Abstract
Background
The correlation between serum creatinine levels and the long-term prognosis of patients undergoing percutaneous coronary intervention (PCI) has not yet been systematically investigated. This study aimed to evaluate the association between long-term prognosis and serum creatinine levels in patients after PCI.
Material/Methods
This was an observational cohort study of 2533 patients who received PCI and completed serum creatinine and other tests in China. The study’s primary prognostic indicators were the frequency of clinical adverse events, all-cause death, cardiac death, acute myocardial infarction, and stroke. All-cause death referred to death from all causes during the follow-up period, whereas cardiac death was death due to cardiac injury resulting in severe cardiac dysfunction or failure. Clinical events included death, ischemia, and stroke. Yao et al completed the entire study and uploaded the data to the DATADRYAD website. We used only this data for secondary analysis.
Results
The study involved 2533 participants, with a mean age of 59.9±11.1 years and a median follow-up of 29.8 months. The analysis, controlling for confounding factors, revealed a positive correlation between serum creatinine and all-cause death (OR: 2.178, 95% CI: 1.317–3.603, P<0.05), which was confirmed by the results of sensitivity analysis (P for trend <0.05). However, no direct linear correlation was found between serum creatinine and acute myocardial infarction, cardiac death, or stroke.
Conclusions
There was a linear correlation between serum creatinine and all-cause death in the long-term prognosis of patients after PCI, independent of acute myocardial infarction, cardiac death, and stroke.
Keywords: Creatine, Percutaneous Coronary Intervention, Prognosis
Introduction
Percutaneous coronary intervention (PCI) is globally acknowledged as the primary therapy for ailments such as myocardial ischemia or myocardial infarction, occurring due to coronary artery blockage or stenosis [1]. Nevertheless, ischemic heart disease persists as a leading global cause of mortality [2]. Identifying serious adverse events during the post-procedural period of patients receiving PCI is crucial for clinical decision making and future healthcare management. Basic clinical indicators, such as age, sex, diabetes, and hypertension, can serve as predictors of adverse events, along with biomarkers, such as high-sensitivity C-reactive protein, troponin I, and D-dimer, with limited extent [3–6]. Despite some advancements, predicting adverse events in patients following PCI remains challenging. Establishing prediction models requires large samples of data, due to individual differences in patients, and inconsistent prediction results can arise from different research methods and statistical analyses. Further exploration of accurate and comprehensive predictive factors is necessary for the development of reliable models to guide clinical decisions and improve patient prognosis.
There have been reports of a significant association between acute kidney injury and death after PCI, with renal failure predicting death in patients with ST-segment elevation myocardial (STEMI) independently of other factors [7–9]. Furthermore, heightened serum creatinine levels after coronary angiography imply an increased risk of all-cause mortality, and even mild to moderate increases in serum creatinine after the procedure correlate with significantly higher mortality rates [10]. Although early post-procedural increases in serum creatinine following coronary angiography are infrequent, they carry a more severe prognosis than does preexisting renal insufficiency. However, there are limited studies investigating the correlation between serum creatinine levels and long-term prognosis in patients with coronary artery disease who underwent PCI.
Therefore drug-eluting stents, we conducted a secondary data analysis using previously published data [11] that evaluated the effects of for PCI on the long-term prognosis of patients with coronary artery disease. This study focused on investigating the correlation between serum creatinine levels and long-term prognosis of adverse clinical events in patients after PCI. We conducted an analysis on the correlation between the level of serum creatinine and the long-term prognosis of patients after PCI, which encompassed clinical events, cardiac death, all-cause death, acute myocardial infarction, and stroke.
Material and Methods
Data Sources
The DATADRYAD database (www.Datadryad.org) is an open-source data distribution platform, a multi-stakeholder community of academic and research institutions, research funders, scholarly societies, and publishers committed to leading the way in open data sharing and reuse. With this database, we can use these data for secondary analysis without infringing on the rights of the original authors. We cited the Dryad data package (Yao, Hai-Mu et al (2014) with data from “Long-term follow-up results in patients undergoing PCI (PCI) with drug-eluting stents: results from a single high-volume PCI center” [Dataset] Dryad (https://doi.org/10.5061/dryad.13d31) to examine the correlation between serum creatinine levels and long-term prognosis in patients after PCI.
Study Population
This study was an observational cohort study of patients with coronary heart disease admitted to the First Affiliated Hospital of Zhengzhou University in Henan Province from 2009 to 2011 who received PCI treatment, collected by Yao et al in 2014. We used the research data of Yao et al to collect patients with complete long-term follow-up data, including 2533 patients who received at least 1 drug-eluting stent treatment and completed long-term follow-up. Qualitative and quantitative analysis of coronary angiography was performed according to standard methods. The average age was 59.9±11.1 years, with a median follow-up of 29.8 months. Data collection variables include age, sex, body mass index (BMI), blood pressure, previous PCI, previous coronary artery bypass graft (CABG) surgery, previous stroke, risk factors (hypertension, diabetes, dyslipidemia, smoking, and renal dysfunction), and blood lipid and blood glucose levels. As this study was a secondary analysis of existing data, there was no need to obtain informed consent from the patients.
Data Collection and Definition
Yao et al [11] completed the entire study. The primary outcome indicators were all-cause death, cardiac death, acute myocardial infarction, and stroke. All-cause deaths were deaths from all causes during the follow-up period, and cardiac death was defined as death resulting from severe cardiac dysfunction or failure due to disease or injury to the heart. Clinical event rates, namely composite endpoint events, included death, infarction, and stroke. Patients with STEMI were classified into the urgent-PCI group based on their clinical presentation and time to PCI. The delayed-PCI group consisted of patients with STEMI undergoing delayed PCI, while the SA group comprised patients with stable angina undergoing elective PCI, and the NSTE-ACS group consisted of patients with non-ST-segment elevation myocardial infarction (NSTEMI) and unstable angina. Patients’ past medical history was assessed upon admission. Hypertension was defined as a systolic blood pressure ≥140 mmHg or a diastolic blood pressure ≥90 mmHg, or current antihypertensive medication [12]. Diabetes mellitus was characterized by a fasting blood glucose ≥6.1 mmol/L, a glycosylated hemoglobin level above 6.5%, or currently receiving hypoglycemic medication or insulin [13]. Patients had a history of smoking if they had smoked within the previous decade. Other past medical records confirmed patients’ medical history. Due to the broad range of extremes in serum creatinine levels, a portion of statistical analyses were conducted with log2 Cr.
Statistical Analysis
We analyzed the population characteristics in relation to the occurrence of adverse events and all-cause death, which were categorized according to the different clinical endpoints. Continuous variables were expressed as mean±standard deviation, and one-way ANOVA was used to determine differences between groups when the data distribution was normal, and the Kruskal-Wallis H test was used to determine differences between groups when the data distribution was not normal. Categorical variables were expressed as frequency percentages, and the chi-square test was used to determine differences between groups. We then evaluated the correlation between the factors and each clinical event using one-way ANOVA. To observe the nonlinear relationship between serum creatinine levels and various endpoint events, we tested the nonlinear relationship between serum creatinine levels and adverse clinical events, all-cause death, cardiac death, acute myocardial infarction, and stroke using generalized estimating equations. Finally, we performed multiple regression equations to obtain stratified analyses, adjusting for different covariates, to quantify the effect of serum creatinine after controlling for different covariates, and to analyze the independent effect of serum creatinine levels on all-cause death. All statistical analyses were conducted using the software packages R and EmpowerStats (https://EmpowerStats.com). Statistical significance was deemed when the P value was less than 0.05.
Results
Characterization of the Population
Classification based on the occurrence of clinical adverse events and all-cause death is shown in Table 1. There were statistically significant differences in age, BMI, blood glucose, creatinine, and cardiac function, whereas there were no statistically significant differences in blood pressure, heart rate, bilirubin levels, lipid levels, sex, clinical characteristics, history of peripheral vascular disease, previous PCI, previous CABG, hypertension, and smoking history between the groups of clinical events. As for all-cause death, there were statistically significant differences in age, blood glucose levels, creatinine levels, total cholesterol levels, and cardiac function between the groups. The percentage of previous heart failure, cardiogenic shock, old myocardial infarction, chronic obstructive pulmonary disease (COPD), third-degree AV block, and diabetes mellitus was higher among those who experienced clinical adverse events and all-cause death.
Table 1.
Description of the study population.
| Clinical events | P value | P value* | All cause death | P value | P value* | |||
|---|---|---|---|---|---|---|---|---|
| No | Yes | No | Yes | |||||
| No. | 2223 | 310 | 2348 | 185 | ||||
| Age, years | 59.20±10.83 | 65.31±11.41 | <0.001 | <0.001 | 59.27±10.81 | 68.61±10.88 | <0.001 | <0.001 |
| BMI | 23.72±4.16 | 24.37±3.78 | 0.047 | 0.019 | 23.75±4.14 | 24.43±3.84 | 0.115 | 0.069 |
| SBP, mmHg | 103.39±28.50 | 102.34±30.68 | 0.557 | 0.432 | 103.52±28.55 | 99.95±31.30 | 0.112 | 0.082 |
| DBP, mmHg | 77.16±11.90 | 77.51±12.88 | 0.636 | 0.629 | 77.17±11.95 | 77.62±12.92 | 0.633 | 0.809 |
| Heart rate, bpm | 72.09±11.42 | 72.65±13.27 | 0.447 | 0.807 | 72.16±11.46 | 72.13±14.00 | 0.976 | 0.513 |
| Glycemia, mmol/L | 6.00±3.14 | 6.40±3.13 | 0.047 | <0.001 | 6.01±3.13 | 6.53±3.22 | 0.040 | <0.001 |
| Creatinine, μmol/L | 71.64±29.79 | 78.94±55.22 | <0.001 | 0.002 | 71.61±29.38 | 84.22±69.04 | <0.001 | 0.003 |
| Uric acid, μmol/L | 301.46±88.51 | 314.98±120.72 | 0.023 | 0.277 | 302.23±91.81 | 314.41±108.03 | 0.103 | 0.269 |
| Bilirubin, μmol/L | 9.81±7.70 | 10.37±5.53 | 0.245 | 0.043 | 9.81±7.56 | 10.80±6.15 | 0.097 | 0.047 |
| Triglyceride, mmol/L | 4.26±1.07 | 4.26±1.04 | 0.956 | 0.966 | 4.27±1.07 | 4.13±0.97 | 0.103 | 0.176 |
| Total cholesterol, mmol/L | 1.91±1.32 | 1.90±1.57 | 0.877 | 0.256 | 1.93±1.37 | 1.67±1.01 | 0.020 | 0.019 |
| HDL-C, mmol/L | 1.06±0.31 | 1.06±0.37 | 0.921 | 0.408 | 1.06±0.31 | 1.05±0.40 | 0.691 | 0.146 |
| LDL-C, mmol/L | 2.66±0.94 | 2.72±0.91 | 0.313 | 0.180 | 2.67±0.94 | 2.67±0.91 | 0.968 | 0.866 |
| Ejection fraction, % | 61.35±6.71 | 57.73±10.77 | <0.001 | 0.002 | 61.39±6.69 | 55.00±11.99 | <0.001 | <0.001 |
| Sex | 0.899 | 0.820 | 0.101 | 0.110 | ||||
| Female | 707 (31.80%) | 101 (32.58%) | 736 (31.35%) | 72 (38.92%) | ||||
| Male | 1515 (68.15%) | 209 (67.42%) | 1611 (68.61%) | 113 (61.08%) | ||||
| Clinical presentation | 0.145 | – | 0.204 | – | ||||
| Urgent PCI | 80 (3.60%) | 19 (6.13%) | 87 (3.71%) | 12 (6.49%) | ||||
| Delayed PCI | 453 (20.38%) | 68 (21.94%) | 481 (20.49%) | 40 (21.62%) | ||||
| NSTE-ACS | 1320 (59.38%) | 175 (56.45%) | 1387 (59.07%) | 108 (58.38%) | ||||
| SA | 370 (16.64%) | 48 (15.48%) | 393 (16.74%) | 25 (13.51%) | ||||
| Medical history | ||||||||
| Heart failure | <0.001 | – | <0.001 | – | ||||
| No | 1985 (89.45%) | 248 (80.26%) | 2097 (89.50%) | 136 (73.51%) | ||||
| Yes | 234 (10.55%) | 61 (19.74%) | 246 (10.50%) | 49 (26.49%) | ||||
| Atrial fibrillation | 0.010 | – | 0.003 | – | ||||
| No | 2185 (98.29%) | 298 (96.13%) | 2307 (98.25%) | 176 (95.14%) | ||||
| Yes | 38 (1.71%) | 12 (3.87%) | 41 (1.75%) | 9 (4.86%) | ||||
| Cardiac shock | <0.001 | 0.007 | <0.001 | 0.001 | ||||
| No | 2222 (99.96%) | 307 (99.03%) | 2347 (99.96%) | 182 (98.38%) | ||||
| Yes | 1 (0.04%) | 3 (0.97%) | 1 (0.04%) | 3 (1.62%) | ||||
| Old myocardial infarction | <0.001 | – | <0.001 | – | ||||
| No | 2033 (91.45%) | 265 (85.48%) | 2146 (91.40%) | 152 (82.16%) | ||||
| Yes | 190 (8.55%) | 45 (14.52%) | 202 (8.60%) | 33 (17.84%) | ||||
| COPD | 0.031 | – | 0.005 | – | ||||
| No | 2207 (99.28%) | 304 (98.06%) | 2331 (99.28%) | 180 (97.30%) | ||||
| Yes | 16 (0.72%) | 6 (1.94%) | 17 (0.72%) | 5 (2.70%) | ||||
| Third degree AVB | 0.001 | 0.010 | 0.001 | 0.016 | ||||
| No | 2219 (99.82%) | 306 (98.71%) | 2343 (99.79%) | 182 (98.38%) | ||||
| Yes | 4 (0.18%) | 4 (1.29%) | 5 (0.21%) | 3 (1.62%) | ||||
| Stroke | 0.022 | – | 0.081 | – | ||||
| No | 2113 (95.05%) | 285 (91.94%) | 2228 (94.89%) | 170 (91.89%) | ||||
| Yes | 110 (4.95%) | 25 (8.06%) | 120 (5.11%) | 15 (8.11%) | ||||
| Perpheral vascular disease | 0.114 | 0.161 | 0.014 | 0.065 | ||||
| No | 2219 (99.82%) | 308 (99.35%) | 2344 (99.83%) | 183 (98.92%) | ||||
| Yes | 4 (0.18%) | 2 (0.65%) | 4 (0.17%) | 2 (1.08%) | ||||
| Post-PCI | 0.797 | – | 0.288 | – | ||||
| No | 2073 (93.29%) | 288 (92.90%) | 2185 (93.10%) | 176 (95.14%) | ||||
| Yes | 149 (6.71%) | 22 (7.10%) | 162 (6.90%) | 9 (4.86%) | ||||
| Post-CABG | 0.104 | – | 0.004 | – | ||||
| No | 2207 (99.28%) | 305 (98.39%) | 2332 (99.32%) | 180 (97.30%) | ||||
| Yes | 16 (0.72%) | 5 (1.61%) | 16 (0.68%) | 5 (2.70%) | ||||
| Hypertension | 0.252 | – | 0.651 | – | ||||
| No | 1138 (51.22%) | 148 (47.74%) | 1195 (50.92%) | 91 (49.19%) | ||||
| Yes | 1084 (48.78%) | 162 (52.26%) | 1152 (49.08%) | 94 (50.81%) | ||||
| Diabetes mellitus | <0.001 | – | <0.001 | – | ||||
| No | 1787 (80.42%) | 223 (72.17%) | 1887 (80.43%) | 123 (66.49%) | ||||
| Yes | 435 (19.58%) | 86 (27.83%) | 459 (19.57%) | 62 (33.51%) | ||||
| Smoking | 0.417 | – | 0.932 | – | ||||
| No | 1514 (68.11%) | 204 (65.81%) | 1592 (67.80%) | 126 (68.11%) | ||||
| Yes | 709 (31.89%) | 106 (34.19%) | 756 (32.20%) | 59 (31.89%) | ||||
| Number of diseased vessel | <0.001 | – | <0.001 | – | ||||
| 0 | 824 (37.07%) | 100 (32.26%) | 868 (36.97%) | 56 (30.27%) | ||||
| 1 | 911 (40.98%) | 94 (30.32%) | 955 (40.67%) | 50 (27.03%) | ||||
| 2 | 1 (0.04%) | 0 (0.00%) | 1 (0.04%) | 0 (0.00%) | ||||
| 3 | 471 (21.19%) | 111 (35.81%) | 506 (21.55%) | 76 (41.08%) | ||||
| 4 | 16 (0.72%) | 5 (1.61%) | 18 (0.77%) | 3 (1.62%) | ||||
| Location of the lesion | ||||||||
| LM | 0.030 | – | 0.047 | – | ||||
| No | 2154 (96.90%) | 293 (94.52%) | 2273 (96.81%) | 174 (94.05%) | ||||
| Yes | 69 (3.10%) | 17 (5.48%) | 75 (3.19%) | 11 (5.95%) | ||||
| LAD | 0.079 | – | 0.024 | – | ||||
| No | 398 (17.90%) | 43 (13.87%) | 420 (17.89%) | 21 (11.35%) | ||||
| Yes | 1825 (82.10%) | 267 (86.13%) | 1928 (82.11%) | 164 (88.65%) | ||||
| LCX | <0.001 | – | 0.003 | – | ||||
| No | 1176 (52.90%) | 133 (42.90%) | 1233 (52.51%) | 76 (41.08%) | ||||
| Yes | 1047 (47.10%) | 177 (57.10%) | 1115 (47.49%) | 109 (58.92%) | ||||
| RCA | <0.001 | – | <0.001 | – | ||||
| No | 1151 (51.78%) | 125 (40.32%) | 1210 (51.53%) | 66 (35.68%) | ||||
| Yes | 1072 (48.22%) | 185 (59.68%) | 1138 (48.47%) | 119 (64.32%) | ||||
Adjust-P value.
BMI – body mass index; SBP – systolic blood pressure; DBP – diastolic blood pressure; HDL-C – high-density lipoprotein cholesterol; LDL-C – low-density lipoprotein cholesterol; COPD – chronic obstructive pulmonary disease; third-degree AVB – third-degree atrioventricular block; LM – left main artery; LAD – left anterior descending artery; LCX – left circumflex artery; RCA – right coronary artery.
Analysis of Factors Associated with Clinical Events
We investigated the correlation between the factors and endpoint events by one-way ANOVA, as shown in Table 2. The results of univariate analysis showed that elevated creatinine levels, age, and BMI increased the incidence of adverse events, while patients with previous atrial fibrillation, cardiogenic shock, old infarction, COPD, third-degree atrioventricular block, stroke, and peripheral vascular disease had a greater likelihood of adverse events. Also, patients who experienced all-cause death were likely to have higher creatinine levels, age, and triglyceride levels, and most were male. In contrast, the occurrence of cardiac death, acute myocardial infarction, and stroke may not be associated with serum creatinine levels.
Table 2.
The results of univariate analysis.
| Statistics Mean±SD or N (%) |
Clinical events OR (95%CI) |
P value | All cause death OR (95%CI) |
P value | Cardiac death OR (95%CI) |
P value | AMI OR (95%CI) |
P value | Stroke OR (95%CI) |
P value | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Creatinine, μmol/L | 72.531±33.999 | 1.004 (1.001, 1.007) | 0.0037 | 1.006 (1.003, 1.009) | 0.0005 | 1.000 (0.993, 1.008) | 0.9734 | 0.998 (0.990, 1.005) | 0.5347 | 1.001 (0.993, 1.009) | 0.8398 |
| Cr quartile | |||||||||||
| Q1 (1.39–197.28) | 565 (24.469%) | Ref | Ref | Ref | Ref | Ref | |||||
| Q2 (197.29–275.64) | 554 (23.993%) | 0.984 (0.672, 1.441) | 0.9348 | 1.304 (0.802, 2.123) | 0.2846 | 0.955 (0.467, 1.951) | 0.8992 | 0.737 (0.420, 1.295) | 0.2891 | 1.150 (0.440, 3.002) | 0.7756 |
| Q3 (275.65–354.00) | 579 (25.076%) | 1.120 (0.775, 1.619) | 0.5463 | 1.210 (0.742, 1.973) | 0.4451 | 0.481 (0.204, 1.132) | 0.0938 | 0.738 (0.423, 1.286) | 0.2836 | 0.852 (0.307, 2.365) | 0.7586 |
| Q4 (354.01–785.00) | 611 (26.462%) | 1.569 (1.111, 2.216) | 0.0105 | 1.876 (1.197, 2.941) | 0.0061 | 0.982 (0.491, 1.963) | 0.9590 | 1.051 (0.634, 1.741) | 0.8473 | 1.395 (0.566, 3.438) | 0.4696 |
| Sex | |||||||||||
| Female | 808 (31.899%) | Ref | Ref | Ref | Ref | Ref | |||||
| Male | 1724 (68.062%) | 0.966 (0.749, 1.245) | 0.7873 | 0.717 (0.527, 0.976) | 0.0344 | 0.736 (0.439, 1.236) | 0.2467 | 1.117 (0.746, 1.673) | 0.5913 | 1.519 (0.716, 3.224) | 0.2763 |
| Age | 59.951±11.085 | 1.056 (1.043, 1.069) | <0.0001 | 1.095 (1.077, 1.114) | <0.0001 | 1.047 (1.021, 1.073) | 0.0003 | 1.000 (0.984, 1.017) | 0.9611 | 1.057 (1.024, 1.092) | 0.0006 |
| BMI | 23.788±4.120 | 1.040 (1.001, 1.081) | 0.0464 | 1.042 (0.990, 1.096) | 0.1128 | 0.876 (0.788, 0.974) | 0.0141 | 1.007 (0.947, 1.070) | 0.8324 | 1.163 (1.052, 1.287) | 0.0032 |
| SBP, mmHg | 103.262±28.767 | 0.999 (0.994, 1.003) | 0.5569 | 0.996 (0.990, 1.001) | 0.1121 | 1.005 (0.996, 1.014) | 0.2741 | 1.006 (1.000, 1.013) | 0.0563 | 1.009 (0.997, 1.020) | 0.1333 |
| DBP, mmHg | 77.200±12.020 | 1.002 (0.992, 1.013) | 0.6362 | 1.003 (0.990, 1.016) | 0.6330 | 1.030 (1.009, 1.051) | 0.0051 | 1.003 (0.988, 1.019) | 0.6983 | 1.007 (0.979, 1.035) | 0.6402 |
| Glycemia | 6.046±3.137 | 1.031 (0.998, 1.064) | 0.0631 | 1.034 (0.999, 1.071) | 0.0573 | 1.056 (1.014, 1.100) | 0.0083 | 1.034 (0.994, 1.075) | 0.0962 | 1.020 (0.947, 1.099) | 0.5928 |
| Bilirubin, μmol/L | 9.882±7.468 | 1.007 (0.994, 1.021) | 0.2686 | 1.011 (0.997, 1.025) | 0.1378 | 1.014 (0.998, 1.030) | 0.0944 | 1.006 (0.989, 1.025) | 0.4801 | 1.002 (0.964, 1.042) | 0.9131 |
| Triglyceride, mmol/L | 4.261±1.063 | 0.997 (0.885, 1.122) | 0.9557 | 0.878 (0.750, 1.027) | 0.1026 | 0.846 (0.641, 1.115) | 0.2352 | 1.069 (0.894, 1.280) | 0.4642 | 1.186 (0.894, 1.573) | 0.2360 |
| Total cholesterol, mmol/L | 1.908±1.350 | 0.993 (0.903, 1.092) | 0.8770 | 0.813 (0.688, 0.962) | 0.0158 | 0.665 (0.467, 0.946) | 0.0232 | 1.054 (0.933, 1.190) | 0.4001 | 1.027 (0.822, 1.282) | 0.8171 |
| HDL-C, mmol/L | 1.063±0.316 | 1.020 (0.686, 1.518) | 0.9211 | 0.901 (0.541, 1.503) | 0.6906 | 0.701 (0.276, 1.780) | 0.4551 | 0.901 (0.476, 1.706) | 0.7479 | 1.507 (0.589, 3.851) | 0.3920 |
| LDL-C, mmol/L | 2.670±0.936 | 1.071 (0.938, 1.223) | 0.3134 | 0.997 (0.840, 1.182) | 0.9684 | 0.904 (0.661, 1.236) | 0.5267 | 1.100 (0.896, 1.351) | 0.3611 | 1.108 (0.790, 1.554) | 0.5513 |
| Clinical presentation | |||||||||||
| Urgent PCI | 99 (3.908%) | Ref | Ref | Ref | Ref | Ref | |||||
| Delayed PCI | 521 (20.568%) | 0.632 (0.361, 1.108) 0.1092 | 0.603 (0.304, 1.195) | 0.1473 | 0.200 (0.090, 0.447) | <0.0001 | 0.695 (0.309, 1.564) | 0.3795 | 0.853 (0.181, 4.007) | 0.8399 | |
| NSTE-ACS | 1495 (59.021%) | 0.558 (0.330, 0.943) 0.0293 | 0.565 (0.299, 1.065) | 0.0774 | 0.113 (0.055, 0.235) | <0.0001 | 0.534 (0.249, 1.145) | 0.1068 | 0.558 (0.127, 2.449) | 0.4394 | |
| SA | 418 (16.502%) | 0.546 (0.305, 0.979) 0.0423 | 0.461 (0.223, 0.954) 0.0368 | 0.233 (0.103, 0.527) | 0.0005 | 0.365 (0.147, 0.907) | 0.0299 | 1.189 (0.256, 5.513) | 0.8252 | ||
| Medical history | |||||||||||
| Atrial fibrillation | |||||||||||
| No | 2233 (88.331%) | Ref | Ref | Ref | Ref | Ref | |||||
| Yes | 295 (11.669%) | 2.087 (1.529, 2.848) | <0.0001 | 3.071 (2.159, 4.368) | <0.0001 | 3.794 (2.195, 6.556) | <0.0001 | 1.706 (1.047, 2.781) | 0.0321 | 0.417 (0.100, 1.739) | 0.2297 |
| Cardiac shock | |||||||||||
| No | 2483 (98.026%) | Ref | Ref | Ref | Ref | Ref | |||||
| Yes | 50 (1.974%) | 2.315 (1.196, 4.481) | 0.0127 | 2.877 (1.376, 6.016) | 0.0050 | 2.622 (0.794, 8.667) | 0.1139 | 1.314 (0.403, 4.286) | 0.6504 | 1.349 (0.181, 10.032) | 0.7699 |
| OMI | |||||||||||
| No | 2529 (99.842%) | Ref | Ref | Ref | Ref | – | |||||
| Yes | 4 (0.158%) | 21.713 (2.251, 209.408) | 0.0078 | 38.687 (4.004, 373.804) | 0.0016 | 125.593 (12.873, 1225.301) <0.0001 | 20.802 (2.904, 148.988) 0.0025 | – | |||
| COPD | |||||||||||
| No | 2298 (90.722%) | Ref | Ref | Ref | Ref | Ref | |||||
| Yes | 235 (9.278%) | 1.817 (1.281, 2.576) | 0.0008 | 2.306 (1.541, 3.452) | <0.0001 | 1.920 (0.962, 3.829) | 0.0642 | 0.901 (0.465, 1.747) | 0.7582 | 0.836 (0.255, 2.740) | 0.7675 |
| Third degree AVB | |||||||||||
| No | 2511 (99.131%) | Ref | Ref | Ref | – | Ref | |||||
| Yes | 22 (0.869%) | 2.722 (1.057, 7.011) | 0.0380 | 3.809 (1.389, 10.443) | 0.0094 | 1.913 (0.253, 14.449) | 0.5297 | – | 3.184 (0.417, 24.297) | 0.2640 | |
| Stroke | |||||||||||
| No | 2525 (99.684%) | Ref | Ref | Ref | Ref | – | |||||
| Yes | 8 (0.316%) | 7.252 (1.804, 29.145) | 0.0052 | 7.724 (1.831, 32.579) | 0.0054 | 25.078 (5.856, 107.391) | <0.0001 | 6.922 (1.382, 34.671) | 0.0186 | – | |
| PVD | |||||||||||
| No | 2398 (94.670%) | Ref | Ref | Ref | Ref | Ref | |||||
| Yes | 135 (5.330%) | 1.685 (1.073, 2.647) | 0.0235 | 1.638 (0.937, 2.865) | 0.0835 | 1.580 (0.623, 4.008) | 0.3358 | 1.899 (0.995, 3.625) | 0.0517 | 3.439 (1.413, 8.373) | 0.0065 |
| Post-PCI | |||||||||||
| No | 2527 (99.763%) | Ref | Ref | – | – | – | |||||
| Yes | 6 (0.237%) | 3.602 (0.657, 19.750) | 0.1399 | 6.404 (1.165, 35.200) | 0.0327 | – | – | – | |||
| Post-CABG | |||||||||||
| No | 2512 (99.171%) | Ref | Ref | Ref | – | – | |||||
| Yes | 21 (0.829%) | 2.261 (0.823, 6.217) | 0.1138 | 4.049 (1.466, 11.178) | 0.0070 | 2.009 (0.265, 15.212) | 0.4994 | – | – | ||
| Hypertension | |||||||||||
| No | 1286 (50.790%) | Ref | Ref | Ref | Ref | Ref | |||||
| Yes | 1246 (49.210%) | 1.149 (0.906, 1.458) | 0.2521 | 1.072 (0.794, 1.446) | 0.6511 | 0.792 (0.476, 1.316) | 0.3676 | 1.235 (0.853, 1.790) | 0.2640 | 1.033 (0.544, 1.960) | 0.9218 |
| Diabetes mellitus | |||||||||||
| No | 2010 (79.415%) | Ref | Ref | Ref | Ref | Ref | |||||
| Yes | 521 (20.585%) | 1.584 (1.209, 2.075) | 0.0008 | 2.072 (1.502, 2.859) | <0.0001 | 1.238 (0.687, 2.232) | 0.4775 | 0.931 (0.584, 1.484) | 0.7636 | 0.869 (0.381, 1.986) | 0.7398 |
| Smoking | |||||||||||
| No | 1718 (67.825%) | Ref | Ref | Ref | Ref | Ref | |||||
| Yes | 815 (32.175%) | 1.110 (0.863, 1.426) | 0.4169 | 0.986 (0.715, 1.359) | 0.9317 | 0.792 (0.450, 1.393) | 0.4180 | 1.174 (0.798, 1.728) | 0.4161 | 1.234 (0.635, 2.398) | 0.5356 |
| Medications | |||||||||||
| Aspirn | |||||||||||
| No | 33 (1.304%) | Ref | Ref | – | Ref | – | |||||
| Yes | 2498 (98.696%) | 1.012 (0.353, 2.899) | 0.9821 | 0.786 (0.238, 2.600) | 0.6930 | – | 1.572 (0.213, 11.608) | 0.6572 | – | ||
| Clopidogrel | |||||||||||
| No | 100 (3.953%) | Ref | Ref | Ref | Ref | Ref | |||||
| Yes | 2426 (95.889%) | 0.721 (0.417, 1.248) | 0.2427 | 0.563 (0.302, 1.050) | 0.0706 | 0.370 (0.156, 0.881) | 0.0246 | 0.751 (0.322, 1.752) | 0.5079 | 0.473 (0.143, 1.566) | 0.2204 |
| β-Blocker | |||||||||||
| No | 813 (32.096%) | Ref | Ref | Ref | Ref | Ref | |||||
| Yes | 1720 (67.904%) | 0.827 (0.645, 1.061) | 0.1357 | 0.886 (0.646, 1.214) | 0.4499 | 0.605 (0.363, 1.006) | 0.0528 | 0.849 (0.577, 1.249) | 0.4051 | 1.163 (0.574, 2.356) | 0.6755 |
| ACEI | |||||||||||
| No | 1178 (46.524%) | Ref | Ref | Ref | Ref | Ref | |||||
| Yes | 1354 (53.476%) | 1.417 (1.112, 1.807) | 0.0049 | 1.469 (1.080, 1.999) | 0.0144 | 0.811 (0.490, 1.344) | 0.4168 | 0.928 (0.641, 1.343) | 0.6913 | 1.686 (0.858, 3.310) | 0.1293 |
| CCB | |||||||||||
| No | 1934 (76.352%) | Ref | Ref | Ref | Ref | Ref | |||||
| Yes | 599 (23.648%) | 0.915 (0.688, 1.216) | 0.5388 | 0.823 (0.569, 1.191) | 0.3022 | 0.471 (0.223, 0.996) | 0.0488 | 0.817 (0.517, 1.291) | 0.3870 | 1.500 (0.752, 2.992) | 0.2494 |
| Statin | |||||||||||
| No | 230 (9.080%) | Ref | Ref | Ref | Ref | Ref | |||||
| Yes | 2303 (90.920%) | 0.963 (0.640, 1.450) | 0.8574 | 1.142 (0.662, 1.973) | 0.6329 | 0.578 (0.282, 1.188) | 0.1362 | 1.392 (0.670, 2.891) | 0.3752 | 0.526 (0.218, 1.272) | 0.1538 |
OR – odds ratio; CI – confidence interval; Ref – reference; OMI – old myocardial infarction; PVD – peripheral vascular disease; ACEI – angiotensin-converting enzyme inhibitor; CCB – calcium channel blockers.
Nonlinear Correlation Analysis
To visualize more intuitively the relationship between serum creatinine levels and the occurrence of clinical events, all-cause death, cardiogenic death, acute myocardial infarction, and stroke in patients after PCI, we plotted a smoothed curve of serum creatinine versus the clinical endpoint events, as shown in Figure 1. It was evident that the relationship between serum creatinine and clinical events and all-cause death was a parabola, whereas there was no significant correlation with cardiogenic death, acute myocardial infarction, and stroke.
Figure 1. Smoothing curve of serum creatinine vs clinical endpoint events.
(A) The relationship between clinical events and serum creatinine. (B) The relationship between all-cause death and serum creatinine. (C) The relationship between cardiac death and serum creatinine. (D) The relationship between acute myocardial infarction and serum creatinine. (E) The relationship between stroke and serum creatinine. The blue points indicate 95% confidence intervals.
Univariate Linear Regression Model
Multiple linear regression models were used in this study to examine the association between serum creatinine levels and clinical events and all-cause death. Given the substantial variation in serum creatinine extremes, a Log2 transformation was performed for this analysis, as displayed in Table 3. In the model without covariate adjustments, serum creatinine was found to significantly increase the incidence of clinical events (OR: 1.379, 95%CI: 1.066–1.783, P<0.05) and all-cause death (OR: 1.776, 95%CI: 1.300–2.326, P<0.001). Serum creatinine remained positively associated with the incidence of all-cause mortality adjusted for sex, age, systolic blood pressure, diastolic blood pressure, glucose levels, bilirubin levels, triglyceride levels, low-density lipoprotein cholesterol levels, high-density lipoprotein cholesterol levels, total cholesterol levels, number of diseased vessels, medical histories of previous disease (heart failure, atrial fibrillation, hypertension, diabetes mellitus, COPD, old myocardial infarction, stroke, peripheral vascular disease, previous PCI, previous CABG, and third-degree atrioventricular block), history of smoking, medication use (aspirin, clopidogrel, angiotensin-converting enzyme inhibitors, β-blockers, calcium channel blockers, and statins), and type of stent (OR: 2.178, 95%CI: 1.317,3.603, P<0.05). We performed sensitivity analysis with serum creatinine as a categorical variable (quadruple categorical), and the P value for trend yielded the same results.
Table 3.
Relationship between sCr (μmol/L) and clinical events/all cause death in different models.
| Model I OR (95%CI) |
P value | Model II OR (95%CI) |
P value | Model III OR (95%CI) |
P value | |
|---|---|---|---|---|---|---|
| Clinical events | ||||||
| Log2 creatinine | 1.379 (1.066, 1.783) | 0.01431 | 1.211 (0.925, 1.585) | 0.16438 | 1.306 (0.902, 1.889) | 0.15719 |
| Creatinine quartile | ||||||
| Q1 (1.39–197.28) | Ref | Ref | Ref | |||
| Q2 (197.29–275.64) | 0.98 (0.67, 1.44) | 0.9348 | 0.98 (0.66, 1.44) | 0.9030 | 1.20 (0.69, 2.10) | 0.5219 |
| Q3 (275.65–354.00) | 1.12 (0.77, 1.62) | 0.5463 | 1.14 (0.76, 1.71) | 0.5188 | 1.70 (0.96, 3.01) | 0.0710 |
| Q4 (354.01–785.00) | 1.57 (1.11, 2.22) | 0.0105 | 1.36 (0.92, 2.00) | 0.1220 | 1.64 (0.93, 2.89) | 0.0865 |
| P for trend | 0.006 | 0.077 | 0.064 | |||
| All cause death | ||||||
| Log2 creatinine | 1.776 (1.300, 2.426) | 0.00031 | 1.570 (1.130, 2.182) | 0.00724 | 2.178 (1.317, 3.603) | 0.00243 |
| Creatinine quartile | ||||||
| Q1 (1.39–197.28) | Ref | Ref | Ref | |||
| Q2 (197.29–275.64) | 1.30 (0.80, 2.12) | 0.2846 | 1.34 (0.81, 2.21) | 0.2552 | 2.27 (1.01, 5.12) | 0.0477 |
| Q3 (275.65–354.00) | 1.21 (0.74, 1.97) | 0.4451 | 1.40 (0.82, 2.40) | 0.2132 | 3.02 (1.30, 7.05) | 0.0105 |
| Q4 (354.01–785.00) | 1.88 (1.20, 2.94) | 0.0061 | 1.66 (1.00, 2.76) | 0.0494 | 2.57 (1.12, 5.91) | 0.0259 |
| P for trend | 0.009 | 0.058 | 0.041 |
The 3 multiple linear regression models. Model I: we did not adjust for other covariants. Model II: we adjusted for sex and age. Model III: we adjusted for sex, age, systolic blood pressure, diastolic blood pressure, glucose, bilirubin, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, total cholesterol, number of diseased vessels, histories of heart failure, atrial fibrillation, hypertension, diabetes mellitus, chronic obstructive pulmonary disease (COPD), old myocardial infarction, stroke, peripheral vascular disease, previous percutaneous coronary intervention (PCI), previous coronary artery bypass graft (CABG), and third-degree atrioventricular block, smoking, type of stent, and use of aspirin, clopidogrel, angiotensin-converting enzyme inhibitors (ACEI), β-blockers, calcium channel blockers (CCB), and statins.
Discussion
In a secondary analyses, our study found that serum creatinine was linearly associated with the incidence of clinical events in patients after PCI, but this relationship was absent in the fully adjusted model. Instead, serum creatinine was linearly associated with the incidence of all-cause death, and this relationship remained after adjusting for covariates, and the same trend was observed when we treated serum creatinine as a categorical variable.
We searched PubMed for literature up to October 2023 using the keywords ‘creatinine’, ‘percutaneous coronary intervention’ and ‘long-term prognosis’, and there was a total of 28 relevant papers, most of which explored the effect of different causes of renal injury on the long-term prognosis of patients after coronary intervention. Most of these papers explored the effects of different causes of renal injury on long-term prognosis after coronary intervention, and creatinine was an important indicator for assessing renal function; therefore, there were also papers that reported that creatinine was associated with long-term prognosis of patients after PCI [14]. However, no article has systematically described this. In the present study, the correlation between serum creatinine level and long-term prognosis of patients after PCI was systematically described by generalized linear modeling and generalized additive modeling, which filled the research gap.
Serum creatinine is a classic marker for assessing renal injury and long-term prognosis. Elevation of serum creatinine in the early postoperative period after coronary angiogram has been shown to be associated with serious adverse events in the short and long term. In general, after PCI, often due to contrast administration, serum creatinine begins to rise within 24 h after surgery, peaks after 2 to 5 days, and returns to a new baseline level after 1 to 3 weeks and remains relatively stable [15–18]. Guo et al found that within 24 h after PCI, elevated serum creatinine was usually strongly associated with postoperative mortality [19]. Fumiki found that the urinary liver-type fatty acid-binding protein/creatinine ratio could be a predictor and was related to worsening renal function in patients undergoing elective PCI [20]. What is more, the research showed that in patients with normal BMI undergoing PCI, low serum creatinine was associated with higher overall mortality and cardiovascular mortality [21]. Therefore, the present study focused on the effect of baseline postoperative serum creatinine levels on the long-term prognosis of patients after PCI. Our results showed that higher baseline levels of postoperative serum creatinine were associated with a higher incidence of all-cause death in long-term follow-up outcomes, but were not significantly associated with cardiac death, acute myocardial infarction, and stroke. Therefore, although contrast-associated renal injury may not occur after PCI, attention to creatinine levels after PCI remains critical.
Compared with other previous studies on creatinine and prognosis of patients after PCI, this study has many points. We used a combination of generalized linear and generalized additive models, which not only smoothed out the nonparameters and fitted them into a regression linear model, but also better identified the genuine relationship between serum creatinine and endpoint events. Since this study was an observational study and therefore undeniably subject to potential confounders, we used sophisticated statistical methods to minimize the effect of these confounders. Because of this, we identified a linear relationship between serum creatinine levels and clinical events, but not after adjusting for other confounders, such as age and sex. Also, our study of serum creatinine was not limited to patients with renal insufficiency; therefore, our findings would be more applicable to most of the post-PCI population as a warning to clinicians to monitor the serum creatinine levels in patients after PCI.
However, our study had some limitations. First, this was an analytic cross-sectional study and therefore lacked a strong evidence base for the association between serum creatinine and outcome indicators. Second, due to the limitations of the original data, we lacked detailed data on the time of follow-up to complete further studies, such as survival analyses, and we were unable to observe the effects of other potential indicators on the long-term prognosis of patients after PCI. Third, the results of this study may not be generalizable to other ethnic groups, because this study observed only a Chinese population.
Conclusions
Serum creatinine levels were positively associated with all-cause death in the long-term prognosis of patients after PCI, whereas there may have been no direct independent effect with clinical events. Moreover, serum creatinine levels were not linearly associated with cardiac death, acute myocardial infarction, or stroke in the long-term prognosis of patients after PCI.
Acknowledgements
We are very grateful to the data providers of the study, who completed the entire study. They are (the rankings and institutions of these researchers were ranked according to the “reference [11]”) Hai-Mu Yao (Department of Cardiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China), You-Dong Wan, Xiao-Juan Zhang (Department of Integrated ICU, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China), De-Liang Shen, Jin-Ying Zhang, Ling Li, Luo-Sha Zhao (Department of Cardiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China), and Tong-Wen Sun (corresponding author, Department of Integrated ICU, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China). The authors also thank Chang-zhong Chen and Xin-Lin Chen of Yi-er college.
Footnotes
Conflict of interest: None declared
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher
Department and Institution Where Work Was Performed: This work was done in Department of Cardiology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Declaration of Figures’ Authenticity: All figures submitted have been created by the authors, who confirm that the images are original with no duplication and have not been previously published in whole or in part.
Financial support: This work was supported by the New Round (2023–2026) of Clinical Medical Discipline Construction Plan of Shanghai Putuo District Health and Wellness System (no. 2023ysxk01)
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