Development of shielding evaluation and management program for O-ring type linear accelerators.

The shielding parameters can vary depending on the geometrical structure of the linear accelerators (LINAC), treatment techniques, and beam energies. Recently, the introduction of O-ring type linear accelerators is increasing. The objective of this study is to evaluate the shielding parameters of new type of linac using a dedicated program developed by us named ORSE (O-ring type Radiation therapy equipment Shielding Evaluation). The shielding evaluation was conducted for a total of four treatment rooms including Elekta Unity, Varian Halcyon, and Accuray Tomotherapy. The developed program possesses the capability to calculate transmitted dose, maximum treatable patient capacity, and shielding wall thickness based on patient data. The doses were measured for five days using glass dosimeters to compare with the results of program. The IMRT factors and use factors obtained from patient data showed differences of up to 65.0% and 33.8%, respectively, compared to safety management report. The shielding evaluation conducted in each treatment room showed that the transmitted dose at every location was below 1% of the dose limit. The results of program and measurements showed a maximum difference of 0.003 mSv/week in transmitted dose. The ORSE program allows for the shielding evaluation results to the clinical environment of each institution based on patient data.

Treatment of chronic-phase chronic myeloid leukemia in patients randomized to dasatinib or imatinib after suboptimal responses to three months of imatinib therapy: final 5-year results from DASCERN.

Early molecular response (EMR) at 3 months is predictive of improved overall survival (OS) and progression-free survival (PFS) in patients with chronic myeloid leukemia in the chronic phase (CML-CP). Although about one-third of patients treated with first-line imatinib do not achieve EMR, long-term OS and PFS are still observed in most patients. DASCERN (NCT01593254) is a prospective, phase IIb, randomized trial evaluating a switch to dasatinib in patients who have not achieved EMR after 3 months of treatment with first-line imatinib. Early analysis demonstrated an improved major molecular response (MMR) rate at 12 months with dasatinib versus imatinib (29% vs. 13%, P=0.005). Here, we report results from the final 5-year follow-up. In total, 174 patients were randomized to dasatinib and 86 to remain on imatinib. Forty-six (53%) patients who remained on imatinib but subsequently experienced failure were allowed to cross over to dasatinib per protocol. At a minimum follow-up of 60 months, the cumulative MMR rate was significantly higher in patients randomized to dasatinib versus imatinib (77% vs. 44%, P.

Dose-toxicity surface histogram-based prediction of radiation dermatitis severity and shapes.

OBJECTIVE: This study aimed to develop a new approach to predict radiation dermatitis (RD) by using the skin dose distribution in the actual area of RD occurrence to determine the predictive dose by grade. Approach: Twenty-three patients with head and neck cancer treated with volumetric modulated arc therapy were prospectively and retrospectively enrolled. A framework was developed to segment the RD occurrence area in skin photography by matching the skin surface image obtained using a 3D camera with the skin dose distribution. RD predictive doses were generated using the dose-toxicity surface histogram (DTH) calculated from the skin dose distribution within the segmented RD regions classified by severity. We then evaluated whether the developed DTH-based framework could visually predict RD grades and their occurrence areas and shapes according to severity. Main results: The developed framework successfully generated the DTH for three different RD severities: faint erythema (grade 1), dry desquamation (grade 2), and moist desquamation (grade 3); 48 DTHs were obtained from 23 patients: 23, 22, and 3 DTHs for grades 1, 2, and 3, respectively. The RD predictive doses determined using DTHs were 28.9 Gy, 38.1 Gy, and 54.3 Gy for grades 1, 2, and 3, respectively. The estimated RD occurrence area visualized by the DTH-based RD predictive dose showed acceptable agreement for all grades compared with the actual RD region in the patient. The predicted RD grade was accurate, except in two patients. Significance: The developed DTH-based framework can classify and determine RD predictive doses according to severity and visually predict the occurrence area and shape of different RD severities. The proposed approach can be used to predict the severity and shape of potential RD in patients and thus aid physicians in decision making.

Asciminib monotherapy in patients with chronic-phase chronic myeloid leukemia with the T315I mutation after ≥1 prior tyrosine kinase inhibitor: 2-year follow-up results.

Asciminib targets the BCR::ABL1 myristoyl pocket, maintaining activity against BCR::ABL1T315I, which is resistant to most approved adenosine triphosphate-competitive tyrosine kinase inhibitors. We report updated phase I results (NCT02081378) assessing safety/tolerability and antileukemic activity of asciminib monotherapy 200 mg twice daily in 48 heavily pretreated patients with T315I-mutated chronic-phase chronic myeloid leukemia (CML-CP; data cutoff: January 6, 2021). With 2 years' median exposure, 56.3% of patients continued receiving asciminib. Overall, 62.2% of evaluable patients achieved BCR::ABL1 ≤1% on the International Scale (IS); 47.6% and 81.3% of ponatinib-pretreated and -naive patients, respectively, achieved BCR::ABL1IS ≤1%. Of 45 evaluable patients, 48.9% achieved a major molecular response (MMR, BCR::ABL1IS ≤0.1%), including 34.6% and 68.4% of ponatinib-pretreated and -naive patients, respectively. MMR was maintained until data cutoff in 19 of 22 patients who achieved it. The most common grade ≥3 adverse events (AEs) included increased lipase level (18.8%) and thrombocytopenia (14.6%). Five (10.4%) patients experienced AEs leading to discontinuation, including 2 who discontinued asciminib and died due to COVID-19; these were the only deaths reported. These results show asciminib's effectiveness, including in almost 50% of ponatinib pretreated patients, and confirm its risk-benefit profile, supporting its use as a treatment option for T315I-mutated CML-CP.

Standardized multi-institutional data analysis of fixed and removable prosthesis: estimation of life expectancy with regards to variable risk factors.

PURPOSE: This study aims to assess and predict lifespan of dental prostheses using newly developed Korean Association of Prosthodontics (KAP) criteria through a large-scale, multi-institutional survey. MATERIALS AND METHODS: Survey was conducted including 16 institutions. Cox proportional hazards model and principal component analysis (PCA) were used to find out relevant factors and predict life expectancy. RESULTS: 1,703 fixed and 815 removable prostheses data were collected and evaluated. Statistically significant factors in fixed prosthesis failure were plaque index and material type, with a median survival of 10 to 18 years and 14 to 20 years each. In removable prosthesis, factors were national health insurance coverage, antagonist type, and prosthesis type (complete or partial denture), with median survival of 10 to 13 years, 11 to 14 years, and 10 to 15 years each. For still-usable prostheses, PCA analysis predicted an additional 3 years in fixed and 4.8 years in removable prosthesis. CONCLUSION: Life expectancy of a prosthesis differed significantly by factors mostly controllable either by dentist or a patient. Overall life expectancy was shown to be longer than previous research.

Real-world effectiveness of a single conventional disease-modifying anti-rheumatic drug (cDMARD) plus an anti-TNF agent versus multiple cDMARDs in rheumatoid arthritis: a prospective observational study.

Objective: The objective of this prospective, observational multicenter study (NCT03264703) was to compare the effectiveness of single conventional disease-modifying anti-rheumatic drug (cDMARD) plus anti-tumor necrosis factor (TNF) therapy versus multiple cDMARD treatments in patients with moderate-to-severe rheumatoid arthritis (RA) following cDMARD failure in the real-world setting in South Korea. Methods: At the treating physicians' discretion, patients received single cDMARD plus anti-TNF therapy or multiple cDMARDs. Changes from baseline in disease activity score 28-joint count with erythrocyte sedimentation rate (DAS28-ESR), corticosteroid use, and Korean Health Assessment Questionnaire (KHAQ-20) scores were evaluated at 3, 6, and 12 months. Results: Of 207 enrollees, the final analysis included 45 of 73 cDMARD plus anti-TNF and 91 of 134 multiple-cDMARD recipients. There were no significant between-group differences (BGDs) in ANCOVA-adjusted changes from baseline in DAS28-ESR at 3, 6 (primary endpoint), and 12 months (BGDs -0.18, -0.38, and -0.03, respectively). More cDMARD plus anti-TNF than multiple-cDMARD recipients achieved a >50% reduction from baseline in corticosteroid dosage at 12 months (35.7% vs 14.6%; p=0.007). Changes from baseline in KHAQ-20 scores at 3, 6, and 12 months were significantly better with cDMARD plus anti-TNF therapy than with multiple cDMARDs (BGD -0.18, -0.19, and -0.19 points, respectively; all p≤0.024). Conclusion: In the real-world setting, relative to multiple cDMARDs, single cDMARD plus anti-TNF therapy significantly improved quality-of-life scores and reduced corticosteroid use, with no significant BGD in disease activity, in RA patients in whom previous cDMARD therapy had failed.

Qualitative evaluation of artificial intelligence-generated weight management diet plans.

Importance: The transformative potential of artificial intelligence (AI), particularly via large language models, is increasingly being manifested in healthcare. Dietary interventions are foundational to weight management efforts, but whether AI techniques are presently capable of generating clinically applicable diet plans has not been evaluated. Objective: Our study sought to evaluate the potential of personalized AI-generated weight-loss diet plans for clinical applications by employing a survey-based assessment conducted by experts in the fields of obesity medicine and clinical nutrition. Design setting and participants: We utilized ChatGPT (4.0) to create weight-loss diet plans and selected two control diet plans from tertiary medical centers for comparison. Dietitians, physicians, and nurse practitioners specializing in obesity medicine or nutrition were invited to provide feedback on the AI-generated plans. Each plan was assessed blindly based on its effectiveness, balanced-ness, comprehensiveness, flexibility, and applicability. Personalized plans for hypothetical patients with specific health conditions were also evaluated. Main outcomes and measures: The primary outcomes measured included the indistinguishability of the AI diet plan from human-created plans, and the potential of personalized AI-generated diet plans for real-world clinical applications. Results: Of 95 participants, 67 completed the survey and were included in the final analysis. No significant differences were found among the three weight-loss diet plans in any evaluation category. Among the 14 experts who believed that they could identify the AI plan, only five did so correctly. In an evaluation involving 57 experts, the AI-generated personalized weight-loss diet plan was assessed, with scores above neutral for all evaluation variables. Several limitations, of the AI-generated plans were highlighted, including conflicting dietary considerations, lack of affordability, and insufficient specificity in recommendations, such as exact portion sizes. These limitations suggest that refining inputs could enhance the quality and applicability of AI-generated diet plans. Conclusion: Despite certain limitations, our study highlights the potential of AI-generated diet plans for clinical applications. AI-generated dietary plans were frequently indistinguishable from diet plans widely used at major tertiary medical centers. Although further refinement and prospective studies are needed, these findings illustrate the potential of AI in advancing personalized weight-centric care.

Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT.

This work aims to investigate the clinical feasibility of deep learning-based synthetic CT images for cervix cancer, comparing them to MR for calculating attenuation (MRCAT). Patient cohort with 50 pairs of T2-weighted MR and CT images from cervical cancer patients was split into 40 for training and 10 for testing phases. We conducted deformable image registration and Nyul intensity normalization for MR images to maximize the similarity between MR and CT images as a preprocessing step. The processed images were plugged into a deep learning model, generative adversarial network. To prove clinical feasibility, we assessed the accuracy of synthetic CT images in image similarity using structural similarity (SSIM) and mean-absolute-error (MAE) and dosimetry similarity using gamma passing rate (GPR). Dose calculation was performed on the true and synthetic CT images with a commercial Monte Carlo algorithm. Synthetic CT images generated by deep learning outperformed MRCAT images in image similarity by 1.5% in SSIM, and 18.5 HU in MAE. In dosimetry, the DL-based synthetic CT images achieved 98.71% and 96.39% in the GPR at 1% and 1 mm criterion with 10% and 60% cut-off values of the prescription dose, which were 0.9% and 5.1% greater GPRs over MRCAT images.

Transcriptional Changes in Radiation-Induced Lung Injury: A Comparative Analysis of Two Radiation Doses for Preclinical Research.

In a recent stereotactic body radiation therapy animal model, radiation pneumonitis and radiation pulmonary fibrosis were observed at around 2 and 6 weeks, respectively. However, the molecular signature of this model remains unclear. This study aimed to examine the molecular characteristics at these two stages using RNA-seq analysis. Transcriptomic profiling revealed distinct transcriptional patterns for each stage. Inflammatory response and immune cell activation were involved in both stages. Cell cycle processes and response to type II interferons were observed during the inflammation stage. Extracellular matrix organization and immunoglobulin production were noted during the fibrosis stage. To investigate the impact of a 10 Gy difference on fibrosis progression, doses of 45, 55, and 65 Gy were tested. A dose of 65 Gy was selected and compared with 75 Gy. The 65 Gy dose induced inflammation and fibrosis as well as the 75 Gy dose, but with reduced lung damage, fewer inflammatory cells, and decreased collagen deposition, particularly during the inflammation stage. Transcriptomic analysis revealed significant overlap, but differences were observed and clarified in Gene Ontology and KEGG pathway analysis, potentially influenced by changes in interferon-gamma-mediated lipid metabolism. This suggests the suitability of 65 Gy for future preclinical basic and pharmaceutical research connected with radiation-induced lung injury.

Optogenetic control of mRNA condensation reveals an intimate link between condensate material properties and functions.

Biomolecular condensates, often assembled through phase transition mechanisms, play key roles in organizing diverse cellular activities. The material properties of condensates, ranging from liquid droplets to solid-like glasses or gels, are key features impacting the way resident components associate with one another. However, it remains unclear whether and how different material properties would influence specific cellular functions of condensates. Here, we combine optogenetic control of phase separation with single-molecule mRNA imaging to study relations between phase behaviors and functional performance of condensates. Using light-activated condensation, we show that sequestering target mRNAs into condensates causes translation inhibition. Orthogonal mRNA imaging reveals highly transient nature of interactions between individual mRNAs and condensates. Tuning condensate composition and material property towards more solid-like states leads to stronger translational repression, concomitant with a decrease in molecular mobility. We further demonstrate that ß-actin mRNA sequestration in neurons suppresses spine enlargement during chemically induced long-term potentiation. Our work highlights how the material properties of condensates can modulate functions, a mechanism that may play a role in fine-tuning the output of condensate-driven cellular activities.

Assessment of radio-activation using spectroscopy in medical linear accelerators.

In radiotherapy, when photon energy exceeding 8 MV is utilized, photoneutrons can activate the components within the gantry of the linear accelerator (linac). At the end of the linac's lifecycle, radiation workers are tasked with its dismantling and disposal, potentially exposing them to unintentional radiation. This study aims to identify and measure the radioisotopes generated by this activation through spectroscopy, and to evaluate the effective dose rate. We selected nine medical linacs, considering various factors such as manufacturer (Siemens, Varian, and Elekta), model, energy, period of operation, and workload. We identified the radionuclides in the linac head by employing an in situ high-purity germanium (HPGe) detector. Spectroscopy and dose-rate measurements were conducted post-shutdown. We also measured the dose rates at the beam-exit window following irradiation with 10 MV and 15 MV photon beams. As a result of the spectroscopy, we identified approximately 20 nuclides including those with half-lives of 100 days or longer, such as 54Mn, 60Co, 65Zn, 122Sb, and 198Au. The dose rate measurements after 10 MV irradiation decreased to the background level in 10 min. By contrast, on 15 MV irradiation, the dose rate was 628 nSv/h after 10 min and decreased to 268 nSv/h after 1.5 hours. It was confirmed that the difference in the level of radiation and the type of nuclide depends on the period of use, energy, and workload. However, the type of nuclide does not differ significantly between the linacs. It is necessary to propose appropriate guidelines for the safety of workers, and disposal/move-install should be planned while taking into consideration the equipment's energy usage rate.

Local Structures of Ex-Solved Nanoparticles Identified by Machine-Learned Potentials.

In this study, we identify the local structures of ex-solved nanoparticles using machine-learned potentials (MLPs). We develop a method for training machine-learned potentials by sampling local structures of heterointerface configurations as a training set with its efficacy tested on the Ni/MgO system, illustrating that the error in interface energy is only 0.004 eV/Å2. Using the developed scheme, we train an MLP for the Ni/La0.5Ca0.5TiO3 ex-solution system and identify the local structures for both exo- and endo-type particles. The established model aligns well with the experimental observations, accurately predicting a nucleation size of 0.45 nm. Lastly, the density functional theory calculations on the established atomistic model verify that the kinetic barrier for the dry reforming of methane are substantially reduced by 0.49 eV on the ex-solved catalysts compared to that on the impregnated catalysts. Our findings offer insights into the local structures, growth mechanisms, and underlying origin of the catalytic properties of ex-solved nanoparticles.

Comparative analysis of adverse drug reactions associated with new antiseizure medications from the Korea Adverse Event Reporting System database.

OBJECTIVE: This study aimed to compare and characterize the safety profiles of new antiseizure medications (ASMs) using a nationwide pharmacovigilance database from a long-term perspective in Korea. METHODS: We reviewed adverse event reports from the Korea Adverse Event Reporting System database between January 2013 and December 2022 for descriptive analysis of six new ASMs (lacosamide, levetiracetam, lamotrigine, oxcarbazepine, topiramate, and zonisamide). We investigated the frequency and characteristics of adverse drug reactions (ADRs) based on the MedDRA terminology, system organ classes, and modified WHO classification. RESULTS: We identified 5,733 reported cases of ADRs. The commonly reported ADRs associated with total ASMs were rash/urticaria (1,822, 31.8 %), dizziness (409, 7.1 %), somnolence/drowsiness (311, 5.4 %), and hepatotoxic effects (273, 4.8 %). Type B (idiosyncratic) effects (2,932; 51.1 %) were more commonly reported than Type A (related to known drug mechanisms) effects (2,613; 45.6 %). Skin and subcutaneous tissue disorders and type B effects were most commonly reported for lamotrigine and oxcarbazepine, whereas nervous system disorders and type A effects were most commonly reported for lacosamide, topiramate, and zonisamide. The pediatric group (<18 years) exhibited skin and subcutaneous tissue disorders and type B effects relatively more frequently than the adult and older adult groups. CONCLUSION: Hypersensitivity skin reactions and type B effects remained significant ADRs in the new ASMs; however, type A effects were more commonly reported in some ASMs. The pediatric group showed a higher rate of type B effects. Overall, new ASMs should also be used with caution.

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders.

BACKGROUND: In the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions. RESULTS: We trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data. CONCLUSIONS: 3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.

Evaluation of the deliverability of dynamic conformal arc therapy (DCAT) by gantry wobble and its influence on dose.

We aimed to investigate the deliverability of dynamic conformal arc therapy (DCAT) by gantry wobble owing to the intrinsic inter-segment break of the Elekta linear accelerator (LINAC) and its adverse influence on the dose to the patient. The deliverability of DCAT was evaluated according to the plan parameters, which affect the gantry rotation speed and resultant positional inaccuracies; the deliverability according to the number of control points and dose rates was investigated by using treatment machine log files and dosimetry devices, respectively. A non-negligible degradation in DCAT deliverability due to gantry wobble was observed in both the treatment machine log files and dosimetry devices. The resulting dose-delivery error occurred below a certain number of control points or above a certain dose rate. Dose simulations in the patient domain showed a similar impact on deteriorated deliverability. For targets located primarily in the isocenter, the dose differences were negligible, whereas for organs at risk located mainly off-isocenter, the dose differences were significant up to - 8.77%. To ensure safe and accurate radiotherapy, optimal plan parameters should be selected, and gantry angle-specific validations should be conducted before treatment.

Whole Process of Standardization of Diffusion-Weighted Imaging: Phantom Validation and Clinical Application According to the QIBA Profile.

Background: To use the apparent diffusion coefficient (ADC) as reliable biomarkers, validation of MRI equipment performance and clinical acquisition protocols should be performed prior to application in patients. This study aims to validate various MRI equipment and clinical brain protocols for diffusion weighted imaging (DWI) using commercial phantom, and confirm the validated protocols in patients' images. Methods: The performance of four different scanners and clinical brain protocols were validated using a Quantitative Imaging Biomarker Alliance (QIBA) diffusion phantom and cloud-based analysis tool. We evaluated the performance metrics regarding accuracy and repeatability of ADC measurement using QIBA profile. The validated clinical brain protocols were applied to 17 patients, and image quality and repeatability of ADC were assessed. Results: The MRI equipment performance of all four MRI scanners demonstrated high accuracy in ADC measurement (ADC bias, -2.3% to -0.4%), excellent linear correlation to the reference ADC value (slope, 0.9 to 1.0; R2, 0.999-1.000), and high short-term repeatability [within-subject-coefficient-of-variation (wCV), 0% to 0.3%]. The clinical protocols were also validated by fulfilling QIBA claims with high accuracy (ADC bias, -3.1% to -0.7%) and robust repeatability (wCV, 0% to 0.1%). Brain DWI acquired using the validated clinical protocols showed ideal image quality (mean score ≥ 2.9) and good repeatability (wCV, 1.8-2.2). Conclusions: The whole process of standardization of DWI demonstrated the robustness of ADC with high accuracy and repeatability across diverse MRI equipment and clinical protocols in accordance with the QIBA claims.

Brain-Decellularized ECM-Based 3D Myeloid Sarcoma Platform: Mimicking Adaptive Phenotypic Alterations in the Brain.

Leukemia circulates in the bloodstream and induces various symptoms and complications. Occasionally, these cells accumulate in non-marrow tissues, forming a tumor-like myeloid sarcoma (MS). When the blast-stage leukemia cells invade the brain parenchyma, intracranial MS occurs, leading to a challenging prognosis owing to the limited penetration of cytostatic drugs into the brain and the development of drug resistance. The scarcity of tissue samples from MS makes understanding the phenotypic changes occurring in leukemia cells within the brain environment challenging, thereby hindering development of effective treatment strategies for intracranial MS. This study presents a novel 3D in vitro model mimicking intracranial MS, employing a hydrogel scaffold derived from the brain-decellularized extracellular matrix in which suspended leukemia cells are embedded, simulating the formation of tumor masses in the brain parenchyma. This model reveals marked phenotypic changes in leukemia cells, including altered survival, proliferation, differentiation, and cell cycle regulation. Notably, proportion of dormant leukemia stem cells increases and expression of multidrug resistance genes is upregulated, leading to imatinib resistance, mirroring the pathological features of in vivo MS tissue. Furthermore, suppression of ferroptosis is identified as an important characteristic of intracranial MS, providing valuable insights for the development of targeted therapeutic strategies.

Harnessing Metabolic Indices as a Predictive Tool for Cardiovascular Disease in a Korean Population without Known Major Cardiovascular Event.

Background: This study evaluated the usefulness of indices for metabolic syndrome, non-alcoholic fatty liver disease (NAFLD), and insulin resistance (IR), as predictive tools for cardiovascular disease in middle-aged Korean adults. Methods: The prospective data obtained from the Ansan-Ansung cohort database, excluding patients with major adverse cardiac and cerebrovascular events (MACCE). The primary outcome was the incidence of MACCE during the follow-up period. Results: A total of 9,337 patients were included in the analysis, of whom 1,130 (12.1%) experienced MACCE during a median follow-up period of 15.5 years. The metabolic syndrome severity Z-score, metabolic syndrome severity score, hepatic steatosis index, and NAFLD liver fat score were found to significantly predict MACCE at values above the cut-off point and in the second and third tertiles. Among these indices, the hazard ratios of the metabolic syndrome severity score and metabolic syndrome severity Z-score were the highest after adjusting for confounding factors. The area under the receiver operating characteristic curve (AUC) of the 10-year atherosclerotic cardiovascular disease (ASCVD) score for predicting MACCE was 0.716, and the metabolic syndrome severity Z-score had an AUC of 0.619. Conclusion: The metabolic syndrome severity score is a highly reliable indicator and was closely associated with the 10-year ASCVD risk score in predicting MACCE in the general population. Given the specific characteristics and limitations of metabolic syndrome severity scores as well as the indices of NAFLD and IR, a more practical scoring system that considers these factors is essential to achieve greater accuracy in forecasting cardiovascular outcomes.