HomeLarge Type Edition
HOME ARCHIVE SEARCH TABLE OF CONTENTS
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Services
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
PubMed
Right arrow PubMed Citation
The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 62:286-295 (2007)
© 2007 The Gerontological Society of America

SPECIAL SECTION

Framework for Evaluating Disease Severity Measures in Older Adults With Comorbidity

Cynthia M. Boyd, Carlos O. Weiss, Jeff Halter, K. Carol Han, William B. Ershler and Linda P. Fried

1 Division of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
2 Center on Aging and Health, Johns Hopkins Medical Institutions, Baltimore, Maryland.
3 Division of Geriatric Medicine and Institute of Gerontology, University of Michigan, Ann Arbor.
4 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
5 Institute for Advanced Studies in Aging, Washington, D.C.

Address correspondence to Cynthia M. Boyd, MD, MPH, Johns Hopkins University School of Medicine, Center on Aging and Health, Mason F. Lord Building, 7th floor, Center Tower, 5200 Eastern Avenue, Baltimore, MD 21224. E-mail: cyboyd{at}jhmi.edu


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 References
 
Background. Accounting for the influence of concurrent conditions on health and functional status for both research and clinical decision-making purposes is especially important in older adults. Although approaches to classifying severity of individual diseases and conditions have been developed, the utility of these classification systems has not been evaluated in the presence of multiple conditions.

Methods. We present a framework for evaluating severity classification systems for common chronic diseases. The framework evaluates the: (a) goal or purpose of the classification system; (b) physiological and/or functional criteria for severity graduation; and (c) potential reliability and validity of the system balanced against burden and costs associated with classification.

Results. Approaches to severity classification of individual diseases were not originally conceived for the study of comorbidity. Therefore, they vary greatly in terms of objectives, physiological systems covered, level of severity characterization, reliability and validity, and costs and burdens. Using different severity classification systems to account for differing levels of disease severity in a patient with multiple diseases, or, assessing global disease burden may be challenging.

Conclusions. Most approaches to severity classification are not adequate to address comorbidity. Nevertheless, thoughtful use of some existing approaches and refinement of others may advance the study of comorbidity and diagnostic and therapeutic approaches to patients with multimorbidity.

COMORBIDITY affects the progression of concurrent disease (1,2), decreases quality of life (3,4), and increases the risk and severity of disability (5,6) and mortality (3). Comorbidity may alter treatment efficacy and the risk of adverse effects and has been observed to be associated with utilization of standard treatments (7). The efficacy of complex, interacting regimens often administered to older persons has not been tested in most cases (8–11). Thus, understanding comorbidity severity can be the key to understanding the difference between inappropriate treatment, realistic prioritization, and justifiably avoiding standard treatment on the basis of concerns about decreased benefit or increased harm.

Inadequate attention to comorbidity stems in part from the lack of established tools and procedures for classifying the severity of coexisting conditions. Improved attention to comorbidity-related research and clinical issues requires the development and validation of new ways for establishing the presence and severity of coexisting and potentially interacting disease states and their treatments. Although understanding the true impact and consequences of one disease requires accounting for the severity of specific comorbid diseases, this is rarely done (12).

Research on and clinical decision making for patients with comorbidity require accurate and comparable characterization of severity of individual, concurrent diseases (Table 1). Consider the question of how to identify the nursing home residents with osteoporosis who are most likely to benefit from bisphosphonate treatment (13). Eighty percent of nursing home residents have osteoporosis (14), but many will die without a new fracture and thus would never experience a benefit from therapy. The severity of comorbid diseases affects life expectancy and risk of falls and adverse drug events, all of which affect treatment decisions. Also, osteoarthritis interacts with heart disease to synergistically increase the risk of disability (6). From preventive or therapeutic perspectives, would decreasing the severity of either disease prevent the interaction (15)? Evaluation of how severity of common diseases can be currently characterized, and when such characterizations are compatible in an overall assessment, is needed.


View this table:
[in this window]
[in a new window]

  		Table 1. Why Does Disease Severity Matter?.

 
The goal of this narrative review is to develop and apply a framework to the classification of severity for common chronic diseases and utilize it to consider the suitability of existing severity classification systems for use, jointly, in persons with comorbidity. We hypothesize that severity classification systems are meaningfully distinguished in several ways: (a) goal or purpose of the system; (b) physiological and/or functional level at which severity is characterized; and, (c) potential reliability and validity of the data, and burden and costs associated with obtaining required classification information. Implications of the findings for research and care for persons with multiple comorbidities are considered.

Framework for Classifying Severity of Disease
Severity of disease is assessed for diverse purposes. One goal is to create a standardized basis for evaluation and communication. We hypothesized that severity of disease is measured to meet one or more of the following objectives: (a) to establish prognosis (risk of death or other common morbid sequela or events); (b) to characterize the impact of the disease on the person's well-being at a given point in time (experiential classification); (c) to establish the basis for treatment decisions; (d) to evaluate disease activity and response to treatment; or (e) other.

Measurement of severity may use information from any level or levels along the pathway from disease to disablement: from the cellular pathologic processes that initiate the disease, to limitations in basic physiologic functions, to impairments in organ system performance, to symptoms, to complex function at the level of the whole person, and finally through quality of life and dependency (Figure 1) (16).


Figure 01
View larger version (9K):
[in this window]
[in a new window]

  		Figure 1. Causal pathway of disease to disability, permitting classification of severity at each point

 
Severity classification may also differ greatly with respect to feasibility, patient or participant burden, and costs of ascertainment. In addition, data from self-report, laboratory tests, direct examination or functional testing, diagnostic tests administered and/or interpreted by an advanced specialist, and treatment information vary in reliability and validity.


   METHODS
Top
 Abstract
 Methods
Results
 Discussion
 References
 
Chronic diseases and conditions of interest were selected on the basis of: (a) high prevalence in older adults, (b) measurable impact on mortality or disability, and/or (c) causal relationship to other prevalent diseases. Diseases selected (and their prevalence among older persons) were: osteoarthritis and/or rheumatoid arthritis (47%–57%) (17,18); hypertension (42%) (17); hypercholesterolemia (41%) (19); chronic kidney disease (CKD) (moderate to severe CKD: 26%) (20); type 2 diabetes mellitus (20%–25%) (21,22); angina (21%) (18); cancer (15%–19%) (17,18); lower extremity peripheral vascular disease (PVD) (15%) (23); congestive heart failure (CHF) (10%) (24); chronic obstructive pulmonary disease (10%–40%) (25); anemia (11%) (26); stroke (9%) (18); osteoporosis (9%) (18); and asthma (7%–9%) (27).

Severity classification systems were first identified through a search of the Medline database using Medical Subject Heading terms for each condition, along with the key words "severity" and either "stage" or "classification," limited to all adults aged 19+ years, humans, and English language. Results were reviewed by one author (C.M.B., L.P.F., or C.O.W.), and appropriate articles were retrieved. References in the first round of articles were reviewed; additional appropriate studies were retrieved. Opinions of an expert for each disease were solicited to identify additional classification systems. This search was intended to provide important and representative examples.

No standard exists to define severity classification systems. For this review, a severity classification system was defined as "any categorization approach that distinguishes, among those with a given disease, the presence of greater or lesser disease (i.e., a minimum of two levels)." In some instances and for some conditions there may be no specifically identified severity system (e.g., diabetes), but there have been systematic attempts to categorize gradients of subclinical disease or risk for overt disease. As severity can fluctuate over time with acute and chronic phases for some diseases (e.g., CHF, stroke, asthma), classification systems that incorporate acute markers of severity have also been included. Indices that seek to account for "global" severity of all comorbidities simultaneously or the severity of all diseases within an organ system are less useful for studying the relationships between diseases, and are discussed elsewhere (28).

Using a disease-specific approach, the authors (C.M.B., L.P.F., or C.O.W.) reviewed each severity classification system based on the framework described above. Examples of such reviews are presented to illustrate how methods to classify disease severity differ and to describe the problems that would arise when trying to consider severity of multiple diseases concurrently. Due to space considerations, results are shown in tables limited to three conditions that were chosen to illustrate the diversity of classification systems.


   RESULTS
Top
 Abstract
 Methods
 Results
Discussion
 References
 
Differences in Number of Existing Severity Classification Systems
The number of methods available to classify severity varied greatly by disease. For example, for angina, CHF, and osteoarthritis we found at least five severity classification systems (29–46), whereas for other conditions fewer systems existed (47–53).

Varied Goals of Severity Classification Systems
Most systems were not designed to be used in the assessment of comorbidity. Thus, some components of the classification scheme may not be unique to a specific condition (e.g., shortness of breath) (33). Both within and among diseases, the goals of severity classification systems varied as is displayed in Table 2 (29–47,49,50,56).


View this table:
[in this window]
[in a new window]

  		Table 2. Purpose of Disease Classification Systems for Angina, Hip and Knee Osteoarthritis, and Hypertension.

 
Levels of Severity Classification Systems
For different diseases and within the same disease, severity has been characterized at different and sometimes multiple levels. Some classifications are based on pathologic or physiologic status (e.g., glomerular filtration rate, cholesterol); others used impairments or specific symptoms (e.g., pain); and still others characterized severity based on exercise tolerance or functional status (32–34,37,47,50,57). For stroke, different severity classification systems drew on the extent of cytotoxic edema, infarct size, symptoms, physical function, and quality of life, drawing from multiple levels simultaneously (54,55,58–74). Many severity classification systems included a functional component either as the main domain or as a contributing factor (e.g., angina, CHF, stroke, osteoarthritis, PVD) (29,30,34–38,44,46,57).

The level of focus for severity classification systems varied in objective and the nature of the condition. Symptoms and outcomes tended to be used in systems designed to reflect the patient experience, to guide treatment, or to measure treatment response. Pathologic or physiologic measures were incorporated in systems used to determine prognosis, guide treatment, or measure response to treatment. For example, disease activity measures for rheumatoid arthritis included the presence of tender and swollen joints and the laboratory demonstration of serum acute-phase reactants in addition to pain, patient and physician global assessment of disease activity, and physical function (75). For relatively asymptomatic diseases (osteoporosis, CKD, hypertension, hypercholesterolemia), severity classifications included fewer symptoms or functional components, and some relied largely on a single biological measure (e.g., bone density or cholesterol) to quantify risk (47,50,52,56,76–80).

Given diversity in purpose and levels of characterization of severity, we did not identify any compatible severity classification systems suitable for simultaneous ascertainment of disease severity across multiple conditions. Using two or more scales would result in comparison of fundamentally different aspects of severity. Angina severity classification systems were largely based on degree of patient-reported functional limitations and symptoms associated with specific activities. By contrast, some osteoarthritis severity measures relied on pathologic information obtained by radiographic imaging; some measures included symptoms and physical function (Table 3). The ability to understand the mechanisms by which these two particular diseases interact to increase the risk of disability synergistically is thus hampered.


View this table:
[in this window]
[in a new window]

  		Table 3. Domains Used for Classification of Severity of Angina, Hip and Knee Osteoarthritis, and Hypertension.

 
Certain additional components do not fit into this framework. Some severity classification systems included presence of other conditions, diseases, or behaviors in their staging if they were risk factors for poorer prognosis (52). Level of treatment required for disease control was included in some systems [e.g., asthma (49,81)]. Several systems developed for prognostic purposes incorporated family history, age, race, or gender (52,76,78–80).

Cost and Burden of Components of Severity Classification Systems
Time and invasiveness, costs, and feasibility varied markedly for severity classifications within and across diseases (Table 4). Patient-reported measures are among the least expensive, but can be time-consuming to ascertain. Medical record–based information is sometimes more objective, but less complete. Standardized tests administered by a well-trained certified examiner are less costly than those requiring interpretation by an expert (e.g., echocardiogram or magnetic resonance imaging). Both may be equally burdensome on patients or participants (e.g., time, discomfort, or fatigue) (45,46,52).


View this table:
[in this window]
[in a new window]

  		Table 4. Classification Systems by Source of Information for Angina, Hip and Knee Osteoarthritis, and Hypertension.

 
Reliability and Validity of Components of Severity Classification Systems
Patient-reported measures may be most subject to criticisms over reliability and validity, but, depending on the goal of measurement, could be most relevant. Chronic diseases, in contrast with acute diseases, manifest a broader spectrum of clinical findings, and symptoms and function sometimes correlate weakly with underlying pathology (82). Some objective measures (e.g., peak expiratory flow) can be quite variable as well (83). Symptoms from CHF correlate poorly with ejection fraction (84), and radiologic features of hip osteoarthritis do not correlate well with clinical symptoms (85). Patient-reported measures do not exist for asymptomatic problems (such as diabetes and hypertension) for which severity classification is dependent on testing by health providers (48,49,51,52).


   DISCUSSION
Top
 Abstract
 Methods
 Results
 Discussion
References
 
Improved attention to and management of comorbidity-related issues requires systematic and standardized characterization of the presence and severity of coexisting diseases and understanding of potentially interacting disease states and their treatments. The classification of disease severity is particularly critical in clinical care and research in older adults due to increased heterogeneity (86,87). A framework for considering methods to classify severity was developed and applied for illustration purposes here. For some diseases, there were many severity classification systems; for initially asymptomatic diseases, there were few. The goals, level of categorization, cost, burden, reliability, and validity of severity classification measures varied widely within and across diseases. The different methods identified were largely incompatible due to diversity in purpose and level of ascertainment within and across diseases. Many situations would require classification of severity along parallel domains for all diseases, but existing severity classification systems draw from divergent domains for differing purposes. Thus much work remains in developing a unified system of severity classification.

The lack of integration among severity classification systems with a single-disease focus presents a chaotic array that does not work when multiple diseases are present. Some examples of why simultaneous use of different severity assessments may be problematic follow. Many severity classifications incorporate other diseases or sociodemographic risk factors, leading to overadjustment of risk in stratified or multivariate analyses. In addition, many severity classification systems incorporate functional decline and symptoms (e.g., dyspnea). This raises several issues. Studies of disability are limited if function is included in both the independent (i.e., disease severity) and dependent (i.e., outcome) variables. Also, severity classification systems incorporating physical function, symptoms, or performance reflect the impact of multiple conditions or one physiologic system (e.g., cardiopulmonary), making it difficult to distinguish the true impact of a single disease. Finally, severity classification systems relying on experiential components may be most affected by the patient's response to the disease. In PVD, for instance, improvement in symptoms could signify improvement in underlying disease or cutting activity back to a level below the claudication threshold.

Severity classification systems have varied etiologic specificity. The underlying pathophysiology of comorbid diseases may overlap (e.g., metabolic syndrome) (88). A challenge in considering the optimal physiologic or clinical level for measuring severity is that etiologic specificity may be highest at the level of pathology or physiology, but the components most relevant and important to patients may be much less disease specific (Figure 2). For example, although ejection fraction is highly specific to severity of CHF, dyspnea on exertion, which is more salient to patients, can result from several common diseases. Choosing the right severity classification system is dependent on issues such as the compatibility measures for both independent and dependent variables, the specificity required, and the costs and burden of ascertainment. Identification of disease-specific markers of severity is important, and choice and development of measures to classify disease severity should be based on the markers that are most relevant for the question of interest.


Figure 02
View larger version (15K):
[in this window]
[in a new window]

  		Figure 2. Components for assessing disease severity

 
Is the solution to measure health status globally? For many important and unanswered research questions, the answer is no. Specific relationships between diseases and their treatments are critical. Because of the demonstrated mix of purposes and components, it does not appear reasonable to create an overall index of disease severity by aggregating across the different available existing severity classification systems. Among patients with Parkinson's disease, stroke, or coronary heart disease in isolation, specific measures of disease severity are associated in different ways with health-related quality of life (89). These relationships may be more complex and even less uniform in older adults with comorbidity. A noteworthy limitation of this review is that it did not include syndromes, such as dementia, that share functional outcomes with the diseases considered here.

The study of common diseases is hampered by an inability to simultaneously account for their severity. There is a need for methods to classify severity across diseases, in patients with multimorbidity, with categorization of severity based on comparable domains or physiologic levels. It is unlikely that researchers will be able to use a single severity classification system for every situation. The thoughtful use of current systems, with attention to purpose, domains, and source of information, is necessary for sound research in older adults with multiple chronic diseases. Furthermore, this work supports the observation that additional models of illness, beyond the disease model, may be needed (82). In some situations, severity of disease may be less relevant than a continuum of impairments, conditions, and subclinical and clinical diseases representing derangement of homeostatic equilibrium (90,91) (Table 5). The theoretical framework for classifying the severity of disease proposed here can serve as a useful guide for articulating goals and creating compatible severity classifications across diseases.


View this table:
[in this window]
[in a new window]

  		Table 5. Subclassification of Disorders.

 


   Acknowledgments
Top
 Abstract
 Methods
 Results
 Discussion
 References
 
This work was supported by a task order from the National Institute on Aging. Drs. Boyd and Fried are supported by the National Institute on Aging's Older Americans Independence Center, P30 AG021334. Dr. Boyd is a Bayview Scholar at the Center for Innovative Medicine. Dr. Weiss was supported by Training Grant T32 AG00247 from the National Institute on Aging.

This work was inspired and guided by issues raised in the National Institute of Aging's Task Force on Comorbidity and was presented, in part, at a meeting of the National Institute on Aging's Task Force on Comorbidity (Bethesda, Maryland, July 2004) and at the National Institute on Aging–funded Comorbidity Conference (Atlanta, Georgia, March, 2005). We thank all members of the Task Force. We are grateful to Dr. William Hazzard, of the Division of Gerontology and Geriatric Medicine, University of Washington School of Medicine, for his thoughtful insights into this manuscript. We are grateful to Dr. Cynthia Brown of the Department of Medicine, University of Alabama at Birmingham, and Dr. Lisa Walke of the Department of Internal Medicine, Yale University School of Medicine, for their thoughtful reviews of this manuscript.


   Footnotes
Top
 Abstract
 Methods
 Results
 Discussion
 References
 
Decision Editor: Luigi Ferrucci, MD

Received August 7, 2006

Accepted December 7, 2006


   References
Top
 Abstract
 Methods
 Results
 Discussion
 References
 

  1. Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837-1847.[Abstract/Free Full Text]
  2. Yokota C, Minematsu K, Hasegawa Y, Yamaguchi T. Long-term prognosis, by stroke subtypes, after a first-ever stroke: a hospital-based study over a 20-year period. Cerebrovasc Dis. 2004;18:111-116.[Medline]
  3. Gijsen R, Hoeymans N, Schellevis FG, Ruwaard D, Satariano WA, van den Bos GA. Causes and consequences of comorbidity: a review. J Clin Epidemiol. 2001;54:661-674.[Medline]
  4. Fortin M, Lapointe L, Hudon C, Vanasse A, Ntetu AL, Maltais D. Multimorbidity and quality of life in primary care: a systematic review. Health Qual Life Outcomes. 2004;2:51.[Medline]
  5. Verbrugge LM, Lepkowski JM, Imanaka Y. Comorbidity and its impact on disability. Milbank Q. 1989;67:450-484.[Medline]
  6. Fried LP, Bandeen-Roche K, Kasper JD, Guralnik JM. Association of comorbidity with disability in older women: the Women's Health and Aging Study. J Clin Epidemiol. 1999;52:27-37.[Medline]
  7. Redelmeier DA, Tan SH, Booth GL. The treatment of unrelated disorders in patients with chronic medical diseases. N Engl J Med. 1998;338:1516-1520.[Abstract/Free Full Text]
  8. Gurwitz JH. Polypharmacy: a new paradigm for quality drug therapy in the elderly? Arch Intern Med. 2004;164:1957-1959.[Free Full Text]
  9. Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005;294:716-724.[Abstract/Free Full Text]
  10. Kravitz RL, Duan N, Braslow J. Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages. Milbank Q. 2004;82:661-687.[Medline]
  11. Tinetti ME, Bogardus ST, Jr, Agostini JV. Potential pitfalls of disease-specific guidelines for patients with multiple conditions. N Engl J Med. 2004;351:2870-2874.[Free Full Text]
  12. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351:1296-1305.[Abstract/Free Full Text]
  13. Wallace RB. Bone health in nursing home residents. JAMA. 2000;284:1018-1019.[Free Full Text]
  14. Zimmerman SI, Girman CJ, Buie VC, et al. The prevalence of osteoporosis in nursing home residents. Osteoporos Int. 1999;9:151-157.[Medline]
  15. Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care. J Gerontol A Biol Sci Med Sci. 2004;59A:255-263.
  16. Pope AM, Taylor AR. Disability in America. Washington, D.C.: National Academy Press; 1991.
  17. Dawson DA, Adams PF. Current Esimates from the National Health Interview Survey, United States, 1986. National Center for Health Statistics. Vital Health Stat Series No. 10(164). 1987.
  18. Freedman VA, Martin LG. Contribution of chronic conditions to aggregate changes in old-age functioning. Am J Public Health. 2000;90:1755-1760.[Abstract/Free Full Text]
  19. Disparities in screening for and awareness of high blood cholesterol–United States, 1999–2002. MMWR Morb Mortal Wkly Rep. 2005;54:117-119.[Medline]
  20. Coresh J, Astor BC, Greene T, Eknoyan G, Levey AS. Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey. Am J Kidney Dis. 2003;41:1-12.[Medline]
  21. Wahl PW, Savage PJ, Psaty BM, Orchard TJ, Robbins JA, Tracy RP. Diabetes in older adults: comparison of 1997 American Diabetes Association classification of diabetes mellitus with 1985 WHO classification. Lancet. 1998;352:1012-1015.[Medline]
  22. Rodriguez BL, Curb JD, Burchfiel CM, et al. Impaired glucose tolerance, diabetes, and cardiovascular disease risk factor profiles in the elderly. The Honolulu Heart Program. Diabetes Care. 1996;19:587-590.[Abstract]
  23. Selvin E, Erlinger TP. Prevalence of and risk factors for peripheral arterial disease in the United States: results from the National Health and Nutrition Examination Survey, 1999–2000. Circulation. 2004;110:738-743.[Abstract/Free Full Text]
  24. Anderson G, Horvath J. Chronic Conditions: Making the Case for Ongoing Care. Partnership for Solutions; 2002. Available at: http://www.partnershipforsolutions.org/DMS/files/chronicbook2002.pdf. Last accessed December 21, 2006.
  25. Mannino DM, Homa DM, Akinbami LJ, Ford ES, Redd SC. Chronic obstructive pulmonary disease surveillance–United States, 1971–2000. Respir Care. 2002;47:1184-1199.[Medline]
  26. Guralnik JM, Eisenstaedt RS, Ferrucci L, Klein HG, Woodman RC. Prevalence of anemia in persons 65 years and older in the United States: evidence for a high rate of unexplained anemia. Blood. 2004;104:2263-2268.[Abstract/Free Full Text]
  27. Braman SS. Asthma in the elderly. Clin Geriatr Med. 2003;19:57-75.[Medline]
  28. Lash TL, Mor V, Wieland D, Ferrucci L, Satariano W, Silliman RA. Methodology, design and analytic techniques to address measurement of comorbid disease. J Gerontol A Biol Sci Med Sci. 2007;62A:281-285.[Abstract/Free Full Text]
  29. Killip T, 3rd, Kimball JT. Treatment of myocardial infarction in a coronary care unit. A two year experience with 250 patients. Am J Cardiol. 1967;20:457-464.[Medline]
  30. Dagenais GR, Armstrong PW, Theroux P, Naylor CD. Revisiting the Canadian Cardiovascular Society grading of stable angina pectoris after a quarter of a century of use. Can J Cardiol. 2002;18:941-944.[Medline]
  31. Braunwald E. Unstable angina. A classification. Circulation. 1989;80:410-414.[Free Full Text]
  32. Goldman L, Hashimoto B, Cook EF, Loscalzo A. Comparative reproducibility and validity of systems for assessing cardiovascular functional class: advantages of a new specific activity scale. Circulation. 1981;64:1227-1234.[Abstract/Free Full Text]
  33. Consensus Committee of the New York Heart Association. Nomenclature and Criteria for Diagnosis of Diseases of the Heart and Great Vessels. Ninth Ed. Boston: Little, Brown & Co.; 1994.
  34. Bellamy N, Buchanan WW, Goldsmith CH, Campbell J, Stitt LW. Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. J Rheumatol. 1988;15:1833-1840.[Medline]
  35. Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis. 1957;16:494-502.[Free Full Text]
  36. LaValley MP, McAlindon TE, Chaisson CE, Levy D, Felson DT. The validity of different definitions of radiographic worsening for longitudinal studies of knee osteoarthritis. J Clin Epidemiol. 2001;54:30-39.[Medline]
  37. Insall JN, Dorr LD, Scott RD, Scott WN. Rationale of the Knee Society clinical rating system. Clin Orthop. 1989:13–14.
  38. Ahlback S. Osteoarthrosis of the knee: a radiographic investigation. Acta Radiologica Suppl. 1968;277:7-72.
  39. Lequesne MG, Maheu E. Clinical and radiological evaluation of hip, knee and hand osteoarthritis. Aging Clin Exp Res. 2003;15:380-390.[Medline]
  40. Peterfy CG, Gold G, Eckstein F, Cicuttini F, Dardzinski B, Stevens R. MRI protocols for whole-organ assessment of the knee in osteoarthritis. Osteoarthritis Cartilage. 2006;14:(Suppl A): A95-A111.
  41. Peterfy CG, Guermazi A, Zaim S, et al. Whole-Organ Magnetic Resonance Imaging Score (WORMS) of the knee in osteoarthritis. Osteoarthritis Cartilage. 2004;12:177-190.[Medline]
  42. Riegel B, Moser DK, Glaser D, et al. The Minnesota Living With Heart Failure Questionnaire: sensitivity to differences and responsiveness to intervention intensity in a clinical population. Nurs Res. 2002;51:(4): 209-218.[Medline]
  43. Grigioni F, Carigi S, Grandi S, et al. Distance between patients' subjective perceptions and objectively evaluated disease severity in chronic heart failure. Psychother Psychosom. 2003;72:166-170.[Medline]
  44. Tranmer JE, Heyland D, Dudgeon D, Groll D, Squires-Graham M, Coulson K. Measuring the symptom experience of seriously ill cancer and noncancer hospitalized patients near the end of life with the memorial symptom assessment scale. J Pain Symptom Manage. 2003;25:420-429.[Medline]
  45. Aaronson KD, Schwartz JS, Chen TM, Wong KL, Goin JE, Mancini DM. Development and prospective validation of a clinical index to predict survival in ambulatory patients referred for cardiac transplant evaluation. Circulation. 1997;95:2660-2667.[Abstract/Free Full Text]
  46. Zugck C, Kruger C, Kell R, et al. Risk stratification in middle-aged patients with congestive heart failure: prospective comparison of the Heart Failure Survival Score (HFSS) and a simplified two-variable model. Eur J Heart Fail. 2001;3:577-585.[Abstract/Free Full Text]
  47. National Institutes of Health, National Heart, Lung, and Blood Institute. Third Report of the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Full Report. Vol. 2004. Available at: http://www.nhlbi.nih.gov/guidelines/cholesterol/atp3full.pdf. Last accessed December 21, 2006.
  48. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998;15:539-553.[Medline]
  49. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care. 1997;20:1183-1187.[Medline]
  50. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002;39:(2 Suppl 1): S1-S266.[Medline]
  51. National Institutes of Health, National Heart, Lung, and Blood Institute. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC VII). Vol. 2005. Available at: http://www.nhlbi.nih.gov/guidelines/hypertension/jnc7full.pdf. Last accessed December 21, 2006.
  52. Whitworth JA. 2003 World Health Organization (WHO)/International Society of Hypertension (ISH) statement on management of hypertension. J Hypertens. 2003;21:1983-1992.[Medline]
  53. Apelqvist J, Van Houtem WH, Nabuurs-Fransen MH, Shaper NC. International Consensus on the Diabetic Foot. The International Working Group on the Diabetic Foot. Amsterdam, The Netherlands: Wiley & Sons; 1999.
  54. Recommendations for clinical trial evaluation of acute stroke therapies. Stroke. 2001;32:1598-1606.[Abstract/Free Full Text]
  55. Meyer BC, Hemmen TM, Jackson CM, Lyden PD. Modified National Institutes of Health Stroke Scale for use in stroke clinical trials: prospective reliability and validity. Stroke. 2002;33:1261-1266.[Abstract/Free Full Text]
  56. Miller PD, Barlas S, Brenneman SK, et al. An approach to identifying osteopenic women at increased short-term risk of fracture. Arch Intern Med. 2004;164:1113-1120.[Abstract/Free Full Text]
  57. Leng GC, Fowkes FG. The Edinburgh Claudication Questionnaire: an improved version of the WHO/Rose Questionnaire for use in epidemiological surveys. J Clin Epidemiol. 1992;45:1101-1109.[Medline]
  58. Mao HF, Hsueh IP, Tang PF, Sheu CF, Hsieh CL. Analysis and comparison of the psychometric properties of three balance measures for stroke patients. Stroke. 2002;33:1022-1027.[Abstract/Free Full Text]
  59. Duncan PW, Bode RK, Min Lai S, Perera S. Rasch analysis of a new stroke-specific outcome scale: the Stroke Impact Scale. Arch Phys Med Rehabil. 2003;84:950-963.[Medline]
  60. Duncan PW, Wallace D, Lai SM, Johnson D, Embretson S, Laster LJ. The stroke impact scale version 2.0. Evaluation of reliability, validity, and sensitivity to change. Stroke. 1999;30:2131-2140.[Abstract/Free Full Text]
  61. Duncan PW, Jorgensen HS, Wade DT. Outcome measures in acute stroke trials: a systematic review and some recommendations to improve practice. Stroke. 2000;31:1429-1438.[Abstract/Free Full Text]
  62. Lyden P, Lu M, Jackson C, et al. Underlying structure of the National Institutes of Health Stroke Scale: results of a factor analysis. NINDS tPA Stroke Trial Investigators. Stroke. 1999;30:2347-2354.[Abstract/Free Full Text]
  63. Johnston KC, Wagner DP, Haley EC, Jr, Connors AF, Jr. Combined clinical and imaging information as an early stroke outcome measure. Stroke. 2002;33:466-472.[Abstract/Free Full Text]
  64. Roberts L, Counsell C. Assessment of clinical outcomes in acute stroke trials. Stroke. 1998;29:986-991.[Abstract/Free Full Text]
  65. Muir KW, Grosset DG, Lees KR. Interconversion of stroke scales. Implications for therapeutic trials. Stroke. 1994;25:1366-1370.[Abstract]
  66. van Straten A, de Haan RJ, Limburg M, van den Bos GA. Clinical meaning of the Stroke-Adapted Sickness Impact Profile-30 and the Sickness Impact Profile-136. Stroke. 2000;31:2610-2615.[Abstract/Free Full Text]
  67. Bushnell CD, Johnston DC, Goldstein LB. Retrospective assessment of initial stroke severity: comparison of the NIH Stroke Scale and the Canadian Neurological Scale. Stroke. 2001;32:656-660.[Abstract/Free Full Text]
  68. Nakajima M, Kimura K, Ogata T, Takada T, Uchino M, Minematsu K. Relationships between angiographic findings and National Institutes of Health stroke scale score in cases of hyperacute carotid ischemic stroke. AJNR Am J Neuroradiol. 2004;25:238-241.[Abstract/Free Full Text]
  69. Brott T, Adams HPJ, Olinger CP, et al. Measurements of acute cerebral infarction–a clinical examination scale. Stroke. 1989;20:864-870.[Abstract/Free Full Text]
  70. Cote R, Battista RN, Wolfson C, Boucher J, Adam J, Hachinski V. The Canadian Neurological Scale: validation and reliability assessment. Neurology. 1989;39:638-643.[Abstract/Free Full Text]
  71. Kidd D, Stewart G, Baldry J, et al. The Functional Independence Measure: a comparative validity and reliability study. Disabil Rehabil. 1995;17:10-14.[Medline]
  72. Bonita R, Beaglehole R. Recovery of motor function after stroke. Stroke. 1988;19:1497-1500.[Abstract/Free Full Text]
  73. Bergner M, Bobbitt RA, Carter WB, Gilson BS. The Sickness Impact Profile: development and final revision of a health status measure. Med Care. 1981;19:787-805.[Medline]
  74. Duncan PW, Wallace D, Studenski S, Lai SM, Johnson D. Conceptualization of a new stroke-specific outcome measure: the stroke impact scale. Top Stroke Rehabil. 2001;8:19-33.[Medline]
  75. Felson DT, Anderson JJ, Boers M, et al. The American College of Rheumatology preliminary core set of disease activity measures for rheumatoid arthritis clinical trials. The Committee on Outcome Measures in Rheumatoid Arthritis Clinical Trials. Arthritis Rheum. 1993;36:729-740.[Medline]
  76. Lydick E, Cook K, Turpin J, Melton M, Stine R, Byrnes C. Development and validation of a simple questionnaire to facilitate identification of women likely to have low bone density. Am J Manag Care. 1998;4:37-48.[Medline]
  77. Chobanian AV, Bakris GL, Black HR, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289:2560-2572.[Abstract/Free Full Text]
  78. Cadarette SM, Jaglal SB, Kreiger N, McIsaac WJ, Darlington GA, Tu JV. Development and validation of the Osteoporosis Risk Assessment Instrument to facilitate selection of women for bone densitometry. CMAJ. 2000;162:1289-1294.[Abstract/Free Full Text]
  79. Weinstein L, Ullery B. Identification of at-risk women for osteoporosis screening. Am J Obstet Gynecol. 2000;183:547-549.[Medline]
  80. Michaelsson K, Bergstrom R, Mallmin H, Holmberg L, Wolk A, Ljunghall S. Screening for osteopenia and osteoporosis: selection by body composition. Osteoporos Int. 1996;6:120-126.[Medline]
  81. Liard R, Leynaert B, Zureik M, Beguin FX, Neukirch F. Using Global Initiative for Asthma guidelines to assess asthma severity in populations. Eur Respir J. 2000;16:615-620.[Abstract]
  82. Tinetti ME, Fried T. The end of the disease era. Am J Med. 2004;116:179-185.[Medline]
  83. Bellia V, Pistelli F, Giannini D, et al. Questionnaires, spirometry and PEF monitoring in epidemiological studies on elderly respiratory patients. Eur Respir J. 2003;21:(40 Suppl): 21S-27S.[Abstract/Free Full Text]
  84. Smith RF, Johnson G, Ziesche S, Bhat G, Blankenship K, Cohn JN. Functional capacity in heart failure. Comparison of methods for assessment and their relation to other indexes of heart failure. The V-HeFT VA Cooperative Studies Group. Circulation. 1993;87:(6 Suppl): VI88-VI93.[Medline]
  85. Hart DJ, Spector TD. The classification and assessment of osteoarthritis. Baillieres Clin Rheumatol. 1995;9:407-432.[Medline]
  86. Dannefer D. What's in a name? An account of the neglect of variability in the study of aging. In: Birren JE, Bengston VL, eds. Emergent Theories of Aging. New York: Springer Publishing Company; 1988:356–384.
  87. Maddox GL. Aging differently. Gerontologist. 1987;27:557-564.[Medline]
  88. Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet. 2005;365:1415-1428.[Medline]
  89. Ferrucci L, Baldasseroni S, Bandinelli S, et al. Disease severity and health-related quality of life across different chronic conditions. J Am Geriatr Soc. 2000;48:1490-1495.[Medline]
  90. Kuller LH, Shemanski L, Psaty BM, et al. Subclinical disease as an independent risk factor for cardiovascular disease. Circulation. 1995;92:720-726.[Abstract/Free Full Text]
  91. Karlamangla A, Tinetti M, Guralnik J, Studenski S, Wetle T, Reuben D. Comorbidity in older adults: nosology of impairments, diseases and conditions. J Gerontol A Biol Sci Med Sci. 2007;62A:296-300.[Abstract/Free Full Text]



This article has been cited by other articles:


Home page
 Journals of Gerontology Series A: Biological Sciences and Medical SciencesHome page
R. Yancik, W. Ershler, W. Satariano, W. Hazzard, H. J. Cohen, and L. Ferrucci
Report of the National Institute on Aging Task Force on Comorbidity
J. Gerontol. A Biol. Sci. Med. Sci., March 1, 2007; 62(3): 275 - 280.
[Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Services
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
PubMed
Right arrow PubMed Citation

HOME ARCHIVE SEARCH TABLE OF CONTENTS