Patient With Gender Dysphoria Diagnosed With Multiple Sclerosis Following Takotsubo Cardiomyopathy and Ventricular Arrhythmia: A Case Report and Systematic Literature Review.

A 34-year-old transgender woman presented with ventricular tachycardia and was diagnosed with takotsubo cardiomyopathy. Further evaluation revealed an underlying diagnosis of multiple sclerosis (MS) with brainstem lesions that may have triggered takotsubo cardiomyopathy. In this report, we also systematically reviewed published cases of takotsubo cardiomyopathy and MS and found that basal type takotsubo cardiomyopathy was the most common, and most patients presented with brainstem involvement of MS. An awareness of these associations by physicians, along with multidisciplinary collaboration, may facilitate the early diagnosis and improve the prognosis of these patients.

A novel missense mutation (FGG c.1168G > T) in the gamma chain of fibrinogen causing congenital hypodysfibrinogenemia with bleeding phenotype.

BACKGROUND: Fibrinogen plays pivotal roles in multiple biological processes. Genetic mutation of the fibrinogen coding genes can result in congenital fibrinogen disorders (CFDs). We identified a novel heterozygous missense mutation, FGG c.1168G > T (NCBI NM_000509.6), and conducted expression studies and functional analyses to explore the influence on fibrinogen synthesis, secretion, and polymerization. METHODS: Coagulation tests were performed on the patients to detect the fibrinogen concentration. Whole-exome sequencing (WES) and Sanger sequencing were employed to detect the novel mutation. Recombinant fibrinogen-producing Chinese hamster ovary (CHO) cell lines were built to examine the recombinant fibrinogen synthesis and secretion by western blotting and enzyme-linked immunosorbent assay (ELISA). The functional analysis of fibrinogen was performed by thrombin-catalyzed fibrin polymerization assay. In silico molecular analyses were carried out to elucidate the potential molecular mechanisms. RESULTS: The clinical manifestations, medical history, and laboratory tests indicated the diagnosis of hypodysfibrinogenemia with bleeding phenotype in two patients. The WES and Sanger sequencing revealed that they shared the same heterozygous missense mutation, FGG c.1168G > T. In the expression studies and functional analysis, the missense mutation impaired the recombinant fibrinogen's synthesis, secretion, and polymerization. Furthermore, the in silico analyses indicated novel mutation led to the hydrogen bond substitution. CONCLUSION: The study highlighted that the novel heterozygous missense mutation, FGG c.1168G > T, would change the protein secondary structure, impair the "A: a" interaction, and consequently deteriorate the fibrinogen synthesis, secretion, and polymerization.

Myocardial DNA Damage Predicts Heart Failure Outcome in Various Underlying Diseases.

BACKGROUND: Reliable predictors of treatment efficacy in heart failure have been long awaited. DNA damage has been implicated as a cause of heart failure. OBJECTIVES: The purpose of this study was to investigate the association of DNA damage in myocardial tissue with treatment response and prognosis of heart failure. METHODS: The authors performed immunostaining of DNA damage markers poly(ADP-ribose) (PAR) and γ-H2A.X in endomyocardial biopsy specimens from 175 patients with heart failure with reduced ejection fraction (HFrEF) of various underlying etiologies. They calculated the percentage of nuclei positive for each DNA damage marker (%PAR and %γ-H2A.X). The primary outcome was left ventricular reverse remodeling (LVRR) at 1 year, and the secondary outcome was a composite of cardiovascular death, heart transplantation, and ventricular assist device implantation. RESULTS: Patients who did not achieve LVRR after the optimization of medical therapies presented with significantly higher %PAR and %γ-H2A.X. The ROC analysis demonstrated good performance of both %PAR and %γ-H2A.X for predicting LVRR (AUCs: 0.867 and 0.855, respectively). There was a negative correlation between the mean proportion of DNA damage marker-positive nuclei and the probability of LVRR across different underlying diseases. In addition, patients with higher %PAR or %γ-H2A.X had more long-term clinical events (PAR HR: 1.63 [95% CI: 1.31-2.01]; P < 0.001; γ-H2A.X HR: 1.48 [95% CI: 1.27-1.72]; P < 0.001). CONCLUSIONS: DNA damage determines the consequences of human heart failure. Assessment of DNA damage is useful to predict treatment efficacy and prognosis of heart failure patients with various underlying etiologies.

Self-supervised tumor segmentation and prognosis prediction in osteosarcoma using multiparametric MRI and clinical characteristics.

BACKGROUND AND OBJECTIVE: Osteosarcoma has a high mortality among malignant bone tumors. MRI-based tumor segmentation and prognosis prediction are helpful to assist doctors in detecting osteosarcoma, evaluating the patient's status, and improving patient survival. Current intelligent diagnostic approaches focus on segmentation with single-parameter MRI, which ignores the nature of MRI resulting in poor performance, and lacks the connection with prognosis prediction. Besides, osteosarcoma is a rare disease, and their few labeled data may lead to model overfitting. METHODS: We propose a three-stage pipeline for segmentation and prognosis prediction of osteosarcoma to assist doctors in diagnosis. First, we propose the Multiparameter Fusion Contrast Learning (MPFCLR) algorithm to share pre-training weights for the segmentation model using unlabeled data. Then, we construct a multiparametric fusion network (MPFNet), which fuses the complementary features from multiparametric MRI (CE-T1WI, T2WI). It can automatically segment tumor and necrotic regions. Finally, a fusion nomogram is constructed by segmentation masks and clinical characteristics (volume, tumor spread) to predict the patient's prognostic status. RESULTS: Our experiments used data from 136 patients at the Second Xiangya Hospital in China. According to experiments, the MPFNet achieves 84.19 % mean DSC and 84.56 % mean F1-score in segmenting tumor and necrotic regions, surpassing existing models and single-parameter MRI input for osteosarcoma segmentation. Besides, MPFCLR improves the segmentation performance and convergence speed. In prognosis prediction, our fusion nomogram (C-index: 0.806, 95 %CI: 0.758-0.854) is better than radiomics (C-index: 0.753, 95 %CI: 0.685-0.841) and clinical (C-index: 0.794, 95 %CI: 0.735-0.854) nomograms in predictive performance. Compared to the comparison models, our model is closest to the prediction model based on physician annotations. Moreover, it can accurately distinguish the patients' prognostic status with good or poor. CONCLUSION: Our proposed solution can provide references for clinicians to detect osteosarcoma, evaluate patient status, and make personalized decisions. It can reduce delayed treatment or overtreatment and improve patient survival.

FOXO3-Activated circFGFBP1 Inhibits Extracellular Matrix Degradation and Nucleus Pulposus Cell Death via miR-9-5p/BMP2 Axis in Intervertebral Disc Degeneration In Vivo and In Vitro.

(1) Background: intervertebral disc degeneration (IVDD) defined as the degenerative changes in intervertebral disc is characterized by extracellular matrix (ECM) degradation and death in nucleus pulposus (NP) cells. (2) Methods: The model of IVDD was established in male Sprague Dawley rats using a puncture of a 21-gauge needle at the endplates located in the L4/5 intervertebral disc. Primary NP cells were stimulated by 10 ng/mL IL-1ß for 24 h to mimic IVDD impairment in vitro. (3) Results: circFGFBP1 was downregulated in the IVDD samples. circFGFBP1 upregulation inhibited apoptosis and extracellular matrix (ECM) degradation and promoted proliferation in IL-1ß-stimulated NP cells. Additionally, circFGFBP1 upregulation mitigated the loss of NP tissue and the destruction of the intervertebral disc structure in vivo during IVDD. FOXO3 could bind to the circFGFBP1 promoter to enhance its expression. circFGFBP1 upregulated BMP2 expression in NP via sponging miR-9-5p. FOXO3 enhanced the protection of circFGFBP1 in IL-1ß-stimulated NP cells, whereas a miR-9-5p increase partly reversed the protection. miR-9-5p downregulation contributed to the survival of IL-1ß-stimulated NP cells, which was partially reversed by BMP2 silence. (4) Conclusions: FOXO3 could activate the transcription of circFGFBP1 via binding to its promoter, which resulted in the enhancement of BMP2 via sponging miR-9-5p and then inhibited apoptosis and ECM degradation in NP cells during IVDD.

Polyvascular Disease and the Incidence of Cancer in Patients with Coronary Artery Disease.

Introduction: Based on the possible relation of atherosclerotic cardiovascular disease to the development of cancer, we examined whether polyvascular disease, as a surrogate marker of the severity of atherosclerosis, is associated with the incidence of cancer in patients with coronary artery disease (CAD). Methods: A total of 8,856 patients with CAD between January 2009 and July 2014 were eligible for this observational study. Two cohorts were established based on the presence or absence of polyvascular disease (i.e., polyvascular disease and CAD only) and tracked for the incidence of cancer and all causes of death. Polyvascular disease was defined when accompanied by diagnosed aortic and/or peripheral arterial disease or other arterial diseases at enrollment. Results: With a median follow-up of 1,095 d, the incidence of cancer was markedly higher in the cohort of 716 patients with polyvascular disease than in the cohort of 8,140 patients with CAD only (8.8% vs. 4.9%, P = 0.0001). A large difference in the incidence of cancer was also found in accordance with a number of the coexisting vascular disease with CAD. With the adjustment of shared common risks, polyvascular disease was an independent contributor to the incidence of cancer (hazard ratio, 1.362; 95% confidence interval [CI], 1.029-1.774). In a total of 548 patients (6.2% of participants) died during follow-up, and all-cause, cardiovascular, and cancer mortalities were all higher in the cohort with polyvascular disease than in the cohort with CAD only. Conclusion: The presence of polyvascular disease may be associated with the incidence of cancer in patients with CAD, implying a pivotal role of the severity of atherosclerosis in cancer development (ClinicalTrials.gov. number: NCT04198896).

Auxiliary Segmentation Method of Osteosarcoma MRI Image Based on Transformer and U-Net.

One of the most prevalent malignant bone tumors is osteosarcoma. The diagnosis and treatment cycle are long and the prognosis is poor. It takes a lot of time to manually identify osteosarcoma from osteosarcoma magnetic resonance imaging (MRI). Medical image processing technology has greatly alleviated the problems faced by medical diagnoses. However, MRI images of osteosarcoma are characterized by high noise and blurred edges. The complex features increase the difficulty of lesion area identification. Therefore, this study proposes an osteosarcoma MRI image segmentation method (OSTransnet) based on Transformer and U-net. This technique primarily addresses the issues of fuzzy tumor edge segmentation and overfitting brought on by data noise. First, we optimize the dataset by changing the precise spatial distribution of noise and the data-increment image rotation process. The tumor is then segmented based on the model of U-Net and Transformer with edge improvement. It compensates for the limitations of U-semantic Net by using channel-based transformers. Finally, we also add an edge enhancement module (BAB) and a combined loss function to improve the performance of edge segmentation. The method's accuracy and stability are demonstrated by the detection and training results based on more than 4,000 MRI images of osteosarcoma, which also demonstrate how well the method works as an adjunct to clinical diagnosis and treatment.

Intelligent Assistant Diagnosis System of Osteosarcoma MRI Image Based on Transformer and Convolution in Developing Countries.

Osteosarcoma is a malignant bone tumor commonly found in adolescents or children, with high incidence and poor prognosis. Magnetic resonance imaging (MRI), which is the more common diagnostic method for osteosarcoma, has a very large number of output images with sparse valid data and may not be easily observed due to brightness and contrast problems, which in turn makes manual diagnosis of osteosarcoma MRI images difficult and increases the rate of misdiagnosis. Current image segmentation models for osteosarcoma mostly focus on convolution, whose segmentation performance is limited due to the neglect of global features. In this paper, we propose an intelligent assisted diagnosis system for osteosarcoma, which can reduce the burden of doctors in diagnosing osteosarcoma from three aspects. First, we construct a classification-image enhancement module consisting of resnet18 and DeepUPE to remove redundant images and improve image clarity, which can facilitate doctors' observation. Then, we experimentally compare the performance of serial, parallel, and hybrid fusion transformer and convolution, and propose a Double U-shaped visual transformer with convolution (DUconViT) for automatic segmentation of osteosarcoma to assist doctors' diagnosis. This experiment utilizes more than 80,000 osteosarcoma MRI images from three hospitals in China. The results show that DUconViT can better segment osteosarcoma with DSC 2.6% and 1.8% higher than Unet and Unet++, respectively. Finally, we propose the pixel point quantification method to calculate the area of osteosarcoma, which provides more reference basis for doctors' diagnosis.

Cost-effectiveness analysis of patent foramen ovale closure versus medical therapy alone after cryptogenic stroke.

BACKGROUND: Closure of a patent foramen ovale reduces the risk of recurrent stroke compared with medical therapy alone in young patients with cryptogenic strokes revealed by randomized control trials. Some cost-effectiveness analyses outside Japan have shown that patent foramen ovale closure is cost-effective, but no studies have examined cost-effectiveness in Japan. The objective of this study is to assess cost-effectiveness, from the perspective of a Japanese healthcare payer, of patent foramen ovale closure versus medical therapy alone for patients with patent foramen ovale related to cryptogenic strokes. METHODS: A cost-effectiveness study was conducted by developing a decision tree and a Markov model. Probabilities and a 5.9-year time horizon followed the RESPECT study. Utilities and costs were based upon published studies and assumptions. All assumptions were assessed by experts, including a cardiologist and a statistical expert. The target population comprised patients with cryptogenic stroke and patent foramen ovale, aged 60 years or younger. The model was discounted at 2.0% and its cycle was one month. A willingness-to-pay threshold is set at $50,000 / quality-adjusted life years (QALYs). Incremental cost-effectiveness ratio was evaluated. Then one-way sensitivity analyses as deterministic sensitivity analysis, and probabilistic sensitivity analyses were performed to assess data robustness. RESULTS: Incremental quality-adjusted life years, incremental costs, and incremental cost-effectiveness ratio were 0.464, $13,562, and $29,208 per QALY gained, respectively. One-way sensitivity analysis showed that the stable state utility score difference between patent foramen ovale closure and medical therapy had the largest impact on incremental cost-effectiveness ratio. Patent foramen ovale closure is cost-effective at a stable state utility score difference of >0.051, compared with medical therapy. Probabilistic sensitivity analyses demonstrated that patent foramen ovale closure was 50.3% cost-effective. CONCLUSIONS: Patent foramen ovale closure was cost-effective compared with medical therapy for Japanese patients with cryptogenic stroke who were ≤60 years.

An Artificial Intelligence Multiprocessing Scheme for the Diagnosis of Osteosarcoma MRI Images.

Osteosarcoma is the most common malignant osteosarcoma, and most developing countries face great challenges in the diagnosis due to the lack of medical resources. Magnetic resonance imaging (MRI) has always been an important tool for the detection of osteosarcoma, but it is a time-consuming and labor-intensive task for doctors to manually identify MRI images. It is highly subjective and prone to misdiagnosis. Existing computer-aided diagnosis methods of osteosarcoma MRI images focus only on accuracy, ignoring the lack of computing resources in developing countries. In addition, the large amount of redundant and noisy data generated during imaging should also be considered. To alleviate the inefficiency of osteosarcoma diagnosis faced by developing countries, this paper proposed an artificial intelligence multiprocessing scheme for pre-screening, noise reduction, and segmentation of osteosarcoma MRI images. For pre-screening, we propose the Slide Block Filter to remove useless images. Next, we introduced a fast non-local means algorithm using integral images to denoise noisy images. We then segmented the filtered and denoised MRI images using a U-shaped network (ETUNet) embedded with a transformer layer, which enhances the functionality and robustness of the traditional U-shaped architecture. Finally, we further optimized the segmented tumor boundaries using conditional random fields. This paper conducted experiments on more than 70,000 MRI images of osteosarcoma from three hospitals in China. The experimental results show that our proposed methods have good results and better performance in pre-screening, noise reduction, and segmentation.

Rethinking U-Net from an Attention Perspective with Transformers for Osteosarcoma MRI Image Segmentation.

Osteosarcoma is one of the most common primary malignancies of bone in the pediatric and adolescent populations. The morphology and size of osteosarcoma MRI images often show great variability and randomness with different patients. In developing countries, with large populations and lack of medical resources, it is difficult to effectively address the difficulties of early diagnosis of osteosarcoma with limited physician manpower alone. In addition, with the proposal of precision medicine, existing MRI image segmentation models for osteosarcoma face the challenges of insufficient segmentation accuracy and high resource consumption. Inspired by transformer's self-attention mechanism, this paper proposes a lightweight osteosarcoma image segmentation architecture, UATransNet, by adding a multilevel guided self-aware attention module (MGAM) to the encoder-decoder architecture of U-Net. We successively perform dataset classification optimization and remove MRI image irrelevant background. Then, UATransNet is designed with transformer self-attention component (TSAC) and global context aggregation component (GCAC) at the bottom of the encoder-decoder architecture to perform integration of local features and global dependencies and aggregation of contexts to learned features. In addition, we apply dense residual learning to the convolution module and combined with multiscale jump connections, to improve the feature extraction capability. In this paper, we experimentally evaluate more than 80,000 osteosarcoma MRI images and show that our UATransNet yields more accurate segmentation performance. The IOU and DSC values of osteosarcoma are 0.922 ± 0.03 and 0.921 ± 0.04, respectively, and provide intuitive and accurate efficient decision information support for physicians.

Deep Active Learning Framework for Lymph Node Metastasis Prediction in Medical Support System.

Assessing the extent of cancer spread by histopathological analysis of sentinel axillary lymph nodes is an important part of breast cancer staging. With the maturity and prevalence of deep learning technology, building auxiliary medical systems can help to relieve the burden of pathologists and increase the diagnostic precision and accuracy during this process. However, such histopathological images have complex patterns that are difficult for ordinary people to understand and require professional medical practitioners to annotate. This increases the cost of constructing such medical systems. To reduce the cost of annotating and improve the performance of the model as much as possible, in other words, using as few labeled samples as possible to obtain a greater performance improvement, we propose a deep learning framework with a three-stage query strategy and novel model update strategy. The framework first trains an auto-encoder with all the samples to obtain a global representation in a low-dimensional space. In the query stage, the unlabeled samples are first selected according to uncertainty, and then, coreset-based methods are employed to reduce sample redundancy. Finally, distribution differences between labeled samples and unlabeled samples are evaluated and samples that can quickly eliminate the distribution differences are selected. This method achieves faster iterative efficiency than the uncertainty strategies, representative strategies, or hybrid strategies on the lymph node slice dataset and other commonly used datasets. It reaches the performance of training with all data, but only uses 50% of the labeled. During the model update process, we randomly freeze some weights and only train the task model on new labeled samples with a smaller learning rate. Compared with fine-tuning task model on new samples, large-scale performance degradation is avoided. Compared with the retraining strategy or the replay strategy, it reduces the training cost of updating the task model by 79.87% and 90.07%, respectively.

Use of the NOBLADS Score to Predict Endoscopic Treatment in Patients with Colonic Diverticular Bleeding by Age Stratification.

Objective A high NOBLADS score reflecting the severity of lower gastrointestinal bleeding contributes to the identification of stigmata of recent hemorrhage (SRH) in colonic diverticular bleeding (CDB). The burden of colonoscopy is particularly high in elderly patients; therefore, we investigated the utility of the NOBLADS score for managing CDB by age stratification. The NOBLADS score performance in SRH prediction was estimated by the area under the receiver operating characteristic calculation and a multiple logistic regression model. Methods This was a single-center, retrospective cohort study. Patients who underwent initial colonoscopy with CDB between April 2008 and December 2019 were divided into a young group (<65 years old) and an elderly group (≥65 years old). We further categorized patients according to colonoscopy findings as SRH-positive, with successful endoscopic hemostasis performance, and SRH-negative, with suspected CDB. The main outcome measure was successful SRH identification. Results Four-hundred and seventeen CDB patients were included, of whom 250 (60.0%) were elderly. There were 72 (43.1%) SRH-positive patients in the young group and 94 (37.6%) in the elderly group. The areas under the receiver operating characteristic curves of the NOBLADS score predicting SRH identification were 0.76, 0.71, and 0.81 for all ages, young patients, and elderly patients, respectively. A multiple logistic regression analysis showed that SRH identification was significantly associated with NOBLADS scores in both groups. Eighty-one patients (32.4%) scored ≥4 in the elderly group, and 60 of those were SRH-positive (74.1%). All 27 patients (10.8%) who scored ≥4 with extravasation on computed tomography were found to have SRH. Conclusion The NOBLADS score is useful for predicting SRH identification, especially in elderly patients.

Intelligent Segmentation Medical Assistance System for MRI Images of Osteosarcoma in Developing Countries.

Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. It has a high degree of malignancy and a poor prognosis in developing countries. The doctor manually explained that magnetic resonance imaging (MRI) suffers from subjectivity and fatigue limitations. In addition, the structure, shape, and position of osteosarcoma are complicated, and there is a lot of noise in MRI images. Directly inputting the original data set into the automatic segmentation system will bring noise and cause the model's segmentation accuracy to decrease. Therefore, this paper proposes an osteosarcoma MRI image segmentation system based on a deep convolution neural network, which solves the overfitting problem caused by noisy data and improves the generalization performance of the model. Firstly, we use Mean Teacher to optimize the data set. The noise data is put into the second round of training of the model to improve the robustness of the model. Then, we segment the image using a deep separable U-shaped network (SepUNet) and conditional random field (CRF). SepUnet can segment lesion regions of different sizes at multiple scales; CRF further optimizes the boundary. Finally, this article calculates the area of the tumor area, which provides a more intuitive reference for assisting doctors in diagnosis. More than 80000 MRI images of osteosarcoma from three hospitals in China were tested. The results show that the proposed method guarantees the balance of speed, accuracy, and cost under the premise of improving accuracy.

Temporal trends in clinical outcomes after percutaneous coronary intervention: a systematic review of 66,327 patients from 25 all-comers trials.

BACKGROUND: With the improvements of percutaneous coronary intervention (PCI) technology and post-PCI patient management, several registry studies reported temporal trends in post-PCI clinical outcomes. However, their results are inconclusive, potentially reflecting region-specific trends, based on site-reported events without external validity. AIMS: This study aimed to investigate temporal trends in post-PCI clinical outcomes in all-comers randomised controlled trials (RCTs) involving coronary stents. METHODS: We performed a systematic review identifying RCTs comparing a clinical outcome as a primary endpoint among different coronary stents with an all-comers design and independent clinical event adjudication, extracting the study start year, patient baseline characteristics, and one- and five-year clinical outcomes. Temporal trends in clinical outcomes (cardiac death, myocardial infarction [MI], target lesion revascularisation [TLR], stent thrombosis [ST]) were assessed using random-effects meta-regression analyses, estimating the relationship between clinical outcomes and study start year. RESULTS: Overall, 25 all-comers trials (51 device arms, 66,327 patients) conducted between 2003 and 2018 fulfilled the eligibility criteria. Random-effects meta-regression analysis revealed significant decreasing trends in one- and five-year cardiac death, one-year TLR, and five-year ST incidences (relative risk per 10-year increase: 0.69 [0.51-0.92], 0.66 [0.44-0.98], 0.60 [0.41-0.88], and 0.18 [0.07-0.44], respectively). There was no significant trend in myocardial infarction incidences. CONCLUSIONS: This is the first attempt to clarify and quantify the temporal trends of post-PCI outcome incidence. The 15-year improvements in PCI therapy and post-therapeutic patient management are associated with reduced incidences of cardiac death and PCI-related adverse events.

The relationship between coronary stent strut thickness and the incidences of clinical outcomes after drug-eluting stent implantation: A systematic review and meta-regression analysis.

BACKGROUND: Drug-eluting stents (DESs) have been developed with thinner stent struts, and more biocompatible polymers and anti-proliferative drugs to improve the clinical performance. However, it remains unclear whether thinner struts are associated with favorable short- and long-term clinical outcomes such as target lesion revascularization (TLR), periprocedural myocardial infarction (PMI), and stent thrombosis (ST). METHODS: We searched MEDLINE, Embase and other online sources for randomized controlled trials (RCTs) comparing clinical outcomes between a DES and other stent(s), with independent clinical event adjudication. We investigated stent-related events (TLR, PMI, and ST) in 5 years. Each outcome was analyzed with random-effects meta-regression model against strut thickness, then adjusted for DES generation and patient and lesion characteristics. RESULTS: We identified 49 RCTs enrolling 97,465 patients, of which strut thickness ranged from 60 to 140 µm. Incidences of 1-year TLR, PMI, and early ST were reduced with thinner stent struts, when adjusted for stent generation (adjusted relative risk [RR] per 10 µm increase 1.12 [95% CI 1.04-1.21], 1.15 [95% CI 1.05-1.26], and 1.15 [95% CI 1.06-1.25], respectively). Strut thickness was not independently associated with incidences of 5-year TLR, late and very late ST. In addition, early DESs contributed to a higher incidence of very late ST (adjusted RR 2.97 [95% CI 1.36-6.50]). CONCLUSIONS: In this meta-regression analysis, a thinner strut thickness was associated with reduced incidences of early stent-related adverse events (1-year TLR, PMI, and early ST), but not with later events (5-year TLR, late ST, and very late ST).