Abstract
Acute cerebrovascular accident (CVA) after percutaneous coronary intervention (PCI) for acute coronary syndrome (ACS) and coronary artery disease (CAD) is associated with high morbidity and mortality. Nationwide Inpatient Sample from 1998 to 2008 was utilized to identify 1,552,602 PCIs performed for ACS and CAD. We assessed temporal trends in the incidence, predictors and prognostic impact of CVA in a broad range of patients undergoing PCI. The overall incidence of CVA was 0.56% (95% confidence interval (CI), 0.55%–0.57%). The incidence of CVA remained unchanged over the study period (adjusted p for trend = 0.2271). The overall mortality in the CVA group was 10.76% (95% CI, 10.1%–11.4%). The adjusted odds ratio (OR) of CVA for in-hospital mortality was 7.74 (95% CI, 7.00–8.57; p<0.0001); this remained high but decreased over the study period (adjusted p for trend <0.0001). Independent predictors of CVA included older age (OR, 1.03, 95% CI, 1.02–1.03; p<0.0001), disorder of lipid metabolism (OR, 1.31, 95% CI, 1.24–1.38; p<0.001), history of tobacco use (OR, 1.21, 95% CI, 1.10–1.34; p=0.0002), coronary atherosclerosis (OR 1.56, 95% CI, 1.43–1.71; p<0.0001), and IABP use (OR 1.39, 95% CI, 1.09–1.77; p=0.0073). A nomogram for predicting the probability of CVA achieved a concordance index of 0.73 and was well calibrated. In conclusion, the incidence of CVA associated with PCI has remained unchanged from 1998–2008 in face of improved equipment, techniques and adjunctive pharmacology. The risk of CVA associated in-hospital mortality is high; however, this risk has declined over the study period.
Keywords: Acute Cerebral Hemorrhage, Acute Cerebral Infarction, Percutaneous Coronary Intervention
INTRODUCTION
The field of interventional cardiology has advanced tremendously since the advent of coronary angioplasty in 1977. Despite improvements in techniques, equipment and adjunctive pharmacology, acute cerebrovascular accidents (CVA) after percutaneous coronary intervention (PCI) remains one of the most devastating adverse complications with high rates of mortality and morbidity (1–4). The incidence of CVA in patients undergoing PCI ranges from 0.07% to 1.4% (3–8). Patients with CVA complications after PCI have a markedly high in-hospital mortality rate in the range of 10% to 37% (3,6,9–11). The Healthcare Cost and Utilization Project (HCUP) database encompasses an extensive collection of longitudinal hospital care data in the United States; this enables research on a broad range of health care policy issues. The purpose of this study was to assess the temporal trends in the incidence, predictors and prognostic impact of CVA in a broad range of patients with acute coronary syndrome (ACS) and coronary artery disease (CAD) undergoing PCI from 1998 to 2008, and to build a nomogram for predicting the likelihood of CVA with available patient profile information. The data in this study is a representation of real-world clinical practice in the United States (US) in the last decade.
METHODS
The Nationwide Inpatient Sample (NIS) is the largest all-payer US inpatient care database that contains over a hundred clinical and nonclinical data elements from approximately 8 million hospital stays each year. From 1998 to 2008, a total of 1,552,602 PCI procedures performed in patients for symptomatic CAD and acute myocardial infarction (AMI) diagnoses, which encompass ST elevation myocardial infarction (STEMI) and non-ST elevation myocardial infarction (NSTEMI), were identified. The Clinical Classifications Software (CCS) developed by HCUP was used in analyzing our dependent and independent variables. CVA was defined as any new focal neurological deficit lasting ≥ 24 h and transient cerebral ischemia as any new deficits lasting < 24 h. The specific single-level CCS diagnosis categories used to define CVA in this study were “109 – Acute Cerebrovascular Disease and 112 – Transient Cerebral Ischemia.” The subtypes of CVA were defined as following: “Hemorrhagic CVA – ICD-9 codes 430, 431, and 432; Ischemic CVA – ICD-9 codes 433 and 434; and, Transient Cerebral Ischemia – CCS code 112.” CVA was the dependent variable. Additional data on covariates were collected and are listed in Table 1.
Table 1.
Baseline patient characteristics
| CVA | |||
|---|---|---|---|
| Variable | No (N= 1,543,858) |
Yes (N= 8,744) |
p-Value |
| Age, mean ± SD (years) | 64 ± 12 | 70 ± 12 | < 0.0001 |
| Women | 34.1 % | 47.6 % | < 0.0001 |
| White | 81.9 % | 79.0 % | |
| Black | 6.7 % | 9.2 % | |
| Hispanic | 6.1 % | 6.9 % | < 0.0001 |
| Asian | 1.7 % | 1.7 % | |
| Native American | 0.4 % | 0.3 % | |
| Other | 3.2 % | 2.9 % | |
| Died during hospitalization, total | 11,798 (0.8 %) | 939 (10.8 %) | < 0.0001 |
| Length of stay, mean ± SD (days) | 2.8 ± 3.1 | 7.9 ± 8.0 | < 0.0001 |
| Total charge, mean ± SD ($) | 39,952 ± 29948 | 68,516 ± 63381 | < 0.0001 |
| Hypertension | 975,346 (63.2 %) | 5,623 (64.3 %) | 0.0288 |
| Dyslipidemia | 839,967 (54.4 %) | 3,434 (39.3 %) | <0.0001 |
| Diabetes Mellitus | 445,889 (28.9 %) | 2,683 (30.7 %) | 0.0002 |
| Chronic Renal Failure | 44,201 (2.9 %) | 448 (5.1 %) | < 0.0001 |
| Chronic Obstructive Pulmonary Disease and Bronchiectasis | 150,545 (9.8 %) | 1,254 (14.3 %) | < 0.0001 |
| Pulmonary Heart Disease | 21,603 (1.4 %) | 200 (2.3 %) | < 0.0001 |
| Tobacco Use Disorder | 259,563 (16.8 %) | 974 (11.1 %) | < 0.0001 |
| History of Tobacco Use | 148,677 (9.6 %) | 543 (6.2 %) | < 0.0001 |
| Peripheral and Visceral Atherosclerosis | 104,620 (6.8 %) | 942 (10.8 %) | < 0.0001 |
| Occlusion or Stenosis of Precerebral Arteries | 22,879 (1.5 %) | 637 (7.3 %) | < 0.0001 |
| Other and Ill-defined cerebrovascular disease | 3,152 (0.2 %) | 137 (1.6 %) | < 0.0001 |
| Congestive Heart Failure | 171,936 (11.1 %) | 2,131 (24.4 %) | < 0.0001 |
| Heart Valve Disorders | 109,748 (7.1 %) | 1,050 (12.0 %) | < 0.0001 |
| Aortic Valve Disorder | 7,695 (0.5 %) | 145 (1.7 %) | <0.0001 |
| Acute Myocardial Infarct | 531,130 (34.4 %) | 4,471 (51.1 %) | < 0.0001 |
| Coronary Atherosclerosis and other Heart | 1,484,506 (96.2 %) | 7,644 (87.4 %) | < 0.0001 |
| Disease | |||
| Atrial Fibrillation/Flutter | 121,235 (7.9 %) | 1,609 (18.4 %) | < 0.0001 |
| Peri- endo- and myocarditis, Cardiomyopathy | 45,167 (2.9 %) | 432 (4.9 %) | < 0.0001 |
| Aortic/peripheral/visceral artery aneurysm, embolism or thrombosis | 23,296 (1.5 %) | 233 (2.7 %) | < 0.0001 |
| Cardiac and circulatory congenital anomalies | 4,264 (0.3 %) | 64 (0.7 %) | < 0.0001 |
| Shock | 13,574 (0.9 %) | 361 (4.1 %) | < 0.0001 |
| Malignancy, Any | 95,696 (6.2 %) | 598 (6.8 %) | 0.0133 |
| Headache, including Migraine | 8,620 (0.6 %) | 115 (1.3 %) | < 0.0001 |
| Systemic Lupus Erythematosus/Connective | 46,831 (3.0 %) | 575 (6.6 %) | < 0.0001 |
| Tissue Disease | |||
| Coagulation and Hemorrhagic Disorders | 21,142 (1.4 %) | 292 (3.3 %) | < 0.0001 |
| Anemia | 85,394 (5.5 %) | 892 (10.2 %) | < 0.0001 |
| Intra-aortic balloon pump | 8,143 (0.5 %) | 147 (1.7 %) | < 0.0001 |
International Classification of Diseases, 9th Revision Codes used for:
Anemia - 285.1 282.41 282.42 282.5 282.60 282.61 282.62 282.63 282.64 282.68 282.69 280.1 280.8 280.9 281.0 281.1 281.2 281.3 281.4 281.8 281.9 284.0 284.01 284.09 284.1 284.11 284.12 284.19 284.8 284.81 284.89 284.9 280.0 283.0 283.1 283.10 283.11 283.19 283.2 283.9 282.0 282.1 282.2 282.3 282.4 282.40 282.43 282.44 282.45 282.46 282.47 282.49 282.7 282.8 282.9 284.2 285.0 285.21 285.22 285.29 285.8 285.9
Disorders of lipid metabolism - 272.0 272.1 272.2 272.3 272.4
Tobacco Use Disorder - 305.1
Our primary analysis was to assess the incidence of CVA and its trend over the study period. Our secondary analysis looked at independent predictors of CVA, mortality rate in patients with CVA, and temporal trend in mortality rate in patients with CVA over the study years. In addition, we built a nomogram that predicts the likelihood of developing CVA for patients undergoing PCI with readily available demographic and clinical characteristic variables.
The study population was separated into two groups – those with CVA and without CVA. The summary statistics with baseline characteristics were generated for the entire population separated into the two groups as well as for the subpopulations stratified by the year.
All tests were 2-tailed, and a P value of less than 0.05 was considered significant for all tests. Univariate analysis was initially conducted to summarize the data. The Pearson chi-square tests were used to test for categorical variables and are presented as percentages. The nonparametric Wilcoxon rank sum test was employed to test for all continuous variables and is presented as mean ± standard deviation.
Logistic regressions were fit to the data to evaluate the trend for incidence of CVA over the years 1998 to 2008. The Wald test was used to test the null hypothesis of no trend. The logistic regression model was then used to assess predictors of CVA after adjusting for the observed baseline demographic and clinical characteristics. The logistic regression model was also used to investigate the trends for incidence of in-hospital mortality with and without CVA as well as to assess the trends for the adjusted and unadjusted odds ratio (OR) for the association between death and CVA over the study years. We used the propensity-score method to evaluate the association between CVA and the mortality rate. Propensity scores were estimated using a logistic regression model with CVA as the outcome and all the observed baseline demographic and clinical characteristic variables. We used the method of regression adjustment by the estimated propensity scores to estimate the association between CVA and the mortality rate, taking into account all the other observed baseline demographic and clinical characteristic variables. Advantage of this two-step propensity score procedure is that this allows us to fit a complicated propensity score model with interactions and higher order terms for more accurate estimation of CVA probability (12).
The missing data were omitted as follows: in the No CVA group (n=1543858) age (n=36, 0.002%), length of stay (n=22, 0.001%), mean total charge (n=20721, 1.3%), female gender (n=137, 0.009%), race (n=419744, 27.2%), death (n=315, 0.02%). In the CVA group (n=8744) mean total charge (n=141, 1.6%), female gender (n=1, 0.01%), race (n=2283, 26.1%), death (n=20, 0.23%).
A multivariable logistic regression model was built to link the demographic and clinical characteristic variables with CVA, which served as the basis of the nomogram for predicting the probability of developing CVA. To relax the common modeling assumption that the association between risk factors and outcome is linear, we applied restricted cubic splines to continuous variables. The nomogram was internally validated with 1000 bootstrap resamples to objectively evaluate the predictive performance after correcting over-fit bias. First, model discrimination ability was quantified using the c-index, which is equivalent to the area under the receiver operating characteristic (ROC), and, ranges from 0.5 to 1, with 0.5 indicating no different from chance and 1 for perfect prediction. In addition, the agreement between the observed and the predicted outcomes was visually checked with a calibration plot.
All analyses were performed using SAS statistical software version 9.2 (SAS Institute Inc., Cary, NC, USA) or the open source statistical package R-2.15.2 (R core team, 2012).
All authors have read and agree to the manuscript as written. The authors are solely responsible for the design and conduct of this study, all study analyses, the drafting and editing of the paper and its final contents. This study has been approved by the University of Illinois at Chicago Institutional Review Board.
RESULTS
From 1998 to 2008, there were 1,552,602 PCI procedures performed for AMI and CAD diagnoses. Patients’ baseline characteristics and clinical presentation are shown in Table 1. CVA during PCI hospitalization occurred in 8744 patients (0.56%, 95% confidence interval (CI), 0.55% – 0.57%). The overall incidence of CVA associated with PCI remained unchanged during the study period from 0.60% in 1998 to 0.66% in 2008 (p for trend =0.2308 univariate analysis, p=0.2271 multivariate analysis) (Figure 1.). Incidences of various types of CVA are shown in Table 2.
Figure 1.
Incidence of cerebrovascular accidents from 1998 to 2008.
Table 2. Incidence of Various Types of Cerebrovascular Accident.
Acute Cerebrovascular Accidents (N=8,744)
| Hemorrhagic CVA | 715 (8.2 %) |
| Ischemic CVA | 4669 (53.4 %) |
| Transient Cerebral Ischemia | 2889 (33.0 %) |
The predictors of CVA after adjusting for covariates were identified (Figure 2). Important predictors in this multivariable analysis included older age (adjusted OR, 1.03, 95% CI, 1.02–1.03; p<0.0001), disorder of lipid metabolism (OR, 1.31, 95% CI, 1.24–1.38; p<0.001), history of tobacco use (OR, 1.21, 95% CI, 1.10–1.34; p=0.0002), coronary atherosclerosis/other heart disease (OR 1.56, 95% CI, 1.43–1.71; p<0.0001), and IABP (OR 1.39, 95% CI, 1.09–1.77; p=0.0073). Female gender, DM, hypertension, CRF, AMI, CHF, aortic valve disorder, atrial fibrillation/flutter, peripheral/visceral atherosclerosis, and shock were not significant predictors for CVA in multivariable analysis. The length of stay (p<0.0001) and mean total charge (p=0.0463) were significantly higher and associated with CVA after adjusting for covariates.
Figure 2.
Independent predictors for cerebrovascular accidents after adjusting for covariates.
The overall mortality in the CVA group was 10.8% (95% CI, 10.1%–11.4%). Patients who died were 15.66 (95% CI, 14.60–16.80; p<0.0001) times more likely to have CVA than the patients who were alive; this OR decreased to 7.74 (95% CI, 7.00–8.57; p<0.0001) after adjusting for covariates. The in-hospital mortality in patients with CVA (adjusted p for trend <0.0001) and without CVA (adjusted p for trend <0.0001) decreased from 1998 to 2008 (Figure 3A and Figure 3B, respectively). Both the adjusted and unadjusted OR for in-hospital mortality associated with CVA were high for each individual year when compared to in-hospital mortality without CVA (p<0.0001 for all years). The temporal trend of the unadjusted and adjusted OR for in-hospital mortality associated with acute CVA (p for trend = 0.0026 and <0.0001, respectively) decreased over the study period (Table 3).
Figure 3.
A Incidence of in-hospital mortality with cerebrovascular accidents from 1998 to 2008.
B Incidence of in-hospital mortality without cerebrovascular accidents from 1998 to 2008.
Table 3.
Association between Death and Cerebrovascular Accidents by Year With and Without Adjusting for Covariates
| Without adjusting for covariates | With adjusting for covariates* | |||
|---|---|---|---|---|
| Variable | OR (95% CI) | p-value | OR (95% CI) | p-value |
| Overall | ||||
| Died | 15.66 (14.60;16.80) | < 0.0001 | 7.74 (7.00;8.57) | < 0.0001 |
| Individual Year | ||||
| 1998 | 15.13 (11.77;19.43) | < 0.0001 | 9.89 (6.93;14.10) | < 0.0001 |
| 1999 | 19.21 (15.34;24.06) | < 0.0001 | 9.31 (6.70;12.92) | < 0.0001 |
| 2000 | 19.45 (15.64;24.19) | < 0.0001 | 10.80 (7.76;15.03) | < 0.0001 |
| 2001 | 20.21 (16.32;25.01) | < 0.0001 | 10.70 (7.86;14.56) | < 0.0001 |
| 2002 | 13.73 (10.76;17.52) | < 0.0001 | 7.71 (5.46;10.89) | < 0.0001 |
| 2003 | 14.58 (11.34;18.74) | < 0.0001 | 9.10 (6.39;12.96) | < 0.0001 |
| 2004 | 13.91 (10.84;17.85) | < 0.0001 | 6.26 (4.28;9.14) | < 0.0001 |
| 2005 | 14.53 (11.45;18.43) | < 0.0001 | 6.50 (4.59;9.20) | < 0.0001 |
| 2006 | 17.07 (13.81;21.10) | < 0.0001 | 8.99 (6.66;12.13) | < 0.0001 |
| 2007 | 15.54 (12.24;19.72) | < 0.0001 | 6.21 (4.30;8.97) | < 0.0001 |
| 2008 | 10.38 (8.15;13.21) | < 0.0001 | 4.40 (3.14;6.18) | < 0.0001 |
The adjusting covariates are year, age, gender, race, in-hospital mortality, length of stay, total charge of hospitalization, hypertension, disorder of lipid metabolism, diabetes mellitus, chronic renal failure, chronic obstructive pulmonary disease/bronchiectasis, pulmonary heart disease, tobacco use disorder, history of tobacco use, peripheral/visceral atherosclerosis, occlusion or stenosis of precerebral arteries, other/ill-defined cerebrovascular disease, congestive heart failure, heart valve disorders, aortic valve disorders, acute myocardial infarct, coronary atherosclerosis/other heart disease, atrial fibrillation/flutter, peri-/endo-/myocarditis cardiomyopathy, aortic/peripheral/visceral artery aneurysm/embolism/thrombosis, cardiac/circulatory congenital anomalies, shock, any malignancy, headache including migraine, systemic lupus erythematosus/connective tissue disease, coagulation/hemorrhagic disorders, anemia, and intra-aortic balloon pump.
Propensity matching analysis was also performed to reduce the confounding effects of baseline demographics and clinical characteristic variables. After propensity matching the association of CVA with in-hospital mortality remained statistically significant (p<0.0001).
Based on the multivariable logistic regression, a nomogram incorporating the baseline characteristics was constructed to predict the probability of CVA (Figure 4A). To use the nomogram, a patient’s profile is first located on each predictor variable scale. Each scale location has corresponding prognostic points at the top point’s axis. The points from each individual predictor scale were summed to arrive at a total point value. This value is then plotted on the total point scale (second scale from the bottom), and the corresponding probability of CVA is determined by drawing a vertical line straight down to the predicted probability of acute CVA axis. In the internal validation, the nomogram achieved a C-index of 0.73 after using bootstrapping to correct for over-fitting. Figure 4B shows the model calibration that looks reasonable for the majority of patients.
Figure 4.
A Nomogram for predicting the likelihood of cerebrovascular accidents using patient demographic and clinical characteristic variables.
B Calibration curve demonstrating the agreement between the observed and the predicted outcome.
DISCUSSION
The observed CVA rates associated with PCI (0.56%) in this largest-to-date observational study analyzing the incidence, predictors and clinical outcomes of CVA associated with PCI in “real world” clinical practice are comparable to the 0.07% to 1.4% range reported in previous studies (3–8).
CVA is a devastating complication associated with PCI (1,4,13,14). Origins of cerebral emboli associated with catheterization may include plaque disruption (5,7,15,16), catheter-related thrombus formation (4,7) or air embolism (7,17). Despite the pharmacological and technical advances in PCI, the incidence of CVA complicating PCI did not change significantly from 1998 to 2008 in our study. This is consistent with two registries from similar observation periods (6,18). A possible explanation for the unchanging incidence of CVA associated with PCI may be due to the parallel balance between the increase in the number of complex PCIs performed over the years and the improved, contemporary management strategies for patients undergoing PCI.
We observed ischemic CVA in 53.4%, transient cerebral ischemia in 33.0% and hemorrhagic CVA in 8.2% of cases. This is comparable to a single-center registry study between 1994 and 2009 that reported TIA and hemorrhagic stroke rates at 22% and 7%, respectively. In another study, the primary hemorrhagic stroke rate was 5%, and ischemic stroke with hemorrhagic conversion was noted in 15% of patients with stroke complicating PCI (7). In nearly 10,000 patients from 1991 to 1999, overall stroke rate was 0.38%; 46.5% were hemorrhagic and 48.8% ischemic (3). The dissimilarity noted between our and these very high rates in the incidence of hemorrhagic stroke may be explained by different study periods, markedly smaller patient population and stroke diagnosis methods. While NIS in an administrative database without event adjudication, a neurologist performed the diagnosis of stroke using computed tomography in the other study, likely accounting for the observed differences.
From 2005 to 2009, the use of unfractionated heparin, low molecular weight heparin and glycoprotein IIb/IIIa inhibitors (GPI) decreased significantly and the use of bivalirudin increased (19). The use of bivalirudin has been associated with reduced access- and non-access site bleeding; no difference has been reported in stroke incidence (15,20). The use of GPI has not been associated with increased stroke rates (21). Finally, adoption of higher loading doses of clopidogrel during the study period likely had a neutral effect on the incidence of periprocedural CVA. Therefore, it is unlikely that the changes in antiplatelet and antithrombotic pharmacology management of ACS contributed to the lower incidence of hemorrhagic CVA (22). Another likely explanation could be attributed to a possible decrease in the utilization of thrombolytic therapy over the study period. Both systemic and intracoronary thrombolytic therapy in the setting of an AMI has been well established as an independent risk factor for hemorrhagic stroke (23,24).
Emergent catheterization is a predictor for periprocedural stroke (9). Increased risk of CVA in patients undergoing an emergent catheterization is possibly hemodynamic compromise increasing the risk of ischemic stroke and cursory advancement of catheters increasing the risk of plaque dislodgement (9,25). Given these findings we would have expected AMI to be a predictor for CVA associated with PCI as noted in previous studies (11). A reason for AMI not being a predictor of CVA is possibly due to the refinements in the devices used for PCI (15,25,26).
The increased risk of CVA with IABP use requires special mention. Stone et al. observed a 2.4% stroke rate in patients with AMI randomized to IABP compared to the control group (27). A few other studies have also noted the use of IABP as a predictor for stroke complicating PCI (3,9). We similarly noted that patients who received an IABP were 1.4 times more likely to have CVA associated with PCI. Lastly, the IABP-SHOCK II trial, however, showed no difference in CVA associated with IABP use in AMI.(28)
The large sample size in this study helped clarify the role of predictors for stroke complicating PCI. These individual risk predictors were then used to develop a risk calculator for stroke associated with PCI. The nomogram achieved a C-index of 0.73, indicating good discrimination ability to separate patients who developed CVA from others who did not. The calibration plot for the nomogram indicates reasonable correspondence between predicted probabilities and observed proportions. An online version of the risk calculator can be found at http://www.r-calc.com.
The overall mortality in our CVA cohort was 10.76% consistent with other studies (3,6,9–11). The high mortality rate in patients with CVA associated with PCI is likely multifactorial. These patients are older with several comorbid conditions (3,7,9,11), lower ejection fractions (7) and have hemodynamic instability requiring IABP placement (3,9). A reassuring finding in this NIS-PCI registry is that the risk-adjusted in-hospital mortality rate in patients with CVA associated with PCI significantly decreased from 1998 to 2008 (Table 3). These data are consistent with reported decreases in stroke mortality during the same period and reflects improvement in acute stroke care (29,30).
Data about management patterns, utilization rates or dosing of the various medications (e.g., thrombolytics) that have been associated with CVA were not collected. The database does not allow for evaluation of practice patterns and precludes us from confirming an association between the trends in the utilization of specific medications, equipment’s and techniques (e.g., radial vs. femoral access) with the incidence of CVA. The details on the type and temporal relationship of CVA to PCI were not available. The CVA might have occurred prior to PCI - this is exceedingly unlikely since the first procedure performed in this dataset was PCI. The identification and definition of CVA was determined by the local sites and not centrally adjudicated. The underreporting of CVA cannot be excluded, and could have biased the estimation of the true incidence of CVA and the mortality rate associated with CVA. Additional unmeasured confounders may have accounted for the observed differences. NIS does not collect long-term outcomes. Participation in the NIS registry is voluntary and only selected centers may have participated in this registry, the results may not be generalized to all U.S. hospitals/population at large.
Supplementary Material
Acknowledgments
Funding Sources
This study is supported in part by the Division of Cardiology, University of Illinois at Chicago and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1TR000050. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
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