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- v.137(9); 2024 May 5
- PMC11062708
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Chin Med J (Engl). 2024 May 5; 137(9): 1069–1077.
Published online 2023 Aug 24. doi:10.1097/CM9.0000000000002806
PMCID: PMC11062708
PMID: 37620281
Juncheng Wang,1,2 Hanning Liu,
1,3 Chao Yue,1,2 Limeng Yang,1,2 Kai Yang,4 Yan Zhao,1 Huan Ren,1 Ying Zhang,1 and Zhe Zheng1,2
Monitoring Editor: Ting Gao and Xiuyuan Hao
Author information Article notes Copyright and License information PMC Disclaimer
Abstract
Background:
Patients who undergo coronary artery bypass grafting (CABG) are known to be at a significant risk of experiencing long-term adverse events, emphasizing the importance of regular assessments. Evaluating health-related quality of life (HRQoL) serves as a direct method to gauge prognosis. Our objective is to ascertain the prognostic significance of consecutive HRQoL assessments using the Physical Component Summary (PCS) and Mental Component Summary (MCS) derived from the Short-Form 36 (SF-36) health survey in CABG patients.
Methods:
The study population consisted of 433 patients who underwent isolated elective CABG at Fuwai Hospital between 2012 and 2013. SF-36 assessments were conducted during both the hospitalization period and follow-up. The primary endpoint of the study was all-cause mortality, while the secondary outcome was a composite measure including death, myocardial infarction, stroke, and repeat revascularization. We assessed the relationships between the PCS and MCS at baseline, as well as their changes during the first 6 months after the surgery (referred to as ΔPCS and ΔMCS, respectively), and the observed outcomes.
Results:
The patients were followed for an average of 6.28 years, during which 35 individuals (35/433, 8.1%) died. After adjusting for clinical variables, it was observed that baseline MCS scores (hazard ratio [HR] for a 1-standard deviation [SD] decrease, 1.57; 95% confidence interval [CI], 1.07–2.30) and ΔMCS (HR for a 1-SD decrease, 1.67; 95% CI, 1.09–2.56) were associated with all-cause mortality. However, baseline PCS scores and ΔPCS did not exhibit a significant relationship with all-cause mortality. Notably, there was a dose-response relationship observed between ΔMCS and the likelihood of all-cause mortality (HRs for the 2nd, 3rd and 4th quartiles compared to the 1st quartile, 0.33, 0.45 and 0.11, respectively).
Conclusions:
Baseline MCS and changes in MCS were independent predictors for long-term mortality of CABG. Better mental health status and recovery indicated better prognosis.
Keywords: Coronary artery bypass grafting, Coronary artery disease, Prognosis, Quality of life, Short-Form 36, Risk factor, Serial measurements
Introduction
Coronary artery bypass grafting (CABG) is one of the most effective treatments for coronary artery disease (CAD) patients.[1–5] CABG patients remain at a high risk for experiencing long-term events, with reported 10-year rates for major cardiovascular events ranging from 26.1% to 47.6%.[6–11] While several preoperative cross-sectional variables have been identified as being associated with long-term prognosis in CAD patients, few tools are available that help physicians to evaluate surgical efficacy and tailor treatment over time.[12–18] This information is particularly crucial for CABG patients, as significant changes occur across various domains following the surgery,[19–22] which should be monitored by serial assessments.
An ideal serial assessment tool should meet certain criteria, such as being well-validated, interpretable, sensitive to clinical and surgical changes, and easy to implement. Health-related quality of life (HRQoL) fits these criteria and holds potential as a candidate for a serial assessment. HRQoL is defined as the perception of the discrepancy between actual and desired functional status and the overall impact of the disease on well-being.[23] It has been found to be valuable not only as an outcome measure but also as a predictor for multiple cardiovascular diseases.[12,23–26]
Studies have shown that changes in health status provide additional prognostic information beyond baseline measurements in patients with heart failure.[12] As a self-perceived indicator, HRQoL can be used to evaluate the broad impact of a disease on a patient and the effectiveness of interventions, which is closely related to therapeutics, but may not reflect in objective assessments, for example, the clinical impression of one's "wellness", physical capacity, or mental status.[26] CABG patients typically experience an improvement in HRQoL after surgery, particularly within the first 6 months, making it a sensitive indicator for serial assessment.[2,12,19–21,23] The Short-Form 36 (SF-36) is one of the most widely used and validated measurements for HRQoL and researchers had demonstrated that HRQoL measured by SF-36 is predictive of cardiovascular outcomes even after adjusting for demographic and clinical variables.[26] For CABG patients, a single preoperative SF-36 measurement could predict 6-month mortality.[26] However, it remains to be determined whether serial HRQoL assessments using SF-36 can provide additional information for long-term prognosis in CABG patients. Further research is needed to explore this potential.
To this end, our study aimed to examine the association between changes in SF-36 scores and long-term mortality and cardiovascular outcomes in a cohort of CABG patients followed for more than 6 years. Our hypothesis was that patients with poorer preoperative HRQoL and less improvement in HRQoL after undergoing CABG would have a higher risk of mortality and cardiovascular events.
Methods
Study design
The study had been approved by the Ethics Committee of Fuwai Hospital (No. 2019-1140) and adheres to the Declaration of Helsinki. Written informed consent was obtained from all participants. The current study derived from a perspective study aiming to provide evidence regarding the effects of statin therapy on perioperative depression in CABG patients (registered at clinicaltrials.gov, No. NCT01624857). Briefly, consecutive patients admitted for their first CABG at Fuwai Hospital between July 2012 and November 2013 were screened and 433 were included. Patients <18 years of age, undergoing emergency or urgent surgery, with concomitant operations other than CABG were excluded.
Health status measurements
After enrollment, patients were interviewed for demographic information and medical history. Routine preoperative examinations were performed to obtain baseline clinical variables. HRQoL was measured with Mandarin version SF-36.[27] The SF-36 measured physical and mental health status using 8 multi-item scales (36 items in total), which includes Physical Functioning (PF, 10 items), Role-Physical (RP, 4 items), Bodily Pain (BP, 2 items), General Health (GH, 5 items), Vitality (VT, 4 items), Social Functioning (SF, 2 items), Role-Emotional (RE, 3 items), and Mental Health (MH, 5 items). The 8 scales can be aggregated into two summary scores, named Physical Component Summary (PCS, mainly composed of PF, RP, BP, and GH) score and Mental Component Summary (MCS, mainly composed of VT, SF, RE, and MH) score.[28] Calculation of PCS and MCS scores followed the methods described by Ware et al[28]. It has been reported that PCS and MCS scores had advantages over the original 8 scales.[28,29] For both PCS and MCS scores, higher values indicate better performance. The Mandarin version SF-36 was translated according to lifestyle of Chinese and has been validated in several Chinese population with good validity.[27,30–32] Assessments of patients' health status with SF-36 were performed preoperatively, 1-month, and 6-month after surgery. Six-month measurement was chosen because it was reported that HRQoL after CABG improved 3–6 months postoperatively and remained stable through 3 years.[21]
Assessments of health status changes
To calculate the change in HRQoL between the preoperative period and the 6-month postoperative follow-up, we subtracted the preoperative PCS or MCS scores from the corresponding 6-month PCS or MCS scores, respectively. Larger values of the resulting ΔPCS (change in PCS scores between the preoperative period and the 6-month postoperative follow-up) and ΔMCS (change in MCS scores between the preoperative period and the 6-month postoperative follow-up) indicated greater improvements in HRQoL. It was important to note that patients who died within 6 months after surgery were excluded from the analyses of ΔPCS and ΔMCS.
Since CABG patients were expected to experience dramatic changes both physically and mentally during the perioperative period,[19–21] we also measured PCS and MCS scores 1-month after CABG. Subsidiary analyses were conducted by subtracting 1-month PCS or MCS scores from 6-month PCS or MCS scores to obtain respective ΔPCSsubanalysis and ΔMCSsubanalysis.
Study endpoints
The primary endpoint of the current study was all-cause mortality and the secondary endpoint was a composite outcome of all-cause death, myocardial infarction (MI), stroke, and repeat revascularization at the last follow-up. Included patients were followed at 6 time points (at discharge, 1 month, 6 months, 12 months, 3 years, and 6 years after CABG) by trained research nurses to obtain outcome information. All endpoints were adjudicated by a critical events committee.
Statistical analysis
Continuous variables were expressed as mean ± standard deviation (SD) and compared using one-way analysis of variance (ANOVA). Categorical variables were presented as counts and percentages and compared using the chi-squared or Fisher's exact test as appropriate. Preoperative PCS, MCS scores and ΔPCS, ΔMCS were standardized using the z-transform. Pearson correlation analyses at each timepoint were conducted to quantify the relations between PCS and MCS scores. First, we investigated the association of baseline PCS and MCS scores with each outcome so that the additional prognostic value of follow-up measurement and ΔPCS or ΔMCS could be demonstrated. The associations between baseline PCS and MCS scores with each outcome were tested by Cox regression analysis adjusted for clinical variables. The covariates in the adjusted model (model 1) included age, sex, body mass index (BMI), prior smoker, hypertension, diabetes mellitus, prior stroke or transient ischemic attack, peripheral vascular disease, chronic obstructive pulmonary disease, preoperative left ventricular ejection fraction (LVEF), prior MI, chronic heart failure, on- or off-pump CABG, and number of conduits. In model 2, baseline PCS or MCS scores were incorporated in addition to those of model 1 to illustrate the predictive value of baseline MCS or PCS scores.
The crude (adjusted for baseline PCS score or MCS score only, respectively) association between ΔPCS and ΔMCS with each outcome was also tested using Cox regression analysis (model 3). To demonstrate whether ΔPCS and ΔMCS were independent predictors for long-term prognosis, clinical variables were further adjusted in the final model (model 4). Additionally, ΔPCS and ΔMCS were stratified into quartiles to facilitate clinical interpretability of change in SF-36 summary scores. All analyses were conducted with R version 4.2.1 (R Core Team, Vienna, Austria). A P <0.05 was considered statistically significant.
Results
The current study included a total of 433 participants who were followed for an average of 6.28 years. During this follow-up period, 35 patients (35/433, 8.1%) experienced mortality, and 88 patients (88/433, 20.3%) suffered from the composite outcome. Four patients who died within the first 6 months after CABG were excluded from the analyses specifically concerning ΔPCS and ΔMCS.
PCS and MCS scores at baseline
The average preoperative PCS and MCS scores in our study were found to be 37.6 and 50.1, respectively. These scores were comparable to those reported by Lam et al[27] in a Chinese CAD population, where the PCS score was 36.0 and the MCS score was 49.2. Figure Figure11 illustrated the correlation between preoperative PCS and MCS scores, demonstrated no significant correlation between the two summary scores. At 1-month after surgery, a weak correlation between PCS and MCS scores was observed (Pearson r = –0.14, P = 0.011) [Supplementary Figure 1, http://links.lww.com/CM9/B728].
Figure 1
Correlation of preoperative SF-36 PCS scores and MCS scores. The blue line is the fitted line for the PCS scores and MCS scores. MCS: Mental Component Summary; PCS: Physical Component Summary; SF-36: Short-Form 36.
Baseline characteristics
The baseline characteristics of the current study were demonstrated in Table Table1.1. The mean age was 60.3 years and 79.9% (346/433) of patients were men. Half of the patients (53.1%, 230/433) received on-pump CABG, and averagely 3.2 conduits were bypassed. When comparing the deceased patients with the surviving ones, it was observed that the deceased patients were older (64.6 ± 8.0 vs. 59.9 ± 8.4, t = –3.309, P = 0.002) and had a higher proportion of diabetes (51.4% vs. 32.2%, χ2 = 5.344, P = 0.021) and prior MI (60.0% vs. 28.4%, χ2 = 15.040, P <0.001). It was also found that these patients had lower PCS scores at the 1-month post-surgery assessment (31.0 ± 13.5 vs. 36.2 ± 12.5, t = 1.997 P = 0.046). Patients grouped by composite outcome obtained similar results except for diabetes and 1-month PCS socres [Supplementary Table 1, http://links.lww.com/CM9/B728].
Table 1
Baseline characteristics of all the participants.
| Variables | All(n = 433) | Alive(n = 398) | Dead(n = 35) | Statistics values | P-value |
|---|---|---|---|---|---|
| Demographics | |||||
| Age (years) | 60.3 ± 8.5 | 59.9 ± 8.4 | 64.6 ± 8.0 | –3.309* | 0.002 |
| Male | 346 (79.9) | 316 (79.4) | 30 (85.7) | 0.800† | 0.371 |
| BMI (kg/m2) | 25.9 ± 3.1 | 25.9 ± 3.1 | 26.6 ± 3.0 | –1.429* | 0.154 |
| Smoker | 110 (25.4) | 103 (25.9) | 7 (20.0) | 0.587† | 0.444 |
| Comorbidities | |||||
| Hypertension | 295 (68.1) | 268 (67.3) | 27 (77.1) | 1.425† | 0.233 |
| Diabetes mellitus | 146 (33.7) | 128 (32.2) | 18 (51.4) | 5.344† | 0.021 |
| Stroke or TIA | 50 (11.5) | 44 (11.1) | 6 (17.1) | 1.167† | 0.272 |
| PVD | 9 (2.1) | 8 (2.0) | 1 (2.9) | 0.113† | 0.535 |
| COPD | 4 (1.0) | 3 (0.8) | 1 (2.9) | 1.555† | 0.287 |
| Disease severity | |||||
| LVEF (%) | 60.9 ± 8.4 | 61.3 ± 8.2 | 55.4 ± 8.8 | 4.050* | <0.001 |
| Disease severity | |||||
| MI | 134 (30.9) | 113 (28.4) | 21 (60) | 15.040† | <0.001 |
| CHF | 10 (2.3) | 8 (2.0) | 2 (5.7) | 1.957† | 0.190 |
| Baseline HRQoL | |||||
| Preoperative PCS | 37.6 ± 13.2 | 37.9 ± 13.6 | 35.2 ± 13.4 | 1.087* | 0.277 |
| Preoperative MCS | 50.1 ± 11.3 | 50.4 ± 11.5 | 48.4 ± 11.1 | 0.873* | 0.383 |
| 1-month PCS | 35.9 ± 11.7 | 36.2 ± 12.5 | 31.0 ± 13.5 | 1.997* | 0.046 |
| 1-month MCS | 57.6 ± 8.4 | 57.6 ± 8.7 | 55.9 ± 10.1 | 0.972* | 0.333 |
| Surgical information | |||||
| ONCAB | 230 (53.1) | 210 (52.8) | 20 (57.1) | 0.248† | 0.619 |
| Number of conduits | 3.2 ± 1.0 | 3.2 ± 1.0 | 3.3 ± 0.9 | –0.696* | 0.487 |
| Vein grafts | 2.3 ± 1.0 | 2.3 ± 1.0 | 2.5 ± 0.8 | –1.205* | 0.229 |
| Atrial grafts | 1.0 ± 0.1 | 1.0 ± 0.2 | 0.9 ± 0.3 | 1.554* | 0.129 |
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Data are presented as n (%) or mean ± standard deviation. *The statistics were calculated using Student's t-test. †The statistics were calculated using chi-squared test. BMI: Body mass index; CHF: Chronic heart failure; COPD: Chronic obstructive pulmonary disease; HRQoL: Health-related quality of life; LVEF: Left ventricular ejection fraction; MCS: Mental Component Summary; MI: Myocardial infarction; ONCAB: On-pump coronary artery bypass grafting; PCS: Physical Component Summary; PVD: Peripheral vascular disease; TIA: Transient ischemic attack.
Change of PCS and MCS before and after CABG
At 1-month after undergoing CABG, the patients experienced a temporary decrease in PCS scores, with an average decline of 1.7 points compared to the preoperative level (35.9 vs. 37.6, t = 1.952, P = 0.047). Conversely, MCS scores showed improvement, with an average increase of 7.5 points (57.6 vs. 50.1, t = 9.750, P <0.001). These changes reflect a temporary limitation in physical activity but a relief in mental stress for the patients.
By the 6-month postoperative mark, PCS scores demonstrated a significant improvement compared to the 1-month assessment, with an average increase of 12.5 points (48.4 vs. 35.9, t = 14.055, P <0.001). However, there was no significant difference in MCS scores between the 6-month and 1-month assessments (57.0 vs. 57.6, t = 1.267, P = 0.210), and on average, the MCS scores decreased by 0.6 points compared to the 1-month level [Figure [Figure22].
Figure 2
Change of PCS and MCS scores during the first 6 months after surgery. The PCS scores showed a slight decrease one month after the surgery but significantly improved after six months. On the other hand, the MCS scores significantly improved one month after the surgery compared to preoperative levels and remained stable at this level for six months post-surgery. MCS: Mental Component Summary; PCS: Physical Component Summary.
Prognostic value of preoperative PCS and MCS scores
PCS and MCS scores were standardized using the z-transform. To assess the predictive value of preoperative MCS and PCS scores, we conducted unadjusted and adjusted analyses. As shown in Tables Tables22 and and3,3, in unadjusted analyses, lower preoperative MCS scores were found to be predictive of 6-year all-cause mortality (hazard ratio [HR] for a 1-SD decrease, 1.64; 95% CI, 1.10–2.46), and showed a marginal association with the composite outcome (HR for a 1-SD decrease, 1.24; 95% CI, 0.97–1.58). However, preoperative PCS scores were not predictive of all-cause mortality (HR for a 1-SD decrease, 1.17; 95% CI, 0.85–1.61) or the composite outcome (HR for a 1-SD decrease, 1.17; 95% CI, 0.96–1.44).
Table 2
Univariable and multivariable predictors for all-cause mortality.
| Variables | Univariable | Multivariable-model 1-PCS | Multivariable-model 1-MCS | Multivariable-model 2 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HR | 95% CI | χ2 | P-value | HR | 95% CI | χ2 | P-value | HR | 95% CI | χ2 | P-value | HR | 95% CI | χ2 | P-value | |
| Demographics | ||||||||||||||||
| Age (years) | 1.08 | 1.03–1.13 | 9.365 | 0.002 | 1.08 | 1.03–1.13 | 6.192 | 0.013 | 1.09 | 1.04–1.14 | 11.271 | 0.001 | 1.08 | 1.03–1.14 | 9.529 | 0.002 |
| Male | 1.52 | 0.59–3.92 | 0.751 | 0.386 | 1.34 | 0.49–3.64 | 0.260 | 0.610 | 1.30 | 0.48–3.54 | 0.248 | 0.618 | 1.25 | 0.45–3.45 | 0.170 | 0.681 |
| BMI (kg/m2) | 1.08 | 0.97–1.20 | 1.907 | 0.167 | 1.13 | 1.01–1.27 | 3.858 | 0.049 | 1.17 | 1.04–1.31 | 6.254 | 0.012 | 1.16 | 1.03–1.31 | 3.901 | 0.048 |
| Smoker | 0.72 | 0.32–1.65 | 0.600 | 0.438 | 0.73 | 0.29–1.81 | 0.418 | 0.518 | 0.75 | 0.30–1.88 | 0.368 | 0.544 | 0.77 | 0.71–4.77 | 0.271 | 0.603 |
| Comorbidities | ||||||||||||||||
| Hypertension | 1.64 | 0.75–3.61 | 1.512 | 0.219 | 1.70 | 0.74–3.92 | 1.625 | 0.202 | 1.68 | 0.74–3.82 | 1.655 | 0.198 | 1.64 | 0.72–3.75 | 1.317 | 0.251 |
| Diabetes mellitus | 2.14 | 1.10–4.14 | 5.032 | 0.025 | 2.16 | 1.11–4.22 | 5.215 | 0.022 | 2.19 | 1.12–4.29 | 6.712 | 0.010 | 2.54 | 1.23–5.28 | 5.244 | 0.022 |
| Stroke or TIA | 1.68 | 0.68–3.94 | 1.207 | 0.272 | 1.65 | 0.64–4.23 | 1.373 | 0.241 | 1.61 | 0.64–4.09 | 1.490 | 0.222 | 1.84 | 0.71–4.77 | 2.145 | 0.143 |
| PVD | 1.44 | 0.20–10.54 | 0.131 | 0.718 | 0.93 | 0.07–12.64 | 0.037 | 0.848 | 0.87 | 0.65–11.49 | 0.035 | 0.852 | 0.74 | 0.06–8.56 | 0.115 | 0.735 |
| COPD | 3.21 | 0.44–23.44 | 1.319 | 0.251 | 2.55 | 0.19–35.24 | 0.392 | 0.531 | 2.28 | 0.17–31.11 | 1.113 | 0.291 | 3.53 | 0.28–43.79 | 1.273 | 0.261 |
| Disease severity | ||||||||||||||||
| LVEF (%) | 0.94 | 0.91–0.97 | 15.016 | <0.001 | 0.96 | 0.93–1.00 | 4.210 | 0.040 | 0.96 | 0.92–1.00 | 4.801 | 0.028 | 0.95 | 0.91–0.99 | 4.371 | 0.035 |
| MI | 3.48 | 1.77–6.84 | 13.054 | <0.001 | 2.91 | 1.40–6.04 | 8.973 | 0.003 | 2.80 | 1.33–5.90 | 7.012 | 0.008 | 2.77 | 1.31–5.89 | 8.382 | 0.004 |
| CHF | 2.63 | 0.63–10.94 | 1.757 | 0.185 | 1.76 | 0.36–8.73 | 0.553 | 0.457 | 1.70 | 0.34–8.38 | 0.273 | 0.601 | 1.53 | 0.31–7.59 | 0.334 | 0.563 |
| Baseline HRQoL | ||||||||||||||||
| Preoperative PCS* | 1.17 | 0.85–1.61 | 0.072 | 0.788 | 1.05 | 0.77–1.44 | 0.108 | 0.743 | – | – | – | – | 1.01 | 0.73–1.39 | 0.001 | 0.972 |
| Preoperative MCS* | 1.64 | 1.10–2.46 | 6.200 | 0.013 | – | – | – | – | 1.57 | 1.07–2.30 | 6.455 | 0.011 | 1.57 | 1.07–2.31 | 5.703 | 0.017 |
| Surgical information | ||||||||||||||||
| ONCAB | 1.13 | 0.58–2.20 | 0.123 | 0.725 | 0.97 | 0.45–2.09 | 0.009 | 0.924 | 1.05 | 0.51–2.16 | 0.011 | 0.916 | 1.08 | 0.50–2.33 | 0.015 | 0.903 |
| Number of conduits† | 1.10 | 0.79–1.54 | 0.341 | 0.559 | 0.96 | 0.78–1.16 | 0.125 | 0.723 | 0.96 | 0.78–1.16 | 0.022 | 0.881 | 1.04 | 0.69–1.55 | 0.016 | 0.901 |
| Vein grafts† | 1.21 | 0.86–1.69 | 1.185 | 0.276 | 1.08 | 0.79–1.48 | 0.065 | 0.799 | 1.08 | 0.76–1.56 | 0.435 | 0.509 | 1.15 | 0.76–1.73 | 0.341 | 0.559 |
| Atrial grafts† | 0.35 | 0.13–0.94 | 4.333 | 0.035 | 0.27 | 0.09–0.78 | 5.851 | 0.016 | 0.52 | 0.23–1.18 | 1.872 | 0.171 | 0.32 | 0.11–0.94 | 5.064 | 0.024 |
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*HR for a 1-SD decrease. PCS and MCS were standardized using the z-transform. †Number of conduits equals to sum of vein grafts and atrial grafts; therefore, they were separately included in the model to avoid collinearity. BMI: Body mass index; CHF: Chronic heart failure; CI: Confidence interval; COPD: Chronic obstructive pulmonary disease; HR: Hazard ratio; HRQoL: Health-related quality of life; LVEF: Left ventricular ejection fraction; MCS: Mental Component Summary; MI: Myocardial infarction; ONCAB: On-pump coronary artery bypass grafting; PCS: Physical Component Summary; PVD: Peripheral vascular disease; SD: Standard deviation; TIA: Transient ischemic attack. –: Not available.
Table 3
Univariable and multivariable predictors for all composite outcome.
| Variables | Univariable | Multivariable-model 1-PCS | Multivariable-model 1-MCS | Multivariable-model 2 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HR | 95% CI | χ2 | P-value | HR | 95% CI | χ2 | P-value | HR | 95% CI | χ2 | P-value | HR | 95% CI | χ2 | P-value | |
| Demographics | ||||||||||||||||
| Age (years) | 1.04 | 1.01–1.07 | 8.194 | 0.004 | 1.04 | 1.02–1.07 | 8.737 | 0.003 | 1.05 | 1.02–1.08 | 11.202 | 0.001 | 1.05 | 1.02–1.08 | 10.491 | 0.001 |
| Male | 0.99 | 0.59–1.67 | 0.001 | 0.980 | 0.82 | 0.46–1.44 | 0.779 | 0.378 | 0.78 | 0.45–1.37 | 1.068 | 0.301 | 0.81 | 0.46–1.43 | 0.804 | 0.370 |
| BMI (kg/m2) | 1.04 | 0.97–1.11 | 1.296 | 0.255 | 1.08 | 1.00–1.16 | 4.382 | 0.036 | 1.09 | 1.01–1.17 | 5.460 | 0.019 | 1.09 | 1.01–1.17 | 5.558 | 0.018 |
| Smoker | 1.05 | 0.65–1.69 | 0.036 | 0.849 | 1.20 | 0.71–2.03 | 0.227 | 0.636 | 1.23 | 0.73–2.08 | 0.351 | 0.554 | 1.22 | 0.73–2.06 | 0.301 | 0.583 |
| Comorbidities | ||||||||||||||||
| Hypertension | 1.36 | 0.84–2.19 | 1.595 | 0.207 | 1.33 | 0.81–2.17 | 0.804 | 0.370 | 1.39 | 0.82–2.18 | 0.914 | 0.339 | 1.57 | 0.99–2.50 | 0.788 | 0.375 |
| Diabetes mellitus | 1.36 | 0.89–2.08 | 2.001 | 0.157 | 1.42 | 0.91–2.20 | 2.240 | 0.135 | 1.42 | 0.91–2.20 | 2.213 | 0.137 | 1.41 | 0.89–2.22 | 2.143 | 0.143 |
| Stroke or TIA | 1.77 | 1.02–3.09 | 4.060 | 0.044 | 1.74 | 1.00–3.04 | 4.642 | 0.031 | 1.74 | 1.00–3.04 | 4.652 | 0.031 | 1.41 | 0.91–2.17 | 2.251 | 0.133 |
| PVD | 0.53 | 0.07–3.78 | 0.407 | 0.524 | 0.21 | 0.02–1.92 | 2.035 | 0.154 | 0.19 | 0.02–1.46 | 2.432 | 0.119 | 0.20 | 0.02–1.78 | 2.245 | 0.134 |
| COPD | 4.17 | 1.02–16.97 | 3.967 | 0.046 | 5.97 | 2.65–13.47 | 5.069 | 0.024 | 6.99 | 1.41–34.76 | 5.958 | 0.015 | 6.90 | 1.40–33.96 | 5.977 | 0.014 |
| Disease severity | ||||||||||||||||
| LVEF (%) | 0.96 | 0.94–0.98 | 12.161 | <0.001 | 0.97 | 0.95–0.99 | 5.656 | 0.017 | 0.96 | 0.94–0.98 | 6.067 | 0.014 | 0.97 | 0.94–0.99 | 5.998 | 0.014 |
| MI | 1.70 | 1.11–2.59 | 6.001 | 0.014 | 1.58 | 0.99–2.51 | 3.791 | 0.052 | 1.58 | 0.99–2.51 | 3.773 | 0.053 | 1.57 | 0.99–2.50 | 3.527 | 0.054 |
| CHF | 1.82 | 0.57–5.75 | 1.031 | 0.310 | 1.44 | 0.43–4.85 | 0.297 | 0.586 | 1.41 | 0.42–4.74 | 0.238 | 0.626 | 1.39 | 0.41–4.66 | 0.210 | 0.646 |
| Baseline HRQoL | ||||||||||||||||
| Preoperative PCS* | 1.17 | 0.96–1.44 | 2.383 | 0.123 | 1.16 | 0.94–1.42 | 1.769 | 0.183 | – | – | – | – | 1.12 | 0.91–1.39 | 0.438 | 0.508 |
| Preoperative MCS* | 1.24 | 0.97–1.58 | 2.508 | 0.113 | – | – | – | – | 1.21 | 0.96–1.52 | 2.957 | 0.085 | 1.18 | 0.94–1.49 | 2.526 | 0.112 |
| Surgical information | ||||||||||||||||
| ONCAB | 1.05 | 0.69–1.60 | 0.055 | 0.815 | 1.12 | 0.71–1.76 | 0.017 | 0.895 | 1.13 | 0.72–1.78 | 0.007 | 0.935 | 1.12 | 0.70–1.77 | 0.023 | 0.880 |
| Number of conduits† | 0.97 | 0.78–1.20 | 0.099 | 0.753 | 0.89 | 0.71–1.11 | 0.981 | 0.322 | 0.91 | 0.73–1.15 | 0.495 | 0.482 | 1.04 | 0.69–1.55 | 0.502 | 0.479 |
| Vein grafts† | 0.98 | 0.79–1.21 | 0.038 | 0.845 | 0.90 | 0.71–1.13 | 0.722 | 0.396 | 0.92 | 0.72–1.16 | 0.403 | 0.526 | 0.92 | 0.72–1.16 | 0.391 | 0.532 |
| Atrial grafts† | 0.82 | 0.36–1.85 | 0.238 | 0.625 | 0.80 | 0.34–1.86 | 0.512 | 0.474 | 0.88 | 0.38–2.06 | 0.195 | 0.659 | 0.86 | 0.37–2.02 | 0.250 | 0.617 |
Open in a separate window
*HR for a 1-SD decrease. PCS and MCS were standardized using the z-transform. †Number of conduits equals to sum of vein grafts and atrial grafts; therefore, they were separately included in the model to avoid collinearity. BMI: Body mass index; CHF: Chronic heart failure; CI: Confidence interval; COPD: Chronic obstructive pulmonary disease; HR: Hazard ratio; HRQoL: Health-related quality of life; LVEF: Left ventricular ejection fraction; MCS: Mental Component Summary; MI: Myocardial infarction; ONCAB: On-pump coronary artery bypass grafting; PCS: Physical Component Summary; PVD: Peripheral vascular disease; SD: Standard deviation; TIA: Transient ischemic attack. –: Not available.
After adjusting for clinical variables, lower preoperative MCS scores remained a significant predictor of 6-year all-cause mortality (HR for a 1-SD decrease, 1.57; 95% CI, 1.07–2.30), while lower preoperative PCS scores did not show a significant association (HR for a 1-SD decrease, 1.05; 95% CI, 0.77–1.44). Neither lower preoperative MCS nor PCS scores were independent predictors of the composite outcome.
In model 2, where preoperative MCS and PCS scores were included in the same model, lower preoperative MCS scores still showed a significant relation to all-cause mortality (HR for a 1-SD decrease, 1.57; 95% CI, 1.07–2.31 and a marginal relation to composite outcome.
Prognostic value of ΔPCS and ΔMCS
In the crude adjusted model (model 3), lower ΔMCS (HR for a 1-SD decrease, 1.60; 95% CI, 1.06–2.42) was associated with a higher risk of all-cause mortality, while lower ΔPCS did not show a significant association. For the composite outcome, both lower ΔMCS (HR for a 1-SD decrease, 1.38; 95% CI, 1.03–1.86) and ΔPCS (HR for a 1-SD decrease, 1.40; 95% CI, 1.06–1.85) were significant predictors.
After further adjustment for clinical variables (model 4), ΔMCS remained independently correlated with all-cause mortality (HR for a 1-SD decrease, 1.67; 95% CI, 1.09–2.56), while ΔPCS did not show a significant association. In the case of the composite outcome, both ΔMCS (HR for a 1-SD decrease, 1.49; 95% CI, 1.10–2.01) and ΔPCS (HR for a 1-SD decrease, 1.36; 95% CI, 1.02–1.81) demonstrated significant prognostic value after adjustment [Table [Table44].
Table 4
Predictive values for ΔPCS and ΔMCS.
| Outcomes | Crude adjusted-model 3 | Fully adjusted-model 4 | ||||||
|---|---|---|---|---|---|---|---|---|
| HR | 95% CI | χ2 | P-value | HR | 95% CI | χ2 | P-value | |
| All-cause mortality | ||||||||
| Lower ΔPCS* | 1.51 | 0.98–2.31 | 3.529 | 0.060 | 1.38 | 0.88–2.17 | 1.037 | 0.309 |
| Lower ΔMCS* | 1.60 | 1.06–2.42 | 5.273 | 0.022 | 1.67 | 1.09–2.56 | 9.933 | 0.002 |
| Composite outcome | ||||||||
| Lower ΔPCS* | 1.40 | 1.06–1.85 | 4.872 | 0.027 | 1.36 | 1.02–1.81 | 4.203 | 0.040 |
| Lower ΔMCS* | 1.38 | 1.03–1.86 | 5.805 | 0.016 | 1.49 | 1.10–2.01 | 5.930 | 0.015 |
Open in a separate window
*HR for a 1-SD decrease. ΔPCS: Change in PCS scores between the preoperative period and the 6-month postoperative follow-up. ΔMCS: Change in MCS scores between the preoperative period and the 6-month postoperative follow-up. ΔPCS and ΔMCS were standardized using the z-transform. CI: Confidence interval; HR: Hazard ratio; SD: Standard deviation; ΔMCS: Change in MCS scores between the preoperative period and the 6-month postoperative follow-up; ΔPCS: Change in PCS scores between the preoperative period and the 6-month postoperative follow-up.
Furthermore, ΔMCS and ΔPCS were stratified into quartiles, revealing a dose-response relationship between ΔMCS and both all-cause mortality and the composite outcome [Figure [Figure33 and Supplementary Figures 2 and 3, http://links.lww.com/CM9/B728]. Stratified ΔPCS was not a predictor for all-cause mortality but showed marginal predictive value for the composite outcome.
Figure 3
Dose-response relationship between DMCS, DPCS, and all-cause mortality (A) and composite outcome (B). The PCS scores showed a slight decrease one month after the surgery but significantly improved after six months. On the other hand, the MCS scores significantly improved one month after the surgery compared to preoperative levels and remained stable at this level for six months post-surgery. ΔPCS: Change in PCS scores between the preoperative period and the 6-month postoperative follow-up. ΔMCS: Change in MCS scores between the preoperative period and the 6-month postoperative follow-up. CI: Confidence interval; HR: Hazard ratio; MCS: Mental Component Summary; PCS: Physical Component Summary; Ref: Reference.
In subsidiary analyses [Supplementary Table 2, http://links.lww.com/CM9/B728] where 1-month postoperative PCS and MCS scores were served as baseline, similar results were obtained for crude ΔMCSsubanalysis, which was adjusted only for 1-month MCS. When further adjusted for clinical variables, lower ΔMCSsubanalysis was also a significant predictor for both all-cause mortality (HR for a 1-SD decrease, 1.78; 95% CI, 1.19–2.66) and composite outcome (HR for a 1-SD decrease, 1.46; 95% CI, 1.11–1.94). ΔPCSsubanalysis was correlated with the composite outcome (HR for a 1-SD decrease, 1.38; 95% CI, 1.04–1.83) but not all-cause mortality.
Discussion
The findings of our study indicated that serial measurements of HRQoL using SF-36 PCS and MCS scores could serve as a valuable tool for predicting prognosis in CABG patients, with MCS showing particular significance. Lower preoperative and ΔMCS scores were associated with a higher risk of all-cause mortality, even after adjusting for demographic, clinical, and operation-related variables. Considering the significant changes in physical and mental health status during the perioperative period, subsidiary analyses using 1-month MCS or PCS scores as the baseline also yielded similar results. These findings suggested that both single-timepoint measurements and changes in health status between timepoints provide prognostic information, which can complement traditional clinical variables. This information can be utilized to identify high-risk patients who may require more intensive medical interventions even after undergoing CABG.
Previous studies have highlighted the predictive value of health status as an outcome and predictor in various cardiovascular diseases. For instance, scores on the Kansas City Cardiomyopathy Questionnaire have been found to predict mortality and rehospitalization in heart failure patients,[12] while scores on the Seattle Angina Questionnaire have been associated with acute coronary syndrome and mortality in patients with CAD.[33] Rumsfeld et al[26] demonstrated that preoperative SF-36 PCS scores could predict short-term (180 days after surgery) mortality in a CABG cohort. In their study, 10-point lower preoperative PCS score had an odds ratio of 1.39 (P = 0.006) for predicting mortality but not for lower preoperative MCS (P = 0.31). It should be noted that short-term death after CABG was more relevant to surgery itself[34] and longer follow-up was necessary to establish the relationship between health status and long-term prognosis. In our current study, participants were followed for over 6 years, with a low rate of loss to follow-up (less than 3%). After adjusting for clinical risk factors, it was observed that preoperative MCS scores, but not PCS scores, were predictive of long-term mortality.
One notable strength of our study was the use of serial measurements for health status. The American Heart Association (AHA) had recommended longitudinal assessments as a means to identify patients with changes in health status, which can provide more comprehensive information compared to a single measurement.[23] This had been particularly demonstrated in studies involving heart failure patients, where changes in health status have been shown to independently predict both mortality and rehospitalization, even after adjusting for baseline health status.[12] In the case of CABG patients, the intervention itself was a significant and transformative procedure. Previous research had indicated that both physical and mental health statuses undergo significant changes during the perioperative period, underscoring the importance and necessity of serial measurements in this population.[19] Our study demonstrated that not only single-timepoint HRQoL measurements but also the changes observed between timepoints can serve as informative predictors of long-term prognosis. Importantly, our findings showed that using either preoperative or early postoperative HRQoL as the baseline level did not alter the main conclusions, further supporting the robustness of HRQoL change as a risk prognostic factor.
The finding that MCS measurements were more predictive than PCS in our study was indeed intriguing and differs from some previous studies.[26] We observed that MCS improved more rapidly than PCS after CABG, and in some cases, PCS scores even declined below the preoperative level during the early recovery period. This might reflect the unique characteristics of the early postoperative phase for patients, where mental health improved more quickly compared to physical health. It was plausible to speculate that perioperative PCS might be more relevant to short- or medium-term prognosis, as suggested by previous research conducted by Rumsfeld et al[26].
However, when considering long-term prognosis, mental health, as measured by MCS, might play a more prominent role. It is well-established that depression is an independent risk factor for both CABG and CAD patients, contributing to nearly a twofold increase in long-term mortality.[35–37] The scales included in the SF-36 questionnaire partially capture depressive symptoms, and given the extended follow-up period of our cohort (more than 6 years), the predictive value of MCS scores might outweigh that of PCS scores. Furthermore, studies focusing on the development of the Chinese version of the SF-36 had identified some differences between the Chinese and Western populations. Specifically, the factor load of GH on MCS is stronger than on PCS in the Chinese population.[30] In contrast, in the U.S. population, GH is more strongly associated with PCS than with MCS. This discrepancy may contribute to the differences observed between our study and previous ones conducted in Western populations.[28]
There are some limitations of the current study. First, the results were obtained from a single-center cohort, which may limit the generalizability of our findings. Although patients referred to our institute represent a diverse population from the entire country, caution should be exercised when extrapolating the conclusions to other settings or populations. Second, the sample size of our cohort might be relatively small to fully explore the relationship between PCS and prognosis. However, we compensated for this limitation by conducting a comprehensive and prolonged follow-up period of more than 6 years, during which the events were carefully adjudicated. This meticulous approach helps to partially compensate for the limited sample size. Third, we utilized the SF-36 as a measurement tool, which was a general HRQoL survey rather than a disease-specific instrument. Although disease-specific surveys might capture more detailed symptomatic and functional information[23]; however, few had been developed specifically for CABG patients, and none has been translated and validated in the Chinese population like SF-36. Nevertheless, we acknowledged the potential limitations of using a generic survey and recognize the need for future studies to explore the utility of disease-specific HRQoL measurements in larger populations.
In summary, baseline MCS scores and change in MCS are independent predictors for long-term mortality of CABG. Better mental health status and recovery indicate better prognosis. Preoperative and postoperative HRQoL measurements should be performed to identify high-risk populations and appropriate treatment, for example, psychological or social support, may reduce the risk. Future studies should focus on whether serial HRQoL measured by disease-specific surveys in larger populations is more informative than the current study.
Funding
This work was supported by a grant from the National Natural Science Foundation of China (No. 81830072).
Conflicts of interest
None.
Supplementary Material
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Footnotes
How to cite this article: Wang JC, Liu HN, Yue C, Yang LM, Yang K, Zhao Y, Ren H, Zhang Y, Zheng Z. Identifying coronary artery bypass grafting patients at high risk for adverse long-term prognosis using serial health-related quality of life assessments. Chin Med J 2024;137:1069–1077. doi: 10.1097/CM9.0000000000002806
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