The COVID-19 pandemic has severely affected health systems and medical research worldwide but its impact on the global publication dynamics and non-COVID-19 research has not been measured. We ...hypothesized that the COVID-19 pandemic may have impacted the scientific production of non-COVID-19 research.
We conducted a comprehensive meta-research on studies (original articles, research letters and case reports) published between 01/01/2019 and 01/01/2021 in 10 high-impact medical and infectious disease journals (New England Journal of Medicine, Lancet, Journal of the American Medical Association, Nature Medicine, British Medical Journal, Annals of Internal Medicine, Lancet Global Health, Lancet Public Health, Lancet Infectious Disease and Clinical Infectious Disease). For each publication, we recorded publication date, publication type, number of authors, whether the publication was related to COVID-19, whether the publication was based on a case series, and the number of patients included in the study if the publication was based on a case report or a case series. We estimated the publication dynamics with a locally estimated scatterplot smoothing method. A Natural Language Processing algorithm was designed to calculate the number of authors for each publication. We simulated the number of non-COVID-19 studies that could have been published during the pandemic by extrapolating the publication dynamics of 2019 to 2020, and comparing the expected number to the observed number of studies.
Among the 22,525 studies assessed, 6319 met the inclusion criteria, of which 1022 (16.2%) were related to COVID-19 research. A dramatic increase in the number of publications in general journals was observed from February to April 2020 from a weekly median number of publications of 4.0 (IQR: 2.8-5.5) to 19.5 (IQR: 15.8-24.8) (p < 0.001), followed afterwards by a pattern of stability with a weekly median number of publications of 10.0 (IQR: 6.0-14.0) until December 2020 (p = 0.045 in comparison with April). Two prototypical editorial strategies were found: 1) journals that maintained the volume of non-COVID-19 publications while integrating COVID-19 research and thus increased their overall scientific production, and 2) journals that decreased the volume of non-COVID-19 publications while integrating COVID-19 publications. We estimated using simulation models that the COVID pandemic was associated with a 18% decrease in the production of non-COVID-19 research. We also found a significant change of the publication type in COVID-19 research as compared with non-COVID-19 research illustrated by a decrease in the number of original articles, (47.9% in COVID-19 publications vs 71.3% in non-COVID-19 publications, p < 0.001). Last, COVID-19 publications showed a higher number of authors, especially for case reports with a median of 9.0 authors (IQR: 6.0-13.0) in COVID-19 publications, compared to a median of 4.0 authors (IQR: 3.0-6.0) in non-COVID-19 publications (p < 0.001).
In this meta-research gathering publications from high-impact medical journals, we have shown that the dramatic rise in COVID-19 publications was accompanied by a substantial decrease of non-COVID-19 research. META-RESEARCH REGISTRATION: https://osf.io/9vtzp/ .
Although the gold standard of monitoring kidney transplant function relies on glomerular filtration rate (GFR), little is known about GFR trajectories after transplantation, their determinants, and ...their association with outcomes. To evaluate these parameters we examined kidney transplant recipients receiving care at 15 academic centers. Patients underwent prospective monitoring of estimated GFR (eGFR) measurements, with assessment of clinical, functional, histological and immunological parameters. Additional validation took place in seven randomized controlled trials that included a total of 14,132 patients with 403,497 eGFR measurements. After a median follow-up of 6.5 years, 1,688 patients developed end-stage kidney disease. Using unsupervised latent class mixed models, we identified eight distinct eGFR trajectories. Multinomial regression models identified seven significant determinants of eGFR trajectories including donor age, eGFR, proteinuria, and several significant histological features: graft scarring, graft interstitial inflammation and tubulitis, microcirculation inflammation, and circulating anti-HLA donor specific antibodies. The eGFR trajectories were associated with progression to end stage kidney disease. These trajectories, their determinants and respective associations with end stage kidney disease were similar across cohorts, as well as in diverse clinical scenarios, therapeutic eras and in the seven randomized control trials. Thus, our results provide the basis for a trajectory-based assessment of kidney transplant patients for risk stratification and monitoring.
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Why are there so few biomarkers accepted by health authorities and implemented in clinical practice, despite the high and growing number of biomaker studies in medical research ? In this ...meta-epidemiological study, including 804 studies that were critically appraised by expert reviewers, the authors have identified all prognostic kidney transplant biomarkers and showed overall suboptimal study designs, methods, results, interpretation, reproducible research standards, and transparency. The authors also demonstrated for the first time that the limited number of studies challenged the added value of their candidate biomarkers against standard-of-care routine patient monitoring parameters. Most biomarker studies tended to be single-center, retrospective studies with a small number of patients and clinical events. Less than 5% of the studies performed an external validation. The authors also showed the poor transparency reporting and identified a data beautification phenomenon. These findings suggest that there is much wasted research effort in transplant biomarker medical research and highlight the need to produce more rigorous studies so that more biomarkers may be validated and successfully implemented in clinical practice.
Despite the increasing number of biomarker studies published in the transplant literature over the past 20 years, demonstrations of their clinical benefit and their implementation in routine clinical practice are lacking. We hypothesized that suboptimal design, data, methodology, and reporting might contribute to this phenomenon.
We formed a consortium of experts in systematic reviews, nephrologists, methodologists, and epidemiologists. A systematic literature search was performed in PubMed, Embase, Scopus, Web of Science, and Cochrane Library between January 1, 2005, and November 12, 2022 (PROSPERO ID: CRD42020154747). All English language, original studies investigating the association between a biomarker and kidney allograft outcome were included. The final set of publications was assessed by expert reviewers. After data collection, two independent reviewers randomly evaluated the inconsistencies for 30% of the references for each reviewer. If more than 5% of inconsistencies were observed for one given reviewer, a re-evaluation was conducted for all the references of the reviewer. The biomarkers were categorized according to their type and the biological milieu from which they were measured. The study characteristics related to the design, methods, results, and their interpretation were assessed, as well as reproducible research practices and transparency indicators.
A total of 7372 publications were screened and 804 studies met the inclusion criteria. A total of 1143 biomarkers were assessed among the included studies from blood ( n =821, 71.8%), intragraft ( n =169, 14.8%), or urine ( n =81, 7.1%) compartments. The number of studies significantly increased, with a median, yearly number of 31.5 studies (interquartile range IQR, 23.8-35.5) between 2005 and 2012 and 57.5 (IQR, 53.3-59.8) between 2013 and 2022 ( P < 0.001). A total of 655 studies (81.5%) were retrospective, while 595 (74.0%) used data from a single center. The median number of patients included was 232 (IQR, 96-629) with a median follow-up post-transplant of 4.8 years (IQR, 3.0-6.2). Only 4.7% of studies were externally validated. A total of 346 studies (43.0%) did not adjust their biomarker for key prognostic factors, while only 3.1% of studies adjusted the biomarker for standard-of-care patient monitoring factors. Data sharing, code sharing, and registration occurred in 8.8%, 1.1%, and 4.6% of studies, respectively. A total of 158 studies (20.0%) emphasized the clinical relevance of the biomarker, despite the reported nonsignificant association of the biomarker with the outcome measure. A total of 288 studies assessed rejection as an outcome. We showed that these rejection studies shared the same characteristics as other studies.
Biomarker studies in kidney transplantation lack validation, rigorous design and methodology, accurate interpretation, and transparency. Higher standards are needed in biomarker research to prove the clinical utility and support clinical use.
The differential pathogenicity of anti-HLA donor-specific antibodies (DSAs) is not fully understood. The presence of complement-binding DSAs helps in better defining the prognosis of acute ...antibody-mediated rejection (ABMR). The evolution of these antibodies after the treatment of ABMR is unknown.
We included patients from the French multicenter RITUX ERAH study diagnosed with acute ABMR within the first year of renal transplantation, with circulating anti-HLA DSAs and treated randomly by rituximab or placebo (and intravenous immunoglobulins, plasma exchange). We centrally analyzed serum samples at the time of ABMR, 3 and 6 months after ABMR, with anti-HLA DSAs specificities and C1q-binding capacity assessment.
Twenty-five patients were included: 68% had C1q-binding DSAs at the time of ABMR. The presence of C1q-binding DSAs was associated with a poorer evolution of chronic glomerulopathy at 6 months (P = 0.036). The persistence of C1q-binding DSAs at 3 and/or 6 months after ABMR was associated with more severe chronic glomerulopathy (P = 0.006), greater C4d score deposition score at 6 months after ABMR (P = 0.008), and graft loss 5 years after ABMR (P = 0.029). C1q-binding capacity was associated with the DSA MFI but 5 C1q-binding DSAs in 4 patients had low MFI values without a prozone effect.
The presence and persistence of anti-HLA C1q-binding DSAs after ABMR is a detrimental marker, leading to transplant glomerulopathy and graft loss. Assessment of the complement-binding capacities of DSAs could help decide treatment intensification.
Alloimmune responses driven by donor-specific antibodies (DSAs) can lead to antibody-mediated rejection (ABMR) in organ transplantation. Yet, the cellular states underlying alloreactive B cell ...responses and the molecular components controlling them remain unclear. Using high-dimensional profiling of B cells in a cohort of 96 kidney transplant recipients, we identified expanded numbers of CD27+CD21- activated memory (AM) B cells that expressed the transcription factor T-bet in patients who developed DSAs and progressed to ABMR. Notably, AM cells were less frequent in DSA+ABMR- patients and at baseline levels in DSA- patients. RNA-Seq analysis of AM cells in patients undergoing ABMR revealed these cells to be poised for plasma cell differentiation and to express restricted IGHV sequences reflective of clonal expansion. In addition to T-bet, AM cells manifested elevated expression of interferon regulatory factor 4 and Blimp1, and upon coculture with autologous T follicular helper cells, differentiated into DSA-producing plasma cells in an IL-21-dependent manner. The frequency of AM cells was correlated with the timing and severity of ABMR manifestations. Importantly, T-bet+ AM cells were detected within kidney allografts along with their restricted IGHV sequences. This study delineates a pivotal role for AM cells in promoting humoral responses and ABMR in organ transplantation and highlights them as important therapeutic targets.
Abstract
Background
Clinical decisions are mainly driven by the ability of physicians to apply risk stratification to patients. However, this task is difficult as it requires complex integration of ...numerous parameters and is impacted by patient heterogeneity. We sought to evaluate the ability of transplant physicians to predict the risk of long-term allograft failure and compare them to a validated artificial intelligence (AI) prediction algorithm.
Methods
We randomly selected 400 kidney transplant recipients from a qualified dataset of 4000 patients. For each patient, 44 features routinely collected during the first-year post-transplant were compiled in an electronic health record (EHR). We enrolled 9 transplant physicians at various career stages. At 1-year post-transplant, they blindly predicted the long-term graft survival with probabilities for each patient. Their predictions were compared with those of a validated prediction system (iBox). We assessed the determinants of each physician’s prediction using a random forest survival model.
Results
Among the 400 patients included, 84 graft failures occurred at 7 years post-evaluation. The iBox system demonstrates the best predictive performance with a discrimination of 0.79 and a median calibration error of 5.79%, while physicians tend to overestimate the risk of graft failure. Physicians’ risk predictions show wide heterogeneity with a moderate intraclass correlation of 0.58. The determinants of physicians’ prediction are disparate, with poor agreement regardless of their clinical experience.
Conclusions
This study shows the overall limited performance and consistency of physicians to predict the risk of long-term graft failure, demonstrated by the superior performances of the iBox. This study supports the use of a companion tool to help physicians in their prognostic judgement and decision-making in clinical care.
AbstractObjectiveTo develop and validate an integrative system to predict long term kidney allograft failure.DesignInternational cohort study.SettingThree cohorts including kidney transplant ...recipients from 10 academic medical centres from Europe and the United States.ParticipantsDerivation cohort: 4000 consecutive kidney recipients prospectively recruited in four French centres between 2005 and 2014. Validation cohorts: 2129 kidney recipients from three centres in Europe and 1428 from three centres in North America, recruited between 2002 and 2014. Additional validation in three randomised controlled trials (NCT01079143, EudraCT 2007-003213-13, and NCT01873157).Main outcome measureAllograft failure (return to dialysis or pre-emptive retransplantation). 32 candidate prognostic factors for kidney allograft survival were assessed.ResultsAmong the 7557 kidney transplant recipients included, 1067 (14.1%) allografts failed after a median post-transplant follow-up time of 7.12 (interquartile range 3.51-8.77) years. In the derivation cohort, eight functional, histological, and immunological prognostic factors were independently associated with allograft failure and were then combined into a risk prediction score (iBox). This score showed accurate calibration and discrimination (C index 0.81, 95% confidence interval 0.79 to 0.83). The performance of the iBox was also confirmed in the validation cohorts from Europe (C index 0.81, 0.78 to 0.84) and the US (0.80, 0.76 to 0.84). The iBox system showed accuracy when assessed at different times of evaluation post-transplant, was validated in different clinical scenarios including type of immunosuppressive regimen used and response to rejection therapy, and outperformed previous risk prediction scores as well as a risk score based solely on functional parameters including estimated glomerular filtration rate and proteinuria. Finally, the accuracy of the iBox risk score in predicting long term allograft loss was confirmed in the three randomised controlled trials.ConclusionAn integrative, accurate, and readily implementable risk prediction score for kidney allograft failure has been developed, which shows generalisability across centres worldwide and common clinical scenarios. The iBox risk prediction score may help to guide monitoring of patients and further improve the design and development of a valid and early surrogate endpoint for clinical trials.Trial registrationClinicaltrials.gov NCT03474003.
Although antibody-mediated rejection (ABMR) has been long recognized as a leading cause of allograft failure after kidney transplantation, the cellular and molecular processes underlying the ...induction of deleterious donor-specific antibody (DSA) responses remain poorly understood.
Using high-dimensional flow cytometry,
assays, and RNA sequencing, we concomitantly investigated the role of T follicular helper (T
) cells and B cells during ABMR in 105 kidney transplant recipients.
There were 54 patients without DSAs; of those with DSAs, ABMR emerged in 20 patients, but not in 31 patients. We identified proliferating populations of circulating T
cells and activated B cells emerging in blood of patients undergoing ABMR. Although these circulating T
cells comprised heterogeneous phenotypes, they were dominated by activated (ICOS
PD-1
) and early memory precursor (CCR7
CD127
) subsets, and were enriched for the transcription factors IRF4 and c-Maf. These circulating T
cells produced large amounts of IL-21 upon stimulation with donor antigen and induced B cells to differentiate into antibody-secreting cells that produced DSAs. Combined analysis of the matched circulating T
cell and activated B cell RNA-sequencing profiles identified highly coordinated transcriptional programs in circulating T
cells and B cells among patients with ABMR, which markedly differed from those of patients who did not develop DSAs or ABMR. The timing of expansion of the distinctive circulating T
cells and activated B cells paralleled emergence of DSAs in blood, and their magnitude was predictive of IgG3 DSA generation, more severe allograft injury, and higher rate of allograft loss.
Patients undergoing ABMR may benefit from monitoring and therapeutic targeting of T
cell-B cell interactions.
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