Abstract
Background:
Health status is increasingly recognized as an important patient-centered outcome after acute myocardial infarction (AMI). Yet drivers of decline in health status after AMI remain largely unknown in older adults. We sought to develop and validate a predictive risk model for health status decline among older adult survivors of AMI.
Methods:
Using data from a prospective cohort study conducted from 2013-2017 of 3,041 patients age ≥75 years hospitalized with acute myocardial infarction at 94 U.S. hospitals, we examined a broad array of demographic, clinical, functional, and psychosocial variables for their association with health status decline, defined as a decrease of ≥5 points in the Short Form-12 (SF-12) physical component score from hospitalization to six months post discharge. Model selection was performed in logistic regression models of twenty imputed datasets to yield a parsimonious risk prediction model. Model discrimination and calibration were evaluated using c-statistics and calibration plots, respectively.
Results:
Of the 2,571 participants included in the main analyses, thirty percent of patients experienced health status decline from hospitalization to six months post-discharge. The risk model contained fourteen factors, ten associated with higher risk of health status decline (age, pre-existing AMI, pre-existing cancer, pre-existing COPD, pre-existing diabetes, history of falls, presenting Killip class, acute kidney injury, baseline health status, and mobility impairment) and four associated with lower risk of health status decline (male sex, higher hemoglobin, receipt of revascularization, and arrhythmia during hospitalization). The model displayed good discrimination (c-statistic=0.74 in validation cohort) and calibration (p>.05) in both development and validation cohorts.
Conclusions:
We used split sampling to develop and validate a risk model for health status decline in older adults after hospitalization for AMI and identified several risk factors that may be modifiable to mitigate the threat of this important patient-centered outcome. External validation of this risk model is warranted.
Keywords: health status, myocardial infarction, geriatric vulnerabilities
Introduction
As the threat to quantity of life after AMI abates due to marked improvements in survival,1 quality of life has emerged as an important patient-centered outcome. This is particularly true for older adults, many of whom prioritize function, independence, and freedom from symptoms over living as long as possible.2 Health status is an important patient-centered outcome that assesses “manifestation of disease in a given patient including symptoms, functional limitation, and quality of life.”3 Health status, an important measure in its own right as well as a robust prognostic indicator for clinical outcomes,4–6 can be measured quickly and accurately in the clinical setting using validated surveys, the most common of which is the Short Form 12 (SF-12).7
Despite the importance of health status and its ease of measurement, there is limited evidence about drivers of poor health status among older adults with AMI. Previous investigations of health status decline after AMI8 were not focused on older patients (who are at greatest risk for this outcome) and did not include functional impairments associated with aging. To address these gaps, we sought to develop and validate a predictive risk model for health status decline after AMI that considers the impact of functional impairment in a large prospective cohort of older AMI patients.
Methods
This study used data from the Comprehensive Evaluation of Risk Factors in Older Patients with Acute Myocardial Infarction (SILVER-AMI) study, a prospective cohort study of 3,041 adults age ≥75 years hospitalized with AMI.9 Briefly, participants were recruited from 94 academic and community hospitals across the United States. Site coordinators reviewed admission records daily to identify potentially eligible patients and performed medical record review to confirm AMI diagnosis in accordance with the Third Universal Definition of Myocardial Infarction.10 Patients were ineligible if they had initial troponin elevation >24 hours after admission, were transferred from another hospital after >48 hours, experienced AMI as a result of an in-hospital procedure, were incarcerated, or were unable to provide informed consent with no proxy available. Study protocols were approved by the Institutional Review Boards at all sites and all participants completed written informed consent.
Participants underwent a structured interview and performance-based assessments during AMI hospitalization and at six months post-discharge to collect information on demographics, health status, functional impairments (including mobility, hearing, vision, and cognition), and psychosocial factors. Six months post-discharge was chosen as the follow-up point for patient-centered outcomes such as health status in SILVER-AMI for two reasons: (1) recovery from AMI is generally “complete” by six months (i.e., patients have recovered from invasive treatments (e.g., CABG) and secondary preventive strategies have been fully implemented), with patient-reported outcomes generally reflecting patients’ new “baseline” and (2) missing data on patient-reported outcomes due to competing risk of death is lower at six months than at one year. Research nurses abstracted data on clinical variables from the medical record.
Given the importance of physical function to maintaining independence and quality of life in older adults, we used the physical component score (PCS) of the SF-12 to measure health status during hospitalization and six months post-discharge.7 The SF-12 was chosen over cardiac-specific health status measures such as the Seattle Angina Questionnaire because its measure of universal health status is potentially more applicable to older AMI patients who tend to present atypical symptoms and less angina (21.4% of SILVER-AMI participants did not presented without angina11). SF-12 PCS scores range from 0 to 100 (higher scores indicate better health status) and are standardized to population norms so that a score of 50 represents “average” health status and a deviation of 10 points indicates a one standard deviation from normal. We defined decline in health status as a decrement of ≥5 points on the PCS of the SF-12 from hospitalization to six months post-discharge, which corresponds to a half standard deviation change (i.e., moderate effect size) and indicates a minimally clinically important difference.12
We examined a broad array of demographic factors, clinical factors (including presenting characteristics, medical history, hospitalization characteristics, treatments, and complications), geriatric vulnerabilities (e.g., physical function, cognitive function, sensory impairments), psychosocial, and lifestyle factors, selected based on clinical judgment and evidence in the literature,8,13 to evaluate for associations with health status decline at six months post-discharge after AMI. All factors considered as candidates for the health status decline risk model are listed in Table 1. We also included receipt of guideline-recommended medications during hospitalization in Table 1, but these were not evaluated as candidate predictors due to risk of confounding by indication.
Table 1.
Participant Characteristics, by Decline in Health Status at 6 Months Post-discharge
| Variable | Full cohort | Health Status Decline at 6 months | ||
|---|---|---|---|---|
|
| ||||
| N=2571 | No Decline N=1794 |
Decline N=777 |
P | |
|
| ||||
| Demographic | ||||
| Age, in years, mean, (SD) | 81.3 (4.9) | 81.0 (4.7) | 81.8 (5.2) | <.001 |
| Sex, male | 1451 (56.4) | 1061 (59.1) | 390 (50.2) | <.001 |
| Race, non-white | 248 (9.8) | 163 (9.2) | 85 (11.2) | .14 |
| Ethnicity, Hispanic | 75 (3.0) | 53 (3.0) | 22 (2.9) | .87 |
| Education, ≤12 years | 1442 (56.6) | 979 (55.0) | 463 (60.1) | .017 |
| Marital status, married | 1225 (47.7) | 812 (45.3) | 413 (53.3) | <.001 |
| Cohabitation status, alone | 950 (40.0) | 625 (34.9) | 325 (41.8) | .001 |
|
| ||||
| Clinical | ||||
| MI type | ||||
| STEMI | 701 (27.3) | 500 (27.9) | 201 (25.9) | .30 |
| NSTEMI | 1870 (72.7) | 1294 (72.1) | 576 (74.1) | |
| Presentation Variables | ||||
| Chest pain | 1050 (42.1) | 730 (41.9) | 320 (42.6) | .76 |
| Killip Class II-IV | 291 (11.3) | 186 (10.4) | 105 (13.5) | .021 |
| Systolic BP, mean (SD) | 147 (31) | 146 (31) | 147 (31) | .48 |
| Diastolic BP, mean(SD) | 78 (18) | 78 (17) | 78 (18) | .92 |
| Heart rate, mean(SD) | 83 (23) | 83 (23) | 83 (22) | .87 |
| Initial hemoglobin, mean(SD) | 12.9 (2.0) | 13.1 (2.0) | 12.7 (2.0) | <.001 |
| Peak troponin, ratio of institution upper limit, median (IQR) | 53 (13-220) | 53 (13-226) | 53 (13-212) | .87 |
| Ejection fraction <40% | 466 (19.9) | 319 (19.5) | 147 (21.1) | .73 |
| Comorbidities | ||||
| Arrhythmia | 615 (23.9) | 410 (22.9) | 205 (26.4) | .054 |
| Heart failure | 428 (16.7) | 283 (15.8) | 145 (18.7) | .07 |
| Hypertension | 2172 (84.5) | 1500 (83.6) | 672 (86.5) | .07 |
| Peripheral vascular disease | 274 (10.7) | 192 (10.7) | 82 (10.6) | .91 |
| Stroke | 371 (14.4) | 262 (14.6) | 109 (14.0) | .70 |
| Prior MI | 696 (27.1) | 460 (25.6) | 236 (30.4) | .013 |
| COPD | 334 (13.0) | 209 (11.7) | 125 (16.1) | .002 |
| Chronic kidney disease | 1490 (58.0) | 1010 (56.4) | 480 (61.8) | .011 |
| Diabetes mellitus | 937 (36.4) | 609 (34.0) | 328 (42.2) | <.001 |
| Cancer | 542 (21.1) | 365 (20.4) | 177 (22.8) | .17 |
| Charlson score, median (IQR) | 3 (2-5) | 3 (2-5) | 3 (2-5) | <.001 |
| Length of stay, in days, median (IQR) | 4 (2-7) | 4 (2-7) | 4 (3-7) | .07 |
| Medications within 24 hours of admission | ||||
| Aspirin | 2465 (95.9) | 1723 (96.0) | 742 (95.5) | .52 |
| Antiplatelet agent | 1639 (63.8) | 1156 (64.4) | 483 (62.2) | .27 |
| IV antithrombotic agent | 2141 (83.3) | 1515 (84.5) | 626 (80.6) | .015 |
| ACEI/ARB | 1194 (46.4) | 862 (48.1) | 332 (42.7) | .013 |
| Beta blocker | 2024 (78.7) | 1418 (79.0) | 606 (78.0) | .55 |
| Statin | 1961 (76.3) | 1389 (77.4) | 572 (73.6) | .037 |
| In-hospital revascularization | ||||
| None | 317 (12.3) | 197 (11.0) | 120 (15.4) | <.001 |
| Catheterization only | 413 (16.1) | 286 (16.0) | 127 (16.3) | |
| PCI | 1523 (59.2) | 1062 (59.2) | 461 (59.3) | |
| CABG | 318 (12.4) | 249 (13.9) | 69 (8.9) | .005 |
| In-hosp complications | .68 | |||
| Arrhythmia | 446 (17.4) | 336 (18.7) | 110 (14.2) | .20 |
| Heart Failure | 317 (12.3) | 218 (12.2) | 99 (12.7) | .29 |
| Cardiogenic Shock | 88 (3.4) | 56 (3.1) | 32 (4.1) | .054 |
| Bleeding event | 644 (25.1) | 460 (25.6) | 184 (23.7) | |
| AKI | 535 (20.8) | 355 (19.8) | 180 (23.2) | |
|
| ||||
| Geriatric Vulnerabilities | ||||
| Pre-admission ADL impairment | 302 (11.8) | 196 (10.9) | 106 (13.6) | .049 |
| Not able to walk ¼ mile pre-admission | 809 (31.5) | 560 (31.3) | 249 (32.0) | .69 |
| Mobility impairment (from TUG) | ||||
| ≤15 seconds | 795 (36.9) | 575 (38.4) | 220 (33.4) | .007 |
| >15-<25 seconds | 560 (30.0) | 402 (26.8) | 158 (24.0) | |
| ≥25 seconds | 412 (19.1) | 268 (17.9) | 144 (21.9) | |
| Unable to complete | 390 (18.1) | 253 (16.9) | 137 (20.8) | |
| Hearing impairment | 322 (12.7) | 219 (12.4) | 103 (13.4) | .74 |
| Vision impairment | 298 (7.7) | 129 (7.2) | 69 (8.9) | .14 |
| Grip strength weakness | 1498 (60.6) | 1042 (60.5) | 456 (61.0) | .82 |
| Global cognitive impairment (from TICS) | 370 (14.6) | 226 (12.8) | 144 (18.9) | <.001 |
| History of Falls | 480 (18.7) | 307 (17.1) | 173 (22.3) | .002 |
| Unintentional weight loss | 514 (20.0) | 350 (19.6) | 164 (21.2) | .35 |
|
| ||||
| Psychosocial & Lifestyle | ||||
| SF-12 PCS, baseline, mean (SD) | 42 (10) | 40 (10) | 46 (9) | <.001 |
| Depressive symptoms | 337 (13.5) | 232 (13.3) | 105 (14.0) | .62 |
| Social support, median (IQR) | 24 (20-25) | 24 (20-25) | 24 (20-25) | .44 |
| Low Physical Activity | 373 (14.6) | 263 (14.8) | 110 (14.2) | .90 |
| BMI, obese | 707 (27.5) | 499 (27.8) | 208 (26.8) | .54 |
| Smoking status, ever | 1411 (55.2) | 995 (55.8) | 416 (53.9) | .38 |
ADL= Activities of Daily Living. TUG=Timed Up and Go. TICS=Telephone Interview for Cognitive Status. PCS= Physical Component Score. ACEI/ARB=angiotensin-converting enzyme inhibitor/angiotensin receptor blocker. Numbers represent n(%) unless otherwise indicated
Participants who died during the index hospitalization (n=35) were excluded from analyses. T-tests, Wilcoxon tests, and χ2 tests were used to examine differences in candidate variables among participants with and without health status decline at six months post-discharge. The cohort was randomized 2:1 into development and validation cohorts, and backward selection was used in a multivariable-adjusted logistic regression model with a p-value <.05% for retention to identify predictors of health status decline. Missing data, <1% for most covariates, was addressed via multiple imputation (20 datasets); candidate variables present in >50% of the imputed models were included in the final model. Discrimination and calibration of the model were evaluated with c-statistics and Hosmer-Lemeshow tests and calibration plots, respectively, in development and validation datasets. To address the competing risk of death during follow-up, we performed sensitivity analyses to simulate extreme hypothetical scenarios that 0% and 100% of the 266 decedents experienced health status decline. Analyses were performed in Stata 15.
Results
After excluding participants due to death after hospital discharge (n=266) and other loss to follow-up at six months post-discharge, (n=169), 2,571 participants comprised the analytic cohort. The average age was 81 (SD=5) years, 56% were male, and 13% identified as a race/ethnicity other than white/non-Hispanic (Table 1). The mean change in PCS score from baseline to six months post-discharge was 0.1 (SD=10.9), with a maximum decline of 36.7 points and a maximum improvement of 37.4 points. Based on the ≥5 point decline threshold, thirty percent (30%, n=777) experienced health status decline from baseline to six months post-discharge. In bivariate analyses, participants who experienced health status decline were older, more likely to be female, married, and live alone. They were more likely to present with heart failure and lower hemoglobin, had more comorbidities, and were less likely to undergo revascularization. They also had more geriatric vulnerabilities, including impairments in activities of daily living, mobility, cognition, and falls, as well as higher physical component scores at baseline (Table 1).
The model selection process yielded 14 independent predictors of health status decline (Figure 1). Factors associated with higher risk of health status decline included: age; prior AMI, cancer, COPD, and diabetes; history of falls; presenting Killip class; acute kidney injury; baseline physical component score; and mobility impairment. Factors associated with lower risk of health status decline included male sex, hemoglobin at presentation, receipt of coronary revascularization, and development of arrhythmia during hospitalization. The final model displayed good discrimination (c-statistic=0.75 in development cohort, 0.74 in validation cohort) and good calibration (Figure 2). Model performance was robust to competing risk analyses simulating that 0% and 100% of decedents experienced health status decline (c-statistic=0.73 for 0% scenario, 0.72 For 100% scenario; Tables S1 and S2).
Figure 1.
Independent Predictors of Health Decline at 6 Months Post-AMI in the SILVER-AMI Derivation Cohort
aper level of mobility impairment. bKillip classes II-IV vs. I. CABG=coronary artery bypass grafting. PCI=percutaneous coronary intervention. SF-12= Short Form-12. PCS=physical component score. AMI=acute myocardial infarction. COPD=chronic obstructive pulmonary disease.
Figure 2.
Observed vs. Expected Probability of Health Status Decline in Development Cohort, by Quintile
Discussion
In this prospective cohort study, nearly one-third of older adults hospitalized with AMI experienced a clinically meaningful decline in health status by six months after discharge. A combination of fourteen factors (demographics, presenting characteristics, comorbidities, treatments, and in-hospital complications) predicted risk of health status decline with good discrimination and calibration.
To our knowledge, this is the first risk model developed and validated to predict risk of health status decline in older patients hospitalized for AMI. Our work extends the previous literature on predictors of health status decline after AMI,8,14 which has shown similar associations of age, sex, and receipt of coronary revascularization, by demonstrating the significance of geriatric vulnerabilities to health status decline in the context of AMI. Two geriatric vulnerabilities, mobility impairment and recurrent falls, were highly associated with health status decline and may be amenable to intervention through physical therapy or cardiac rehabilitation, which are underutilized in this population.15
In addition to geriatric vulnerabilities, several chronic comorbidities (e.g., prior AMI, cancer, COPD, diabetes) and presenting comorbidities (e.g., acute kidney injury) were identified as risk factors for health status decline. This finding confirms prior evidence about the association of multimorbidity and health status decline as applicable in the post-AMI population,16 and moreover, underscores the need to consider transitioning from disease-centered care to patient goals-directed care to maximize outcomes for older AMI patients with multiple co-occurring conditions.17 The identification of higher hemoglobin at presentation as a protective factor against health status decline indicates there may be utility in increasing efforts to identify and resolve anemia among older AMI patients as a preventive measure against health status decline, as has been shown among patients with cancer18 and heart failure.19 The association of higher baseline health status with greater risk of decline may indicate either a floor effect for participants with low baseline health status or a greater influence of an intervening health event such as AMI on older adults with high baseline health status. While we did address potential multicollinearity issues between candidate predictor variables, we believe the association of development of arrhythmia during hospitalization with lower risk of health status decline may be connected to receipt of CABG (40% of CABG recipients developed arrhythmia in-hospital vs. 15% of non-recipients) and could represent confounding by indication in that (1) healthier older adults tend to be deemed better candidates for CABG and (2) receipt of CABG is associated with improved health status.
The discrimination and calibration of our prediction model for health status decline in AMI were good, as indicated by c-statistics in the mid-seventies and closely aligned predicted vs. observed probabilities. While certainly adequate, these performance metrics do not indicate the model’s readiness for use in the clinical setting, but rather serves as a starting point for further exploration of drivers of health status decline in the older AMI population. Other factors not captured in SILVER-AMI, particularly social and environmental determinants of health, may add additional predictive value to the model. Future work should explore associations of factors not measured here, such as socioeconomic disadvantage, the built environment, or social isolation with health status changes after AMI. If future work is able to improve upon the discriminatory ability of our current model, it would be feasible to translate this risk model into a brief assessment for use in outcomes prioritization and shared decision-making for older adults hospitalized with AMI.
The main limitation of this study is that our risk model has not been externally validated. Furthermore, because this model was developed to predict risk of health status decline at the time of discharge, intervening health events that occur after discharge (e.g., readmissions) were not evaluated, despite their known associations with health status change after AMI.6 Older adults from minoritized groups were underrepresented in this study, relative to the U.S. population during the study period. Lastly, our findings might not be generalizable to older adults age <75 years and AMI patients who die in the first six months after hospitalization.
The goal of refocusing health care to improve outcomes that matter most to patients is increasingly recognized in cardiology.17,20 This work represents an important step toward identifying and mitigating threats to health status, a key patient-centered outcome, after AMI.
Supplementary Material
Supplemental Materials: A Risk Model for Decline in Health Status after Acute Myocardial Infarction Among Older Adults
Key Points
This prospective cohort study shows that fourteen demographic, clinical, and functional characteristics predicted risk of decline in health status (as measured by the Short Form 12) among adults 75 years of age or older six months after hospitalization for acute myocardial infarction.
Why does this matter?
This study’s findings may be useful to ascertain older adults’ risk of health status decline after acute myocardial infarction in a concise and well-performing risk model.
Funding:
National Institutes of Health NHLBI R01HL115295 (AMH, JAD, TEM, SIC), NIA T32 AG019134 (AMH), NIA P30AG021342 (AMH, TEM, SIC), American Heart Association 17MCPRP33370062 (AMH)
Footnotes
The authors declare no conflicts of interest.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental Materials: A Risk Model for Decline in Health Status after Acute Myocardial Infarction Among Older Adults