Article ID: CJ-19-0467
Background: Octogenarians, who are frequently frail, represent a large proportion of patients admitted for ST-segment elevation myocardial infarction (STEMI). We investigated the relationship between frailty, assessed by the Canadian Study of Health and Aging Clinical Frailty Scale (CFS), and short- and mid-term prognoses in octogenarian STEMI patients.
Methods and Results: We used a multicenter registry data of 1,301 patients with STEMI undergoing percutaneous coronary intervention (PCI) between January 2014 and December 2016. Of them, 273 were retrospectively analyzed after categorization into 3 groups based on the preadmission CFS (CFS 1–3, 140 patients; CFS 4–5, 99 patients; and CFS 6–8, 34 patients). We evaluated the influence of CFS on overall mortality at 2 years and on non-home discharge, defined as the composite of in-hospital death and new transfer to a hospital or nursing home. During the study period (median, 565 days), the overall mortality and ratio of non-home discharge increased as CFS increased. After adjustment for multivariable analysis, the severely frail continued to be significantly associated with an increased risk of overall mortality (adjusted hazard ratio 2.37; 95% confidence interval [CI] 1.11–5.05; P=0.026) and non-home discharge (adjusted odds ratio 9.50; 95% CI 3.48–25.99; P<0.001).
Conclusions: Frailty, as assessed by CFS, had an influence on short- and mid-term prognoses in octogenarian patients with STEMI.
Percutaneous coronary intervention (PCI) for the treatment of ST-elevation myocardial infarction (STEMI) has become more common in recent decades. The proportion of octogenarian patients with STEMI undergoing PCI is increasing in accordance with aging of populations. These patients are at high risk of death;1–4 even if they survive STEMI, they are likely to be transferred to a hospital or nursing home. Similarly, the concept of “frailty” is often used to describe octogenarian patients. Frailty represents a state in which there is an increased likelihood of dependency and/or death when a patient is exposed to a stressor.5–7 Recently, the semiquantitative 9-level Canadian Study of Health and Aging Clinical Frailty Scale (CFS)8 has gained attention for its ability to measure frailty based on a single interview.5,9 However, the prognostic value of CFS among octogenarian patients with STEMI receiving PCI has not been evaluated sufficiently with regard to overall mortality and non-home discharge. The aim of this study was to evaluate the association of CFS with these clinical outcomes of octogenarian patients with STEMI.
We screened the Nagoya multicenter registry for the data of consecutive patients with STEMI who underwent primary PCI between January 2014 and December 2016 (n=1,301). The registry is an ongoing, retrospective, multicenter registry of coronary artery disease and current data include information from 5 institutions in the Tokai area, details of which have been described previously.10 Of the total screened, 273 consecutive octogenarian patients were enrolled in this study after excluding 15 patients who had been in a hospital or nursing home immediately before admission. STEMI was defined as: (a) clinical evidence of ischemia; (b) ECG showing new ST elevation at the J-point in 2 contiguous leads, with cutoff points of ≥0.2 mV in men and ≥0.15 mV in women in leads V2–V3, or ≥0.1 mV in the other leads; and (c) at least 1 high myocardial biomarker level, which was defined as a serum troponin I/T or creatine kinase level above the 99th percentile of the normal reference population during the first 24 h after admission.11 We excluded patients with suspected STEMI who did not undergo coronary angiography, those who exhibited coronary vasospasm or takotsubo cardiomyopathy, and those who had culprit lesions but did not undergo PCI. The institutional medical ethics committees approved the study in accordance with the Declaration of Helsinki.
Frailty ClassificationPatients were categorized into 3 groups based on their preadmission CFS level determined by the validation study:9,12 CFS 1–3 (not frail), CFS 4–5 (at risk to mildly frail), and CFS 6–9 (severely frail).
The assessment of CFS data was retrospectively performed by at least 2 investigators in each institute between July 2017 and May 2018.
The CFS assessment was conducted with the primary physician, using chart reviews evaluated regarding the preadmission status by the rehabilitation therapists, nurses, and doctors who made first contact with the patients. Rehabilitation therapists made detailed medical records of patient exercise capacity before admission and during rehabilitation. Furthermore, at admission, the nurses and physicians in the emergency department interviewed patients and their relatives and immediately recorded patients’ preadmission life history, including daily activities and cognitive function. In the case of the primary physician not being present, we determined CFS based on the chart review by at least 2 people’s checks.
Study EndpointsThe study endpoint was overall mortality during 2 years as the mid-term prognostic endpoint and non-home discharge as the short-term prognostic endpoint according to the CFS. Non-home discharge was defined as the composite of in-hospital death and new transfer to a hospital or nursing home. We did not set a limit of days in non-home discharge. Furthermore, the independent predictors of each endpoint were evaluated.
Treatment ProtocolUpon admission, patients underwent a rapid evaluation that included sharing their medical history, a physical examination, ECG, chest radiography, echocardiography, and blood tests. In this study, all patients underwent immediate coronary angiography and primary PCI. Written informed consent was given by each patient and/or the patient’s relatives before PCI. None of the patients received systemic thrombolytic therapy. Other therapeutic interventions were performed at the discretion of the attending physician, including blood transfusion for anemia, mechanical ventilation for respiratory failure or shock, the administration of diuretics for congestive heart failure, and administration of inotropes for hypotension or hypoperfusion.
Data Definitions and Data CollectionHypertension was defined as current or previous treatment with antihypertensive medication. Diabetes mellitus was defined as current or previous treatment with antidiabetic medication (insulin or oral hypoglycemic drugs) or a hemoglobin A1c level ≥6.5%, in accordance with the National Glycohemoglobin Standardization Program.13 Dyslipidemia was defined as current or previous treatment with antidyslipidemic medication. Chronic kidney disease was defined in this study as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2.14 We calculated the eGFR according to the Japanese equation: eGFR (mL/min/1.73 m2)=194×serum creatinine−1.094×age−0.287×0.739 (if female).15 Previous MI, congestive heart failure, ischemic stroke, intracranial bleeding, gastrointestinal bleeding, peripheral artery disease (PAD), PCI, and coronary artery bypass grafting were recorded based on interviews with the patients and/or their relatives. The time from the onset of MI to reperfusion was recorded. The recorded laboratory findings included the baseline white blood cell count and levels of C-reactive protein (CRP), hemoglobin, blood glucose, hemoglobin A1c, peak creatine kinase, peak creatine kinase MB isoenzyme, and albumin. The echocardiographic findings included the left ventricular ejection fraction (LVEF). The Killip classification was determined from the physical examination and systolic blood pressure. Clinical data were collected during hospital visits or by telephone interviews with the patients and their relatives at 6-month intervals.
Statistical AnalysisContinuous variables are expressed as mean±standard deviation (SD) or median and interquartile range (IQR: 25–75%). Categorical variables are expressed as number and percentages. Comparisons of clinical, echocardiographic, angiographic, and procedure-related characteristics were performed using the chi-square test for categorical covariates, one-way analysis of variance for continuous normally distributed covariates (expressed as mean±SD), and the Kruskal-Wallis test for continuous non-normally distributed variables (expressed as median with IQR), according to the CFS.
In-hospital clinical outcomes and medications at discharge were analyzed using the chi-square test. Comparisons of event-free survival (Kaplan-Meier curves) were performed with the log-rank test. Cox regression analysis was performed to determine the independent predictors of overall mortality during the follow-up period after STEMI-PCI. Logarithmic transformation was performed for non-normally distributed variables. The covariates that were introduced into the multivariate Cox model were predictors associated with overall mortality (P<0.05) [CFS, body mass index (BMI), prior heart failure, Killip classification, target lesion in right coronary artery (RCA), albumin, hemoglobin, and CRP] and patients’ age and sex. An assessment of proportional hazard assumption in the Cox regression analysis on overall mortality was also performed using a backward stepwise selection method. Logistic regression analysis was performed to detect the independent predictors of non-home discharge. Logarithmic transformation was performed for non-normally distributed variables. The covariates that were introduced into the multivariate logistic model were predictors associated with non-home discharge (P<0.05) [CFS, BMI, age, prior ischemic stroke, Killip classification, CKD, target lesion in left main trunk (LMT), albumin, hemoglobin, WBC, CRP, and blood glucose] and the patient’s sex. Similarly, these parameters were assessed by the backward stepwise selection method. The results are reported as adjusted hazard ratios (HR) or odds ratios (OR) with associated 95% confidence intervals (CIs). All statistical analyses were performed using SPSS version 23 (SPSS Inc., Chicago, IL, USA) and R ver. 3.5.1 (The R Project for Statistical Computing, http://www.R-project.org/). All P-values were two-tailed, and results with P<0.05 were considered to be statistically significant for all analyses.
A total of 273 octogenarian patients with STEMI were treated with PCI in our registry during the study period. The median clinical follow-up was 565 days (1st to 3rd quartile: 255–730 days). The number of patients who were followed more than 1 year was 251 (91.9%). The remaining 22 patients could not be contacted during hospital visit or by telephone.
The percentage of patients in each CFS classification was as follows: CFS 1–3, 51.3%; CFS 4–5, 36.3%; and CFS 6–8, 12.5%. There were no patients with CFS 9 in this study (Figure 1). The baseline clinical, laboratory, and procedural characteristics according to the CFS are shown in Table 1. This cohort had a mean age of 84.6±3.8 years and 46.2% were men. A total of 69.2% of the cohort had hypertension, 31.9% diabetes mellitus, and 50.2% dyslipidemia. The median time from onset to reperfusion was 3.5 h (1st to 3rd quartile: 2.4–7.0 h). In this cohort, 20.1% were classified as Killip class III or IV. Among the 3 CFS study groups, significant differences were observed for mean age, sex, BMI, prior ischemic stroke, prior PAD, current smoking, hemoglobin level, serum albumin level, and white blood cell count.
Study population and classification of all patients with detailed Clinical Frailty Scale (CFS) data. STEMI, ST-segment elevation myocardial infarction.
| Overall (n=273) |
CFS 1–3 (n=140) |
CFS 4–5 (n=99) |
CFS 6–8 (n=34) |
P value | |
|---|---|---|---|---|---|
| Clinical characteristics | |||||
| Age, years | 84.6±3.8 | 83.7±3.1 | 85.4±4.1 | 85.7±4.6 | 0.001 |
| Male, n (%) | 126 (46.2) | 75 (53.6) | 38 (38.4) | 13 (38.2) | 0.042 |
| Body mass index, kg/m2 | 22.1 (20.1–24.5) | 22.6 (20.7–24.7) | 21.7 (19.9–24.8) | 21.5 (18.1–22.2) | 0.001 |
| Body mass index <20 kg/m2, n (%) | 58 (21.8) | 22 (15.9) | 24 (25.0) | 12 (37.5) | 0.018 |
| Prior heart failure, n (%) | 11 (4.0) | 4 (2.9) | 5 (5.1) | 2 (5.9) | 0.59 |
| Prior MI, n (%) | 26 (9.5) | 14 (10.0) | 10 (10.1) | 2 (5.9) | 0.74 |
| Prior PCI, n (%) | 35 (12.8) | 15 (10.7) | 18 (18.2) | 2 (5.9) | 0.10 |
| Prior CABG, n (%) | 4 (1.5) | 3 (2.1) | 1 (1.0) | 0 (0) | 0.58 |
| Prior ischemic stroke, n (%) | 28 (10.3) | 10 (7.1) | 9 (9.1) | 9 (26.5) | 0.003 |
| Prior intracranial bleeding, n (%) | 6 (2.2) | 2 (1.4) | 2 (2.0) | 2 (5.9) | 0.28 |
| Prior gastrointestinal bleeding, n (%) | 5 (1.8) | 2 (1.4) | 1 (1.0) | 2 (5.9) | 0.17 |
| Prior PAD, n (%) | 18 (6.6) | 7 (5.0) | 5 (5.1) | 6 (17.6) | 0.021 |
| Dyslipidemia, n (%) | 137 (50.2) | 73 (52.1) | 53 (53.5) | 11 (32.4) | 0.083 |
| Diabetes mellitus, n (%) | 87 (31.9) | 42 (30.0) | 35 (35.4) | 10 (29.4) | 0.65 |
| Hypertension, n (%) | 189 (69.2) | 95 (67.9) | 69 (69.7) | 25 (69.2) | 0.81 |
| Current smoking, n (%) | 34 (12.5) | 17 (12.1) | 17 (17.2) | 0 (0) | 0.031 |
| CKD, n (%) | 167 (61.2) | 79 (56.4) | 69 (69.7) | 19 (55.9) | 0.093 |
| Admission data | |||||
| Systolic blood pressure, mmHg | 131.9±41.5 | 134.2±42.3 | 127.9±39.6 | 134.4±44.0 | 0.48 |
| Diastolic blood pressure, mmHg | 72.8±24.1 | 73.7±24.5 | 70.5±22.0 | 76.0±28.0 | 0.44 |
| Heart rate, beats/min | 75.8±25.5 | 75.5±27.1 | 74.8±22.8 | 79.8±26.4 | 0.60 |
| Killip class | 1.0 (1.0–2.0) | 1.0 (1.0–2.0) | 1.0 (1.0–2.0) | 1.0 (1.0–2.3) | 0.34 |
| Killip class ≥3, n (%) | 55 (20.1) | 25 (17.9) | 22 (22.2) | 8 (23.5) | 0.62 |
| Laboratory data on admission | |||||
| Hemoglobin, g/dL | 12.4±1.9 | 12.7±1.9 | 12.0±1.8 | 12.1±2.1 | 0.017 |
| eGFR, mL/min/1.73 m2 | 51.5±21.5 | 53.9±20.6 | 47.9±21.2 | 51.7±25.2 | 0.10 |
| Albumin, g/dL | 3.8 (3.4–4.1) | 3.9 (3.5–4.1) | 3.7 (3.4–4.1) | 3.5 (3.3–4.0) | 0.015 |
| Albumin <3.5 g/dL, n (%) | 73 (27.2) | 27 (20.0) | 29 (29.3) | 17 (50.0) | 0.002 |
| White blood cell count, ×103/μL | 8.5 (6.9–10.5) | 8.3 (6.4–10.3) | 8.5 (6.9–10.5) | 9.4 (7.5–11.7) | 0.046 |
| CRP, mg/dL | 0.2 (0.1–1.1) | 0.2 (0.1–0.9) | 0.2 (0.1–0.8) | 0.8 (0.2–1.4) | 0.35 |
| Blood glucose, mg/dL | 167.0 (135.0–208.5) | 160.0 (135.0–205.5) | 178.0 (135.0–225.0) | 164.5 (130.0–220.3) | 0.98 |
| HbA1c, % | 5.9 (5.6–6.5) | 5.9 (5.6–6.59) | 5.8 (5.5–6.7) | 5.8 (5.5–6.4) | 0.93 |
| Peak CK, IU/L | 1,086.0 (323.0–2,395.0) |
1,336.5 (663.0–2,430.0) |
1,581.5 (692.0–2,963.0) |
1,778.0 (436.5–2,569.0) |
0.13 |
| Peak CKMB, IU/L | 140.5 (66.5–301.0) | 134.4 (65.0–290.5) | 165.5 (75.0–319.0) | 152.0 (39.5–283.0) | 0.98 |
| LVEF, % | 56.0 (45.0–62.0) | 56.7 (45.6–62.9) | 56.0 (46.0–64.0) | 50.5 (43.8–60.0) | 0.23 |
| Lesion characteristics | |||||
| LMT, n (%) | 17 (6.2) | 10 (7.1) | 6 (6.1) | 1 (2.9) | 0.66 |
| LAD, n (%) | 103 (37.7) | 51 (36.4) | 37 (37.4) | 15 (44.1) | 0.71 |
| LCx, n (%) | 23 (8.4) | 9 (6.4) | 10 (10.1) | 4 (11.8) | 0.46 |
| RCA, n (%) | 118 (43.2) | 62 (44.3) | 43 (43.4) | 13 (38.2) | 0.81 |
| 2VD, n (%) | 12 (4.4) | 8 (5.7) | 3 (3.0) | 1 (2.9) | 0.55 |
| Time to reperfusion | |||||
| Onset to reperfusion time, h | 3.5 (2.4–7.0) | 3.5 (2.5–9.0) | 4.4 (2.5–9.0) | 3.8 (3.0–5.8) | 0.28 |
Data are number (%) or mean±SD. Data are also presented as median (Q1–Q3). CABG, coronary artery bypass grafting; CFS, clinical frailty scale; CK, creatine kinase; CKMB, creatine kinase MB; CKD, chronic kidney disease; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c; LAD, left anterior descending artery; LCx, left circumflex artery; LVEF, left ventricular ejection fraction; LMT, left main trunk; MI, myocardial infarction; PAD, peripheral artery disease; PCI, percutaneous coronary intervention; RCA, right coronary artery; 2VD, 2-vessel disease; LAD+LCx/LAD+RCA/RCA+LCx.
During the follow-up period, 65 patients died after PCI. As the severity of frailty increased, overall mortality also significantly increased (Figure 2). In-hospital outcomes are shown in Table 2. After discharge, 37 patients died (13 with CFS 1–3, 18 with CFS 4–5, 6 with CFS 6–8). The causes of death are shown in Table 3.
Kaplan-Meier analysis of cumulative mortality in the 3 groups of Clinical Frailty Scale (CFS) classification.
| Overall (n=273) |
CFS 1–3 (n=140) |
CFS 4–5 (n=99) |
CFS 6–8 (n=34) |
P value | |
|---|---|---|---|---|---|
| In-hospital clinical outcome | |||||
| All deaths, n (%) | 28 (10.3) | 8 (5.7) | 12 (12.1) | 8 (23.5) | 0.007 |
| Cardiac death, n (%) | 24 (8.8) | 7 (5.0) | 10 (10.1) | 7 (20.6) | 0.013 |
| Non-home discharge, n (%) | 64 (23.4) | 20 (14.3) | 25 (25.3) | 19 (55.9) | <0.001 |
| Mechanical complications, n (%) | 6 (2.2) | 0 (0) | 3 (3.0) | 3 (8.8) | 0.005 |
| Overall (n=245) |
CFS 1–3 (n=132) |
CFS 4–5 (n=87) |
CFS 6–8 (n=26) |
P value | |
| Medication at discharge | |||||
| Aspirin, n (%) | 232 (94.7) | 123 (93.2) | 84 (96.6) | 25 (96.2) | 0.52 |
| Thienopyridine, n (%) | 221 (90.2) | 118 (89.4) | 80 (92.0) | 23 (88.5) | 0.78 |
| Warfarin, n (%) | 6 (2.4) | 5 (3.8) | 1 (1.1) | 0 (0.0) | 0.32 |
| DOAC, n (%) | 20 (8.2) | 14 (10.6) | 5 (5.7) | 1 (3.8) | 0.31 |
| Statin, n (%) | 214 (87.3) | 115 (87.1) | 79 (90.8) | 20 (76.9) | 0.17 |
| ACEI/ARB, n (%) | 179 (73.1) | 92 (69.7) | 71 (81.6) | 16 (61.5) | 0.057 |
| CCB, n (%) | 54 (22.0) | 30 (22.7) | 19 (21.8) | 5 (19.2) | 0.92 |
| β-blocker, n (%) | 171 (69.8) | 88 (66.7) | 64 (73.6) | 19 (73.1) | 0.51 |
Data are number (%). ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; CCB, calcium-channel blocker; CFS, clinical frailty scale; DOAC, direct oral anticoagulant.
| Preadmission CFS / Patient no. |
Age (years) |
Sex | Target lesion | Cause of death | Duration from PCI (days) |
|---|---|---|---|---|---|
| Not frail | |||||
| 1 | 80 | M | LAD | Sudden death | 324 |
| 2 | 82 | M | LAD | Sudden death | 118 |
| 3 | 82 | F | RCA | Sudden death | 242 |
| 4 | 86 | M | RCA | Sudden death | 173 |
| 5 | 82 | F | LAD | Heart failure | 347 |
| 6 | 83 | F | RCA | Heart failure | 315 |
| 7 | 82 | M | LAD | Fatal arrhythmia | 250 |
| 8 | 83 | F | LAD | Infection | 43 |
| 9 | 85 | F | RCA | Infection | 542 |
| 10 | 86 | F | LMT | Infection | 260 |
| 11 | 81 | M | RCA | Infection | 454 |
| 12 | 81 | F | LCx | Infection | 135 |
| 13 | 84 | F | 2VD (LAD+LCx) | Unknown | 104 |
| Mildly frail | |||||
| 1 | 83 | M | RCA | Sudden death | 307 |
| 2 | 86 | F | RCA | Heart failure | 672 |
| 3 | 87 | F | LCx | Heart failure | 214 |
| 4 | 91 | F | LAD | Heart failure | 76 |
| 5 | 80 | M | LAD | Infection | 43 |
| 6 | 84 | F | LAD | Infection | 373 |
| 7 | 91 | F | LAD | Infection | 151 |
| 8 | 85 | M | LAD | Cerebral infarction | 550 |
| 9 | 86 | F | LAD | Cerebral hemorrhage | 313 |
| 10 | 87 | M | RCA | Cerebral hemorrhage | 708 |
| 11 | 82 | F | RCA | Malignancy | 161 |
| 12 | 85 | F | LAD | Malignancy | 96 |
| 13 | 80 | M | LAD | Unknown | 68 |
| 14 | 83 | F | LAD | Unknown | 35 |
| 15 | 84 | M | RCA | Unknown | 57 |
| 16 | 84 | F | LAD | Unknown | 499 |
| 17 | 92 | F | LAD | Unknown | 150 |
| 18 | 94 | M | RCA | Unknown | 96 |
| Severely frail | |||||
| 1 | 85 | F | RCA | Sudden death | 25 |
| 2 | 94 | F | RCA | Heart failure | 549 |
| 3 | 80 | M | RCA | Malignancy | 697 |
| 4 | 86 | M | LAD | Asthenia | 333 |
| 5 | 87 | F | LAD | Asthenia | 83 |
| 6 | 82 | F | LAD | Unknown | 379 |
Abbreviations as in Table 1.
A total of 64 patients were not discharged home, and in-hospital mortality and the possibility of discharge to home significantly worsened as the severity of frailty increased (Figure 3).
Distribution of in-hospital outcome according to the Clinical Frailty Scale (CFS) classification in all patients.
Regarding overall mortality, Cox multivariate regression analysis identified severe frailty (at risk to mildly frail: adjusted HR 1.81; 95% CI 0.99–3.36; P=0.056, severely frail: adjusted HR 2.37; 95% CI 1.11–5.05; P=0.026), lower BMI (adjusted HR 0.88; 95% CI 0.81–0.95; P=0.001), Killip class ≥3 (adjusted HR 2.20; 95% CI 1.26–3.83; P=0.006), prior heart failure (adjusted HR 2.88; 95% CI 1.22–6.77; P=0.015), and target lesion in the RCA (adjusted HR 0.53; 95% CI 0.31–0.93; P=0.026) as independent predictors of mid-term death (Table 4). Similarly, regarding non-home discharge as a short-term prognosis, multivariate analysis identified severe frailty (at risk to mildly frail: adjusted OR 1.99; 95% CI 0.86–4.63; P=0.11, severely frail: adjusted OR 9.50; 95% CI 3.48–25.99; P<0.001), Killip class ≥3 (adjusted OR 4.42; 95% CI 1.99–9.84; P<0.001), higher CRP (adjusted OR 1.30; 95% CI 1.05–1.59; P=0.014), and target lesion in the LMT (adjusted OR 5.06; 95% CI 1.38–18.55; P=0.015) as independent predictors of non-home discharge (Table 5).
| Factors for predicting | Overall mortality | |||||
|---|---|---|---|---|---|---|
| Univariate analysis | Multivariate analysis | |||||
| HR | 95% CI | P value | HR | 95% CI | P value | |
| Patients’ clinical characteristics | ||||||
| CFS (1–3) | 1.00 (Ref.) | 1.00 (Ref.) | ||||
| CFS (4–5) | 2.24 | 1.28–3.91 | 0.005 | 1.81 | 0.99–3.33 | 0.056 |
| CFS (6–8) | 3.47 | 1.76–6.82 | <0.001 | 2.37 | 1.11–5.05 | 0.026 |
| Age, years | 1.05 | 0.99–1.12 | 0.13 | |||
| Male, n (%) | 0.74 | 0.45–1.21 | 0.23 | |||
| Body mass index, kg/m2 | 0.022 | 0.005–0.10 | <0.001 | 0.88 | 0.81–0.95 | 0.001 |
| Prior heart failure, n (%) | 2.71 | 1.17–6.27 | 0.020 | 2.88 | 1.22–6.77 | 0.015 |
| Prior MI, n (%) | 1.63 | 0.81–3.29 | 0.18 | |||
| Prior PCI, n (%) | 1.34 | 0.70–2.56 | 0.38 | |||
| Prior CABG, n (%) | 0.048 | 0.001–183.56 | 0.47 | |||
| Prior ischemic stroke, n (%) | 1.56 | 0.77–3.15 | 0.22 | |||
| Prior intracranial bleeding, n (%) | 0.75 | 0.10–5.39 | 0.77 | |||
| Prior gastrointestinal bleeding, n (%) | 0.75 | 0.10–5.39 | 0.77 | |||
| Prior PAD, n (%) | 1.20 | 0.48–2.98 | 0.70 | |||
| Dyslipidemia, n (%) | 0.88 | 0.54–1.43 | 0.60 | |||
| Diabetes mellitus, n (%) | 0.79 | 0.46–1.36 | 0.39 | |||
| Hypertension, n (%) | 1.03 | 0.60–1.76 | 0.92 | |||
| Current smoking, n (%) | 1.12 | 0.55–2.26 | 0.76 | |||
| CKD, n (%) | 1.11 | 0.67–1.82 | 0.69 | |||
| Admission data | ||||||
| Killip class ≥3, n (%) | 3.42 | 2.08–5.62 | <0.001 | 2.20 | 1.26–3.83 | 0.006 |
| LMT | 1.43 | 0.57–3.56 | 0.44 | |||
| LAD | 1.54 | 0.94–2.51 | 0.084 | |||
| LCx | 1.24 | 0.53–2.86 | 0.62 | |||
| RCA | 0.58 | 0.35–0.98 | 0.040 | 0.53 | 0.31–0.93 | 0.026 |
| 2-vessel disease | 0.69 | 0.17–2.82 | 0.61 | |||
| Hemoglobin, g/dL | 0.82 | 0.72–0.93 | 0.003 | |||
| Albumin <3.5 g/dL, n (%) | 2.37 | 1.45–3.88 | 0.001 | 1.65 | 0.95–2.85 | 0.074 |
| White blood cell count, ×103/μL | 1.99 | 0.91–4.38 | 0.086 | |||
| CRP, mg/dL | 1.27 | 1.11–1.45 | 0.001 | |||
| Blood glucose, mg/dL | 1.17 | 0.58–2.36 | 0.67 | |||
| Peak CK, IU/L | 1.12 | 0.92–1.36 | 0.25 | |||
| Onset to reperfusion time, h | 1.07 | 0.80–1.43 | 0.67 | |||
Covariates introduced into the multivariate model were: CFS, age, sex, BMI, prior heart failure, Killip classification, RCA (target lesion), hemoglobin, albumin, and CRP. CI, confidence interval; HR, hazard ratio; other abbreviations as in Table 1.
| Factors for predicting | Non-home discharge | |||||
|---|---|---|---|---|---|---|
| Univariate analysis | Multivariate analysis | |||||
| OR | 95% CI | P value | OR | 95% CI | P value | |
| Patients’ clinical characteristics | ||||||
| CFS (1–3) | 1.00 (Ref.) | 1.00 (Ref.) | ||||
| CFS (4–5) | 2.03 | 1.05–3.90 | 0.035 | 1.99 | 0.86–4.63 | 0.11 |
| CFS (6–8) | 7.60 | 3.32–17.36 | <0.001 | 9.50 | 3.48–25.99 | <0.001 |
| Age, years | 1.10 | 1.03–1.18 | 0.007 | |||
| Male, n (%) | 0.63 | 0.35–1.12 | 0.11 | |||
| Body mass index, kg/m2 | 0.045 | 0.007–0.29 | 0.001 | |||
| Prior heart failure, n (%) | 0.72 | 0.15–3.41 | 0.68 | |||
| Prior MI, n (%) | 0.98 | 0.38–2.55 | 0.96 | |||
| Prior PCI, n (%) | 0.64 | 0.25–1.62 | 0.35 | |||
| Prior CABG, n (%) | <0.001 | – | >0.99 | |||
| Prior ischemic stroke, n (%) | 2.78 | 1.24–6.25 | 0.013 | |||
| Prior PAD, n (%) | 1.28 | 0.44–3.73 | 0.65 | |||
| Dyslipidemia, n (%) | 1.27 | 0.72–2.22 | 0.41 | |||
| Diabetes mellitus, n (%) | 0.72 | 0.39–1.34 | 0.30 | |||
| Hypertension, n (%) | 0.74 | 0.41–1.33 | 0.31 | |||
| Current smoking, n (%) | 1.01 | 0.43–2.36 | 0.98 | |||
| CKD, n (%) | 2.05 | 1.10–3.81 | 0.023 | |||
| Admission data | ||||||
| Killip class ≥3, n (%) | 5.24 | 2.77–9.93 | <0.001 | 4.42 | 1.99–9.84 | <0.001 |
| LMT | 4.11 | 1.52–11.15 | 0.005 | 5.06 | 1.38–18.55 | 0.015 |
| LAD | 0.99 | 0.55–1.76 | 0.97 | |||
| LCx | 1.17 | 0.44–3.10 | 0.76 | |||
| RCA | 0.62 | 0.34–1.11 | 0.10 | |||
| 2-vessel disease | 1.09 | 0.29–4.16 | 0.90 | |||
| Hemoglobin, g/dL | 0.73 | 0.63–0.86 | <0.001 | 0.84 | 0.69–1.02 | 0.070 |
| Albumin <3.5 g/dL, n (%) | 2.75 | 1.52–4.98 | 0.001 | |||
| White blood cell count, ×103/μL | 4.27 | 1.72–10.60 | 0.002 | |||
| CRP, mg/dL | 1.44 | 1.22–1.71 | <0.001 | 1.30 | 1.05–1.59 | 0.014 |
| Blood glucose, mg/dL | 2.56 | 1.16–5.64 | 0.020 | |||
| Peak CK, IU/L | 1.12 | 0.90–1.39 | 0.31 | |||
| Onset to reperfusion time, h | 0.99 | 0.69–1.43 | 0.97 | |||
Covariates introduced into the multivariate model were: CFS, age, sex, BMI, prior ischemic stroke, CKD, Killip classification, left main trunk (target lesion), hemoglobin, albumin, WBC, CRP, and blood glucose. Abbreviations as in Tables 1,4.
The aim of this study was to evaluate the association of CFS with clinical outcomes of octogenarian patients with STEMI. There are 3 important findings from this study. First, the data demonstrated that the overall mortality at 2 years increased as the severity of frailty measured by the CFS increased among octogenarians undergoing PCI for STEMI. Second, the data showed that another frailty-associated parameter, BMI, was also associated with an increased risk of overall mortality. Third, the data suggested the potential prognostic value of CFS for short-term risk stratification among octogenarians undergoing PCI for STEMI.
To the best of our knowledge, this is the first multicenter study to show that the CFS level was independently associated with mid-term mortality among octogenarian STEMI patients undergoing PCI.
Furthermore, our study clearly showed that risk classification using the CFS is applicable to the Asian octogenarian population with STEMI. Importantly, this study demonstrated that the CFS level had greater prognostic value for overall mortality in patients with STEMI than other characteristics such as renal dysfunction, diabetes mellitus, female sex, and older age, which have previously been reported to have a significant impact on prognosis in patients with acute coronary syndrome (ACS).16,17
In terms of risk stratification in elderly people with ACS, there are several well-established approaches to predicting prognosis that incorporate health and function. For example, gait speed is well known to predict an increased risk of cardiovascular events in patients with STEMI;18 however, it should be mentioned that the assessment of gait speed takes time and effort, and it is sometimes difficult to evaluate very frail patients. In addition to gait speed, other frailty parameters must be evaluated by a functional physical test and checklists associated with lifestyle,19,20 which are useful for predicting outcomes. However, it can be difficult to evaluate these parameters in the emergency setting. On the other hand, CFS is a simple and effective semiquantitative marker for a baseline objective and non-invasive assessment of patients’ characteristics. Frailty can be conceptualized as a phenotype of weight loss, fatigue, and weakness, or a multidimensional state of vulnerability arising from a complex interplay of biological, cognitive, and social factors.21–24 Indeed, the significance of the CFS has been supported by research conducted by Shimura et al, who identified a positive correlation between the CFS and several other indicators of frailty, including BMI <20 kg/m2, serum albumin level <3.5 g/dL, gait speed, and grip strength in patients with severe aortic stenosis.25
In addition to the CFS level, our study demonstrated that another frailty-associated parameter, BMI, was associated with an increased risk of mid-term overall mortality in the multivariate analysis. This further emphasizes the influence of frailty, which is in line with findings of previous reports.26–28 In general, higher BMI and obesity are associated with cardiovascular risk factors such as hypertension, diabetes mellitus, and dyslipidemia.29 However, a markedly lower BMI was related to a poor outcome following PCI in octogenarian patients with STEMI, which is possibly related to cardiac cachexia, malnutrition, or depression, as previously reported.30 Considering that the patients of the study were octogenarians, this finding may be explainable by the “obesity paradox”.31,32
As with the CFS, a higher Killip score also had an association with in-hospital outcome and overall mortality, which is consistent with findings of previous reports on patients with ACS.33,34 Therefore, the fact that simple parameters such as the CFS, BMI, and Killip classification have a strong association with clinical outcomes highlights the importance of early assessment of frailty, as well as hemodynamic severity, and introduces the possibility of improving mortality in frail patients through early intervention.
Notably, we showed that the rate of discharge to home decreased as the severity of frailty increased, as measured by the CFS. It is possible that there is a close relationship between frailty and non-home discharge in octogenarian patients with STEMI. Sujino et al found that severe frailty and lower BMI were independently associated with non-home discharge in 62 patients.28 In agreement with such findings, the present study demonstrated that severe frailty had a significant association with non-home discharge in a larger number of patients. Although sex, cognitive disorder, living alone, caregiver other than spouse,35 and independence in activities of daily living36 have been previously reported as significant predictors of discharge destination, the CFS is a useful marker of frailty in daily clinical practice when evaluating octogenarian patients with STEMI.
Considering these factors, there is a possibility that our findings are important for the management of frail patients with STEMI in the early hospital phase. Further, early intervention for frailty in concert with social support and cardiac rehabilitation should be prioritized in order to support octogenarian patients with STEMI to live as independently as possible, for as long as possible.23,37,38
Study LimitationsFirst, the study was retrospective in nature. Second, there were no comparative data between STEMI patients with PCI and those without PCI. Third, in this study, we turned the continuous 9-stage CFS level into a 3-stage categorical variable because the data available were relatively few. Considering the inadequate number of enrolled patients, particularly in the high CFS group, larger-scale studies are required to confirm the effect of the CFS on clinical outcomes. Fourth, the CFS classification was not evaluated at admission because of the retrospective design, and in cases where the primary physician was absent, we estimated the CFS based on chart review. Therefore, it is possible that the retrospective assessment of CFS with primary physician introduces recall bias, and assessment of CFS by referring to chart review introduces misclassification bias. Fifth, the synergistic effects of social work, home care, and cardiac rehabilitation were not evaluated in this study. Sixth, a significant parameter for both endpoints, LVEF, was not used in the multivariate analysis because this parameter was not available in 16 of the patients in our registry and of these patients, 12 died during hospitalization for STEMI.
Finally, of the 65patient deaths in this study, 28 (43%) occurred in hospital. It is possible that our assessment of the influence of the CFS on mid-term mortality was affected by the in-hospital mortality rate. In addition, it is well known that elderly patients tend to become frail after hospital admission. Therefore, in the future, the change in frailty level during hospitalization should be investigated to determine more appropriate timing for applying the CFS. Further investigations are required to clarify the indications for PCI in frail patients with STEMI.
CFS level was associated with an increased risk of not only mid-term overall mortality, but also non-home discharge in octogenarian patients with STEMI undergoing PCI.
None.
