Abstract
Background:
Sarcopenia is underappreciated in advanced heart failure and is not routinely assessed. In patients receiving a left ventricular assist device (LVAD), preoperative sarcopenia, defined using CT-derived pectoralis muscle area index (muscle area indexed to body surface area), is an independent predictor of post-operative mortality. The association between preoperative sarcopenia and outcomes after heart transplant (HT) is unknown.
Objectives:
The primary aim was to determine if preoperative sarcopenia, diagnosed using pectoralis muscle area index, is an independent predictor of days alive and out of the hospital (DAOH) post-transplant.
Methods:
Patients who underwent HT from January 2018 to June 2022 with available preoperative chest CT scans were included. Sarcopenia was diagnosed as pectoralis muscle area index in the lowest sex-specific tertile. The primary endpoint was DAOH at 1-year post-transplant.
Results:
169 patients were included. Patients with sarcopenia (n=55) had fewer DAOH compared to those without, with a median difference of 17 days (320 vs. 337 days, p=0.004). Patients with sarcopenia had a longer index hospitalization and were also more likely to be discharged to a facility other than home. In a Poisson regression model, sarcopenia was a significant univariable and the strongest multivariable predictor of DAOH at 1-year (Parameter estimate = −0.17, 95% CI −0.19 to −14, p = <0.0001).
Conclusions:
Preoperative sarcopenia, diagnosed using pectoralis muscle area index, is an independent predictor of poor outcomes after HT. This parameter is easily measurable from commonly obtained preoperative CT scans and may be considered in the transplant evaluation.
Keywords: sarcopenia, cardiac transplantation, outcomes
Introduction
The progression of advanced heart failure (HF) is commonly associated with loss of body mass over time due heightened inflammation, poor appetite, and physical inactivity (1). The ultimate consequence of the involuntary loss of body mass over time is cardiac cachexia, which is defined as an unintentional loss of at least 5% of non-edema body mass over at least 6 months and associated with excess morbidity and mortality (2,3). Prior to overt reductions in body weight, reductions in muscle and fat mass may be missed due to reliance on body mass index (BMI), which does not discriminate the proportion of body mass attributed to muscle, fat and/or fluid. Due to an increase in body water in advanced HF, total weight may remain stable despite ongoing loss of muscle and/or fat mass. In this context, sarcopenia, or reduced muscle mass associated with reduced strength and function, is highly underdiagnosed (4). Sarcopenia is associated with reduced functional capacity and quality of life in patients with advanced HF and is strongly associated with clinical frailty, which is highly predictive of morbidity, mortality, and healthcare utilization in patients with HF (5,6).
Patients with advanced HF undergoing evaluation for advanced therapies with left ventricular assist device (LVAD) and/or heart transplantation (HT) are at highest risk for the wasting disorders of advanced HF including sarcopenia and associated frailty. Given the association between frailty and excess morbidity and mortality after HT, preoperative detection of sarcopenia is critically important to optimize patient selection (7). While patients with advanced HF may be unable to perform traditional frailty assessments, CT-derived measures of low muscle mass that may serve as a surrogate for sarcopenia are easily obtainable from CT scans performed for other indications such as preoperative planning (8,9). In patients undergoing LVAD implantation, unilateral pectoralis major muscle area index (muscle area indexed to height, cm2/m2), obtained from routine preoperative chest CT scans, was a powerful predictor of mortality after LVAD implant and outperformed all other traditional risk factors including the HeartMate II Risk Score, which takes into account relevant risk factors including patient age, laboratory parameters (i.e., albumin, creatinine, International Normalized Ratio (INR)) and center experience (10). The overall aim of this study was to establish the association of preoperative sarcopenia, determined by a low unilateral pectoralis muscle area index, with outcomes after HT.
Methods
All adult patients who underwent HT at our center from January 2018-June 2022 were identified. Patients who underwent combined heart-lung transplant were excluded from the analysis. Patients with available chest CT performed within 1 year before HT were included in the analysis. The exposure of interest was sarcopenia, for which we chose the lowest sex-specific tertile of pectoralis muscle area index as a surrogate, which was calculated as pectoralis muscle area (cm2) divided by height (m2) using the methodology outlined by Teigen et al (10). Briefly, pectoralis muscle assessment was performed by a trained reader under the supervision of an attending cardiac radiologist, using Aquarius iNtuition Viewer (ver. 4.5.0.125, TeraRecon Inc., San Mateo, CA, USA). Unilateral pectoralis muscle measurements were performed on a single axial slice directly superior to the aortic arch on the patient’s right side. If a defibrillator was present on the right, the left pectoralis muscle was analyzed instead. Muscles were manually shaded using a predefined attenuation range of −29 to 150 to obtain the mean Hounsfield Units and cross-sectional area (cm2). Cross-sectional area measures were standardized for body size by dividing by height in square meters (m2) which generated the measure of pectoralis muscle area index (cm2/m2). Radiologists performing these measurements were blinded to patient outcomes.
The primary outcome was days alive and out of the hospital (DAOH) at 1-year post-transplant. DAOH was calculated by subtracting from 365 days the sum of the length of the index hospitalization in days post-transplant plus the total number of days rehospitalized in the year after transplant if the patient survived the first year. If the patient died within the first year, DAOH was calculated manually, and counted as zero if the patient died during the index hospitalization. Secondary outcomes included death at 1-year post-transplant, length of stay (index hospitalization), length of intensive care unit (ICU) stay (index hospitalization), percent discharged to facility other than home, and total number of days rehospitalized at 1-year post-transplant (i.e., non-index hospitalization days), need for renal replacement therapy at 1-year post-transplant and incidence of infection at 1-year post-transplant. This analysis was approved by the Institutional Review Board of Columbia University Irving Medical Center.
Statistical Analysis
Baseline characteristics and primary and secondary outcomes of interest were compared according to sarcopenia status. Categorical variables were summarized as counts (percentages) and were compared by group using the chi-square test. Continuous variables were summarized as medians (interquartile range, IQR) and were compared by group using the Wilcoxon rank sum test as the data were non-parametric. For the primary outcome, we generated univariable and multivariable Poisson regression models (with a log link function) to identify predictors of DAOH at 1-year post-transplant. We tested the impact of low unilateral pectoralis muscle area index as well as potential confounding variables in a univariable analysis and variables with a univariable p-value<0.20 were considered, and the final model was selected using Akaike information criterion. A p-value of <0.05 was considered statistically significant in all analyses. Statistical analyses were performed with SAS version 9.4 (SAS Institute, Inc., Cary, North Carolina) and R (version 3.5.3, R Foundation for Statistical Computing).
Results
Baseline characteristics
From January 2018-June 2022, 255 patients underwent HT at our center. After excluding those without available and interpretable chest CT scans performed within 1 year of transplant, 169 patients (66.2%) were included in the final analysis (Table 1). The overall median age of the cohort was 55 years and 29% were female. Those patients determined to have sarcopenia (n=55) had a median unilateral pectoralis muscle area index of 4.6 cm/m2 versus a median of 6.9 cm2/m2 in those without sarcopenia. This corresponded to a lower median unilateral pectoralis muscle density in those with sarcopenia with a median of 30.7 HU versus a median of 35.3 HU in those without sarcopenia. Patients with sarcopenia were older (median age 60 years versus 55 years), more likely to be white (65.5% versus 44.7%) and had lower BMI (median 23.8 kg/m2 versus 27.5 kg/m2), though were otherwise not significantly different in terms of baseline characteristics as compared to those without sarcopenia.
Table 1.
Baseline characteristics stratified by sarcopenia as defined by pectoralis muscle index (pectoralis muscle area indexed to height) at lowest sex-specific tertile
| Variable | All (n=169) | Sarcopenia (n=55) | No sarcopenia (n=114) | p-value |
|---|---|---|---|---|
| Pectoralis muscle area index (cm2/m2) | 6.2 (4.9, 7.6) | 4.6 (3.9, 5.1) | 6.9 (6.1–9.2) | |
| Pectoralis muscle density (HU) | 32.8 (26.8, 38.6) | 30.7 (24.8, 34.0) | 35.3 (24.8, 34.0) | |
| Age | 55.0 (46.0, 63.8) | 60.0 (46.0–67.5) | 54.0 (46.0–61.0) | 0.011 |
| Female sex (%) | 49 (29.4) | 17 (30.9) | 33 (28.9) | 0.935 |
| Blood type (%) | 0.558 | |||
| A | 65 (38.2) | 23 (41.8) | 42 (36.8) | |
| AB | 11 (6.5) | 3 (5.5) | 7 (6.1) | |
| B | 30 (17.6) | 12 (21.8) | 18 (15.8) | |
| O | 64 (37.6) | 17 (30.9) | 47 (41.2) | |
| Race/ethnicity | 0.025 | |||
| Black | 42 (24.7) | 8 (14.5) | 34 (29.8) | |
| White | 87 (51.2) | 36 (65.5) | 51 (44.7) | |
| Hispanic | 27 (15.9) | 5 (9.1) | 21 (18.4) | |
| Other | 14 (8.2) | 6 (10.9) | 8 (7.0) | |
| Heart failure etiology (%) | 0.654 | |||
| Ischemic | 30 (17.6) | 10 (18.2) | 20 (17.5) | |
| Non-ischemic | 106 (62.4) | 34 (61.8) | 71 (62.3) | |
| Congenital | 11 (6.5) | 2 (3.6) | 9 (7.9) | |
| Restrictive/infiltrative | 10 (5.9) | 5 (9.1) | 5 (4.4) | |
| Retransplant | 13 (7.6) | 4 (7.3) | 9 (7.9) | |
| BMI at transplant | 25.9 (22.4, 30.5) | 23.8 (21.3, 26.1) | 27.5 (23.6, 31.2) | <0.001 |
| ICD prior to transplant (%) | 105 (61.8) | 35 (63.6) | 69 (60.5) | 0.825 |
| Diabetes (%) | 35 (20.6) | 13 (23.6) | 22 (19.3) | 0.653 |
| Prior stroke (%) | 25 (14.7) | 6 (10.9) | 19 (16.7) | 0.449 |
| Renal dysfunction (EGFR<60) (%) | 69 (40.6) | 26 (47.3) | 43 (37.7) | 0.309 |
| LVAD at transplant (%) | 22 (12.9) | 7 (12.7) | 15 (13.2) | 1.000 |
| ECMO at transplant (%) | 25 (14.7) | 8 (14.5) | 17 (14.9) | 1.000 |
| IABP at transplant (%) | 60 (35.3) | 22 (40.0) | 38 (33.3) | 0.498 |
| Status at transplant (%) | 0.630 | |||
| 1 | 24 (14.1) | 7 (12.7) | 17 (14.9) | |
| 2 | 94 (53.3) | 33 (60.0) | 61 (53.5) | |
| 3 | 33 (19.4) | 9 (16.4) | 23 (20.2) | |
| 4 | 16 (9.4) | 4 (7.3) | 12 (10.5) | |
| 5 | 2 (1.2) | 1 (1.8) | 1 (0.9) | |
| 6 | 1 (0.6) | 1 (1.8) | 0 (0.0) | |
| Serum albumin (g/dl) | 4.0 (3.5, 4.4) | 4.0 (3.5, 4.4) | 4.0 (3.7, 4.4) | 0.579 |
Sarcopenia and clinical outcomes
Overall, in this cohort, 14 patients died by 1-year post-transplant (8.3%) though this did not differ by the presence of preoperative sarcopenia. At 1-year post-transplant, the median DAOH in this cohort was 333 days (interquartile range (IQR) 310.5–343.0 days). Notably those with sarcopenia had significantly less DAOH at 1 year compared to those without sarcopenia with a median difference of 17 days (median 320 versus 337 days, p=0.003) (Central Illustration, Table 2). In addition, those with sarcopenia had a longer length of stay at the index hospitalization post-transplant (median 29.0 days versus 22.0 days, p=0.003) and were more likely to be discharged to a facility other than home after transplant (33.3% versus 14.2%, p=0.003). With regard to post-transplant morbidity, patients with and without sarcopenia had similar incidence of post-transplant infection requiring hospitalization (18.2% vs. 10.2%, respectively) and need for renal replacement at one year (0% in either group). Length of ICU stay post-transplant and total number of days rehospitalized at 1-year post-transplant also did not differ significantly by sarcopenia status (Table 2).
Central Illustration. Days alive and out of the hospital after heart transplantation according to sarcopenia.
Patients with sarcopenia, defined as the lowest sex-specific tertile of pectoralis muscle area index (muscle area indexed to height) had significantly fewer days alive and out of the hospital at 1-year post-transplant compared to those without sarcopenia. Those with sarcopenia had a longer length of stay that the index hospitalization and were more likely to be discharged to a facility other than home.
Table 2.
Clinical outcomes of interest stratified by sarcopenia as defined by pectoralis muscle index (pectoralis muscle area indexed to height) at lowest sex-specific tertile
| Variable | All | Sarcopenia (n=55) | No sarcopenia (n=114) | P value |
|---|---|---|---|---|
| Median days alive and out of the hospital at 1-year post-transplant | 333.0 (310.5, 343.0) | 320.0 (292.5, 339.5) | 337.0 (315.0, 343.0) | 0.004 |
|
| ||||
| Death at 1-year post-transplant (%) | 14 (8.2) | 5 (7.9) | 9 (9.1) | 1.000 |
| Death during index hospitalization (%) | 8 (4.7) | 3 (5.5) | 5 (4.4) | 0.77 |
|
| ||||
| Length of stay, total post-transplant (days) | 24.0 (17.0, 36.0) | 29.0 (20.0, 45.0) | 22.0 (16.0, 34.0) | 0.003 |
|
| ||||
| Length of stay, ICU post-transplant (days) | 9.0 (6.0, 13.5) | 11.0 (6.0, 15.0) | 9.0 (6.0, 12.0) | 0.29 |
|
| ||||
| Discharge to other than home (%) | 34 (20.2) | 18 (33.3) | 16 (14.2) | 0.014 |
|
| ||||
| Median number of days rehospitalized in 1-year post-transplant | 5.0 (0.0, 16.0) | 4.5 (0.0, 13.3) | 5.0 (0.0, 20.0) | 0.75 |
|
| ||||
| Post-transplant infection requiring hospitalization at 1-year (Events/100 patient years) | 14.6 (9.1–22.4) | 22.1 (10.6–40.7) | 11.2 (5.6–20.5) | 0.11 |
|
| ||||
| Need for renal replacement therapy at 1 year (%) | 0 (0.0) | 0 (0.0) | 0 (0.0) | N/A |
Data are presented as number and percentage, median and interquartile range, or incidence per 100 patient years and the Poisson 95% Confidence Interval
Univariable and multivariable Poisson regression was performed to determine clinical predictors of DAOH at 1 year. Sarcopenia, defined as the lowest sex-specific tertile of pectoralis muscle area index, was a significant univariable predictor of less DAOH as compared to those without (Table 3). Other significant univariable predictors included age, non-white race, obesity, ICD prior to transplant, and high status (UNOS Status 1–2). After multivariable adjustment, low unilateral muscle area index remained significant and the strongest multivariable predictor of DAOH at 1 year (Parameter estimate −0.17, 95% CI −0.19 to −0.14, p<0.0001, Table 4). Notably, we performed a post-hoc sensitivity analysis including only those patients with CT scans available within 3 months prior to transplant, to capture more recent sarcopenia and got the same result (278 days versus 324 days, p=0.003).
Table 3.
Univariable linear regression model for the outcome of days alive and out of the hospital
| Parameter Estimate | 95% CI | p-value | |
|---|---|---|---|
| Sarcopenia | −0.10 | −0.12, −0.08 | <0.0001 |
| Age | 0.0018 | 0.0011, 0.0025 | <0.0001 |
| Male sex | −0.01 | −0.03, 0.008 | 0.25 |
| Non-white race | −0.04 | −0.06, −0.02 | <0.0001 |
| Obesity (BMI ≥ 30 kg/m2) | −0.03 | −0.05, −0.01 | 0.0007 |
| ICD prior to transplant | 0.04 | 0.02, 0.05 | <0.0001 |
| Diabetes | −0.002 | −0.02, 0.02 | 0.89 |
| Prior stroke | 0.006 | −0.02, 0.03 | 0.62 |
| Renal dysfunction (EGFR < 60 ml/min/1.73m2) | 0.002 | −.002, 0.02 | 0.83 |
| Prior LVAD | 0.01 | −0.01, 0.04 | 0.33 |
| High status prior to transplant (UNOS 1–2) | 0.12 | 0.09, 0.14 | <0.0001 |
Table 4.
Multivariable linear regression model for the outcome of days alive and out of the hospital for sarcopenia adjusted by high transplant priority status.
| Parameter Estimate | 95% CI | p-value | |
|---|---|---|---|
| Sarcopenia | −0.17 | −0.19, −0.14 | <0.0001 |
| High status prior to transplant (UNOS 1–2) | 0.12 | 0.10–0.15 | <0.0001 |
| ICD | 0.04 | 0.02, 0.06 | <0.0001 |
| Non-white | −0.03 | −0.05, −0.01 | 0.002 |
| Age | 0.002 | 0.001, 0.003 | <0.0001 |
Discussion
The findings from our analysis indicate that low pectoralis muscle area index is an independent predictor DAOH at 1-year post transplant. Notably, this surrogate for sarcopenia outperformed all other clinical variables to predict the number of DAOH at 1 year. Those with sarcopenia as defined by a unilateral pectoralis muscle area index in the lowest sex-specific tertile spent a median of 17 fewer DAOH compared to those without sarcopenia. In addition, those with a low unilateral pectoralis muscle area index experienced a longer length of stay at the index hospitalization post-transplant and were more likely be discharged to a facility other than home compared to those with a higher pectoralis muscle area index.
The results of our analysis follow those by Teigen et al in which pectoralis muscle area index was a significant independent predictor of worse clinical outcomes in patients with advanced heart failure who underwent LVAD implantation (8). In this single center sample with available chest CT data (n=143), every 1 unit increase in pectoralis muscle area index (1 cm2/m2) decreased the hazard of death by 27% (adjusted HR 0.73, 95% CI 0.58–0.92) (8). In a follow up analysis inclusive of data from 2 centers (n=276), a novel Minnesota Pectoralis Risk Score was developed and incorporated both pectoralis muscle area index and density in addition to other known risk factors for postoperative mortality, reaching receiver-operating characteristic (ROC) curves with an area under the curve (AUC) of 0.76 to predict death at 1 year (10).
Only one retrospective analysis from a single center in Spain examined the impact of pectoralis muscle area index on prognosis after HT, reporting in an abstract that pectoralis muscle area index was not an independent predictor of death over a median follow up period of 3.7 years post-transplant in their cohort (11). In accordance, overall mortality was very low in our cohort who underwent HT with 14 deaths at 1 year (8.2%) and our analysis did not demonstrate excess 1-year mortality in those with sarcopenia compared to those without (9.1% versus 7.9%). In general, given the low mortality after HT in the cotemporary era, with survival greater than 90% at 1 year and greater than 85% at 5 years, alternative clinical outcomes that incorporate estimates of morbidity and/or quality of life in addition to mortality are critical to assess in this population, in whom the life impact of HT is a major concern (12). DAOH is an example of patient-centered clinical outcome that captures this information and also provides an indirect measure of healthcare costs (13). Given that patients who undergo LVAD are likely older with a larger number of comorbidities, they represent a sicker, potentially older population of patients with advanced HF in whom short- and long-term mortality is higher as compared to HT. This may explain why differences in mortality were noted in LVAD cohort according to sarcopenia status but not in our HT cohort.
In this context, our analysis is the first to show that low muscle quantity assessed using pectoralis muscle area index is associated with 17 fewer DAOH at 1-year post-transplant. Very few studies to date focus on DAOH as a primary outcome in the context of HT, however one single center study in Germany reported that use of VA-ECMO for primary graft dysfunction (PGD) was associated with a median if 243 less DAOH compared to 310 days (14). The same group of investigators then sought to identify donor-, recipient-, and/or procedure-related variables that predict DAOH among patients who underwent HT, finding that duration of post-transplant mechanical ventilation, post-transplant RRT, and recipient diabetes showed significant independent association with this outcome (13). While surrogates for sarcopenia and/or frailty were not assessed, one preoperative risk factor in our analysis that was significantly and independently associated with DAOH was high waitlist status, where high status was associated with more DAOH compared to those with lower statuses. This reflects that patients listed at lower status may be older with more comorbidities, and may receive organs from donors with additional comorbidities, contributing to overall more days in the hospital post-transplant (15). In support of this is our finding that age was independently associated with more DAOH. In our cohort, low pectoralis muscle area index was an independent risk factor for less DAOH even after controlling for waitlist status, highlighting its distinctness as a risk factor in this population.
It is also important to note that patients in our cohort with low pectoralis muscle area index did not differ significantly in terms of the incidence of major medical comorbidities compared with those patients with higher muscle quantity. While those with low muscle quantity demonstrated a lower BMI, BMI in the obese range was not a significant predictor of DAOH, which suggests that sarcopenia represents a distinct risk factor independent of body mass which is similar to the results by Teigen et al in the cohort of patients that underwent LVAD implantation (8).
A final key point is that pectoralis muscle quantity is overall easy to measure from routinely obtained preoperative chest CTs, which were available for analysis in 66% of our cohort. While these radiologic measures are unlikely to provide as comprehensive of an assessment of a patient’s functional capacity and physiologic reserve as compared to a multicomponent frailty assessment such as the Fried Frailty Index, they are significantly less time consuming to measure. In addition, the Fried Frailty Index requires assessment of gait speed and handgrip strength, which may be very difficult to perform in patient on temporary mechanical support awaiting HT (7). In an analysis by McDonald et al demonstrating the high prognostic value of preoperative frailty, the Fried Frailty Index was only available for 52% of patients (7). Nonetheless, future studies correlating pectoralis muscle quantity and density with validated measures of clinical frailty such as the Fried Frailty Index will be important. From a radiologic standpoint, the measure of pectoralis area index and density requires processing of a single slice of a CT chest and therefore is relatively easy to perform.
Limitations
There are several notable limitations associated with this analysis. First, this is a single center analysis and the generalizability of the data to other centers is unknown. In addition, 34% of patients who underwent HT from our center did not have available or interpretable preoperative CT data and were therefore excluded from the analysis. Despite this, when we compared baseline characteristics among those with available CT and without, only waitlist status at time of transplant differed between groups, where those without available CT data had overall lower status (Table S1, Supplemental Appendix). Further, while low pectoralis muscle area index indicates low muscle mass, which is critical to the definition of sarcopenia, the diagnosis also requires concomitant low muscle strength (i.e., low handgrip strength) and/or low muscle function (i.e., slow gait speed). These assessments are not available in our single center database and therefore we define low unilateral pectoralis muscle area index as a surrogate for sarcopenia in our cohort, which has been described elsewhere (9). Finally, the number of days spent in subacute rehabilitation, or another longer-term care facility, to better define “days at home” were not readily available in our database though would also be an important measure given the potential impact on quality of life for our patients.
Conclusion
Low unilateral pectoralis muscle area index is an independent predictor of less DAOH after HT, which is an important patient-centered outcome to assess in this population especially given the improving HT survival over time. Given the wide availability of preoperative chest CT scans and the ease of measurement, unilateral pectoralis muscle quantity and density may be used to risk stratify patients prior to HT. However, prospective multicenter studies in large diverse populations are needed to confirm these findings. It also remains to be seen whether sarcopenia and/or frailty can be reversed in HT recipients postoperatively, which has been shown in LVAD recipients, and may influence our selection strategies as well (16). CT may play a role in detecting these changes. There are also various clinical trials in process to determine the role of calorie and protein supplementation to maintain nutritional status and therefore prevent sarcopenia and cachexia among recipients of heart transplant that may be monitored with CT-derived pectoralis area index (NCT05219708; NCT05655910).
Supplementary Material
Lay summary:
Sarcopenia is underdiagnosed in patients with advanced heart failure eligible for cardiac transplantation and may impact post-transplant outcomes.
Patients with sarcopenia, defined by low pectoralis muscle area index (muscle area indexed to body surface area) using available preoperative CT scans, was independently associated with fewer days alive and out of the hospital compared to those without sarcopenia.
Pectoralis muscle area index is easily measurable from routinely obtained pre-operative CT scans and may aid in identifying sarcopenia during the transplant evaluation.
Lay summary paragraph:
Sarcopenia, or low muscle mass and function, is associated with poor outcomes after heart transplant. We measured pectoralis muscle area index, a CT-derived potential surrogate for sarcopenia, in 169 patients who underwent heart transplant. Those with low pectoralis muscle area index had significantly fewer days alive and out of the hospital as well as significantly longer lengths of stay. These patients were also more likely to be discharged to a facility other than home compared to those without. Overall, pectoralis muscle area index is an easy to measure metric that may help us to risk stratify patients for heart transplant.
Financial conflict of interest statement
There were no outside sources of funding for this study.
Biography
Footnotes
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