Abstract
Background
We previously reported that the “Endothelial Activation and Stress Index” (EASIX; ((creatinine×lactate dehydrogenase)÷thrombocytes)) measured before start of conditioning predicts mortality after allogeneic hematopoietic stem cell transplantation (alloSCT) when used as continuous score. For broad clinical implementation, a prospectively validated EASIX-pre cut-off is needed that defines a high-risk cohort and is easy to use.
Method
In the current study, we first performed a retrospective cohort analysis in n=2022 alloSCT recipients and identified an optimal cut-off for predicting non-relapse mortality (NRM) as EASIX-pre=3. For cut-off validation, we conducted a multicenter prospective study with inclusion of n=317 first alloSCTs from peripheral blood stem cell in adult patients with acute leukemia, lymphoma or myelodysplastic syndrome/myeloproliferative neoplasms in the European Society for Blood and Marrow Transplantation network.
Results
Twenty-three % (n=74) of alloSCT recipients had EASIX-pre ≥3 taken before conditioning. NRM at 2 years was 31.1% in the high EASIX group versus 11.5% in the low EASIX group (p<0.001). Patients with high EASIX-pre also had worse 2 years overall survival (51.6% vs 70.9%; p=0.002). We were able to validate the cut-off and found that EASIX ≥3 was associated with more than twofold increased risk for NRM in multivariate analysis (HR=2.18, 95% CI 1.2 to 3.94; p=0.01). No statistically significant difference could be observed for the incidence of relapse.
Conclusions
The results of this study provide a prospectively validated standard laboratory biomarker index to estimate the transplant-related mortality risk after alloSCT. EASIX ≥3 taken before conditioning identifies a population of alloSCT recipients who have a more than twofold increased risk of treatment-related mortality.
Keywords: Inflammation, Hematologic Neoplasms, Immunotherapy
WHAT IS ALREADY KNOWN ON THIS TOPIC
The “Endothelial Activation and Stress Index” (EASIX; ((creatinine×lactate dehydrogenase)÷thrombocytes)) predicts survival in recipients of allogeneic hematopoietic stem cell transplantation (alloSCT). EASIX also predicted survival in patients with COVID-19 infection, sepsis, cancer or chimeric antigen receptor T-cell therapy.
WHAT THIS STUDY ADDS
This study defines and prospectively validates an EASIX cut-off ≥3 taken before conditioning to identify patients with a more than twofold increased risk of alloSCT-related mortality.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
EASIX ≥3 will more broadly be used in alloSCT and will be tested in combination with clinical scores to improve mortality risk assessment. This study will stimulate EASIX studies in different healthcare setting that are related to endothelial pathology, such as infection, inflammation, malignancies and immunotherapy.
Background
The main clinical challenge of allogeneic stem cell transplantation (alloSCT) is high treatment-associated mortality (non-relapse mortality (NRM)). Prediction of NRM is currently done by defining comorbidities, disease-specific risks and donor-related factors with indices such as the Hematopoietic Cell Transplantation-Comorbidity Index (HCT-CI),1 the European Society for Blood and Marrow Transplantation (EBMT)-score,2 3 the Dana-Farber Cancer Institute (DFCI)-score4 and a combination of such scores.5 Further improvement of pre-alloSCT risk assessment could facilitate clinical decision-making.
Endothelial dysfunction plays a crucial role in the pathophysiology of major complications contributing to NRM of alloSCT, such as sepsis, graft-versus-host disease (GVHD), sinusoidal obstruction syndrome (SOS) and transplant-associated microangiopathy.6–8 Risk assessment based on quantification of endothelial dysfunction prior to alloSCT is an attractive option that could help predicting alloSCT-associated mortality. Evidence is accumulating that pre-alloSCT measurement of patient-related endothelial risk factors, such as single-nucleotide-polymorphisms of the thrombomodulin and the CD40 ligand genes, complement activation-related genes, and angiopoetin-2 serum levels, can be used to predict outcome after acute GVHD.9–12 However, general clinical application of these markers for alloSCT risk assessment in the near future is hindered by a lack of standardization and cost-effectiveness.
We have therefore established a biomarker panel related to endothelial dysfunction for pretransplant OS prediction that consists of standardized routine laboratory parameters in order to enable broad clinical use. The “Endothelial Activation and Stress Index” (EASIX; ((lactate dehydrogenase [LDH]×creatinine)/thrombocytes)) taken before start of conditioning has been recently shown to predict the risk of death after alloSCT (EASIX-pre).13 In this previous project, the data analysis was performed with the continuous EASIX-pre score.
For this manuscript, we first analyzed a large retrospective alloSCT cohort to define an optimal EASIX-pre cut-off to predict NRM. The cut-off is very easy to use in clinical routine, as opposed to a continuous EASIX-pre score. We then prospectively validated the EASIX-pre cut-off within the EBMT network to facilitate broad clinical implementation.
Methods
Retrospective study
The basic methodology and transplant procedures for the retrospective cohorts are described in more detail elsewhere.13 For the present manuscript, we re-analyzed the retrospective data and included patients from four independent adult alloSCT cohorts. Cohort I contained 755 adult patients who had undergone alloSCT at the University of Heidelberg between 09/2001 and 06/2014. Cohort II was transplanted at the Charité, Campus Benjamin Franklin, Berlin between 08/1995 and 12/2011. Cohort III consisted of adult patients who had undergone alloSCT at the Seattle Fred Hutchinson Cancer Research Center between 01/2010 and 12/2013. Cohort IV consisted of adult patients transplanted between 01/2009 and 12/2013 at the University Hospital Essen.
Prospective study
Data source, study design and data collection
We asked EBMT centers performing more than 50 alloSCT per year if they were willing to participate in this prospective study. Nine centers in seven countries agreed to participate. Data were prospectively collected between 12/2017 and 3/2020 with a minimal follow-up of 365 days. Adults with acute leukemia, lymphoma or myelodysplastic syndrome (MDS) receiving a first alloSCT from peripheral blood were eligible. All types of conditioning and donors were allowed. Patients had to sign an informed consent document that permitted sharing of clinical data according to national rules. Basic data on patient and disease characteristics as well as longer term follow-up was taken from minimal essential data (MED-A) forms, which are submitted from all consecutive patients to the central EBMT registry. In addition, we designed registration and MED-B/C forms that were prospectively collected and specific to this study.
Endpoints and statistical analyses
Median follow-up time was estimated using the reverse Kaplan-Meier method. Primary endpoint was the incidence of NRM after alloSCT. Secondary endpoints were overall survival (OS), relapse-free survival (RFS), relapse incidence (RI), incidence and severity of acute GVHD and chronic GVHD.
NRM was defined as death without previous relapse. OS was defined as the time from alloSCT to death, regardless of the cause. RFS was defined as time from alloSCT to relapse or death from any cause. Acute GVHD was graded according to the modified Seattle-Glucksberg criteria14 and chronic GVHD according to the revised Seattle criteria.15 All outcomes were measured from the time of stem cell infusion. The probabilities of OS and RFS were calculated with the Kaplan-Meier test, and those of NRM, RI, acute and chronic GVHD, TMA, VOD and sepsis with the cumulative incidence estimator to accommodate for competing risks. For NRM, relapse was the competing risk, and for relapse, the competing risk was NRM. For acute and chronic GVHD, VOD, TMA and sepsis, death without the event and relapse were the competing risks.
For multivariate analysis, Cox proportional hazards regression models were used for OS and RFS. For competing outcomes like NRM, RI, GVHD and sepsis, cause-specific Cox proportional hazards regression models were used. Adjusting variables for multivariable analyses were: donor type (related vs unrelated), Disease Risk Index (DRI—divided in two categories: low and intermediate vs high and very high), patient age, sex (female to male vs other combination) and intensity of conditioning (reduced intensity conditioning (RIC) vs myeloablative conditioning (MAC)) (EBMT definition: MAC was defined as total body irradiation [TBI] >6 Gray or oral busulfan >8 mg/kg or intravenous busulfan >6.4 mg/kg). The definition of complete response excluded patients with incomplete regeneration of haematopoiesis (Cri).
EASIX was calculated by the formula: LDH (U/L)×creatinine (mg/dL)/thrombocytes (nL). To identify an optimal EASIX-pre cut-off for predicting NRM, we used maximally selected log-rank statistics. In addition, we applied conditional inference survival trees to account for differences in the four retrospective cohorts.16 17 The dichotomized EASIX-pre was then analyzed in univariable and multivariable analyses.
Results were expressed as the (cause-specific) HRs with 95% CI. Proportional hazards assumptions were checked systematically for all proposed models using the graphical test as proposed by Grambsch and Therneau.18 Statistical analyses were performed with R V.4.04 (R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/).
Results
Defining an optimal EASIX-pre cut-off to predict NRM in retrospective cohorts
Patient characteristics of the training cohort were already published.13 Median age of the combined adult cohorts I–IV was 53 (17–78) years, 842 (42%) were female patients, 761 (38%) had female donors. Matched related donors were used in 584 (29%), matched unrelated donors in 1074 (53%), mismatched donors in 330 (16%) of patients, whereas only 34 patients (2%) had haplo-identical donors. Diagnoses were mainly acute myeloid leukemia and MDSs (1260 (62%)), lymphoma (332 (16%)); acute lymphoblastic leukemia (183 (9%)) myeloproliferative neoplasms (MPN) (76 (4%)), multiple myeloma (152 (8%)) and aplastic anemia (18 (1%)). 601 (30%) of patients had high disease risk, and 504 (25%) had intermediate disease risk. Stem cell sources were bone marrow in 156 (8%) and peripheral blood stem cells in all others. RIC was received by 1471 (73%) patients.
Using maximally selected log-rank statistics and conditional inference survival trees16 17 with the endpoint NRM for the combined adult cohorts (n=2022) we identified an optimal cut-off point at EASIX-pre=3 (figure 1).
Figure 1.
Definition of an optimal cut-off point for non-relapse mortality at Endothelial Activation and Stress Index-pre=3 in the retrospective cohort.
Prospective study
Patient characteristics
We enrolled 317 patients. The main patients and transplant characteristics that were included in the analysis are described in table 1. We used the last EASIX-pre score that was measured in the individual patients within 30 days before start of conditioning.
Table 1.
Patient characteristics
| EASIX | |||||
| Variable | Level | <3 (n=241) | ≥3 (n=74) | Overall (n=317) | P value |
| Previous autologous transplantation(s) | No | 213 (88.4%) | 69 (93.2%) | 284 (89.6%) | 0.23 |
| Yes | 28 (11.6%) | 5 (6.8%) | 33 (10.4%) | ||
| Year of transplantation | Median (min–max) | 2018 (2017–2020) | 2018 (2017–2020) | 2018 (2017–2020) | 0.94 |
| (IQR) | (2018–2019) | (2018–2019) | (2018–2019) | ||
| Type of donor 1 | Identical | 77 (32.5%) | 23 (31.5%) | 100 (32.1%) | 0.81 |
| Sibling | 128 (54%) | 42 (57.5%) | 172 (55.1%) | ||
| Unrelated | 32 (13.5%) | 8 (11%) | 40 (12.8%) | ||
| Haplo missing | 4 | 1 | 5 | ||
| Diagnosis | Acute leukemia | 161 (66.8%) | 35 (47.3%) | 197 (62.1%) | <0.0001 |
| Lymphoma | 42 (17.4%) | 6 (8.1%) | 48 (15.1%) | ||
| MDS or MPN | 38 (15.8%) | 33 (44.6%) | 72 (22.7%) | ||
| Complete remission at transplant | CR | 163 (68.5%) | 20 (27.4%) | 184 (58.8%) | <0.0001 |
| No CR | 75 (31.5%) | 53 (72.6%) | 129 (41.2%) | ||
| Missing | 3 | 1 | 4 | ||
| DRI | Low–intermediate | 182 (75.5%) | 51 (68.9%) | 235 (74.1%) | 0.26 |
| High–very high | 59 (24.5%) | 23 (31.1%) | 82 (25.9%) | ||
| Low | 24 (10%) | 3 (4.1%) | 27 (8.5%) | ||
| Intermediate | 158 (65.6%) | 48 (64.9%) | 208 (65.6%) | ||
| High | 45 (18.7%) | 16 (21.6%) | 61 (19.2%) | ||
| Very high | 14 (5.8%) | 7 (9.5%) | 21 (6.6%) | ||
| Patient age (years) | Median (min–max) (IQR) |
51.1 (19.3–73.5) (38.3–60.7) |
58.8 (20.3–68.5) (51.3–63.6) |
54.6 (19.3–73.5) (41.5–61.1) |
0.0003 |
| Patient sex | Male | 133 (55.2%) | 50 (67.6%) | 183 (57.7%) | 0.059 |
| Female | 108 (44.8%) | 24 (32.4%) | 134 (42.3%) | ||
| Donor 1 sex | Male | 180 (75%) | 50 (67.6%) | 230 (72.8%) | 0.21 |
| Female | 60 (25%) | 24 (32.4%) | 86 (27.2%) | ||
| Missing | 1 | 0 | 1 | ||
| Patient cytomegaly virus | Negative | 50 (21%) | 8 (11.1%) | 59 (18.9%) | 0.059 |
| Positive | 188 (79%) | 64 (88.9%) | 253 (81.1%) | ||
| Missing | 3 | 2 | 5 | ||
| Donor cytomegaly virus | Negative | 89 (37.2%) | 23 (31.1%) | 114 (36.2%) | 0.33 |
| Positive | 150 (62.8%) | 51 (68.9%) | 201 (63.8%) | ||
| Missing | 2 | 0 | 2 | ||
| Karnofsky score | ≥90 | 133 (57.3%) | 31 (43.7%) | 165 (54.1%) | 0.043 |
| <90 | 99 (42.7%) | 40 (56.3%) | 140 (45.9%) | ||
| Missing | 9 | 3 | 12 | ||
| HCT-CI | 0 | 109 (46.2%) | 20 (27.8%) | 129 (41.6%) | 0.006 |
| 1–2 | 69 (29.2%) | 22 (30.6%) | 91 (29.4%) | ||
| 3+ | 58 (24.6%) | 30 (41.7%) | 90 (29%) | ||
| Missing | 5 | 2 | 7 | ||
| Intensity of conditioning | RIC | 85 (35.3%) | 43 (58.1%) | 129 (40.7%) | 0.0005 |
| MAC | 156 (64.7%) | 31 (41.9%) | 188 (59.3%) | ||
| Total body irradiation | No | 161 (66.8%) | 64 (86.5%) | 227 (71.6%) | 0.001 |
| Yes | 80 (33.2%) | 10 (13.5%) | 90 (28.4%) | ||
| In vivo T-cell depletion | ATG | 80 (33.2%) | 27 (36.5%) | 108 (34.1%) | 0.6 |
| No | 161 (66.8%) | 47 (63.5%) | 209 (65.9%) | ||
DRI, Disease Relapse Index; HCT-CI, Hematopoietic Cell Transplantation Comorbidity Index; MAC, myeloablative conditioning; MDS, myelodysplastic syndrome; MPN, myeloproliferative neoplasms; RIC, reduced intensity conditioning.
Patients were transplanted for acute leukemia (62.1%), MDS/MPN (22.7%) or lymphoma (15.1%), mainly from an unrelated donor (55.1%). Complete remission was achieved at transplant for 58.8%, leading to a higher proportion of low/intermediate DRI (74.1%). Patient median age was 54.6 years, with a majority of male recipients (57.7%) and donors (72.8%). MAC was more frequently performed (59.3%) than RIC, with high-dose TBI in 28.4%. ATG for GVHD prevention was given for 34.1%. Most parameters were balanced between the two cohorts. However, the following factors were higher in the EASIX-pre high group: patient age (median=58.8 vs 51.1 years, p<0.001), not in remission at transplant (72.6% vs 31.5%, p<0.001), diagnosis of MDS/MPN (44.6% vs 15.8%, p<0.001), RIC (58.1% vs 35.3%, p<0.001) and hematopoietic cell transplantation comorbidity index (HCT-CI ≥3, 41.7% vs 24.6%, p=0.006).
EASIX-pre is associated with NRM
The median follow-up time was 23.1 months (95% CI 18.8 to 24.4) in the low EASIX group and 23.6 months (95% CI 20.2 to 25.9) in the high EASIX group.
We found that 23% (n=74) of the 317 alloSCT recipients had EASIX ≥3 taken before conditioning. In univariate analysis NRM at 2 years was 31.1% (95% CI 20.1 to 42.8) in the high EASIX group (11.5% (95% CI 7.7 to 16.1) only in the low EASIX group) (figure 2A). Patients with high EASIX also had worse 2 years OS (51.6% (95% CI 40.6 to 65.6) vs 70.9% (95% CI 64.9 to 77.5)) (figure 2B) and 2 years progression-free survival (49.0% (95% CI 38.1 to 63.1) vs 61.4% (95% CI 55 to 68.5)). No statistically significant difference could be observed for the incidence of relapse. Major reasons for mortality were relapse of the original disease as well as infections in both groups (table 2).
Figure 2.
Univariate outcome graphs in patients with EASIX <3 versus EASIX ≥3 before allogeneic hematopoietic stem cell transplantation. EASIX, Endothelial Activation and Stress Index.
Table 2.
Reasons for deaths
| Cause of death | EASIX-pre low (n=63) |
EASIX-pre high (n=33) |
| Original disease | 39 (63%) | 13 (39%) |
| Infection | 15 (22%) | 9 (27%) |
| GVHD | 3 (5%) | 3 (9%) |
| Multiorgan failure | 0 (0%) | 3 (9%) |
| Secondary malignancy | 0 (0%) | 2 (6%) |
| Other | 5 (8%) | 3 (9%) |
| Missing | 1 | 0 |
EASIX, Endothelial Activation and Stress Index; GVHD, graft-versus-host disease.
However, NRM was responsible for death in 37% in the low EASIX group and in 61% in the high EASIX group, reflecting the increased NRM. To investigate if EASIX is associated with a certain type of NRM, we sub-classified NRM into infection-related, GVHD-related, multi-organ failure and secondary malignancy. TMA or VOD were not primary reasons for death. Results of table 2 show a higher percentage in all sub-categories in the high EASIX group. In multivariate analyses, we were able to validate the cut-off and found that EASIX ≥3 was associated with more than twofold increased risk for NRM (HR=2.18, 95% CI 1.2 to 3.94, p=0.01) (table 3).
Table 3.
Multivariate analyses of non-relapse mortality
| Variable | Level | HR | P value |
| EASIX before conditioning | Reference <3 | ||
| ≥3 | 2.18 (1.2 to 3.94) | 0.01 | |
| Type of donor | Reference related donor Unrelated donor |
0.59 (0.32 to 1.07) | 0.082 |
| Disease Relapse Index (DRI) | Reference: Low–intermediate High–very high |
1.67 (0.88 to 3.16) | 0.12 |
| Patient age (5 years increment) | 1.15 (1 to 1.32) | 0.05 | |
| Donor recipient gender difference | Reference: Female to male Other combination |
0.42 (0.22 to 0.82) | 0.01 |
| Intensity of conditioning | Reference: Reduced intensity Myeloablative |
0.82 (0.44 to 1.55) | 0.54 |
EASIX, Endothelial Activation and Stress Index.
Secondary outcome variables
There was no significant difference according to EASIX in incidence of acute GVHD II–IV (at d180: 22.1% (95% CI 17 to 27.7) vs 29.4% (95% CI 19.1 to 40.6) for EASIX <3 or >3 resp.). Interestingly, high-grade (3–4) acute GVHD differed in univariable analyses (at d180 5.8% (95% CI 3.2 to 9.3) vs 20.6% (95% CI 11.9 to 31) for EASIX <3 or ≥3, respectively). Multivariable Cox regression for high-grade acute GVHD was not possible due to low numbers of events. As expected, incidence of chronic GVHD did not differ between the two EASIX cohorts (at 24 months 15.5% (95% CI 7.8 to 25.6) vs 21.4% (95% CI 15.6 to 27.8) for EASIX <3 or ≥3, respectively).
Discussion
The results of the current study demonstrate that EASIX-pre ≥3 identifies a population of patients at high risk for alloSCT-related mortality. After having tested EASIX-pre before in different alloSCT cohorts,13 we now establish an easy to use cut-off. In the next step, we have validated this ≥3 cut-off in a multinational EBMT prospective study. EASIX-pre is now ready to be used in the clinical standard setting.
EASIX-pre has to be put in perspective with clinical scores estimating alloSCT-associated mortality. The HCT-CI focuses on patient-related factors and includes different pathological conditions.1 The EBMT score consists of patient and donor data including histocompatibility, stage of disease, age and sex of donor and recipient, and time from diagnosis to transplantation.2 3 A combination of both scores may even increase accuracy.5 The DFCI score focuses on disease and disease status to predict mortality.4 Comparing EASIX with the HCT-CI and EBMT scores, respectively, we observed an independent prognostic value of EASIX.13 Interestingly, there was a tendency of improved prediction when these scores were applied in combination.13 In the current analyses there was a higher share of HCT-CI high patients in the EASIX high cohort. However, the current study was not powered to explore synergies. Further analyses are needed to precisely define the synergies and overlaps between the scores.
EASIX is a prognostic rather than a diagnostic tool which is also emphasized by our observation that the marker does not associate with incidence of acute GVHD (II–IV), but has a connection to high-grade acute GVHD, that is, increased NRM after acute GVHD. EASIX was designed to be applicable with minimal costs or efforts in all transplantation centers. Endothelial dysfunction is a common physiopathological mechanism of several severe infectious and non-infectious alloSCT-related complications.6–8
Of note, we have previously shown the clinical utility of EASIX as a prognostic marker in patients with acute GVHD,19 and for prediction of risk of sepsis,20 SOS/VOD21 and transplantation-associated microangiopathy.13 Our results underline the clinical importance of endothelial dysfunction for complications after alloSCT. However, the clinical significance of EASIX as a prognostic tool is not restricted to the alloSCT setting. Recent data demonstrate that EASIX can be used also in other endothelium-related syndromes to predict mortality, such as lower-risk MDSs,22 diffuse large B cell lymphoma,23 multiple myeloma,24 SARS-CoV-2 infections25 26 or CAR-T cell therapy.27 28
A strength of our study is the simplicity of the approach, the retrospective validation in large cohorts of alloSCT recipients13 as well as the current prospective validation of the clinical useful ≥3 EASIX-pre cut-off. A limitation and possible bias is that the conditioning regimens were variable and we do not have information why investigators decided on RIC in some patients. This bias can only be addressed in a randomized study. Another limitation is that the applicability of EASIX-pre has not been shown for pediatric transplants. This is less relevant here since our current study focused on adult patients only, but this also implies that EASIX-pre ≥3 is only ready to be used in the clinical standard setting for adult patients.
In summary, the results of this study provide a prospectively validated standard laboratory biomarker index to estimate transplant-related mortality after alloSCT. EASIX ≥3 taken before conditioning identifies a population of adult alloSCT recipients who have a more than twofold increased risk of treatment-related mortality.
Footnotes
Contributors: CP performed statistical analyses. OP, TL, ZP designed the study and wrote the manuscript. All authors performed research read, edited and approved the manuscript. OP is the guarantor and accepts full responsibility for the work and/or the conduct of the study, had access to the data, and controlled the decision to publish.
Funding: The authors thank the following funding agencies for supporting this work: José Carreras Leukämie-Stiftung (3R/2019, 23R/2021), Deutsche Krebshilfe (70113519), Deutsche Forschungsgemeinschaft (PE 1450/7-1, PE 1450/9-1) and Stiftung Charité BIH (BIH_PRO_549, Focus Group Vascular Biomedicine).
Competing interests: OP has received honoraria or travel support from Gilead, Jazz, MSD, Novartis, Pfizer and Therakos. He has received research support from Incyte and Priothera. He is a member of advisory boards to Equillium Bio, Jazz, Gilead, Novartis, MSD, Omeros, Priothera, Sanofi, Shionogi and SOBI. HS reports having received personal fees from Incyte, Janssen, Novartis, Sanofi and from the Belgian Hematological Society (BHS), as well as research grants from Novartis and the BHS, all paid to her institution. She has also received non-financial support from Gilead, the EBMT (European Society for Blood and Marrow transplantation) and the CIBMTR (Center for International Bone Marrow Transplantation Research).
Provenance and peer review: Not commissioned; externally peer reviewed.
Data availability statement
Data are available upon reasonable request.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
This study involves human participants and was approved by Charité Ethics Committee (approval number: EA2/24/17). Data collection for the EBMT registry was approved by the IRB and/or Ethics Committee in all centers. Written informed consent according to the Declaration of Helsinki was obtained in all eligible patients.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data are available upon reasonable request.