2.03 Quiz Theme And Characterization
Clinical Perspective
What Is New?
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By applying an unsupervised statistical method, 4 distinct trajectories of the long-term progression of cardiac allograft vasculopathy later on middle transplantation were identified in a large and highly phenotyped European cohort.
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These cardiac allograft vasculopathy trajectories manifested consistently in an independent US cohort and were associated with all-crusade mortality.
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Based on an integrative approach assuasive the precise assay of the independent association of each immune and nonimmune potential risk factor, 6 candidate variables independently associated with cardiac allograft vasculopathy were identified.
What Are the Clinical Implications?
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The robustness of the 4-trajectory pattern and the predictive variables in different healthcare systems supports the ground for a trajectory-based assessment.
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Trajectory-based monitoring could allow for the individualization of coronary angiogram monitoring and medical therapies, thus minimizing unnecessary angiograms in low-hazard patients and intensifying treatment and angiogram monitoring in high-take a chance patients, and may inform the design and optimize stop point definition of next-generation clinical trials.
Introduction
Editorial, see p 1968
The number of patients with end-stage eye failure is dramatically increasing worldwide and responsible for tens of thousands of deaths annually in Europe and in the United states.one,2 Orthotopic heart transplantation (HTx) is the ultimate treatment only is dramatically express in numbers.3,4 Although the short-term survival after HTx has improved considerably in recent decades, the survival across 1 year after transplantation has remained unchanged over years despite of import progress in immunosuppression and patient care.5,six In this setting, cardiac allograft vasculopathy (CAV) is the third leading cause of late bloodshed and the leading cause of belatedly allograft dysfunction.seven–nine
The identification of relevant CAV-predicted progression or trajectories and their respective risk factors is an unmet need. The current gilded standard for CAV monitoring later on HTx relies on protocol angiograms that guide the clinical management of patients including prevention strategies, therapeutic changes, and end points for clinical trials.ten,11 Current approaches for CAV investigation have been limited to registry information. No study to engagement has been primarily designed toward a prospective protocol-based and standardized cess of CAV together with ongoing patient characteristics including immunologic and biological profiles, histological phenotypes, and information on treatments. We conducted the nowadays study to determine the characteristics and profiles of the development of CAV over the years with a specific aim: to early identify the factors that contribute the virtually to CAV development, which could become a target for guiding patient intendance.
To achieve this goal, nosotros performed a longitudinal prospective cohort study including consecutive heart transplant recipients in dissimilar cohorts from Europe and the United States. We used information gathered by protocol-driven repeated CAV assessments performed together with clinical, biological, histological, and immunologic phenotyping. We determined whether a trajectory-based approach could identify robust prototypes of CAV course that could stratify patients into singled-out CAV trajectories. Furthermore, we identified the corresponding contribution of immune and nonimmune candidate predictive characteristics on CAV progression over 10 years and its association with overall-cause bloodshed.
Methods
Information, methods, and materials used to behave the inquiry are available from the respective author on reasonable request and in compliance with the European General Data Protection Regulation.
Report Pattern and Participants
Derivation Cohort
The derivation cohort included 815 sequent European patients >18 years of age who were prospectively enrolled at the fourth dimension of HTx in 2 French referral centers (La Pitié-Salpêtrière Hospital, Paris, n=510; Georges Pompidou European Hospital, Paris, northward=99) and i Kingdom of belgium centre (Leuven Academy Hospital, n=206), who underwent transplantation between January 1, 2004 and December 31, 2016, and who were alive at 1 year after transplantation and had undergone at least two coronary angiographies during follow-up (from 1 to 10 years later on transplantation). All data were entered at the time of transplantation, at 3 months, six months, 1 year later transplantation, and at subsequent annual visit evaluations using a standardized protocol to ensure harmonization across study centers. In addition, clinically driven events such every bit symptomatic rejection were collected. Twenty-four hours of transplant and 1-yr posttransplant visits included an extensive clinical, biological, and functional evaluation (run across Methods and the study protocol in the Methods in the Information Supplement for detailed information collection procedures). Data were retrieved from the database in June 2019. The institutional review boards of the Paris Transplant Group (https://paristxgroup.weebly.com/#), Leuven University, and Cedars-Sinai approved the study. Patients provided written informed consent at the fourth dimension of transplantation (URL: https://clinicaltrials.gov; Unique identifier: NCT04117152).
United states Independent Cohort
The robustness of the European trajectory design was evaluated in an independent accomplice of 486 heart transplant recipients >eighteen years of historic period from 1 US eye (Cedars-Sinai Heart Establish, Los Angeles, 2008 and 2016). Data sets from the independent cohort were collected as part of routine clinical practice and entered in the center's databases in compliance with local and national regulatory requirements and sent anonymized to the Paris Transplant Group.
Procedures and Clinical Protocols
All patients were followed from fourth dimension of transplant until retransplantation, death, or date of final data extraction.
We defined the baseline period at 1 twelvemonth after transplantation where the recipients underwent concomitant evaluation of coronary angiography, allograft pathology, usual claret tests, and circulating anti–human leukocyte antigen (HLA) antibiotic cess co-ordinate to a prespecified protocol. All patients included in the study had at least 2 CAV measures after one year after transplantation (see Methods in the Data Supplement for the listing of parameters assessed for the derivation cohort).
CAV angiographies were recorded per center protocol for all patients. CAV was graded per patient according to the International Society for Heart and Lung Transplantation classification every bit CAV 0 (not significant), i (mild), 2 (moderate), and iii (astringent).12 European center protocols for follow-up data included CAV measurement at 1 year afterward transplantation and every 2 years thereafter, and at the time of whatsoever clinical indication, every bit well. The Us center protocol included CAV measurement at six weeks and i year after transplantation, then every year thereafter, and at the time of any clinical indication, besides.
A prespecified protocol was practical to the analysis of coronary angiograms, including independent evaluation by senior cardiologists, consummate review of cases with uncertain CAV class, and boosted analyses of random coronary angiograms. This detailed approach allowed the mitigation of the risk of CAV grade misclassification. In the European derivation accomplice, a total of 2742 reports of coronary angiographies were retrospectively and independently reviewed past 3 senior cardiologists (G.B., G.C., Yard.-C.B.). In the case of discrepancies between observers or an inconclusive report, the coronary angiographies were reviewed to reach an understanding (n=454, 16.half dozen%, mostly International Society for Heart and Lung Transplantation CAV1). In the The states independent cohort, a total of 1968 coronary angiographies were analyzed. Two senior cardiologists graded all coronary angiographies according to the summarized report collected prospectively during patient follow-up.
Anti- HLA donor-specific antibodies (DSA) are antibodies developed by the recipient and directed against donor class I or class II HLA antigens. They can be either preexisting if present earlier transplantation or de novo if adult after transplantation. Circulating DSAs confronting HLA-A, HLA-B, HLA-Cw, HLA-DR, HLA-DQ, and HLA-DP were assessed past using single-antigen menses bead assays (Methods in the Information Supplement). Heart allograft pathology data were recorded and graded according to the International Society for Centre and Lung Transplantation classifications (astute cellular rejection classification: class 0R to 3R; pathological antibody-mediated rejection classification: grade 0–3; Methods in the Information Supplement).xiii,14 During the first yr after transplantation, a total of 12 803 endomycardial biopsies were performed in the derivation cohort.
Outcome Measures
The chief outcome was CAV trajectories after transplantation. The secondary outcome was progression to all-cause mortality or retransplantation. The outcomes were prospectively assessed in both cohorts at each transplant anniversary, upwardly to June 30, 2019.
Statistical Analysis
Continuous variables were described using means and SDs or median and the interquartile range, as appropriate. We compared means and proportions betwixt groups using the Pupil t examination, analysis of variance (Mann-Whitney U test for DSA mean fluorescent intensity) or the χtwo exam (or Fisher verbal test, if appropriate).
Derivation of Posttransplantation CAV Trajectories
So that nosotros would not identify trajectories that may exist influenced by allocation systems and patient management across cohorts and countries, we conducted the analyses on Europe and in the United States separately. CAV trajectories were identified over 10 years after transplantation using latent class mixed models.15 Latent class mixed models characterize trajectories in repeated measurements, with the supposition that several underlying subpopulations (ie, the latent classes) can be detected.16 This arroyo requires neither the same number of measures per patient nor the same fourth dimension points of measurement. Initial CAV grading (1 year afterward transplantation) and CAV slopes were specific to each subpopulation identified. We compared the linear trajectory models with nonlinear models, including quadratic (t 2 effect) and splines, and confirmed the suitability of linear models used for trajectory identification. Furthermore, we tested unlike link functions to identify the best fitting model.
At model convergence, each patient was assigned posterior likelihoods of belonging to each CAV trajectory. Patients were assigned the form to which they had the highest likelihood of belonging.
Definition of the Optimal Number of CAV Trajectories
The number of CAV trajectories was divers according to (1) the Bayesian Information Criterion and the Akaike Data Benchmark, (ii) the bigotry (ie, the ability of the model to specifically allocate patients in the CAV trajectories), (3) the entropy (ie, the ability of the model to identify singled-out CAV trajectories), and (4) the interpretability of the model, every bit previously published (run into further details in the Appendix in the Data Supplement).16–18
Candidate Predictive Variables of CAV Trajectories
In the derivation cohort, the associations betwixt CAV trajectories and clinical, histological, functional, and immunologic parameters at the time of transplantation, during the start year and at 1 year after transplantation were assessed using multinomial logistic regression. Parameters associated with trajectories in the univariate analysis with P<0.x were thereafter included in the multivariable model. Stepwise backward elimination was performed to obtain the last multivariable model (Methods in the Information Supplement).
Missing Information and Imputation
A total of 35 patients (four.29%) in the derivation cohort had at least 1 missing data element for the final multinomial model. Multiple imputation using chained equations was used (MICE R package, using 20 sets of imputations); continuous parameters were imputed with random wood, and categorical parameters were imputed with polynomial regressions.
CAV Trajectories and Association With All-Cause Mortality
Kaplan-Meier curves were used to report fourth dimension to occurrence of all-cause bloodshed according to the identified CAV trajectories. The outcome of expiry or graft failure was defined when whatever patient died or had preemptive retransplantation at the date of final data extraction (June i, 2019).
We used R (version three.five.three, R Foundation for Statistical Computing) and STATA (version 15, Data Analysis and Statistical Software) for the descriptive and survival analyses. Values of P<0.05 were considered statistically significant, and all tests were 2-tailed. Details regarding the interpretation of important statistical concepts are given in the Methods in the Data Supplement.
Results
Characteristics of the European Derivation and US Independent Cohorts
The European derivation accomplice (due north=815) and the US independent cohort (n=486) comprised a full of 1301 included patients from 4 transplant centers betwixt January 1, 2004, and December 31, 2016 respective to 9298 patient-years. The median follow-upwardly after transplantation was 6.59 years (interquartile range, 4–ix.1). The summarized characteristics of patients, and the transplant procedures, policies, and allocation systems per participating heart, as well, are detailed in Tabular array ane and Table I in the Data Supplement. Older donors were used in the European cohort in comparing with the US cohort (43.6±12.ii versus 34.viii±12.nine, P<0.001). Recipients were younger (48.ane±12.8 versus 56.four±12.vii, P<0.001) with fewer ischemic cardiomyopathy and cardiovascular gamble factors at the fourth dimension of transplant in the European accomplice. Detailed baseline characteristics stratified by center in the derivation cohort are displayed in Tabular array II in the Data Supplement.
| Characteristics | All Patients (4 Centers, n=1301) | European Derivation Cohort (3 Centers, n=815) | U.s.a. Contained Accomplice (i Center, n=486) | P Value | |||
|---|---|---|---|---|---|---|---|
| n | due north | northward | |||||
| Donor | |||||||
| Age, y, mean (SD) | 1299 | 40.29 (13.74) | 815 | 43.56 (12.17) | 484 | 34.77 (12.89) | <0.001 |
| Sex male, northward (%) | 1202 | 833 (69.3) | 815 | 556 (68.22) | 385 | 340 (seventy.ten) | 0.48 |
| Tobacco, n (%) | 1220 | 390 (31.97) | 783 | 348 (44.44) | 437 | 42 (nine.61) | <0.001 |
| BMI, ≥25 kg/grand2, n (%) | 1257 | 608 (48.37) | 815 | 347 (42.58) | 442 | 261 (59.05) | <0.001 |
| Recipient | |||||||
| Age, y, mean (SD) | 1301 | 51.23 (13.36) | 815 | 48.14 (12.77) | 486 | 56.43 (12.72) | <0.001 |
| Male sex, n (%) | 1300 | 975 (75) | 815 | 632 (77.55) | 485 | 343 (70.72) | 0.006 |
| White ethnicity, n (%) | 1300 | 933 (71.77) | 815 | 619 (75.95) | 485 | 314 (64.74) | <0.001 |
| Ischemic cardiomyopathy, n (%) | 1298 | 453 (34.90) | 815 | 265 (32.52) | 483 | 188 (38.92) | 0.019 |
| Long-term MCS, n (%) | 1300 | 268 (20.61) | 815 | 150 (18.40) | 485 | 118 (24.33) | 0.009 |
| Previous history of hypertension, n (%) | 1211 | 430 (35.51) | 797 | 219 (27.48) | 486 | 211 (l.97) | <0.001 |
| Diabetes mellitus, n (%) | 1281 | 261 (twenty.37) | 813 | 109 (13.41) | 468 | 152 (32.48) | <0.001 |
| BMI ≥ 25 kg/yardtwo, n (%) | 1296 | 564 (43.52) | 815 | 327 (40.12) | 481 | 237 (49.27) | 0.001 |
| Transplant baseline southward | |||||||
| Combined transplantation, n (%) | 1300 | 83 (half-dozen.38) | 815 | 33 (four.05) | 485 | fifty (10.31) | <0.001 |
| Cold ischemic time, min, mean (SD) | 1282 | 177.5 (58.6) | 815 | 183.0 (55.3) | 467 | 167.8 (62.viii) | <0.001 |
| Sex activity mismatch (female D – male R), n (%) | 1299 | 185 (xv.38) | 815 | 146 (17.91) | 486 | 55 (eleven.36) | 0.002 |
| CMV mismatch (D+/R–), due north (%) | 1266 | 247 (xix.51) | 815 | 164 (20.27) | 486 | 83 (xviii.16) | 0.36 |
| Immunosuppressive therapies | |||||||
| ATG induction therapy, n (%) | 1282 | 991 (77.30) | 815 | 773 (94.85) | 467 | 218 (46.68) | <0.001 |
| 1-yr immunosuppressive regimen | |||||||
| Cyclosporine | 1297 | 505 (38.86) | 815 | 465 (76.35) | 482 | 27 (v.60) | <0.001 |
| Tacrolimus | 1297 | 792 (61.06) | 815 | 144 (23.65) | 482 | 455 (94.forty) | <0.001 |
| MMF | 1297 | one,113 (85.81) | 815 | 732 (89.82) | 482 | 381 (79.05) | <0.001 |
| mTOR-inhibitors | 1297 | 212 (16.35) | 815 | 132 (16.20) | 482 | fourscore (16.60) | 0.85 |
| 1-year posttransplant cardiovascular risk profile | |||||||
| Treated hypertension, n (%) | 813 | 592 (72.82) | 813 | 592 (72.82) | – | – | |
| Diabetes mellitus, north (%) | 813 | 177 (21.77) | 813 | 177 (21.77) | – | - | |
| Statins, n (%) | 1275 | 1,178 (92.39) | 789 | 692 (87.71) | 486 | 470 (96.seventy) | <0.001 |
| LDL-c ≥ane thou/L, n (%) | 1126 | 536 (45.19) | 794 | 386 (48.61) | 392 | 150 (38.27) | 0.001 |
| Tobacco, n (%) | 813 | 47 (5.78) | 813 | 47 (v.78) | – | – | |
| Immunology and histology | |||||||
| Anti-HLA DSA, preexisting and 1-y (MFI ≥500), northward (%) | 1242 | 307 (24.72) | 799 | 218 (27.28) | 443 | 89 (twenty.09) | 0.005 |
| MFI of immunodominant DSA, preexisting & one-y | 1241 | 798 | 443 | ||||
| None | 935 (75.34) | 581 (72.81) | 354 (79.91) | ||||
| MFI: 500–2999 | 151 (12.17) | 135 (sixteen.92) | 16 (three.61) | <0.001 | |||
| MFI: ≥3000 | 155 (12.49) | 82 (10.28) | 73 (16.48) | ||||
| Form Two anti-HLA DSA, preexisting and 1-y, n (%) | 1241 | 215 (17.32) | 798 | 150 (18.fourscore) | 443 | 65 (xiv.67) | 0.07 |
| Acute cellular rejection ≥2R, n (%) | 1301 | 140 (10.76) | 815 | 108 (13.25) | 486 | 32* (6.58) | <0.001 |
| Antibody-mediated rejection ≥ pAMR1, north (%) | 1094 | 104 (9.51) | 608† | 76 (12.48) | 486 | 28* (5.76) | <0.001 |
| Follow-up | |||||||
| Follow-up, y, median (IQR) | 1301 | 6.56 (four.72) | 815 | 7.73 (5.xiv) | 486 | 4.84 (three.23) | <0.001 |
The total number of CAV measures analyzed was 4710 (three.6±1.half-dozen measures per patient), including 2742 coronary angiographies in the derivation cohort (3.4±i.iii per patient) and 1968 in the US contained cohort (4.0±one.8 per patient). The International Club for Heart and Lung Transplantation CAV grades were distributed as follows: 3354 CAV course 0 (71.21%), 847 CAV class one (17.98%), 358 CAV course 2 (7.lx%), and 151 CAV grade 3 (3.21%) from year i to year ten. The CAV grades were higher in the European cohort than in the US cohort (Table Three in the Data Supplement). I hundred sixty-six of 1301 (12.76%) were diagnosed with CAV class >0 in the first twelvemonth after transplantation. The detailed results of coronary angiographies and their evolution across years are described in Table Iv in the Data Supplement.
Two hundred eighteen patients (27.3%) and 89 patients (20.1%) had detectable anti-HLA DSA in the European and United states of america cohorts, respectively, either preexisting or de novo with 69.81% of DSA being course Ii. Overall, 108 patients (thirteen.3%) from the derivation cohort presented with astute cellular rejection ≥2R, and 76 patients (12.v%) from the French derivation cohort presented with antibody-mediated rejection ≥pathological antibody–mediated rejection 1. In the The states contained cohort, where only the information "treated rejections" was available, 32 (6.six%) and 28 (v.viii%) patients presented with treated astute cellular rejection ≥2R and treated antibiotic-mediated rejection ≥pathological antibody–mediated rejection 1, respectively. The detailed results of the 12 803 endomyocardial biopsies are provided in Table Five in the Data Supplement.
Identification and Characterization of CAV Trajectories
Derivation Cohort
We identified 4 distinct CAV trajectories over ten years after transplantation (Figure one, Figures I and II in the Information Supplement); the model showed a good discrimination of 0.92 (0.96, 0.83, 0.93, and 0.88 for trajectory 1, two, 3, and four, respectively) and entropy of 0.82, meaning that the model fairly separated the trajectories (see details regarding the model in Table Half dozen in the Information Supplement and in Figures III and IV in the Information Supplement).
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Figure one. Singled-out profiles of CAV trajectories identified later on transplantation in the European derivation cohort (n=815). This figure represents the chief profile CAV grades identified with latent class mixed models. Each patient, represented past an private CAV trajectory, was assigned to the grade for which the membership probability was the highest. Bigotry=0.92. CAV trajectory one was composed of patients with no CAV at i year that remained stable over time; CAV trajectory two was equanimous of patients with no CAV at ane year, with a slight increase in CAV over fourth dimension starting ≈4years after transplantation; CAV trajectory 3 was equanimous of patients with intermediate CAV class at 1 yr who experienced a moderate increase during follow-upwardly; and patients from CAV trajectory four presented with a pattern of intermediate CAV at one year and acceleration over time. Thick lines stand for latent course trajectory; sparse lines represent CAV private patient trajectory. CAV indicates cardiac allograft vasculopathy.
Usa Contained Accomplice
Latent grade mixed models were practical in the contained US accomplice. The best fitting model identified 4 profiles of CAV trajectories and confirmed the consistency of the 4 profiles previously demonstrated in the derivation cohort (Figure two). In this geographically distinct cohort, the model showed an excellent bigotry of 0.97 (0.93, 0.97, 0.88, and 0.92 for trajectory 1, two, 3, and iv, respectively; see details regarding the model in Table Seven in the Data Supplement).
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Figure 2. Cardiac allograft vasculopathy trajectories: robustness of the 4-trajectory pattern in the Usa independent cohort. Latent course mixed models were applied to the US contained cohorts. CAV profiles identified in these independent analyses were similar to those identified in the derivation accomplice with an fantabulous discrimination of 0.97. Thick lines correspond latent class trajectory; sparse lines represent CAV individual patient trajectory. CAV indicates cardiac allograft vasculopathy.
CAV trajectory one was composed of patients with no CAV at one year who remained stable over time (n=823, 63.26%, CAV slope per year=0.00±0.01). Of annotation, among the 823 and the corresponding 2792 coronary angiographies analyzed in trajectory 1, but six (0.07%) patients had an increase of CAV grade over the 10 years of observation time.
CAV trajectory 2 was composed of patients with no CAV at 1 year, with a tardily-onset increase of CAV starting at ≈4 years after transplantation (n=79, 6.07%, overall CAV slope per year=0.11±0.12). CAV trajectory 3 was composed of patients with intermediate CAV grade at 1 year who experienced a moderate increase during follow-upwardly (n=261, xx.06%, CAV slope per year=0.xviii±0.16). Patients from CAV trajectory iv presented with a blueprint of intermediate CAV at ane year and dispatch over time (n=138, ten.61%, CAV slope per year=0.forty±0.50).
Clinical, Functional, Structural, and Immunologic Candidate Predictive Variables of CAV Trajectories
Donors, recipients, transplant, immunosuppression, and immunologic characteristics according to CAV trajectory are detailed in Tabular array 8 in the Information Supplement.
A total of lx clinical, functional, structural, and immunologic factors of CAV trajectories were investigated. The chief results of the univariate analysis conducted in the derivation accomplice are reported in Tabular array 2. Nosotros identified 17 significant variables associated with the trajectories: eight donor-related hazard factors (historic period: P<0.001; sexual activity: P=0.002; hypertension: P=0.035; diabetes mellitus: P=0.003; tobacco consumption: P=0.004; torso mass index ≥25 kg/m2: P=0.002; creatinine clearance ≤60 mL/min: P<0.001; vascular cause of death: P=0.013), 4 pretransplant recipient variables (sex: P=0.081; ischemic cardiomyopathy: P=0.022; hypertension: P=0.081; body mass alphabetize ≥25 kg/chiliad2: P=0.031), 1 immunosuppressive therapy variable (type of calcineurin inhibitor: P<0.001), 4 recipient cardiovascular profile variables assessed at i year afterward transplantation (diabetes mellitus: P=0.07; statin therapy: P=0.09; low-density lipoprotein cholesterol ≥i g/50: P=0.002; creatinine clearance: P=0.02), four immunologic parameters (presence of preexisting or de novo in the start yr anti-HLA DSA: P=0.007; class II immunodominant DSA: P=0.025; cellular rejection grade ≥2R occurring during the get-go twelvemonth after transplantation: P=0.042; episode of antibody-mediated rejection occurring during the start year: P=0.104).
| Parameters | n | Trajectory 1 (due north=459) | Trajectory two (n=62) | Trajectory 3 (n=188) | Trajectory 4 (northward=106) | P Value | |
|---|---|---|---|---|---|---|---|
| Donor | |||||||
| Age (x-y increment) | 815 | – | i.23 (1.00–one.52) | 1.eighty (1.54–2.ten) | 1.91 (i.57–two.33) | <0.001 | |
| Sex (ref=female person) | 815 | – | one.l (0.83–2.70) | 1.30 (0.90–ane.88) | ii.59 (one.52–1.88) | 0.002 | |
| Hypertension | 788 | – | ane.42 (0.seventy–2.88) | one.56 (0.99–two.49) | 2.08 (i.23–3.53) | 0.035 | |
| Diabetes mellitus | 790 | – | 1.02 (0.12–viii.42) | two.87 (1.03–8.05) | 3.11 (0.97–10.00) | 0.003 | |
| Tobacco consumption | 783 | – | ane.32 (0.77–two.26) | 1.55 (1.09–2.xx) | 2.00 (one.30–3.09) | 0.004 | |
| BMI >25 kg/m2 | 815 | – | i.61 (0.94–2.74) | one.64 (one.17–ii.32) | 1.92 (one.26–2.94) | 0.002 | |
| Creatinine clearance ≤60 (mL·min–1·one.73 chiliad–2) | 815 | – | 1.57 (0.75–3.29) | i.08 (0.64–i.85) | 0.95 (0.48–1.89) | <0.001 | |
| Vascular cause of expiry | 815 | – | 1.11 (0.65–ane.88) | 1.56 (1.xi–2.19) | 1.75 (i.14–ii.68) | 0.013 | |
| Recipient | |||||||
| Age (10-y increment) | 815 | – | 0.98 (0.96–1.00) | 1.01 (0.99–1.02) | 1.01 (0.99–1.03) | 0.153 | |
| Sexual activity (ref=female) | 815 | – | ane.17 (0.62–ii.21) | one.50 (0.98–2.28) | 1.79 (1.02–iii.13) | 0.081 | |
| Ischemic cardiomyopathy | 815 | – | 0.57 (0.30–one.08) | ane.00 (0.70–1.45) | 1.64 (1.07–2.53) | 0.022 | |
| Previous history of hypertension | 797 | – | 0.77 (0.40–1.47) | 1.nineteen (0.81–1.75) | i.71 (i.08–2.lxx) | 0.081 | |
| BMI >25 kg/yard2 | 815 | – | i.11 (0.64–i.92) | 1.43 (one.01–ii.01) | i.77 (one.15–two.lxx) | 0.031 | |
| Transplant baseline | |||||||
| Cold ischemic time (per 1-h increment) | 815 | – | 1.08 (0.81–1.45) | 0.99 (0.82–1.18) | 0.87 (0.69–i.09) | 0.565 | |
| Combined transplantation | 815 | – | 0.66 (0.fifteen–ii.89) | 0.77 (0.32–1.83) | 0.38 (0.09–1.65) | 0.500 | |
| CMV mismatch (D+/R–) | 809 | – | i.xv (0.61–2.eighteen) | one.07 (0.71–1.63) | 0.82 (0.47–one.42) | 0.800 | |
| Immunosuppressive therapies | |||||||
| Interleukin-ii receptor inhibitor induction therapy (ref=ATG) | 815 | – | ane.18 (0.34–i.10) | 1.72 (0.83–1.56) | 1.64 (0.67–4.00) | 0.467 | |
| Tacrolimus at 1 y (ref=cyclosporine) | 815 | – | 0.51 (0.26–1.01) | 0.47 (0.30–0.72) | 0.36 (0.20–0.66) | <0.001 | |
| Everolimus therapy at i y | 815 | – | 1.54 (0.81–2.94) | 1.00 (0.63–1.60) | 0.87 (0.48–1.59) | 0.557 | |
| Cardiovascular risk cistron one-y postal service-HTx | |||||||
| Treated hypertension | 813 | – | 0.78 (0.44–i.36) | 1.x (0.75–ane.61) | ane.71 (1.01–2.90) | 0.123 | |
| Diabetes mellitus | 813 | – | 1.04 (0.53–2.03) | 1.52 (1.02–two.27) | 1.lxx (1.05–2.76) | 0.070 | |
| Statins | 789 | – | two.86 (0.87–9.43) | 0.79 (0.49–one.30) | ane.38 (0.68–2.81) | 0.090 | |
| LDL-c ≥1 m/L | 815 | – | ane.39 (0.82–ii.37) | 1.76 (1.25–two.49) | one.84 (one.20–2.82) | 0.002 | |
| Tobacco | 813 | – | two.02 (0.79–5.eighteen) | 1.06 (0.49–ii.27) | i.54 (0.67–3.55) | 0.462 | |
| Creatinine clearance >lx mL·min–1·one.73 m–two | 813 | – | ane.xxx (0.76–2.21) | 1.10 (0.78–i.54) | 0.55 (0.35–0.86) | 0.020 | |
| Immunology and histology | |||||||
| Anti–HLA DSA ( preexisting +1-y post-HTx) | 799 | – | 0.45 (0.21–0.97) | ane.49 (1.03–two.16) | 1.31 (0.82–ii.10) | 0.007 | |
| Anti-HLA DSA ( preexisting+1-y post-HTx | 798 | 0.025 | |||||
| Grade I | – | 0.51 (0.15–1.71) | one.08 (0.57–2.02) | 1.37 (0.67–2.80) | |||
| Class 2 | – | 0.42 (0.16–ane.10) | ane.72 (1.xiii–2.61) | 1.30 (0.75–2.25) | |||
| Astute cellular rejection ≥2R during the 1st y | 815 | – | 2.39 (i.23–iv.63) | 1.25 (0.75–2.10) | 1.79 (1.00–3.xviii) | 0.042 | |
| Antibody-mediated rejection ≥ pAMR1 during the 1st y | 608 | – | one.78 (0.82–3.84) | ane.21 (0.69–2.14) | 0.53 (0.22–ane.31) | 0.104 | |
Later on the multivariable analysis, the following independent candidate predictive variables of CAV trajectories were identified: (one) donor age (P<0.001), (2) donor sex (P<0.001), (3) donor tobacco consumption (P=0.001), (iv) recipient low-density lipoprotein cholesterol ≥1 g/dL ane year after transplantation (P=0.009), (5) recipient immunologic profile every bit divers by the presence of preexisting or de novo circulating class II anti–HLA donor-specific antibodies (P=0.004), and (vi) allograft injury defined past acute cellular rejection ≥2R occurring in the first year afterward transplantation (P=0.028; Table 3).
| Parameters | Trajectory i (north=459) | Trajectory 2 (n=62) | Trajectory three (n=188) | Trajectory four (north=106) | P Value |
|---|---|---|---|---|---|
| Donor | |||||
| Age (10-y increment) | – | one.27 (1.02–1.58) | 1.90 (one.62–2.24) | 2.17 (ane.75–2.70) | <0.001 |
| Sexual practice(ref=female person) | – | 1.66 (0.90–3.06) | 1.82 (1.23–1.71) | iii.76 (2.14–6.60) | <0.001 |
| Tobacco | – | 1.36 (0.79–2.34) | 1.72 (i.xix–2.48) | ii.23 (one.41–3.53) | 0.001 |
| Cardiovascular hazard cistron i-y posttransplant | |||||
| LDL-c ≥ane g/L | – | one.37 (0.80–2.35) | ane.71 (1.xix–2.46) | 1.79 (1.14–2.82) | 0.009 |
| Immunology and histology | |||||
| Anti-HLA DSA class Two, preexisting + 1-y mail-HTx (ref=none) | – | 0.47 (0.eighteen–i.22) | ane.95 (ane.25–three.04) | 1.47 (0.82–two.62) | 0.004 |
| Acute cellular rejection ≥2R during the 1st y | – | 2.39 (1.22–4.68) | 1.2 9 (0.75–two.21) | 1.98 (one.07–iii.65) | 0.028 |
Projection of Patient Individual x-Year CAV Trajectories Based on Risk Factors Assessed in the First Year After Transplantation
Based on the candidate variables independently associated with CAV trajectories identified in previous analyses, we congenital an online interface to provide clinicians with a ready-to-use tool that projects the CAV trajectories (https://transplant-prediction-organisation.shinyapps.io/CAV_trajectories/). Clinicians can enter the individual parameters of i patient, and 4 likelihoods of belonging for each trajectory are provided, corresponding to the personalized likely future CAV progression of the patient. A patient who is projected to belong to trajectory 1 at 1 yr has a probability of 0.07% of having a coronary angiography showing a CAV grade increment within 10 years of follow-up. Examples on the clinical application of trajectory in real-life patients is provided in Table Nine in the Data Supplement.
Clan Between CAV Trajectories and Overall-Cause Mortality
The trajectories were likewise associated with bloodshed. During the follow-upward, a full of 198 patients died. We found that CAV trajectories 3 and 4 were associated with higher mortality rates (10-twelvemonth patient survival of 73.43% [95% CI, 65.18–80.02] and 51.89% [95% CI, 38.76–63.51], respectively), in comparison with trajectories 1 and 2 that were characterized by 10-year patient survival of 80.01% (95% CI, 76.38–84.82) and 83.49% (95% CI, 71.34–90.eighty), respectively (log-rank test: P<0.001; Effigy 3).
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Figure 3. Overall x-twelvemonth survival probability according to the CAV trajectory in the overall cohort (n=1301). Trajectories 3 and 4 were associated with higher mortality rates (ten-year patient survival of 73.43% [95% CI, 65.18–fourscore.02] and 51.89% [95% CI, 38.76–63.51], respectively) in comparing with trajectories ane and 2 that were characterized by x-year patient survival of lxxx.01 (95% CI, 76.38–84.82) and 83.49% (95% CI, 71.34–90.80), respectively (P<0.001).
Sensitivity Analyses
Various sensitivity analyses were performed to examination the robustness and generalizability of the CAV trajectories and candidate predictive variables in different subpopulations and clinical scenarios.
CAV Trajectories and Center Effect
To take into business relationship a possible bias in the assessment of the candidate variables associated with the CAV trajectories, the center was likewise entered in the final multivariable model and did not modify the set of independent parameters associated with trajectories (Table 10 in the Data Supplement). Also, when the multivariable model was performed adding the Usa cohort to the derivation accomplice, the 6 contained candidate predictive variables that dominated in primary analyses remained unchanged (Table Xi in the Data Supplement).
Allowed Candidate Predictive Variables of CAV Trajectory
When histological antibody-mediated rejection was forced in the final multivariable model, it showed a trend for association (P=0.07) with CAV trajectory, merely was outperformed by the circulating anti-HLA DSA status.
Consistency of CAV Trajectories According to Timing of Outset CAV Assessment
We tested and confirmed the robustness of the profiles of CAV trajectories when the CAV evaluation started before ane twelvemonth after transplantation (bigotry=0.89). Nosotros also confirmed the consistency of the trajectories when the CAV was commencement assessed at 2 years afterward transplantation (bigotry=0.ninety).
Concluding, a similar iv-trajectory pattern was constitute when nosotros performed latent class mixed model analyses stratified by countries in the derivation cohort (France: Figure I in the Information Supplement; Belgium: Figure Ii in the Data Supplement).
Cytomegalovirus Status and CAV
We did non find an association betwixt donor (D) and recipient (R) cytomegalovirus status at the time of transplantation (ie, D–/R–, R+, D+/R–) with CAV trajectories. The occurrence of cytomegalovirus infection during the first year later transplantation (2 viral load thresholds tested: ≥10 000 UI/mL and ≥1000 UI/mL) was not associated with the CAV trajectories.
Proliferation Signal Inhibitors and CAV
We did non find any association betwixt the use of proliferation signal inhibitors at one yr after transplantation and CAV trajectories. The apply of proliferation signal inhibitors did not significantly differ between trajectories (Table 1; P=0.53). Multinomial logistic regression did not notice associations between proliferation signal inhibitor use at 1 year and CAV (P=0.56). Last, when the use of proliferation signal inhibitors was forced in the last multivariate multinomial regression model, the set of parameters independently associated with CAV found in main analyses was non changed.
Discussion
In this international study of 1301 advisedly phenotyped HTx recipients with protocol-based repeated assessment of CAV over ten years (4710 coronary angiographies), combined with histology (12 803 biopsies) and immune profiling, we identified iv distinct trajectories of long-term CAV progression for the offset time by using unsupervised approach. Nosotros demonstrated that the 4 trajectories were consistent in geographically distinct cohorts recruited in Europe and the The states. Nosotros found that these trajectories were quite varied for several unlike conditions related to donor and recipient characteristics, ongoing disease processes, and immunologic profiles that can be determined at an early stage after transplantation, identifying potential triggers for treatment. In addition, the patient trajectory cess that can exist performed at an early phase afterward transplantation may inform the blueprint and optimize stop signal definition of next-generation clinical trials.
Written report Strengths and Novelties
In this study, nosotros applied the following 3-step development procedure: identification of dissimilar CAV trajectories, identification of predictive variables associated with each trajectory, and development of a new risk-stratification tool.
Despite the importance of coronary angiography evaluation every bit standard of intendance after HTx, no study has characterized long-term trajectories using large unselected transplant cohorts with a prespecified longitudinal CAV monitoring together with a patient deep-level phenotyping, including donor and recipient characteristics, histology, immunology and handling based on multiple posttransplant evaluations. Across the novel study design, the original advantage of our trajectory approach over traditional assay is its ability to map the CAV course and classify individuals into singled-out groups. Hence, this approach non only helped to anticipate the long-term trajectories of CAV, but also immune us to probe the population heterogeneity and the susceptibility of change in CAV over the life class. Furthermore, nosotros believe that identifying the profiles of development in time of a chronic disease with an unsupervised approach and investigating whether these profiles manifest consistently beyond centers and countries may provide of import guidance on the potential universality of the disease and its determinants, which has great considerable implications for time to come research, in particular, for counseling patients, and too for choosing therapies. Our holistic approach immune for the evolution of an integrated individual take chances-stratification tool aimed at improving clinical management and follow-up.
Clinical Significance of the CAV Trajectories
The 4 distinct trajectories identified correspond unlike profiles of CAV development over time. CAV trajectory i is characterized by the absence of CAV over time, trajectory ii past a mild and late onset of CAV, trajectory three by an early onset with progressive evolution of CAV, and trajectory four by an early onset with rapid development of CAV. From a clinical perspective, those findings might help to refine CAV direction. All the same, the coronary angiography monitoring protocol could be adapted to the evolving gamble of CAV and, therefore, to the likelihood of belonging to each trajectory. Patients with a high likelihood of belonging to trajectory 1 might be monitored less intensively, resulting in cost savings and avoiding procedural risks. In contrast, patients with a high likelihood of belonging to trajectories iii and 4, at risk of CAV progression, might benefit from closer invasive follow-up and early stratification for apply of proliferation signal inhibitors that accept been demonstrated to reduce progression of CAV.19 These trajectories may also better prognosis stratification after heart transplantation because trajectories 3 and 4 are associated with a significantly higher risk of overall mortality.
Significance of the Allowed Predictive Characteristics Associated With CAV Trajectories
This study likewise emphasizes the interplay between donor and recipient characteristics, the allograft immune profile and existing allograft injury, and the long-term CAV trajectories. The associations between these characteristics and the CAV trajectories provide mechanistic insight. Class 2 DSA appears to exist an of import trigger of CAV in our accomplice. Both experimental and clinical studies support our findings.viii,20–25 The expression of grade II HLA antigens by the endothelial cells requires their activation. Traditional cardiovascular take a chance factors associated with trajectory iii, both derived from the donor and from the recipient, promote endothelial inflammation and activation, and increasing information suggest that the natural-killer cells play a critical part in the development of immunologic atherosclerosis.22,26,27
Nosotros found that acute cellular rejection ≥2R components is an contained hazard factor for CAV progression. On the one hand, astringent cellular rejection might acutely accelerate CAV past promoting local inflammation and the recruitment of other immune cells. On the other manus, cellular rejection has been recognized as a risk factor for the development of de novo DSA.28 In the French derivation cohort, patients with ≥1 episode of acute cellular rejection ≥2R were twice as likely to develop de novo DSA as patients without astute cellular rejection (17/threescore [25.3%] in comparison with 55/315 [14.9%]; P=0.01).
We establish marginal associations between antibody-mediated rejection and CAV trajectories in our cohort, contrasting with a robust association with the presence of preexisting or de novo anti-HLA DSA. DSA may be a more than sensitive mark of allosensitization than antibody-mediated rejection. Although astute and severe antibody-mediated rejection has been associated with fulminant forms of CAV,29 this entity is rare, and chronic allograft inflammation induced by allosensitization may be a trigger of CAV at the population level. Chronic antibody-mediated rejection has not been properly identified in middle allografts in contrast to kidney transplantation. This subclinical illness process might not be captured by 1-twelvemonth histology.
We did detect non an association betwixt cytomegalovirus status and CAV. Considering the present study gives a gimmicky motion-picture show of HTx, it might exist that prophylaxis strategies have modified the clinical expression and thereby the associations between cytomegalovirus and CAV.
Robustness of the four Trajectory Profiles and Prospects for Patient Risk Stratification and Patient Monitoring
It is remarkable that, despite the identification without any preconceptions of 4 CAV trajectories in the derivation cohort, very similar trajectories were too detected in the U.s. cohort. This robustness of the same set of trajectories and predictive variables for patients in different healthcare systems promotes the idea that the same contained factors drive the evolution of CAV across the globe. Although the proportions of patients in each trajectory differed between the cohorts, reflecting intrinsic variability in the demographics and HTx practices across nations, our results suggest using these profiles with HTx recipients first and then tailor the plan for futurity allograft monitoring and therapy based on the category. Consequently, to brand trajectory-based monitoring of patients viable in contemporary do, we adult an easy-to-use online interface that allows clinicians to predict the personalized probable future of CAV trajectory of any given patient.
This approach to determining patient trajectories brings a wider dimension to the traditional approaches that are either derived from 1 single coronary angiography result and selected but limited parameters, or are instead based on a few measurements in non–protocol-based cohorts. Taking into account the development of CAV in the long term could therefore have advantage of more information about subtle changes and could exist an important complement to current medical practices.
Although not validated by drug regulatory authorities, CAV is a major clinical end bespeak for past and ongoing clinical trials in heart transplantation.7,nineteen,30 Endocoronary imaging modalities, either intravascular ultrasound or optical coherence tomography, have the power to observe early coronary lesions while the coronary angiography remains normal. The change in maximal intimal thickness from baseline to 1 year or from ane to 5 years after transplantation has been associated with cardiovascular outcomes.31,32 All ongoing CAV-focused studies registered on clinicaltrials.gov (n=18) have an proliferation betoken inhibitors/optical coherence tomography end indicate. Even so, endocoronary imaging procedures are invasive, have specific complications, and are non available in all centers.33 Moreover, coronary angiography maintains the highest level of evidence for CAV monitoring, although the diagnosis is commonly made after several years of the affliction process.12 The latent class trajectory approach likewise holds hope as an outcome mensurate. For about of trials, the duration of follow-upward is too brief to expect a large change in CAV. This approach could also be used every bit a recruitment tool for prospective studies to enrich a population at loftier risk for CAV.
Study Limitations
Several limitations of the report should exist noted. Get-go, a limited number of trajectories were derived that may not accurately reflect every possible CAV profile. However, the good discrimination of our trajectory-building model indicates that these trajectories can parsimoniously summarize the predominant features of patient allografts in our population without a pregnant loss of information. Second, there are unmeasured characteristics of the recipients and allografts such equally non-HLA antibodies or inflammatory circulatory biomarkers that may well affect CAV trajectories and could be explored in futurity studies. Third, the lack of an early angiogram in the European cohort might be a limitation to interpreting the origin of coronary artery lesions. However, nosotros constitute strong correlations between donor cardiovascular take chances factors and 1-year angiograms, suggesting that donor-transmitted coronary artery disease accounts for a significant part of coronary lesions observed one year after transplantation. Fourth, although performed past an skillful interventional cardiologist, coronary angiograms may lack the sensitivity to detect early signs or lengthened patterns of CAV. The nowadays report was specifically designed based on angiography assessment, because it is broadly available and standard of practise with the advantage of having an international grading scheme. Still, we do agree that assessing CAV by angiogram may be less sensitive than proliferation betoken inhibitors. Fifth, because of the study design, we considered candidate risk factors assessed at the time of transplant, during the kickoff year or at 1 yr after transplantation. We acknowledge that the analysis of the impact of chance/protective factors beyond the beginning year afterward transplantation, such as the apply of proliferation signal inhibitors, remains an important question that deserves specific studies. Last, low-density lipoprotein levels and rejection processes were identified at 1 year subsequently transplantation equally stiff, independent, and potentially modifiable factors associated with the CAV development. Nonetheless, our written report was observational and not designed equally a clinical trial.
In determination, this assay takes an original arroyo to analyzing and characterizing, for the kickoff time, trajectories of long-term CAV after HTx associated with overall bloodshed. We likewise found that the identified CAV trajectories and their immune and nonimmune predictive variables could be generalizable and transportable across centers in different continents. Our results provide an important new tool for improving the arroyo to monitoring and risk stratifying middle transplant recipients, potentially paving the way toward a rationalization of the use of coronary angiograms and therapeutic interventions.
Acknowledgments
The authors thank F. Tacafred, P. Przednowed, and Due south. Messaoudi for their assist in data collection and C. Tritscher for her involvement in the Paris Transplant Group.
Sources of Funding
The study was funded by the Agence de la Biomédecine (2012) and by "MSD Avenir" grant iTransplant. Dr Loupy received funds from the French National Establish for Wellness and Medical Research (INSERM, ATIP-Avenir 2016).
Supplemental Materials
Expanded Methods and Materials
Data Supplement Figures I–Four
Data Supplement Tables I–XI
Appendix
Footnotes
References
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2.03 Quiz Theme And Characterization,
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