. pmid:25653516
- 43. Miravitlles M, Molina J, Quintano JA, Campuzano A, Perez J, Roncero C. Factors associated with depression and severe depression in patients with COPD. Respir Med. 2014;108(11):1615–25. pmid:25312692
- 44. Wacker ME, Hunger M, Karrasch S, Heinrich J, Peters A, Schulz H et al. Health-related quality of life and chronic obstructive pulmonary disease in early stages—longitudinal results from the population-based KORA cohort in a working age population. BMC Pulmonary Medicine. 2014;14(1).
- 45. Janson C, Marks G, Buist S, Gnatiuc L, Gislason T, McBurnie MA et al. The impact of COPD on health status: findings from the BOLD study. Eur Respir J. 2013;42(6):1472–83. pmid:23722617
- 46. Lopez Varela MV, Montes de Oca M, Halbert R, Muino A, Talamo C, Perez-Padilla R et al. Comorbidities and health status in individuals with and without COPD in five Latin American cities: the PLATINO study. Arch Bronconeumol. 2013;49(11):468–74. pmid:23856439
- 47. de Miguel-Diez J, Carrasco-Garrido P, Rejas-Gutierrez J, Martin-Centeno A, Gobartt-Vazquez E, Hernandez-Barrera V et al. The influence of heart disease on characteristics, quality of life, use of health resources, and costs of COPD in primary care settings. BMC Cardiovascular Disorders. 2010;10(8).
- 48. Ng TP, Niti M, Fones C, Yap KB, Tan WC. Co-morbid association of depression and COPD: a population-based study. Respir Med. 2009;103(6):895–901. pmid:19136238
- 49.
Xiang YT, Wong TS, Tsoh J, Ungvari GS, Correll CU, Sareen J et al. Quality of Life in Older Patients With Chronic Obstructive Pulmonary Disease (COPD) in Hong Kong: A Case-Control Study. Perspect Psychiatr Care. 2014. https://doi.org/10.1111/ppc.12073
- 50. van Manen JG, Bindels PJ, Dekker FW, Bottema BJ, van der Zee JS, Ijzermans CJ et al. The influence of COPD on health-related quality of life independent of the influence of comorbidity. J Clin Epidemiol. 2003;56(12):1177–84. pmid:14680668
- 51. Rascon-Aguilar IE, Pamer M, Wludyka P, Cury J, Vega KJ. Poorly treated or unrecognized GERD reduces quality of life in patients with COPD. Dig Dis Sci. 2011;56(7):1976–80. pmid:21221789
- 52. Cully JA, Graham DP, Stanley MA, Ferguson CJ, Sharafkhaneh A, Souchek J et al. Quality of life in patients with chronic obstructive pulmonary disease and comorbid anxiety or depression. Psychosomatics. 2006;47(4):312–9. pmid:16844889
- 53. van Manen JG, Bindels PJ, Dekker EW, Ijzermans CJ, Bottema BJ, van der Zee JS et al. Added value of co-morbidity in predicting health-related quality of life in COPD patients. Respir Med. 2001;95(6):496–504. pmid:11421508
- 54. Koskela J, Kilpelainen M, Kupiainen H, Mazur W, Sintonen H, Boezen M et al. Co-morbidities are the key nominators of the health related quality of life in mild and moderate COPD. BMC Pulm Med. 2014;14:102. pmid:24946786
- 55. Blinderman CD, Homel P, Andrew Billings J, Tennstedt S, Portenoy RK. Symptom Distress and Quality of Life in Patients with Advanced Chronic Obstructive Pulmonary Disease. Journal of Pain and Symptom Management. 2009;38(1):115–23. pmid:19232893
- 56. Putcha N, Puhan MA, Hansel NN, Drummond MB, Boyd CM. Impact of co-morbidities on self-rated health in self-reported COPD: an analysis of NHANES 2001–2008. Copd. 2013;10(3):324–32. pmid:23713595
- 57. Bentsen SB, Miaskowski C, Rustoen T. Demographic and clinical characteristics associated with quality of life in patients with chronic obstructive pulmonary disease. Quality of life research: an international journal of quality of life aspects of treatment, care and rehabilitation. 2014;23(3):991–8.
- 58. Shavro SA, Ezhilarasu P, Augustine J, Bechtel JJ, Christopher DJ. Correlation of health-related quality of life with other disease severity indices in Indian chronic obstructive pulmonary disease patients. International journal of chronic obstructive pulmonary disease. 2012;7:291–6. pmid:22615528
- 59. Wijnhoven HA, Kriegsman DM, Hesselink AE, de Haan M, Schellevis FG. The influence of co-morbidity on health-related quality of life in asthma and COPD patients. Respir Med. 2003;97(5):468–75. pmid:12735662
- 60. Dolan P. Modeling valuations for EuroQol health states. Med Care. 1997;35(11):1095–108. pmid:9366889
- 61. Shaw JW, Johnson JA, Coons SJ. US valuation of the EQ-5D health states: development and testing of the D1 valuation model. Med Care. 2005;43(3):203–20. pmid:15725977
- 62. Badia X, Roset M, Montserrat S, Herdman M, Segura A. [The Spanish version of EuroQol: a description and its applications. European Quality of Life scale]. Med Clin (Barc). 1999;112 Suppl 1:79–85.
- 63. Avis NE, Smith KW, Hambleton RK, Feldman HA, Selwyn A, Jacobs A. Development of the multidimensional index of life quality. A quality of life measure for cardiovascular disease. Med Care. 1996;34(11):1102–20. pmid:8911427
- 64. Burckhardt CS, Woods SL, Schultz AA, Ziebarth DM. Quality of life of adults with chronic illness: a psychometric study. Res Nurs Health. 1989;12(6):347–54. pmid:2602575
- 65. Skevington SM, Lotfy M, O'Connell KA. The World Health Organization's WHOQOL-BREF quality of life assessment: psychometric properties and results of the international field trial. A report from the WHOQOL group. Qual Life Res. 2004;13(2):299–310. pmid:15085902
- 66.
Centers for Disease Control and Prevention Measuring Healthy Days: Population assessment of health-related quality of life. CDC, Atlanta Georgia. 2000. http://www.cdc.gov/hrqol/hrqol14_measure.htm.
- 67. Hunt SM, McKenna SP, McEwen J, Backett EM, Williams J, Papp E. A quantitative approach to perceived health status: a validation study. J Epidemiol Community Health. 1980;34(4):281–6. pmid:7241028
- 68. Mullerova H, Agusti A, Erqou S, Mapel DW. Cardiovascular comorbidity in COPD: systematic literature review. Chest. 2013;144(4):1163–78. pmid:23722528
- 69. Miyazaki M, Nakamura H, Chubachi S, Sasaki M, Haraguchi M, Yoshida S et al. Analysis of comorbid factors that increase the COPD assessment test scores. Respir Res. 2014;15:13. pmid:24502760
- 70. Gläser S, Krüger S, Merkel M, Bramlage P, Herth FJF. Chronic Obstructive Pulmonary Disease and Diabetes Mellitus: A Systematic Review of the Literature. Respiration. 2015;89(3):253–64. pmid:25677307
- 71. Parappil A, Depczynski B, Collett P, Marks GB. Effect of comorbid diabetes on length of stay and risk of death in patients admitted with acute exacerbations of COPD. Respirology. 2010;15(6):918–22. pmid:20546185
- 72. Marquis K, Debigare R, Lacasse Y, LeBlanc P, Jobin J, Carrier G et al. Midthigh muscle cross-sectional area is a better predictor of mortality than body mass index in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2002;166(6):809–13. pmid:12231489
- 73. Swallow EB, Reyes D, Hopkinson NS, Man WD, Porcher R, Cetti EJ et al. Quadriceps strength predicts mortality in patients with moderate to severe chronic obstructive pulmonary disease. Thorax. 2007;62(2):115–20. pmid:17090575
- 74. Cohen S, Nathan JA, Goldberg AL. Muscle wasting in disease: molecular mechanisms and promising therapies. Nat Rev Drug Discov. 2015;14(1):58–74. pmid:25549588
- 75. Engstrom CP, Persson LO, Larsson S, Sullivan M. Health-related quality of life in COPD: why both disease-specific and generic measures should be used. Eur Respir J. 2001;18(1):69–76. pmid:11510808
- 76. Huber MB, Wacker ME, Vogelmeier CF, Leidl R. Excess Costs of Comorbidities in Chronic Obstructive Pulmonary Disease: A Systematic Review. PLoS One. 2015;10(4):e0123292. pmid:25875204
- 77. Leidl R, Reitmeir P. A value set for the EQ-5D based on experienced health states: development and testing for the German population. Pharmacoeconomics. 2011;29(6):521–34. pmid:21247225
- 78. Ara R, Brazier J. Comparing EQ-5D scores for comorbid health conditions estimated using 5 different methods. Med Care. 2012;50(5):452–9. pmid:22002647
- 79. Hanmer J, Vanness D, Gangnon R, Palta M, Fryback DG. Three methods tested to model SF-6D health utilities for health states involving comorbidity/co-occurring conditions. J Clin Epidemiol. 2010;63(3):331–41. pmid:19896802
- 80. Ara R, Wailoo AJ. Estimating health state utility values for joint health conditions: a conceptual review and critique of the current evidence. Med Decis Making. 2013;33(2):139–53. pmid:22927696