Innovation In Clinical Research Research Articles

Trophectoderm grade is associated with the risk of placenta previa in frozen-thawed single-blastocyst transfer cycles.

Do obstetric and perinatal complications vary according to different blastocyst developmental parameters after frozen-thawed single-blastocyst transfer (SBT) cycles? Pregnancies following the transfer of a blastocyst with a grade C trophectoderm (TE) were associated with an increased risk of placenta previa compared to those with a blastocyst of grade A TE. Existing studies investigating the effect of blastocyst morphology grades on birth outcomes have mostly focused on fetal growth and have produced conflicting results, while the risk of obstetric complications has rarely been reported. Additionally, growing evidence has suggested that the appearance of TE cells could serve as the most important parameter for predicting implantation and live birth. Given that the TE ultimately develops into the placenta, it is plausible that this independent predictor may also impact placentation. This retrospective cohort study at a tertiary-care academic medical center included 6018 singleton deliveries after frozen-thawed SBT cycles between January 2017 and December 2021. Singleton pregnancies were grouped into two groups according to blastocyst developmental stage (Days 5 and 6), four groups according to embryo expansion (Stages 3, 4, 5, and 6), three groups according to inner cell mass (ICM) quality (A, B, and C), and three groups according to TE quality (A, B, and C). The main outcomes included pregnancy-induced hypertension, preeclampsia, gestational diabetes mellitus, preterm premature rupture of membrane, placenta previa, placental abruption, placenta accreta, postpartum hemorrhage, preterm birth, low birth weight, small for gestational age, and birth defects. Multivariate logistic regressions were performed to evaluate the effect of blastocyst developmental stage, embryo expansion stage, ICM grade, and TE grade on measured outcomes adjusting for potential confounders. No association was found between blastocyst developmental stage and obstetric or perinatal outcomes both before and after adjusting for potential confounders, and similar results were found with regard to embryo expansion stage and ICM grade. Meanwhile, the incidence of placenta previa derived from a blastocyst with TE of grade C was higher compared with those derived from a blastocyst with TE of grade A (1.7%, 2.4%, and 4.0% for A, B, and C, respectively, P = 0.001 for all comparisons). After adjusting for potential covariates, TE grade C blastocysts had 2.81 times the likelihood of resulting in placenta previa compared to TE grade A blastocysts (adjusted odds ratio 2.81, 95% CI 1.11-7.09). No statistically significant differences were detected between any other measured outcomes and TE grades both before or after adjustment. The study is limited by its retrospective, single-center design. Additionally, although the sample size was relatively large for the study group, the sample size for certain subgroups was relatively small and lacked adequate power, particularly the ICM grade C group. Therefore, these results should be interpreted with caution. The study extends our knowledge of the potential downstream effect of TE grade on placental abnormalities. This study was supported by the National Key Research and Development Program of China (2023YFC2705500, 2023YFC2705501, 2023YFC2705505, 2019YFA0802604); National Natural Science Foundation of China (82130046, 82320108009, 82371660, 32300710); Shanghai leading talent program, Innovative research team of high-level local universities in Shanghai (SHSMU-ZLCX20210201, SHSMU-ZLCX20210200, SHSMU-ZLCX20180401), Shanghai Jiaotong University School of Medicine Affiliated Renji Hospital Clinical Research Innovation Cultivation Fund Program (RJPY-DZX-003), Science and Technology Commission of Shanghai Municipality (23Y11901400), Shanghai's Top Priority Research Center Construction Project (2023ZZ02002), and Three-Year Action Plan for Strengthening the Construction of the Public Health System in Shanghai (GWVI-11.1-36). The authors have no conflicts of interest to declare. N/A.

Development and validation of a deep learning-based framework for automated lung CT segmentation and acute respiratory distress syndrome prediction: a multicenter cohort study

Acute respiratory distress syndrome (ARDS) is a life-threatening condition with a high incidence and mortality rate in intensive care unit (ICU) admissions. Early identification of patients at high risk for developing ARDS is crucial for timely intervention and improved clinical outcomes. However, the complex pathophysiology of ARDS makes early prediction challenging. This study aimed to develop an artificial intelligence (AI) model for automated lung lesion segmentation and early prediction of ARDS to facilitate timely intervention in the intensive care unit. A total of 928 ICU patients with chest computed tomography (CT) scans were included from November 2018 to November 2021at three centers in China. Patients were divided into a retrospective cohort for model development and internal validation, and three independent cohorts for external validation. A deep learning-based framework using the UNet Transformer (UNETR) model was developed to perform the segmentation of lung lesions and early prediction of ARDS. We employed various data augmentation techniques using the Medical Open Network for AI (MONAI) framework, enhancing the training sample diversity and improving the model's generalization capabilities. The performance of the deep learning-based framework was compared with a Densenet-based image classification network and evaluated in external and prospective validation cohorts. The segmentation performance was assessed using the Dice coefficient (DC), and the prediction performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The contributions of different features to ARDS prediction were visualized using Shapley Explanation Plots. This study was registered with the China Clinical Trial Registration Centre (ChiCTR2200058700). The segmentation task using the deep learning framework achieved a DC of 0.734±0.137 in the validation set. For the prediction task, the deep learning-based framework achieved AUCs of 0.916 [0.858-0.961], 0.865 [0.774-0.945], 0.901 [0.835-0.955], and 0.876 [0.804-0.936] in the internal validation cohort, external validation cohort I, external validation cohort II, and prospective validation cohort, respectively. It outperformed the Densenet-based image classification network in terms of prediction accuracy. Moreover, the ARDS prediction model identified lung lesion features and clinical parameters such as C-reactive protein, albumin, bilirubin, platelet count, and age as significant contributors to ARDS prediction. The deep learning-based framework using the UNETR model demonstrated high accuracy and robustness in lung lesion segmentation and early ARDS prediction, and had good generalization ability and clinical applicability. This study was supported by grants from the Shanghai Renji Hospital Clinical Research Innovation and Cultivation Fund (RJPY-DZX-008) and Shanghai Science and Technology Development Funds (22YF1423300).

North American Cancer Center Clinical Research Capacity and Benchmarking in the Postpandemic Era.

Cancer center clinical trial offices (CCTOs) support trial development, activation, conduct, regulatory adherence, data integrity, and compliance. In 2018, the Association of American Cancer Institutes (AACI) Clinical Research Innovation (CRI) Steering Committee conducted and published survey results to benchmark North American CCTOs, including trial volume, accrual, full time equivalents (FTEs), and budget. The survey was readministered in 2023 to assess contemporary CCTO performance and capacity with results presented here. The 28 question 2023 survey was sent to directors of AACI's clinical member cancer centers. Survey participation was voluntary, no compensation was provided, and data requested covered operations during 2022. Definitions were consistent with National Cancer Institute (NCI) CCTO reporting requirements and AACI staff anonymously compiled results for descriptive statistical reporting. The survey response rate was 61% (60/99). The median annual CCTO budget was $11.5 million (M) US dollars (USD) versus $8.2M USD in 2018. These budgets support a median of 150 FTEs versus 104 previously, and a median total of 384 versus 280 interventional treatment trials and a median of 479 versus 531 interventional treatment accruals. Sources of support for CCTO annual budgets were primarily from industry revenue (45.3%) or institutional support (31.7%). Nearly 60% of centers reported activating NCI-sponsored studies within 90 days but only 9% reported meeting a 90-day activation timeline for industry sponsored studies. Contemporary benchmarks for CCTO operations through this survey demonstrate larger staff sizes, larger budgets, more trials supported, but fewer patients enrolled to interventional treatment trials in comparison with 2018. These data shine a critical light on the increasing complexity of cancer clinical trials, the importance of external funding sources, and necessary operational efficiency upgrades to provide cutting-edge cancer research and care.

Ovarian ferroptosis induced by androgen is involved in pathogenesis of PCOS.

Does ovarian ferroptosis play an active role in the development of polycystic ovary syndrome (PCOS)? Increased ovarian ferroptosis was present in PCOS ovaries and the inhibition of ferroptosis with ferrostatin-1 (Fer-1) ameliorated polycystic ovary morphology and anovulation. Programmed cell death plays a fundamental role in ovarian follicle development. However, the types and mechanisms of cell death involved in the ovary are yet to be elucidated. Ferroptosis is a recently discovered iron-dependent programmed cell death. Impaired iron metabolism and cell death have been observed in women with PCOS, the main cause of anovulatory infertility. Additionally, previous studies reported that an abnormal expression of noncoding RNA may promote ferroptosis in immortalized ovarian granulosa cell lines. However, little is known about whether ovarian ferroptosis is increased in PCOS, and there is insufficient direct evidence for a role of ferroptosis in PCOS, and the underlying mechanism. Moreover, the effect of the inhibition of ferroptosis with Fer-1 in PCOS remains unclear. Ferroptosis was evaluated in human granulosa cells (hGCs) from non-PCOS (n = 6-16) and PCOS (n = 7-18) patients. The experimental study was completed in vitro using primary hGCs from women undergoing IVF. Improvements in PCOS indicators following ferroptosis inhibition with Fer-1 were investigated in a dehydroepiandrosterone (DHEA)-induced PCOS rat model (n = 8 per group). Ovarian ferroptosis was evaluated in the following ways: by detecting iron concentrations via ELISA and fluorescent probes; measuring malondialdehyde (MDA) concentrations via ELISA; assessing ferroptosis-related protein abundance with western blotting; observing mitochondrial morphology with transmission electron microscopy; and determining cell viability. Primary hGCs were collected from women undergoing IVF. They were treated with dihydrotestosterone (DHT) for 24 h. The effect of DHT on ferroptosis was examined in the presence or absence of small interfering RNA-mediated knockdown of the putative receptor coregulator for signaling molecules. The role of ovarian ferroptosis in PCOS progression was explored in vivo in rats. The DHEA-induced PCOS rat model was treated with the ferroptosis inhibitor, Fer-1, and the oocytes and metaphase II oocytes were counted after ovarian stimulation. Additionally, rats were treated with the ferroptosis inducer, RSL3, to further explore the effect of ferroptosis. The concentrations of testosterone, FSH, and LH were assessed. Increased ferroptosis was detected in the ovaries of patients with PCOS and in rats with DHEA-induced PCOS. Increased concentrations of Fe2+ (P < 0.05) and MDA (P < 0.05), and upregulated nuclear receptor coactivator 4 protein levels, and downregulated ferritin heavy chain 1 (FTH1) and glutathione peroxidase 4 (GPX4) proteins were observed in the hGCs in patients with PCOS and ovaries of PCOS rats (P < 0.05 versus control). DHT was shown to induce ferroptosis via activation of NOCA4-dependent ferritinophagy. The inhibition of ferroptosis with Fer-1 in rats ameliorated a cluster of PCOS traits including impaired glucose tolerance, irregular estrous cycles, reproductive hormone dysfunction, hyperandrogenism, polycystic ovaries, anovulation, and oocyte quality (P < 0.05). Treating rats with RSL3 resulted in polycystic ovaries and hyperandrogenism (P < 0.05). N/A. Although ovarian-targeted ferroptosis inhibition may be a more targeted treatment for PCOS, the underlying mechanisms in the cycle between ferroptosis and hyperandrogenism require further exploration. Additionally, since PCOS shows high heterogeneity, it is important to investigate whether ferroptosis increases are present in all patients with PCOS. Androgen-induced ovarian ferroptosis appears to play a role in the pathogenesis of PCOS, which potentially makes it a promising treatment target in PCOS. This study was supported by the National Key R&D Program of China (2023YFC2705500, 2023YFC2705505, 2019YFA0802604), National Natural Science Foundation of China (No. 82130046, 82320108009, 82101708, 82101747, and 82001517), Shanghai leading talent program, Innovative research team of high-level local universities in Shanghai (No. SHSMU-ZLCX20210201, No. SSMU-ZLCX20180401), Shanghai Jiaotong University School of Medicine, Affiliated Renji Hospital Clinical Research Innovation Cultivation Fund Program (RJPY-DZX-003) and Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (No. 20161413), Shanghai's Top Priority Research Center Construction Project (2023ZZ02002), and Three-Year Action Plan for Strengthening the Construction of the Public Health System in Shanghai (GWVI-11.1-36). The authors report no competing interests.

Synergizing AI and Healthcare: Pioneering Advances in Cancer Medicine for Personalized Treatment

This paper investigates how Artificial Intelligence (AI) is changing the field of cancer medicine. It is organized into nine major sections that illustrate the profound effects of AI on different aspects of cancer care. Starting from the early phases of the disease, AI shows how it can transform conventional diagnostic methods by providing quick and accurate analyses of medical imaging, pathology slides, and genetic data. The paper then goes into the era of personalized cancer therapies, highlighting the ways in which AI helps to customize treatment based on individual genetic and molecular profiles. Finally, the paper discusses the smart revolution in healthcare, which is driven by AI integration, highlighting the impact of AI on diagnosis precision, treatment optimization, and resource allocation. Moreover, the story delves into how AI is being incorporated into healthcare outside of diagnosis and treatment, including areas like predictive modeling, ongoing monitoring, and after-treatment care. AI has the capacity to revolutionize cancer medicine by improving current practices and fostering innovation in clinical research, diagnosis modalities, and treatment planning. The paper highlights the revolutionary boundaries that AI has created, including liquid biopsies, virtual tumor boards, and the speeding up of drug discovery processes. The narrative weaves a thorough overview of AI's transformative journey in cancer care, offering insights into its current impact and the promising possibilities that lie ahead.

Exploring the role of decentralized clinical trials in oncology: Insights from cancer centers.

105 Background: Decentralized clinical trials (DCT) involve conducting some or all trial-related activities at a location separate from the investigator's site. DCT can help reduce the burden on patients, sites, and sponsors while improving patient accrual and retention rates and increasing the diversity of trial participants. While DCT methods provide patient-centric trial flexibilities, they can impact trial operations. This survey aimed to gain insights into the perspectives and experiences of cancer centers regarding DCT in the field of oncology. Methods: A 13-question survey that addressed prior experience, perceived benefits, and challenges in implementing DCT was developed by members of the Association of American Cancer Institutes (AACI) Clinical Research Innovation (CRI) Steering Committee. The survey was sent via email to the AACI CRI listserv, which consists of members of 90 cancer research centers in North America. Respondents were asked to consider their activity related to treatment and non-treatment interventional trials. Results: There were 75 total responses from members affiliated with 55 individual cancer centers. Of these centers, 73% were NCI-designated cancer centers. Of the individual respondents, 57% were clinical trial office administrators. The issues of highest concern by respondents were 1) meeting study timelines, 2) patient recruitment and 3) cost (83%, 65%, 44% respectively). All respondents had prior experience with DCT tools with the most common components being the use of local facilities for labs/imaging, telemedicine and e-consent (89%, 69%, 68% respectively). The areas of most perceived benefit from DCT include reduced participant burden, increased diversity and participant retention (81%, 80%, 68% respectively) while areas of perceived challenges include quality control, regulatory compliance and cost (61%, 59%, 49% respectively). While 92% respondents agreed/strongly agreed that DCT can improve clinical trial access, 92% agreed/strongly agreed that additional site staff training is required for successful DCT implementation. Conclusions: DCT tools have been widely implemented in oncology trials with all surveyed sites reporting prior experience. This survey emphasizes the perceived benefits of DCT in oncology, while underscoring the importance of addressing challenges and meeting the specific requirements of cancer centers. Future work should identify specific areas that require additional staff training to enable DCT readiness.

Innovation and strategy in clinical research with the SCIOSM method

Innovation in clinical research is a long-term need due to several factors, including the high failure rate and skyrocketing costs. Healthcare has been changing rapidly for many years. The COVID-19 pandemic has accelerated by helping drive the shift to value-based healthcare. Instead, the productivity of pharmaceutical development has been declining due to high failure rates in clinical research. The SCIO method is an innovative and strategic method implementing a tailored approach to drug development. The SCIO method customizes strategy and roadmap for pharmaceutical and biological candidates allowing robust clinical data generation and informed decision-making during the early clinical development stages. The complexity of diseases contributes to the high failure rate and increases the cost of research. Clinical research in developing new drugs and treatments is associated with a high late failure rate. Late-stage trial failures can delay patients' access to potentially lifesaving or disease-modifying treatments. Strategy is valuable in reducing late clinical trial failure. A well-developed and carefully executed strategy helps mitigate risks, optimize trial design and execution, and increase the chances of successful outcomes. A failed trial can harm the credibility by raising concerns about the research methods, the quality of the data, or the reliability of the therapy under investigation. The SCIO is an innovative drug development method, including a strategic approach before initiating clinical trials that ensure a solid scientific basis and a reasonable likelihood of success in human trials.

The COVID-19 pandemic as a catalyst for innovation: a regulatory framework to assess fit-for-purpose innovative approaches in clinical research

The COVID-19 pandemic has had a devastating impact on individuals and multiple aspects of our society including healthcare and clinical research. The silver lining is that the pandemic also served as a catalyst for wider adoption of innovative approaches in clinical research, notably the use of mobile or remote services, and digital technologies. Regulators, clinical study investigators, clinical study participants, sponsors, and other stakeholders collaborated to adopt measures that ensured safe participation in clinical studies whilst maintaining study integrity. In this article, we propose a regulatory framework for assessing fit-for-purpose innovative approaches in clinical research based on Roche/Genentech’s experience during the COVID-19 pandemic with the aim to inform and encourage broader implementation of patient-centric and sustainable innovation in clinical research. Our goal is to contribute to ongoing discussions on introducing innovative approaches in clinical trials and eventually the development of globally harmonised guidelines.

Toxicity spectrum of immunotherapy in advanced lung cancer: A safety analysis from clinical trials and a pharmacovigilance system.

SummaryBackgroundWith the increased use of immune checkpoint inhibitors (ICIs) in advanced lung cancer, adverse events (AEs), particularly immune-related AEs (irAEs), have garnered considerable interest. We conducted a comprehensive assessment of the toxicity profile in advanced lung cancer using multi-source medical data.MethodsFirst, we systematically searched the PubMed, Embase, and Cochrane Library databases (from inception to 10 August 2021) for relevant randomised controlled trials (RCTs) involving ICI-based treatments for advanced lung cancer. The primary outcomes were treatment-related AEs and irAEs, including events that were assigned grade 1–5 and 3–5. The secondary outcomes were grade 5 AEs and irAEs (grade 1–5 and grade 3–5) in specific organs. Network comparisons were conducted for 11 treatments, including chemotherapy (CT), ICI monotherapy (three regimens: programmed death-1 receptor [PD-1] inhibitors, programmed death ligand-1 [PD-L1] inhibitors, and cytotoxic T lymphocyte-associated antigen [CTLA-4] inhibitors), dual-ICI combination therapy (two regimens), and treatment using one or two ICI drugs administered in combination with CT (five regimens). We also conducted a disproportionality analysis by extracting reports of various irAEs associated with ICIs from the FDA Adverse Event Reporting System (FAERS) database. The reporting odds ratios and fatality proportions of different irAEs were calculated and compared. PROSPERO: CRD42021268650.FindingsOverall, 41 RCTs involving 23,121 patients with advanced lung cancer were included. Treatments containing chemotherapy increased the risk of treatment-related AEs compared to ICI-based regimens without chemotherapy. Concerning irAEs, PD-L1 + CTLA-4 + CT was associated with the highest risk of grade 1–5 irAEs, followed by two regimens of dual ICI combination, three regimens of ICI monotherapy, and three regimens of one ICI combined with CT. For 3–5 irAEs, CTLA-4 accounted for most AEs. Detailed comparisons of ICI-based treatment options provided irAE profiles based on specific organs/systems and AE severity. Insights from the FAERS database revealed that signals corresponding to pneumonitis, colitis, thyroiditis, and hypophysitis were observed across all ICI regimens. Further analyses of the outcomes indicated that myocarditis (163 of 367, 44.4%), pneumonitis (1610 of 4497, 35.8%), and hepatitis (290 of 931, 31.1%) had high fatality rates.InterpretationIncluded RCTs showed heterogeneity in a few clinical factors, and reports derived from the FAERS database might have involved inaccurate data. Our results can be used as a basis for improving clinical treatment strategies and designing preventive methods for ICI treatment in advanced lung cancer.FundingThis study was supported by the Research Project of Drug Clinical Comprehensive Evaluation and Drug Treatment Pathway (SHYXH-ZP-2021-001, SHYXH-ZP-2021-006), Clinical Research Innovation and Cultivation Fund of Ren Ji Hospital (RJPY-LX-008), Ren Ji Boost Project of National Natural Science Foundation of China (RJTJ–JX–001), and Shanghai “Rising Stars of Medical Talent” Youth Development Program – Youth Medical Talents – Clinical Pharmacist Program (SHWJRS (2019) 072).

Opportunities and Challenges of Swarm AI for Decentralized Clinical Research

Swarm learning opens new opportunities for collaboration and innovation in clinical research where all members of the swarm have equal rights. Only algorithms and parameters are shared – with no central authority. Swarm creates many new opportunities in clinical research to develop new therapeutics, epidemiology, genetics research and more. This session will unlock swarm principals, challenges and opportunities in clinical R&amp;D driving wider adoption of its application(s) in the health domain. Key learnings will include: Understanding the differences between federated machine vs swarm learning The technical, policy and application barriers when it relates to transferring significant amounts of data The steps and frameworks needed to drive wider understanding and adoption of the technology in clinical R&amp;D The pharma perspective: where does Swarm AI offer new solutions yet to be introduced to the drug development process

Beneficial or an unknown risk: Immunization hesitancy in Indian population

The most victorious intervention in field of Public Health is immunization. It prevents 2-3 million deaths every year from diseases like diphtheria, tetanus, pertussis, influenza, measles and currently to overcome the novel corona virus infection an effective vaccine is the most anticipated resolution. Despite the advancements and innovations in clinical research and healthcare, vaccine hesitancy is a threat globally. Social media has provided unmatched capacity for people to communicate but has also been a major tool for rapid spread of misconceptions and disingenuous information damaging to public health. This article aims to give an overview of vaccine hesitancy of various infectious diseases, people’s perception towards it, how social media has facilitated this movement and how to eliminate the misconception.

Enhancing Patient Centricity and Advancing Innovation in Clinical Research with Virtual Randomized Clinical Trials (vRCTs)

The current public health crisis has highlighted the need to accelerate healthcare innovation. Despite unwavering levels of cooperation among academia, industry, and policy makers, it can still take years to bring a life-saving product to market. There are some obvious limitations, including lack of blinding or masking and small sample size, which render the results less applicable to the real world. Traditional randomized controlled trials (RCTs) are lengthy, expensive, and have a low success rate. There is a growing acknowledgement that the current process no longer fully meets the growing healthcare needs. Advances in technology coupled with proliferation of telehealth modalities, sensors, wearable and connected devices have paved the way for a new paradigm. Virtual randomized controlled trials (vRCTs) have the potential to drastically shorten the clinical trial cycle while maximizing patient-centricity, compliance, and recruitment. This new approach can inform clinical trials in real time and with a holistic view of a patient’s health. This paper provides an overview of virtual clinical trials, addressing critical issues, including regulatory compliance, data security, privacy, and ownership.

Clinical Trial Metrics: The Complexity of Conducting Clinical Trials in North American Cancer Centers.

PURPOSE:Cancer clinical trials offices (CTOs) support the investigation of cancer prevention, early detection, and treatment at cancer centers across North America. CTOs are a centralized resource for clinical trial conduct and typically use research staff with expertise in four functional areas of clinical research: finance, regulatory, clinical, and data operations. To our knowledge, there are no publicly available benchmark data sets that characterize the size, cost, volume, and efficiency of these offices, nor whether the metrics differ by National Cancer Institute (NCI) designation. The Association of American Cancer Institutes (AACI) Clinical Research Innovation (CRI) steering committee developed a survey to address this knowledge gap.METHODS:An 11-question survey that addressed CTO budget, accrual and trial volume, full-time equivalents (FTEs), staff turnover, and activation timelines was developed by the AACI CRI steering committee and sent to 92 academic cancer research centers in North America (n = 90 in the United States; n = 2 in Canada), with 79 respondents completing the survey (86% completion rate).RESULTS:The number of FTE employees working in the CTOs ranged from 4.5 to 811 (median, 104). The median number of analytic cases (ie, newly diagnosed or received first course of treatment) reported by the main center was 3,856. Annual CTO budgets ranged from $250,000 to $23,900,000 (median, $8.2 million). The median trial activation time, based on 61 centers, was 167 days. The median number of accruals per center was 480 (range, 5-6,271) and median number of trials per center was 282 (range, 31-1,833). Budget and FTE ranges varied by NCI designation.CONCLUSION:The response rate to the survey was high. These data will allow cancer centers to evaluate their CTO infrastructure, funding, portfolio, and/or accrual goals as compared with peers. A wide range in each of the outcomes was noted, in keeping with the wide variation in size and scope of cancer center CTOs across the United States and Canada. These variations may warrant additional investigation.

Disruptive Innovations in Clinical Research

Disruptive Innovations in Clinical Research

A Prediction Model of Sepsis-Associated Acute Kidney Injury Based on Antithrombin III

Background: The incidence of sepsis-associated acute kidney injury (AKI) is on the rise. Recent studies have found a correlation between antithrombin III and AKI. We established a predictive model for sepsis-associated AKI based on plasma ATIII levels. Methods: A prospective study (March 2018 to January 2020) was conducted in sepsis patients admitted to the Critical Care Medicine Department at Shanghai General Hospital. ATIII levels were obtained within 48 hours after admission to the ICU and before the diagnosis of sepsis-related AKI was recorded. Renal function was assessed by measuring serum creatinine levels and urine volume. Results: Male, cardiovascular disease, and low ATIII levels were identified as independent risk factors for AKI. Age, immune disease, and low ATIII levels were identified as independent risk factors for death. Plasma ATIII levels in the non-AKI group were higher than those in the AKI group, plasma ATIII levels were higher in the survival group than in the non-survival group, plasma ATIII levels in the non-CRRT group were higher than those in the CRRT group, and plasma ATIII levels in the non-CKD group were higher than those in the CKD group. ATIII was significantly higher in the group with pulmonary infection than in the group without pulmonary infection. ATIII was significantly lower in the celiac infection group than in the nonceliac infection group. The predictive model of sepsis-associated AKI established based on ATIII was ln[P/(1-p)]=-1.211×sex-0.017×ATIII+0.022×Cr+0.004×BUN-2.8192. The model goodness of fit test (P=0.000) and the area under the ROC curve of the model (0.9862) suggested that the model has a high degree of discrimination and calibration. Conclusions: ATIII reduction was an independent risk factor for sepsis-associated AKI and an independent risk factor for mortality in patients with sepsis. ATIII reduction could predict sepsis--associated AKI. Low ATIII predicted a poor prognosis. Funding Statement: The present study was supported by grants from Shanghai Science and Technology Committee Scientific and Technological Support Project (grant no. 18411950600 and 18411950602, respectively), Clinical Research Innovation Plan of Shanghai General Hospital (grant no. CTCCR2016B01) Wu Jieping Medical Foundation (grant no. 320.6750.18546) and Songjiang district science and technology project(19SJKJGG92). Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: Ethics approval was obtained from Shanghai General Hospital Institutional Review Board [approval no. (2018)KY004].

Antithrombin III Protects Against Acute Kidney Injury in Septic Elderly Patients

The elderly are the primary age group of acute kidney injury (AKI). The predictive value of Antithrombin III (ATIII) level in preventing AKI among septic elderly patients has not been studied. The purpose of this study was to evaluate the value of ATIII in predicting AKI nondevelopment and prognosis in septic elderly patients under ICU settings. This is a retrospective study enrolling 107 elderly patients with sepsis who were admitted to ICUs from October 2015 to March 2018. Assessment of renal function was performed daily by measuring serum creatinine level and urine output. The ATIII level was obtained within the first 48 hours of the diagnosis of sepsis (ATIII1). The results were presented using descriptive statistics and area under the receiver operating characteristic curve (AUC-ROC). P value was set 0.05. Among all enrolled patients, 29 (27.1%) developed AKI. The ATIII level was a predictor of AKI nondevelopment (AUC-ROC =0.729, and sensitivity 0.700and specificity0.714). The ATIII/Cr ratio was also a predictor of AKI nondevelopment (AUC-ROC =0.971, and sensitivity 0.900and specificity1). The accuracy of ATIII and ATIII/Cr in predicting survival was intermediate, with AUC-ROC of 0.681 and 0.804 repectively, sensitivity ranging between 0.802 and 0.596, and specificity between 0.542 and 0.875. The ATIII level demonstrated excellent ability in predicting AKI nondevelopment in septic elderly patients in ICUs. Patients were divided into low ATIII group and high ATIII group using 66.95 or 55.7 as cutoff value. In comparison, ICU stay was significantly lower in the high ATIII group(p=0.020 and 0.049, repectively. Off mechanical ventilation time, off CRRT time, and survival time were significantly higher in high ATIII group (p=0.049,0.048,0.014 using 66.95 as cutoff and 0.041,0.036,0.021 using 55.7 as cutoff).This study suggests that ATIII protects against acute kidney injury in septic elderly patients. Lower ATIII predicts worse prognosis. Funding: This project was supported by a grant from the Important and weak subject construction project of Shanghai Health and Family Planning System (No: 2016ZB0205) 2016ZB0205), grants from Shanghai science and technology committee scientific and technological support project (No:18411950600 and No:18411950602) and a grant from Clinical Research Innovation Plan of Shanghai General Hospital(CTCCR-2016B01). Declaration of Interest: The authors declare that they have no competing interests. Ethical Approval: Ethics approval was obtained from Shanghai General Hospital Instutional Review Board (reference number [2018]KY004).

Strengthening Clinical Research in Marriage and Family Therapy: Challenges and Multilevel Solutions.

There is a critical need for high-quality and accessible treatments to improve mental health. Yet, there are indications that the research being conducted by contemporary marriage and family therapy (MFT) scholars focuses less on advancing and disseminating clinical interventions than in previous decades. In this article, we describe challenges to increasing rigorous clinical research in MFT. We use systems mapping and the intervention-level framework to identify strategic goals designed to drive innovation in clinical research in the field. It is our hope this article encourages dialog and action among MFT stakeholder groups to support clinical science that will improve the health and functioning of families.

The application of VR technology in clinical medicine and its influence on medical science and technology periodicals

Objective With the rapid development of network information technology, media convergence is deepening from simple application to media transformation. This article explores the application of virtual reality technology (VR)in clinical medical research management through media virtual reality, so that medical researchers or clinicians can understand the reasonable resource of media VR technology in clinical medicine, Application of scientific researchers play an innovative role. Methods Through the literature review to understand the use of VR technology for clinical research or surgical operation of the three-dimensional simulation of the scene to achieve the feeling of exposure to the scene, so as to achieve a richer environment than the real observation angle of thinking space. And then the use of VR media (including magazines, etc.) to create a simulation platform to make medical research magazine more adapt to the development trend of network information technology. Results Media VR technology is changing the traditional mode of paper journals in medical journals and bringing a whole new way of narration to the media. Journal journals, for example, use the new media convergence method to make use of smart phones such as mobile phones to enable the users of information Participate in live and interactive video at any time, and use VR technology and magazine's virtual platform to train and discuss virtual resources through clinical research and management, so that medical researchers or clinicians (including clinical researchers and clinicians) can learn from others on their own , In the virtual space can be freely discussed, thereby enhancing the medical researchers or clinicians interested in learning and clinical research innovation effect. Conclusions Medical and scientific research institutions and their magazines, which promote the achievements of medical research, need to adapt to the development trend of network information technology, learn VR technology, update the scientific research management and publishing concepts, and on the one hand do a good job in the dissemination of scientific research management and scientific and technological paper media. On the other hand, Computer and Internet technologies, and strive to create a relaxed and comfortable knowledge for medical researchers to gain the reverie of space and technological innovation so that medical researchers or clinicians can enhance their interest in diverse services, increase their desire for information needs and enhance their learning Clinical research thinking and practical skills. Key words: Network information technology; VR technology; Scientific research management; New media integration

Opportunities and barriers for pragmatic embedded trials: Triumphs and tribulations.

ResultsEmbedded pragmatic clinical trials (PCTs) are set in routine health care, have broad eligibility criteria, and use routinely collected electronic data. Many consider them a breakthrough innovation in clinical research and a necessary step in clinical trial development. To identify barriers and success factors, we reviewed published embedded PCTs and interviewed 30 researchers and clinical leaders in 7 US delivery systems.LiteratureWe searched PubMed, the Cochrane library, and clinicaltrials.gov for studies reporting embedded PCTs. We identified 108 embedded PCTs published in the last 10 years. The included studies had a median of 5540 randomized patients, addressed a variety of diseases, and practice settings covering a broad range of interventions. Eighty‐one used cluster randomization. The median cost per patient was $97 in the 64 trials for which it was possible to obtain cost data.InterviewsDelivery systems required research studies to align with operational priorities, existing information technology capabilities, and standard quality improvement procedures. Barriers that were identified included research governance, requirements for processes that were incompatible with clinical operations, and unrecoverable costs.ConclusionsEmbedding PCTs in delivery systems can provide generalizable knowledge that is directly applicable to practice settings at much lower cost than conventional trials. Successful embedding trials require accommodating delivery systems' needs and priorities.

A harmonized and efficient clinical research environment would benefit patients and enhance European competitiveness. Commentary.

The forthcoming implementation of the European Clinical Trial Regulation (Regulation (EU) No. 536/2014), which is expected to facilitate the conduct of clinical trials across the European Union, will require National Authorities to create the best conditions for the implementation of the new Regulation through national guidelines, so that sponsors may reconsider Europe as a prime location for planning clinical trials. During a meeting titled "Innovation in Clinical Research", an expert panel discussed potential local advances fostering competitiveness of European clinical research with representatives of the pharmaceutical industry, patient organisations and Italian regulatory agency in view of the forthcoming implementation of (EU) No. 536/2014 on clinical trials of medicinal products. In this article we summarise the findings of the meeting, describe features characterising clinical research patterns and offer some suggestions on the possible involvement of all stakeholders in order to foster research innovation and allow the timely access to novel medicines for patients.