Nonlinear relationship between platelet count and 30-day in-hospital mortality in ICU acute respiratory failure patients: a multicenter retrospective cohort study.

BACKGROUND: Limited evidence exists regarding the link between platelet count and 30-day in-hospital mortality in acute respiratory failure (ARF) patients. Thus, this study aims to investigate this association among ICU patients experiencing acute respiratory failure. METHODS: We conducted a retrospective cohort study across multiple centers, utilizing data from the US eICU-CRD v2.0 database covering 22,262 patients with ARF in the ICU from 2014 to 2015. Our aim was to investigate the correlation between platelet count and 30-day in-hospital mortality using binary logistic regression, subgroup analyses, and smooth curve fitting. RESULTS: The 30-day in-hospital mortality rate was 19.73% (4393 out of 22,262), with a median platelet count of 213 × 109/L. After adjusting for covariates, our analysis revealed an inverse association between platelet count and 30-day in-hospital mortality (OR = 0.99, 95% CI 0.99, 0.99). Subgroup analyses supported the robustness of these findings. Furthermore, a nonlinear relationship was identified between platelet count and 30-day in-hospital mortality, with the inflection point at 120 × 109/L. Below the inflection point, the effect size (OR) was 0.89 (0.87, 0.91), indicating a significant association. However, beyond this point, the relationship was not statistically significant. CONCLUSION: This study establishes a clear negative association between platelet count and 30-day in-hospital mortality among ICU patients with ARF. Furthermore, we have identified a nonlinear relationship with saturation effects, indicating that among ICU patients with acute respiratory failure, the lowest 30-day in-hospital mortality rate occurs when the baseline platelet count is approximately 120 × 109/L.

Two Decades of Publications in Journals Dedicated to Autoimmunity: A Bibliometric Analysis of the Autoimmunity Field from 2004 to 2023.

To carry out an in-depth analysis of the scientific research on autoimmunity, we performed the first bibliometric analysis focusing on publications in journals dedicated to autoimmunity (JDTA) indexed by science citation index during the period 2004-2023. Using bibliometric analysis, we quantitatively and qualitatively analyzed the country, institution, author, reference and keywords information of publications in JDTA, so as to understand the quantity, publication pattern and publication characteristics of these publications. The co-occurrence networks, clustering map and timeline map were created by CiteSpace and VOSviewer software to visualize the results. The CiteSpace was also used to analyze the strongest citation burst of keywords, which could describe the frequency, intensity and time period of high-frequency keywords, and indicate the research hotspots in the field. A total of 5 710 publications were analyzed, and their annual distribution number was basically stable from 2004 to 2023, fluctuating around 300. The United States and Italy led the way in terms of the number of publications, followed by France and China. For international cooperation, the developed countries represented by the United States cooperate more closely, but the cooperation was localized, reflecting that there was no unified model of autoimmunity among countries. UDICE-French Research Universities had the greatest number of publications. Subsequently, the number of publications decreased slowly with the ranking, and the gradient was not large. Eric Gershwin and Yehuda Shoenfeld stood out among the authors. They had an excellent academic reputation and great influence in the field of autoimmunity. The results of keyword analysis showed that JDTA publications mainly studied a variety of autoimmune diseases, especially SLE and RA. At the same time, JDTA publications also paid special attention to the research of cell function, autoantibody expression, animal experiments, disease activity, pathogenesis and treatment. This study is the first to analyze the publications in JDTA from multiple indicators by bibliometrics, thus providing new insights into the research hotspots and development trends in the field of autoimmunity.

The development status and future trends of lubricant additives technology: Based on patents analysis.

In order to reveal the current status and future trends of lubricant additives, this study analyzes the structured and unstructured data of 77701 lubricant additive patents recorded by Patsnap. The results show that China is the country with the largest number of patents in this field, and the United States is the main exporting country of international technology flow; the current research and development of lubricant additives is dominated by multifunctional composite additives; environmentally friendly additive compositions are the current research hotspot; and more environmentally friendly and economically degradable additives have more development potential in the future. Overall, this study provides a comprehensive understanding of the research and application of lubricant additives and contributes to the future development of the lubricant industry.

Patient-specific reference model estimation for orthognathic surgical planning.

PURPOSE: Accurate estimation of reference bony shape models is fundamental for orthognathic surgical planning. Existing methods to derive this model are of two types: one determines the reference model by estimating the deformation field to correct the patient's deformed jaw, often introducing distortions in the predicted reference model; The other derives the reference model using a linear combination of their landmarks/vertices but overlooks the intricate nonlinear relationship between the subjects, compromising the model's precision and quality. METHODS: We have created a self-supervised learning framework to estimate the reference model. The core of this framework is a deep query network, which estimates the similarity scores between the patient's midface and those of the normal subjects in a high-dimensional space. Subsequently, it aggregates high-dimensional features of these subjects and projects these features back to 3D structures, ultimately achieving a patient-specific reference model. RESULTS: Our approach was trained using a dataset of 51 normal subjects and tested on 30 patient subjects to estimate their reference models. Performance assessment against the actual post-operative bone revealed a mean Chamfer distance error of 2.25 mm and an average surface distance error of 2.30 mm across the patient subjects. CONCLUSION: Our proposed method emphasizes the correlation between the patients and the normal subjects in a high-dimensional space, facilitating the generation of the patient-specific reference model. Both qualitative and quantitative results demonstrate its superiority over current state-of-the-art methods in reference model estimation.

Research Progress on the Correlation Between Hypertension and Gut Microbiota.

Among cardiovascular diseases, hypertension is the most important risk factor for morbidity and mortality worldwide, and its pathogenesis is complex, involving genetic, dietary and environmental factors. The characteristics of the gut microbiota can vary in response to increased blood pressure (BP) and influence the development and progression of hypertension. This paper describes five aspects of the relationship between hypertension and the gut microbiota, namely, the different types of gut microbiota, metabolites of the gut microbiota, sympathetic activation, gut-brain interactions, the effects of exercise and dietary patterns and the treatment of the gut microbiota through probiotics, faecal microbiota transplantation (FMT) and herbal remedies, providing new clues for the future prevention of hypertension. Diet, exercise and traditional Chinese medicine may contribute to long-term improvements in hypertension, although the effects of probiotics and FMT still need to be validated in large populations.

In vivo rescue of genetic dilated cardiomyopathy by systemic delivery of nexilin.

BACKGROUND: Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Multiple identified mutations in nexilin (NEXN) have been suggested to be linked with severe DCM. However, the exact association between multiple mutations of Nexn and DCM remains unclear. Moreover, it is critical for the development of precise and effective therapeutics in treatments of DCM. RESULTS: In our study, Nexn global knockout mice and mice carrying human equivalent G645del mutation are studied using functional gene rescue assays. AAV-mediated gene delivery is conducted through systemic intravenous injections at the neonatal stage. Heart tissues are analyzed by immunoblots, and functions are assessed by echocardiography. Here, we identify functional components of Nexilin and demonstrate that exogenous introduction could rescue the cardiac function and extend the lifespan of Nexn knockout mouse models. Similar therapeutic effects are also obtained in G645del mice, providing a promising intervention for future clinical therapeutics. CONCLUSIONS: In summary, we demonstrated that a single injection of AAV-Nexn was capable to restore the functions of cardiomyocytes and extended the lifespan of Nexn knockout and G645del mice. Our study represented a long-term gene replacement therapy for DCM that potentially covers all forms of loss-of-function mutations in NEXN.

Ethanol responsive lnc171 promotes migration and invasion of HCC cells via mir-873-5p/ZEB1 axis.

BACKGROUNDS: Long nonconding RNAs (lncRNAs) have been found to be a vital regulatory factor in the development process of human cancer, and could regarded as diagnostic or prognostic biomarkers for human cancers. Here, we aim to confirm the expression and molecular mechanism of RP11-171K16.5 (lnc171) in hepatocellular carcinoma (HCC). METHODS: Screening of differentially expressed lncRNAs by RNA sequencing. Expression level of gene was studied by quantitative real-time PCR (qRT-PCR). The effects of lnc171, mir-873-5p, and ethanol on migration and invasion activity of cells were studied used transwell assay, and luciferase reporter assay was used to confirm the binding site. RESULTS: RNA sequencing showed that lnc171 was markedly up-regulated in HCC. siRNA-mediated knockdown of lnc171 repressed the migration and invasion ability of HCC cells. Bioinformatic analysis, dual luciferase reporter assay, and qRT-PCR indicated that lnc171 interacted with mir-873-5p in HCC cells, and Zin-finger E-box binding homeobox (ZEB1) was a downstream target gene of mir-873-5p. In addition, lnc171 could enhance migration and invasion ability of HCC cells by up-regulating ZEB1 via sponging mir-873-5p. More interestingly, ethanol stimulation could up-regulate the increase of lnc171, thereby regulating the expression of competing endogenous RNA (ceRNA) network factors which lnc171 participated in HCC cells. CONCLUSIONS: Our date demonstrates that lnc171 was a responsive factor of ethanol, and plays a vital role in development of HCC via binding of mir-873-5p.

Association of gene polymorphisms and the decreased expression of long non-coding RNA LOC553103 with rheumatoid arthritis.

BACKGROUND: Long non-coding RNAs (lncRNAs) are involved in many key bioprocesses, including the occurrence and development of rheumatoid arthritis (RA). We aimed to analyze the association of genetic variants of long non-coding RNA LOC553103 and its peripheral blood mononuclear cells (PBMC) expression with RA. METHODS: We enrolled 457 RA patients and 551 healthy controls and conducted a case-control study to analyze the relationship between LOC553103 gene rs272879 and the susceptibility of RA by TaqMan single nucleotide polymorphism genotyping. Among them, we sampled 92 cases and 92 controls, respectively, to detect the PBMC level of LOC553103 using quantitative real-time polymerase chain reaction technology. We explored the association between LOC553103 rs272879 and its PBMC expression levels in 71 RA patients. Mann-Whitney, Chi-square, and Spearman correlation analysis were used for statistical analysis and P-value <.05 was considered statistically significant. RESULTS: The genotype frequency of LOC553103 rs272879 CC was increased, and CG was decreased in RA patients compared to the control group (χ2 = 6.772, P = .034). The LOC553103 expression level in PBMC of RA patients was downregulated compared to healthy control (Z = -4.497, P < .001). Moreover, negative correlations were observed between the PBMC level of LOC553103 and erythrocyte sedimentation rate (rs = -0.262, P = .018), white blood cell count (rs = -0.382, P = .004), platelet (rs = -0.293, P = .030), and disease activity score in 28 joints (rs = -0.271, P = .016) in RA patients. CONCLUSIONS: This study provides the first evidence supporting an association between LOC553103 gene polymorphisms and susceptibility of RA and a relationship of PBMC level of LOC553103 with clinical manifestations and laboratory indicators of RA patients.

Long-term outcomes of endoscopic resection for well-differentiated nonampullary duodenal neuroendocrine tumors.

BACKGROUND & AIMS: Nonampullary duodenal neuroendocrine tumors (NAD-NETs) are rare with limited evidence regarding endoscopic treatment. The study aimed to investigate the efficacy and safety of endoscopic resection of well-differentiated NAD-NETs and evaluate long-term outcomes, including local recurrence and metastasis. METHODS: A total of 78 patients with NAD-NETs who underwent endoscopic resection between January 2011 and August 2022 were included. The clinicopathologic characteristics and treatment outcomes were collected and analyzed. RESULTS: En bloc resection was achieved for 74 of the tumors (94.9%) and R0 resection was obtained in 68 of the tumors (87.2%). Univariate analysis identified tumors in the second part of the duodenum, tumor size ≥ 10 mm and muscularis propria invasion as risk factors for non-curative resection. Two patients with R1 resection (vertical margin involvement) and two patients with lymphovascular invasion underwent additional surgery. Four patients experienced adverse events (5.1%), including two cases of delayed bleeding and two cases of perforation, all successfully managed conservatively. During a median follow-up period of 62.6 months, recurrence and lymph node metastasis were only detected in one patient with R1 resection 3 months after the original procedure. CONCLUSION: Endoscopic resection is safe and effective and provides a favorable long-term outcome for patients with well-differentiated NAD-NETs without regional lymph node or distant metastasis.

An evaluation of the replicability of analyses using synthetic health data.

Synthetic data generation is being increasingly used as a privacy preserving approach for sharing health data. In addition to protecting privacy, it is important to ensure that generated data has high utility. A common way to assess utility is the ability of synthetic data to replicate results from the real data. Replicability has been defined using two criteria: (a) replicate the results of the analyses on real data, and (b) ensure valid population inferences from the synthetic data. A simulation study using three heterogeneous real-world datasets evaluated the replicability of logistic regression workloads. Eight replicability metrics were evaluated: decision agreement, estimate agreement, standardized difference, confidence interval overlap, bias, confidence interval coverage, statistical power, and precision (empirical SE). The analysis of synthetic data used a multiple imputation approach whereby up to 20 datasets were generated and the fitted logistic regression models were combined using combining rules for fully synthetic datasets. The effects of synthetic data amplification were evaluated, and two types of generative models were used: sequential synthesis using boosted decision trees and a generative adversarial network (GAN). Privacy risk was evaluated using a membership disclosure metric. For sequential synthesis, adjusted model parameters after combining at least ten synthetic datasets gave high decision and estimate agreement, low standardized difference, as well as high confidence interval overlap, low bias, the confidence interval had nominal coverage, and power close to the nominal level. Amplification had only a marginal benefit. Confidence interval coverage from a single synthetic dataset without applying combining rules were erroneous, and statistical power, as expected, was artificially inflated when amplification was used. Sequential synthesis performed considerably better than the GAN across multiple datasets. Membership disclosure risk was low for all datasets and models. For replicable results, the statistical analysis of fully synthetic data should be based on at least ten generated datasets of the same size as the original whose analyses results are combined. Analysis results from synthetic data without applying combining rules can be misleading. Replicability results are dependent on the type of generative model used, with our study suggesting that sequential synthesis has good replicability characteristics for common health research workloads.

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments.

A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

A refined deep-learning-based algorithm for harmful-algal-bloom remote-sensing recognition using Noctiluca scintillans algal bloom as an example.

Harmful algal blooms (HABs) are challenging to recognize because of their striped and uneven biomass distributions. To address this issue, a refined deep-learning algorithm termed HAB-Ne was developed for the recognition of HABs in GF-1 Wide Field of View (WFV) images using Noctiluca scintillans algal bloom as an example. First, a pretrained image super-resolution model was integrated to improve the spatial resolution of the GF-1 WFV images and minimize the impact of mixed pixels caused by the strip distribution. Side-window convolution was also explored to enhance the edge features of HABs and minimize the effects of uneven biomass distribution. In addition, a convolutional encoder-decoder network was constructed for threshold-free HAB recognition to address the dependence on thresholds in existing methods. HAB-Net effectively recognized HABs from GF-1 WFV images, achieving an average precision of 90.1% and an F1-score of 0.86. HAB-Net showed more fine-grained recognition results than those of existing methods, with over 4% improvement in the F1-Score, especially in the marginal areas of HAB distribution. The algorithm demonstrated its effectiveness in recognizing HABs in different marine environments, such as the Yellow Sea, East China Sea, and northern Vietnam. Additionally, the algorithm was proven suitable for detecting the macroalga Sargassum. This study demonstrates the potential of deep-learning-based fine-grained recognition of HABs, which can be extended to the recognition of other fine-scale and strip-distributed objects, such as oil spills and Ulva prolifera.

Correspondence attention for facial appearance simulation.

In orthognathic surgical planning for patients with jaw deformities, it is crucial to accurately simulate the changes in facial appearance that follow the bony movement. Compared with the traditional biomechanics-based methods like the finite-element method (FEM), which are both labor-intensive and computationally inefficient, deep learning-based methods offer an efficient and robust modeling alternative. However, current methods do not account for the physical relationship between facial soft tissue and bony structure, causing them to fall short in accuracy compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to predict facial changes by correlating facial soft tissue changes with bony movement through a point-to-point attentive correspondence matrix. To ensure efficient training, we also introduce a contrastive loss for self-supervised pre-training of the ACMT-Net with a k-Nearest Neighbors (k-NN) based clustering. Experimental results on patients with jaw deformities show that our proposed solution can achieve significantly improved computational efficiency over the state-of-the-art FEM-based method with comparable facial change prediction accuracy.

Efficient estimation for left-truncated competing risks regression for case-cohort studies.

The case-cohort study design provides a cost-effective study design for a large cohort study with competing risk outcomes. The proportional subdistribution hazards model is widely used to estimate direct covariate effects on the cumulative incidence function for competing risk data. In biomedical studies, left truncation often occurs and brings extra challenges to the analysis. Existing inverse probability weighting methods for case-cohort studies with competing risk data not only have not addressed left truncation, but also are inefficient in regression parameter estimation for fully observed covariates. We propose an augmented inverse probability-weighted estimating equation for left-truncated competing risk data to address these limitations of the current literature. We further propose a more efficient estimator when extra information from the other causes is available. The proposed estimators are consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator is unbiased and leads to estimation efficiency gain in the regression parameter estimation. We analyze the Atherosclerosis Risk in Communities study data using the proposed methods.

Blood urea nitrogen has a nonlinear association with 3-month outcomes with acute ischemic stroke: A second analysis based on a prospective cohort study.

BACKGROUND: Evidence regarding the relationship between blood urea nitrogen (BUN) and 3-month outcomes in acute ischemic stroke (AIS) patients is still scarce. Therefore, the present study was preformed to explore the link between the BUN and 3-month poor outcomes in patients with AIS. METHODS: A retrospective study of 1866 participants with AIS enrolled from January 2010 to December 2016 at a hospital in South Korea. Binary logistic regression, smooth curve fitting, and a set of sensitivity analyses were used to analyze the association between BUN and 3-month poor outcomes. RESULTS: After adjusting covariates, the results of the binary logistic regression model suggested that the relationship between the BUN and the risk of 3-month poor outcomes for AIS patients was not statistically significant. However, there was a special nonlinear relationship between them, and the inflection point of the BUN was 13 mg/dl. On the left side of the inflection point, every unit increase in the BUN reduces the risk of 3-month poor outcomes by 14.1 % (OR = 0.859, 95%CI: 0.780-0.945, p = 0.0019). On the right side of the inflection point, the relationship is not statistically significant. CONCLUSION: There is a nonlinear relationship with saturation effect between BUN level and 3-month poor outcomes in AIS patients. Maintaining the BUN at around 13 mg/dl can reduce the risk of 3-month poor outcome in AIS patients.

Identification of m6A-related lncRNAs-based signature for predicting the prognosis of patients with skin cutaneous melanoma.

BACKGROUND: Skin cutaneous melanoma (SKCM) is one of the fastest developing malignancies with strong aggressive ability and no proper curative treatments. Numerous studies illustrated the importance of N6-methyladenosine (m6A) RNA modification to tumorigenesis. The aim of this study was to identify novel prognostic signature by using m6A-related lncRNAs, thus to improve the survival for SKCM patients and guide SKCM therapy. METHODS: We downloaded the Presentational Matrix data from The Cancer Genome Atlas (TCGA) and analyzed all the expressed lncRNAs among 468 SKCM samples. Pearson correlation analysis was performed to assess the correlations between lncRNAs and 29 m6A-related genes. Least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression analysis were performed to construct m6A-related lncRNAs prognostic signature (m6A-LPS). The accuracy and prognostic value of this signature were validated by using receiver operating characteristic (ROC) curves, Kaplan-Meier (K-M) survival analysis, univariate COX or multivariate COX analyses. After calculating risk scores, patients were divided into low- and high-risk subgroups by the median value of risk scores. RESULTS: A total of 2973 lncRNAs were found expressed among SKCM tissues. Prognostic analysis showed that 98 lncRNAs had a significant effect on the survival of SKCM patients. The m6A-LPS was validated using K-M and ROC analysis and the predictive accuracy of the risk score was also high according to the AUC of the ROC curve in training and testing sets. A nomogram based on tumor stage, gender and risk score that had a strong ability to forecast the 1-, 2-, 3-, 5-year OS of SKCM patients confirmed by calibrations. Enrichment analysis indicated that malignancy-associated biological processes and pathways were more common in the high-risk subgroup. CONCLUSION: Collectively, m6A-related lncRNAs exert as potential biomarkers for prognostic stratification of SKCM patients and may assist clinicians achieving individualized treatment for SKCM.

Mediating effect of cumulative lipid profile burden on the effect of diet and obesity on hypertension incidence: a cohort study of people aged 35-65 in rural China.

BACKGROUND AND OBJECTIVES: Cumulative lipid profile burden is designed to dynamically measure lipid accumulation, and its effect on hypertension has been poorly studied. Our main purpose was to investigate the effect of cumulative lipid profile burden on the incidence of essential hypertension (EH) and to investigate whether cumulative lipid burden mediates the pathogenesis of the effects of diet and obesity on EH. SUBJECTS AND METHODS: A total of 1295 participants were included in the study, which started in 2017. The average follow-up time was 2.98 years. A total of 240 EH patients occurred during the follow-up period. RESULTS: The HR (95% CI) of the highest quartile in cumulative Total cholesterol (TC), triglyceride (TG) and high density lipoprotein (HDL) burden were 1.747 (1.145 - 2.664), 1.502 (1.038 - 2.173), 0.615 (0.413 - 0.917) for incidence of EH respectively, compared to the respective reference groups. Participants with EH consumed more red meat and refined grains, and red meat was positively associated with cumulative TC burden. BMI and Waist-To-Height Ratio (WHtR) increased the incidence of EH, and obesity was positively correlated with cumulative TG burden. Mediating analysis showed that cumulative TG had a partial mediating effect in the causal relationship between obesity and EH, and Mendelian randomization (MR) also proved this result. Diet was not found to influence EHn through cumulative lipid profile burden. CONCLUSIONS: The cumulative TG burden partially mediates the effect of obesity on EH.

MULTI-TASK DEEP LEARNING AND UNCERTAINTY ESTIMATION FOR PET HEAD MOTION CORRECTION.

Head motion occurring during brain positron emission tomography images acquisition leads to a decrease in image quality and induces quantification errors. We have previously introduced a Deep Learning Head Motion Correction (DL-HMC) method based on supervised learning of gold-standard Polaris Vicra motion tracking device and showed the potential of this method. In this study, we upgrade our network to a multi-task architecture in order to include image appearance prediction in the learning process. This multi-task Deep Learning Head Motion Correction (mtDL-HMC) model was trained on 21 subjects and showed enhanced motion prediction performance compared to our previous DL-HMC method on both quantitative and qualitative results for 5 testing subjects. We also evaluate the trustworthiness of network predictions by performing Monte Carlo Dropout at inference on testing subjects. We discard the data associated with a great motion prediction uncertainty and show that this does not harm the quality of reconstructed images, and can even improve it.

Evaluating the Utility and Privacy of Synthetic Breast Cancer Clinical Trial Data Sets.

PURPOSE: There is strong interest from patients, researchers, the pharmaceutical industry, medical journal editors, funders of research, and regulators in sharing clinical trial data for secondary analysis. However, data access remains a challenge because of concerns about patient privacy. It has been argued that synthetic data generation (SDG) is an effective way to address these privacy concerns. There is a dearth of evidence supporting this on oncology clinical trial data sets, and on the utility of privacy-preserving synthetic data. The objective of the proposed study is to validate the utility and privacy risks of synthetic clinical trial data sets across multiple SDG techniques. METHODS: We synthesized data sets from eight breast cancer clinical trial data sets using three types of generative models: sequential synthesis, conditional generative adversarial network, and variational autoencoder. Synthetic data utility was evaluated by replicating the published analyses on the synthetic data and assessing concordance of effect estimates and CIs between real and synthetic data. Privacy was evaluated by measuring attribution disclosure risk and membership disclosure risk. RESULTS: Utility was highest using the sequential synthesis method where all results were replicable and the CI overlap most similar or higher for seven of eight data sets. Both types of privacy risks were low across all three types of generative models. DISCUSSION: Synthetic data using sequential synthesis methods can act as a proxy for real clinical trial data sets, and simultaneously have low privacy risks. This type of generative model can be one way to enable broader sharing of clinical trial data.