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New potential determinants of disability in aged persons with myocardial infarction: results from the KORINNA-study



Elderly individuals with coronary heart disease are a population particularly burdened by disability. However, to date many predictors of disability established in general populations have not been considered in studies examining disability in elderly acute myocardial infarction (AMI) survivors. Our study explores factors associated with the ability to perform basic activities of daily living in elderly patients with AMI.


Baseline data from 333 AMI-survivors older than 64 years included within the randomized controlled KORINNA-study were utilized to examine disability assessed by the Stanford Health Assessment Questionare Disability Index (HAQ-DI). Numerous potential determinants including demographic characteristics, clinical parameters, co-morbidities, interventions, lifestyle, behavioral and personal factors were measured.

Disability was defined as a HAQ-DI ≥ 0.5. After bi-variate testing the probability of disability was modeled with logistic regression. Missing covariate values were imputed using a Markov Chain Monte Carlo method.


Disability was significantly more frequent in older individuals (Odds Ratio (OR): 1.10, 95% Confidence Interval (CI): 1.05-1.16), patients with deficient nutrition (OR: 3.38, 95% CI: 1.60-7.15), coronary artery bypass graft (CABG) (OR: 3.26, 95% CI: 1.29-8.25), hearing loss in both ears (OR: 2.85, 95% CI: 1.41-5.74), diabetes mellitus (OR: 2.56, 95% CI: 1.39-4.72), and heart failure (OR: 3.32, 95% CI: 1.79-6.16). It was reduced in patients with percutaneous transluminal coronary angioplasty (PTCA) (OR: 0.41, 95% CI: 0.21-0.80) and male sex (OR: 0.48, 95% CI: 0.27-0.85).


Effects of nutrition, hearing loss, and diametrical effects of PTCA and CABG on disability were identified as relevant for examination of causality in longitudinal trials.

Trial registration


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Acute myocardial infarction (AMI) and coronary heart disease (CHD) are the most common causes of death and the fourth largest contributor to burden of disease worldwide [1, 2]. In 2008/09 22.9% of all Germans aged 65 and older were diagnosed with CHD [3]. The short term lethality of AMI in industrialized countries is in decline [4, 5] due to new treatment and prevention procedures [5, 6], while the general populations are ageing [7, 8]. These developments result in a growing proportion of elderly AMI-survivors. As elderly individuals with chronic diseases are often multi-morbid, they become vulnerable to development of disability, causing increase of costs for care and treatment [8].

Disability in activities of daily living (ADL) indicates the individual’s ability to perform actions fundamental to daily life [9] under the burden of existing health conditions [10]. The ADL concept of disability addresses the individual’s ability to perform self-care and satisfy basic needs such as washing, toileting, dressing, eating, mobility, etc. [11, 12]. Reduced ability to perform ADL indicates reduced autonomy and increased dependency on assistance in everyday life [11, 12], resulting in need for nursing care and/or other benefits and services. ADL-disability has been shown to be predictive of institutionalization, mortality and healthcare expenditure [1316].

While heart diseases such as CHD have been associated with disability in older adults in several studies [13, 1721], only few studies to date research disability within the growing population of AMI survivors [18, 22, 23]. Elderly AMI-survivors in our study were reported to be particularly burdened by prevalent ADL-disability, when compared to the general population of our study region [24]. Indications to whether there are differences in the causation and development of disability between the general population and AMI survivors remain unknown to date. However many factors considered to be associated with or causally related to disability in general or in other populations [8, 13, 17, 18, 2123, 2527], have not been considered in previous studies examining disability in elderly AMI-survivors [18, 22, 23]. Thus factors affecting the process of disability development in elderly AMI survivors must still be identified and examined. Cross-sectional analyses provide identification of associations relevant for subsequent longitudinal examination of potential causal relationships.

Knowledge of determinants of disability in older aged AMI patients may allow for the development of interventions aimed at preventing or treating disability. Considering the expected increase of this population in the next decades, successful disability prevention and treatment will be a major public health concern.

Thus, the aim of this analysis was to identify clinical parameters, treatment related factors, co-morbidities, socio-demographic and lifestyle factors which show the most substantial association with disability within the population of elderly AMI survivors.


Study sample

The “Koronarinfarkt Nachbehandlung im Alter” (KORINNA) study is a randomized controlled trial with two study arms in which the effect of a nurse-led case management intervention on death and hospital readmission is examined within one year after discharge. Cross-sectional analyses were conducted on the population of the KORINNA-study at randomization. Details on the intervention program, exclusion criteria and the outcome measures were reported elsewhere [28]. The study design was approved by the ethics committee at the Bavarian Chamber of Physicians (Date of approval: 11.11.2008, Reference number: 08064). Furthermore the study was conducted in accordance to German privacy law and in compliance with the Helsinki Declaration. All participants submitted written informed consent before being enrolled in the study. The population of KORINNA consists of 338 home-dwelling patients aged ≥ 65 years who were discharged after treatment of first or recurrent AMI in the central hospital of Augsburg between September 2008 and May 2010 [28]. For the present analysis five patients were excluded due to missing outcome scores.

Study measures

Most of the data were collected by a study nurse in a face-to-face interview with the patient and a physician during an examination shortly before hospital discharge. In addition, further information, specifically on clinical data, was obtained from information collected on the patients within the MONICA/KORA myocardial infarction registry [29].

Assessment of ADL-disability

Disability of study participants was assessed with the Stanford health assessment questionnaire disability index (HAQ-DI). The HAQ-DI is one of five health dimensions which constitute the HAQ [30]. It consists of eight domains: dressing and grooming, arising, eating, walking, hygiene, reach, grip, and activities. The applied scoring procedure takes into account items addressing functional ability to perform ADL as well as items assessing utilization of equipment and personal assistance [30]. Numerous studies confirm validity and applicability of the HAQ-DI as a generic instrument [14, 3032]. Disability was defined as a HAQ-DI score higher or equal to 0.5 as suggested by Siegert et al. [33] and applied as a dichotomous cut-point in a cohort study of the general population of elderly community dwelling adults in the Netherlands [8].

Identification and assessment of associated factors

Based on the results of previous studies [8, 13, 17, 18, 2123, 2527], potentially associated factors were selected from the available data. Chosen variables were assigned to five categories: Demographics, clinical parameters, interventions, co-morbidities, and finally lifestyle, personal & environmental factors (see Tables 1 and 2). Included factors assessed by instrument scores were: (a) social support questionnaire (F-SozU); (b) Mini Mental State Examination (MMSE); (c) Geriatric Depression Scale (GDS); and (d) the abbreviated version of Seniors in the community: risk evaluation for eating and nutrition, version II (SCREENII). Scores were dichotomized according to clinically relevant cut-points developed and confirmed in previous research [3438]. Infarction type was diagnosed as either AMI with ST-segment elevation in electrocardiogram (STEMI) or AMI without ST-segment elevation (NSTEMI) according to guidelines of the European Society for Cardiology [39]. Blood pressure was categorized according to the American Heart Association, which recommends a blood pressure lower or equal to 130/80 for AMI survivors [40]. Pathological hearing loss in both ears and recent decline in visual acuity were measured according to the geriatric assessment by Lachs et al. [41]. Likewise pathological lung sounds and oedema of lower limb were assessed by the study physician. Obesity was defined as a body mass index of ≥ 30 kg/m2[42]. Educational status was defined by the minimal amount of school years necessary to attain the highest school degree held by the participant (9 years/10 years/≥12 years). Heart failure was defined as New York Heart Association classes III and IV diagnosed by physician.

Table 1 Age-stratified distribution of population characteristics
Table 2 Age- and sex-adjusted logistic regression analyses on prevalent disability (Health Assessment Questionnaire-Disability ≥ 0.5)

Statistical analyses

In initial descriptive analyses frequencies of identified variables were examined within the age groups 65–74 and 75–92. The distributions of age in years and BMI were also examined with histograms. Subsequently all variables were subjected to age- and sex- adjusted, explorative testing against the dichotomized HAQ-DI score with logistic regression. The variable sex was tested in a model only adjusted for age. Variables were subjected to model fitting if significant at a chosen α = 0.05 and smallest cell frequencies proved sufficient for stable multivariate analyses.

The probability of disability was modeled by fitting of a dichotomous logistic regression model by stepwise backward elimination and confirming covariate structure with stepwise forward selection each at α = 0.05. To avoid the exclusion of participants due to missing values in the covariates, five imputed data sets were created using the Markov Chain Monte Carlo method for continuous variables with arbitrary missing data pattern [43, 44]. Imputation was not subjected to rounding or setting of interval limits to avoid bias [44]. Model fitting was performed for each imputed data set respectively. Multicollinearity was tested in each imputed data set respectively by assessing variance inflation of the weighted linear model [45]. The continuous variable age was tested for linearity of its association to the logit of the outcome probability using Box-Tidwell tests in both fitted and empty regression models yielding no violation of the linearity assumption. Interactions with sex were tested via interaction terms. Sex-stratified analysis was not performed, since interaction terms showed no significant interactions of sex with any other variables of the finalized model. Finally, results of the fitted logistic regression analysis of the five imputed data sets were subjected to combining analysis to obtain finalized parameter estimates.

All statistical analyses were performed using SAS software, release 9.2 (SAS Institute, Cary, NC).


According to HAQ-DI categories by Siegert et al. 47.1% persons were self-sufficient, 25.2% had minor difficulties, 16.8% had major difficulties, and 10.8% had a severe handicap in performance of ADL [32, 33]. Of 333 patients 52.9% were classified as disabled at hospital discharge. Characteristics of the total sample are displayed stratified according to dichotomized disability status and the age groups 65–75 and 75 and older in Table 1. Disability was less prevalent in the younger age group (36.8%) than in older individuals (66.3%). The sample included more men (62.5%) than women. Males were slightly less common among older (56.4%) than among younger individuals (69.7%). Age of participants ranged from 65 to 92 years with an average of 75.5 years. The majority of the sample (75.7%) had the lowest education level. AMI with ST-segment elevation occurred in 36.3%, and heart failure in 32.7% of the participants. Heart failure and re-infarctions were more frequent among the older (40.9% and 29.4% respectively) when compared to the younger group (23.0% and 21.7% respectively). ST-segment elevation infarctions were slightly less common among older patients (33.2%) than younger (40.1%). The most common co-morbidity was diabetes (32.7%). The rarest health conditions, each of which represented less than 10% of the sample size, were osteoporosis, stroke and moderate to severe depressive symptoms. Among 14 different variables with missing values, school education had the most with 16 missings constituting 4.8% of the complete sample size.

Disabled patients (77 years) significantly differed (p < 0.001) from patients without disability (72 years) in median age. Results of the age- and sex-adjusted explorative analyses are displayed in Table 2. The variable sex was tested in a model only adjusted for age. Sex, heart failure, pathological pulmonary sounds, hearing loss in both ears, oedema of lower limb, diabetes mellitus, circulatory disturbances in legs, pacemaker implant, coronary artery bypass graft (CABG), percutaneous transluminal coronary angioplasty with stent (PTCA), deficient nutrition, cardiac arrest, ST-elevation and cognitive impairment showed significant associations with disability in age- and sex adjusted analyses and were subjected to multivariate analysis (see Table 2). Moderate to severe depressive symptoms were too infrequent for stable multivariate analysis.

The 10 model fitting procedures (forward selection and backward elimination methods in each of the five imputed data sets) produced identical covariate structures. Variance inflation factors within each imputed data set showed values well below 2.5. This delivered no indication of multicollinearity [45].

Results of the finalized regression model for probability of disability are displayed in Table 3. Odds of disability were reduced in males (OR: 0.46, 95% CI: 0.27-0.85) and participants with PTCA (OR: 0.41, 95% CI: 0.21-0.80). The highest odds of disability were found among participants endangered by nutritional deficiency (OR: 3.38, 95% CI: 1.60-7.15), participants who had undergone CABG (OR: 3.26, 95% CI: 1.29-8.25), participants with heart failure (OR: 3.32, 95% CI: 1.79-6.16) and participants with hearing loss in both ears (OR: 2.85, 95% CI: 1.41-5.74). Furthermore the odds of disability increased with each year of rising age (OR: 1.10, 95% CI: 1.05-1.16) and prevalent diabetes mellitus (OR: 2.56, 95% CI: 1.39-4.72).

Table 3 Finalized logistic regression model of the probability of disability (Health Assessment Questionnaire-Disability ≥ 0.5)


In our present study a reduced odds of disability was associated with male sex and patients who had undergone PTCA. On the other hand we could show that increased odds of disability were associated with rising age, diabetes mellitus, hearing loss in both ears, CABG, heart failure and deficient nutritional status.

Our results confirmed associations of the demographic factors age and sex with disability after AMI demonstrated in previous studies. Studies including AMI-survivors found disability or loss of physical function was more frequent in women and older individuals irrespective of applied measure. Instruments utilized in past studies included the Disability Scale by Rosow and Breslau, the SF-12 and the HAQ-DI [18, 22, 23]. Studies examining associations between sex, age and ADL-disability in general elderly populations, confirm our results [8, 21]. Among these, results from a recent cross sectional study by Strobl et al. seem most comparable with our study, since it utilized data representative of our study region and age group from the KORA-Age cohort and applied the same instrument (HAQ-DI) to measure disability [21]. It found increased odds of disability in women (OR: 2.49, 95% CI: 2.06-3.02) and increasing odds of disability with rising years of age (OR: 1.12, 95% CI: 1.11-1.14). These estimates resemble our findings. However, in contrast to our study Strobl et al. defined prevalent disability as a HAQ-DI score > 0 due to less frequent disability in the general population. Only 22.5% of the general elderly population scored higher than 0.49 (minor to severe disability) in the HAQ-DI. In our population of elderly AMI survivors, 52.9% scored higher than 0.49. Furthermore, proportions of women, who have higher probability of being disabled, are considerably higher in the general population (51.2%) than in AMI survivors (37.5%) [21].

In our study AMI treatments showed strong associations with ADL-disability. The increased odds of disability in patients with CABG may be related to the severity of the intervention itself, which initially affects the patients’ general health status negatively. As this study examined patients shortly after the AMI event, it cannot be excluded that CABG will prevent disability in the long term. In contrast, PTCA as a more gently reperfusion intervention was shown to be associated with a reduced odds of disability already at the time of hospital discharge. On the long term, however, Dodson et al. have not found any significant effect of PTCA on disability (measured by EuroQol-5D questionnaire) one year post-AMI in 2002 patients with AMI with a mean age of 59 years [22].

Heart failure emerged as the strongest cardiac indicator of prevalent ADL-disability. Previous research discovered a decline in ability to perform ADL among individuals with heart failure during the first year after AMI [18]. The long-term prediction of disability assessed with patients from the Framingham disability study, identified heart insufficiency to be predictive of disability in women [17]. However, when interpreting the above it must be considered that among the discussed studies none applied the HAQ-DI to assess disability.

Our finding that AMI patients with diabetes were more likely to be disabled is in line with results of studies on the older aged general population. For instance, Gregg et al. found that diabetes was associated with a 2- to 3-fold increased risk of physical disability, accessed by physical performance tests and self-rated ability to walk ¼ mile, climb 10 steps and do housework, in a sample of 6,588 U.S. adults aged 60 years or older [46].

The strong associations of hearing loss in both ears and nutritional status are noteworthy as they have not been taken into account in available studies examining disability within the population of AMI patients. In studies on the general elderly population, hearing impairment was found to be a significant predictor of disability [9, 47]. Strobl et al. investigated determinants of disability within the framework of KORA-Age and found sufficiently nourished individuals, as defined by the SCREEN malnutrition score, to have significantly reduced odds of any ADL-disability as assessed by the HAQ-DI (OR: 0.93, 95% CI: 0.92-1.56). However, as discussed above, since Strobl et al. defined prevalent disability as a HAQ-DI score > 0 due to much more infrequent disability in the general population, coefficients of the regression models are not directly comparable with our present study [21]. Furthermore, a recent review of literature on the association between nutrition and mobility in elderly people suggested that low micronutrients correlate with mobility disability [48]. While this study applies measures so different that comparability to our study remains questionable, the congruence of laboratory-based and questionnaire-based results remains noteworthy. These findings suggest a further examination of potential causal effects nutritional deficiency and hearing loss may have on disability in elderly AMI-survivors. Furthermore adjustment for these factors may be necessary to avoid confounding in future studies.

Sample characteristics and associations found in the bi-variate analysis are mostly confirmatory of observations made in past studies [2123, 18, 13, 17]. A few non-significant findings of the bi-variate baseline analysis may well have clinical relevance. For instance, participants with STEMI showed slightly reduced odds of disability. One possible interpretation is the fact that patients with STEMI may be diagnosed earlier and this leads to earlier access to necessary clinical interventions which reduce further damage of the heart muscle and consequently may prevent disability.

Strengths and limitations

To our knowledge, this study is the first which explores factors associated with disability in patients with AMI aged 65 or older. A large number of different variables including clinical characteristics and cardiac treatment could be tested for their association with disability not considered in any of the related studies to date. As part of the randomized KORINNA-study, instruments were carefully selected and standardized assessments were performed. The covariate structure of the finalized regression model was confirmed to be a very stable model of the most dominant influence factors on the outcome in different selection procedures.

Findings relevant to future research on disability in AMI-survivors were made. Due to the large effect sizes it appears reasonable that adjustment for hearing loss and nutritional deficiency may be relevant to the estimation of other predictors in clinical research. Finally, our study raises the question whether differences in disability prevalence between elderly AMI-survivors and other populations are systematic or arbitrary. If disability differentials are caused by random differences in certain population characteristics such as hearing loss, adjustment is sufficient. If however, the structure in causal relationships between factors predictive of disability differs from other populations, the development of a disease-specific theoretical framework will be essential.

However, some methodological limitations should be considered. Since the analysis was conducted with data from a study not originally designed for this research question, sample size did not allow consideration of potentially relevant factors too infrequent (n < 40) for the execution of a stable multivariate analysis. Examples are the effects of social support and moderate to severe depressive symptoms on disability found in the bi-variate analyses. Furthermore, the exclusion of relevant characteristics with too low frequencies resulted in the unfeasibility of structural equation modeling. Thus, potentially relevant causal relationships between covariates could not be considered. In addition, effects may be overestimated. The ratio of predictors over outcome events in the full model is 1/11.2. Application of the rule of thumb proposed by Steyerberg and Harrell, suggests that with a ratio over 1/10 shrinkage while advisable is not absolutely necessary [49]. Finally, due to the cross sectional design, actual causations between disability and covariates cannot be proven in this study.


Our study has confirmed that factors which are found to be associated with disability in populations other than AMI patients, also account for disability development in AMI patients aged 65 years and older. Since nutrition and hearing loss were identified as most important associations with disability, they should be examined in subsequent longitudinal studies. If causality is shown they should be adjusted for in future studies examining determinants and treatment of disability in AMI survivors. Beside demographic characteristics and co-morbidities, different reperfusion treatments strongly and diametrically influenced the occurrence of disability at hospital discharge. Further studies are needed to clarify whether these effects persist over time.


  1. World Health Organization: The top 10 causes of death - Major causes of death.,

  2. Global Atlas on cardiovascular disease prevention and control. Edited by: Mendis S, Puska P, Norrving B. 2011, Geneva: World Health Organization

    Google Scholar 

  3. Robert Koch-Institut: Coronary heart diseases, diagnosed by a physician (percentage of the respondents). Classification: years, region, age, sex, level of education. German Health Update - Telephone Health Survey (GEDA). []

  4. Yeh RW, Sidney S, Chandra M, Sorel M, Selby JV, Go AS: Population trends in the incidence and outcomes of acute myocardial infarction. N Engl J Med. 2010, 362 (23): 2155-2165. 10.1056/NEJMoa0908610.

    Article  CAS  PubMed  Google Scholar 

  5. Löwel H, Meisinger C, Heier M, Hörmann A: The population-based Acute Myocardial Infarction (AMI) Registry of the MONICA/KORA Study Region of Augsburg. Gesundheitswesen. 2005, 67 (suppl 1): S1-S7.

    Google Scholar 

  6. Tunstall-Pedoe H, Vanuzzo D, Hobbs M, Mähönen M, Cepaitis Z, Kuulasmaa K, Keil U: Estimation of contribution of changes in coronary care to improving survival, event rates, and coronary heart disease mortality across the WHO MONICA Project populations. Lancet. 2000, 355 (9205): 688-700. 10.1016/S0140-6736(99)11181-4.

    Article  CAS  PubMed  Google Scholar 

  7. Federal Statistical Office of Germany: Gesundheit im Alter. 2012, Wiesbaden: Statistisches bundesamt,,

    Google Scholar 

  8. Taş U, Verhagen AP, Bierma-Zeinstra SMA, Hofman A, Odding E, Pols HAP, Koes BW: Incidence and risk factors of disability in the elderly: the Rotterdam study. Prev Med. 2007, 44: 272-278. 10.1016/j.ypmed.2006.11.007.

    Article  PubMed  Google Scholar 

  9. Verbrugge LM, Jette AM: The disablement process. Soc Sci Med. 1994, 38 (1): 1-14. 10.1016/0277-9536(94)90294-1.

    Article  CAS  PubMed  Google Scholar 

  10. Mayo NE, Nadeau L, Levesque L, Miller S, Poissant L, Tamblyn R: Does the Addition of Functional Status Indicators to Case-Mix Adjustment Indices Improve Prediction of Hospitalization, Institutionalization and Death in the Elderly?. Med Care. 2005, 43: 1194-1202. 10.1097/01.mlr.0000185749.04875.cb.

    Article  PubMed  Google Scholar 

  11. Christensen K, Doblhammer G, Rau R, Vaupel JW: Ageing populations: the challenges ahead. Lancet. 2009, 374: 1196-1208. 10.1016/S0140-6736(09)61460-4.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Salvador-Carulla L, Gasca VI: Defining disability, functioning, autonomy and dependency in person-centered medicine and integrated care. Int J Integr Care. 2010, 10 (Suppl): e025-

    PubMed  PubMed Central  Google Scholar 

  13. Plichart M, Barberger-Gateau P, Tzourio C, Amouyel P, Pérès K, Ritchie K, Jouven X, Ducimetière P, Empana JP: Disability and incident coronary heart disease in older community-dwelling adults: the three-city study. J Am Geriatr Soc. 2010, 58 (4): 636-642. 10.1111/j.1532-5415.2010.02758.x.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ramy DR, Raynauld J-P, Fries JF: The Health Assessment Questionare 1992 – status and review. Arthritis Care Res. 1992, 5 (3): 119-129. 10.1002/art.1790050303.

    Article  Google Scholar 

  15. Pincus T, Brooks R, Callahan L: Prediction of long term mortality in patients with reumatoid Arthritis. Ann Intern Med. 1994, 120 (1): 26-34. 10.7326/0003-4819-120-1-199401010-00005.

    Article  CAS  PubMed  Google Scholar 

  16. Lubeck DP, Spitz PW, Fries JF, Wolfe F, Mitchel DM, Roth SH: A multicenter study of annual health service utilization and costs in rheumatoid arthritis. Arthritis Rheum. 1986, 29 (4): 488-493. 10.1002/art.1780290405.

    Article  CAS  PubMed  Google Scholar 

  17. Pinsky JL, Jette AM, Branch LG, Kannel WB, Feinleib M: The Framingham disability study: relationship of various coronary heart disease manifestations to disability in older persons living in the community. Am J Public Health. 1990, 80 (11): 1363-1367. 10.2105/AJPH.80.11.1363.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Van Jaarsveld CH, Sanderman R, Miedema I, Ranchor AV, Kempen GI: Changes in health-related quality of life in older patients with acute myocardial infarction or congestive heart failure: a prospective study. J Am Geriatr Soc. 2001, 49 (8): 1052-1058. 10.1046/j.1532-5415.2001.49208.x.

    Article  CAS  PubMed  Google Scholar 

  19. Guccione AA, Felson DT, Anderson JJ, Anthony JM, Zhang Y, Wilson PW, Kelly-Hayes M, Wolf PA, Kreger BE, Kannel WB: The effects of specific medical conditions on the functional limitations of elders in the framingham study. Am J Public Health. 1994, 84: 351-358. 10.2105/AJPH.84.3.351.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Whitson HE, Landerman LR, Newman AB, Fried LP, Pieper CF, Cohen HJ: Chronic Medical Conditions and the Sex-based disparity in disability: the Cardiovascular Health Study. J Gerontol A Biol Sci Med Sci. 2010, 65A (12): 1325-1331. 10.1093/gerona/glq139.

    Article  PubMed Central  Google Scholar 

  21. Strobl R, Müller M, Emeny R, Peters A, Grill E: Distribution and determinants of functioning and disability in aged adults - results from the German KORA-Age study. BMC Public Health. 2013, 13 (137):,

    Google Scholar 

  22. Dodson JA, Arnold SV, Reid KJ, Grill TM, Rich MW, Masoudi FA, Spertus JA, Krumholz HM, Alexander KP: Physical function and independence 1 year after myocardial infarction: observations from the Translational Research Investigating Underlying disparities in recovery from acute Myocardial infarction: Patients’ Health status registry. Am Heart J. 2012, 163: 790-796. 10.1016/j.ahj.2012.02.024.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Ades PA, Savage PD, Tischler MD, Pochlman ET, Dee J, Niggel J: Determinants of disability in older coronary patients. Am Heart J. 2002, 143 (1): 151-156. 10.1067/mhj.2002.119379.

    Article  PubMed  Google Scholar 

  24. Peters A, Döring A, Ladwig KH, Meisinger C, Linkohr B, Autenrieth C, Baumeister SE, Behr J, Bergner A, Bickel H, Bidlingmaier M, Dias A, Emeny RT, Fischer B, Grill E, Gorzeiniak L, Hänsch H, Heidbreder S, Heier M, Horsch A, Huber D, Huber RM, Jörres RA, Kääb S, Karrasch S, Kirchberger I, Klug G, Kranz B, Kuch B, Lacruz ME, et al: Multimorbidität und erfolgreiches Altern Ein Blick auf die Bevölkerung im Rahmen der KORA-Age-Studie. Z Gerontol Geriat. 2011, 44 (Suppl 2): 41-53.

    Article  Google Scholar 

  25. Smith GL, Masoudi FA, Vaccarino V, Radford MJ, Krumholz HM: Outcomes in Heart Failure Patients With Preserved Ejection Fraction Mortality, Readmission, and Functional Decline. J Am Coll Cardiol. 2003, 41 (9): 1510-1518. 10.1016/S0735-1097(03)00185-2.

    Article  PubMed  Google Scholar 

  26. Vaccarino V, Kasl SV, Abramson J, Krumholz HM: Depressive symptoms and risk of functional decline and death in patients with heart failure. J Am Coll Cardiol. 2001, 38 (1): 199-205. 10.1016/S0735-1097(01)01334-1.

    Article  CAS  PubMed  Google Scholar 

  27. Clarke SP, Frasure-Smith N, Lespérance F, Bourassa MG: Psychosocial Factors as Predictors of Functional Status at 1 Year in Patients with Left Ventricular Dysfunction. Res Nurs Health. 2000, 23: 290-300. 10.1002/1098-240X(200008)23:4<290::AID-NUR5>3.0.CO;2-0.

    Article  CAS  PubMed  Google Scholar 

  28. Kirchberger I, Meisinger C, Seidl H, Wende R, Kuch B, Holle R: Nurse-based case management for aged patients with myocardial infarction: study protocol of a randomized controlled trial. BMC Geriatr. 2010, 10 (29):,

    Google Scholar 

  29. Kuch B, Heier M, Von Scheidt W, Kling B, Hoermann A, Meisinger C: 20-year-trends in clinical characteristics, therapy and short-term prognosis in acute myocardial infarction according to presenting electrocardiogram – results of the MONICA/KORA Augsburg Myocardial Infarction Registry (1985–2004). J Intern Med. 2008, 264 (3): 254-264. 10.1111/j.1365-2796.2008.01956.x.

    Article  CAS  PubMed  Google Scholar 

  30. Fries JF: The Health Assessment Questionare. Measuring Health A Guide to Rating Scales and Questionnaires. Edited by: McDowell I. 2006, Oxford: Oxford University Press, 111-119. 3

    Google Scholar 

  31. Bruce B, Fries JF: The Stanford Health Assessment Questionnaire: dimensions and practical applications. Health Qual Life Outcomes. 2003, 1 (20):,

    Google Scholar 

  32. Bruce B, Fries JF: The Stanford health assessment questionnaire (HAQ): a review of its history, issues, progress, and documentation. J Rheumatol. 2003, 30 (1): 167-78.

    PubMed  Google Scholar 

  33. Siegert CEH, Vleming L-J, Van-Denbroucke JP, Cats A: Measurement of disability in Dutch rheumatoid arthritis patients. Clin Rheumatol. 1984, 3 (3): 305-309. 10.1007/BF02032335.

    Article  CAS  PubMed  Google Scholar 

  34. Fydrich T, Sommer G, Tydechs S, Brähler E: Fragebogen zur sozialen Unterstützung (F-SozU): Normierung der Kurzform (K-14). Z Med Psychol. 2009, 18 (1): 43-48.

    Google Scholar 

  35. Folstein MF, Folstein SE, McHugh PR: “Mini-Mental State” A Practical Method For Grading The Cognitive State Of Patients For The Clinician. J Psychiatr Res. 1975, 12 (3): 189-198. 10.1016/0022-3956(75)90026-6.

    Article  CAS  PubMed  Google Scholar 

  36. O’Connor PA, Pollit JB, Hyde JL, Fellows ND, Miller ND, Brook CPB, Reiss BB: The Reliability and Validity of the Mini-Mental State in a British community survey. J Psychiatr Res. 1989, 23 (1): 87-96. 10.1016/0022-3956(89)90021-6.

    Article  PubMed  Google Scholar 

  37. Alden D, Austin C, Sturgeon R: A correlation between the Geriatric Depression Scale long and short forms. J Gerontol. 1989, 44 (4): 124-125. 10.1093/geronj/44.4.P124.

    Article  Google Scholar 

  38. Keller HH, Goy R, Kane SL: Validity and reliability of SCREEN II (Seniors in the Community: risk evaluation for eating and nutrition, Version II). Eur J Clin Nutr. 2005, 59: 1149-1157. 10.1038/sj.ejcn.1602225.

    Article  CAS  PubMed  Google Scholar 

  39. Van de Werf F, Bax J, Betriu A, Blomstrom-Lundqvist C, Crea F, Falk V, Filippatos G, Fox K, Huber K, Kastrati A, Rosengren A, Steg PG, Tubaro M, Verheugt F, Weidinger F, Weis M, ESC Committee for Practice Guidelines (CPG): ESC-Guidelines - Management of acute myocardial infarction in patients presenting with persistent ST-segment elevation. Eur Heart J. 2008, 29 (23): 2909-2945.

    Article  CAS  PubMed  Google Scholar 

  40. Rosendorff C, Black HR, Cannon CP, Gersh BJ, Gore J, Izzo JL, Kaplan NM, O’Connor CM, O’gara PT, Oparil S: Treatment of hypertension in the prevention and management of ischemic heart disease: a scientific statement from the American Heart Association Council for High Blood Pressure Research and the Councils on Clinical Cardiology and Epidemiology and Prevention. Circulation. 2007, 115 (21): 2761-2788. 10.1161/CIRCULATIONAHA.107.183885.

    Article  PubMed  Google Scholar 

  41. Lachs MS, Feinstein AR, Cooney LM, Drickamer MA, Marottoli RA, Pannill FC, Tinetti ME: A simple procedure for general screening for functional disability in elderly patients. Ann Intern Med. 1990, 112: 699-706. 10.7326/0003-4819-112-9-699.

    Article  CAS  PubMed  Google Scholar 

  42. World Health Organization: Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000, 894 (i-xii): 1-253.

    Google Scholar 

  43. Horton NJ, Kleinman KP: Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models. Am Stat. 2007, 61 (1): 79-90. 10.1198/000313007X172556.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Ake CF: Rounding After Multiple Imputation With Non-binary Covariates. Proceedings of the Thirteith Annual SAS Users Group International Conference: 10-13 April 2005. 2005, Philadelphia: SAS Institute Inc,,

    Google Scholar 

  45. Allison PD: Logistic Regression using SAS: Theory and application. 1999, Cary, NC, USA: SAS Institute Inc.

    Google Scholar 

  46. Gregg EW, Beckles GL, Williamson DF, Leveille SG, Langlois JA, Engelgau MM, Narayan KM: Diabetes and physical disability among older U.S. adults. Diabetes Care. 2000, 23 (9): 1272-1277. 10.2337/diacare.23.9.1272.

    Article  CAS  PubMed  Google Scholar 

  47. Gopinath B, Schneider J, McMahon CM, Teber E, Leeder SR, Mitchell P: Severity of age-related hearing loss is associated with impaired activities of daily living. Age Ageing. 2012, 41 (2): 195-200. 10.1093/ageing/afr155.

    Article  PubMed  Google Scholar 

  48. Milaneschi Y, Tanaka T, Ferrucci L: Nutritional determinants of mobility. Curr Opin Clin Nutr Metab Care. 2010, 13 (6): 625-629. 10.1097/MCO.0b013e32833e337d.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Steyerberg EW, Harrell FE: Chapter 8: Statistical Models for Prognostication.,

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The present trial is conducted within subproject 2 of the KORA Age Consortium and is funded by the German Federal Ministry of Education and Research (01 ET 0713). The KORA research platform is financed by the Helmholtz Zentrum München, German Research Centre for Environmental Health (GmbH), which is funded by the German Federal Ministry of Education and Research and by the State of Bavaria. We thank all members of the Helmholtz Zentrum München who are involved in the conduct of the study. Furthermore, we wish to thank the field staff in Augsburg. Finally, we express our appreciation to all study participants.

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Correspondence to Philip Andrew Quinones.

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The authors declare that they have no competing interests.

Authors’ contributions

PAQ developed the study question, performed all data analyses and drafted the manuscript. HS prepared the data sets performed plausibility checks and variable transformations. CM, IK, RH and HS developed the complete KORINNA study design and data assessment. MH was advisor for statistical methods. IK was general counselor and advisor. All authors reviewed and revised preliminary manuscript drafts and approved the final manuscript.

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Quinones, P.A., Seidl, H., Holle, R. et al. New potential determinants of disability in aged persons with myocardial infarction: results from the KORINNA-study. BMC Geriatr 14, 34 (2014).

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