Construction of diagnostic and prognostic models for premature coronary artery disease based on multiple machine algorithms

BackgroundSince the triglyceride glucose (TyG) index can reflect insulin resistance, it has been proven to be an efficient predictor of glycolipid-metabolism-related diseases. Therefore, this study aimed to investigate the predictive value of the TyG index for visceral obesity (VO) and body fat distribution in patients with type 2 diabetes mellitus (T2DM).MethodsAbdominal adipose tissue characteristics in patients with T2DM, including visceral adipose area (VAA), subcutaneous adipose area (SAA), VAA-to-SAA ratio (VSR), visceral adipose density (VAD), and subcutaneous adipose density (SAD), were obtained through analyses of computed tomography images at the lumbar 2/3 level. VO was diagnosed according to the VAA (> 142 cm2 for males and > 115 cm2 for females). Logistic regression was performed to identify independent factors of VO, and receiver operating characteristic (ROC) curves were used to compare the diagnostic performance according to the area under the ROC curve (AUC).ResultsA total of 976 patients were included in this study. VO patients showed significantly higher TyG values than non-VO patients in males (9.74 vs. 8.88) and females (9.59 vs. 9.01). The TyG index showed significant positive correlations with VAA, SAA, and VSR and negative correlations with VAD and SAD. The TyG index was an independent factor for VO in both males (odds ratio [OR] = 2.997) and females (OR = 2.233). The TyG index ranked second to body mass index (BMI) for predicting VO in male (AUC = 0.770) and female patients (AUC = 0.720). Patients with higher BMI and TyG index values showed a significantly higher risk of VO than the other patients. TyG-BMI, the combination index of TyG and BMI, showed significantly higher predictive power than BMI for VO in male patients (AUC = 0.879 and 0.835, respectively) but showed no significance when compared with BMI in female patients (AUC = 0.865 and 0.835, respectively).Conclusions. TyG is a comprehensive indicator of adipose volume, density, and distribution in patients with T2DM and is a valuable predictor for VO in combination with anthropometric indices, such as BMI.

Outcome prediction by serum calprotectin in patients with COVID-19 in the emergency department

Serious bacterial infections (SBIs) are linked to unplanned hospital admissions and a high mortality rate. The early identification of SBIs is crucial in clinical practice. This study aims to establish and validate clinically applicable models designed to identify SBIs in patients with infective fever. Clinical data from 945 patients with infective fever, encompassing demographic and laboratory indicators, were retrospectively collected from a 2200-bed teaching hospital between January 2013 and December 2020. The data were randomly divided into training and test sets at a ratio of 7:3. Various machine learning (ML) algorithms, including Boruta, Lasso (least absolute shrinkage and selection operator), and recursive feature elimination, were utilized for feature filtering. The selected features were subsequently used to construct models predicting SBIs using logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) with 5-fold cross-validation. Performance metrics, including the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), accuracy, sensitivity, and other relevant parameters, were used to assess model performance. Considering both model performance and clinical needs, 2 clinical timing-sequence warning models were ultimately confirmed using LR analysis. The corresponding predictive nomograms were then plotted for clinical use. Moreover, a physician, blinded to the study, collected additional data from the same center involving 164 patients during 2021. The nomograms developed in the study were then applied in clinical practice to further validate their clinical utility. In total, 69.9% (661/945) of the patients developed SBIs. Age, hemoglobin, neutrophil-to-lymphocyte ratio, fibrinogen, and C-reactive protein levels were identified as important features by at least two ML algorithms. Considering the collection sequence of these indicators and clinical demands, 2 timing-sequence models predicting the SBI risk were constructed accordingly: the early admission model (model 1) and the model within 24 hours of admission (model 2). LR demonstrated better stability than RF and XGBoost in both models and performed the best in model 2, with an AUC, accuracy, and sensitivity of 0.780 (95% CI 0.720-841), 0.754 (95% CI 0.698-804), and 0.776 (95% CI 0.711-832), respectively. XGBoost had an advantage over LR in AUC (0.708, 95% CI 0.641-775 vs 0.686, 95% CI 0.617-754), while RF achieved better accuracy (0.729, 95% CI 0.673-780) and sensitivity (0.790, 95% CI 0.728-844) than the other 2 approaches in model 1. Two SBI-risk prediction nomograms were developed for clinical use based on LR, and they exhibited good performance with an accuracy of 0.707 and 0.750 and a sensitivity of 0.729 and 0.927 in clinical application. The clinical timing-sequence warning models demonstrated efficacy in predicting SBIs in patients suspected of having infective fever and in clinical application, suggesting good potential in clinical decision-making. Nevertheless, additional prospective and multicenter studies are necessary to further confirm their clinical utility.

Актуальность. Новые возможности улучшения функции внешнего дыхания (ФВД) связаны с применением высокочастотной осцилляции грудной клетки (ВЧОГК), которая позволяет восстановить дренаж бронхиального дерева и оптимизировать легочную вентиляцию. Цель исследования — изучение и оценка ФВД у детей с внебольничной пневмонией (ВП). Материалы и методы. Проведено обследование 107 детей (основная группа (ОГ) — 55 человек и контрольная группа (КГ) — 52 человека) в возрасте 6–17 лет с ВП с острым и неосложненным течением. Дети ОГ получали базисную терапию (БТ) с проведением процедур ВЧОГК, тогда как дети КГ получали исключительно БТ. Исследовали ФВД в динамике лечения, применяя спирометрию. Результаты. В начале терапии ВП показатели ФВД в исследуемых группах имели несущественные различия. При анализе параметров ФВД у детей ОГ на 10-й день БТ отмечено повышение объема форсированного выдоха за 1-ю секунду (ОФВ1) (88,36 ± 1,55 %, p = 0,02), жизненной емкости легких (ЖЕЛ) (88,18 ± 1,53 %, p = 0,02), форсированной жизненной емкости легких (ФЖЕЛ) (86,77 ± 1,37 %, p = 0,03), максимальной объемной скорости воздуха на уровне выдоха 25 % (МОШ25) (90,10 ± 2,99 %, p = 0,02) и максимальной вентиляции легких (МВЛ) (88,31 ± 1,70 %, p = 0,04). Сравнивая показатели ФВД детей КГ, выявили менее выраженную динамику, в частности, ОФВ1 (81,65 ± 2,44 %), ЖЕЛ (82,95 ± 2,56), ФЖЕЛ (80,85 ± 2,09), МОШ25 (82,63 ± 3,08 %) и МВЛ (85,65 ± 1,99 %). При проведении ROC-анализа динамика восстановления ФВД у детей ОГ более заметна за счет улучшения МВЛ, о чем свидетельствует самая большая площадь под кривой ROC (ППК) — 0,99, ОФВ1 — ППК 0,94 и ОФВ1/ФЖЕЛ — ППК 0,94. У детей КГ была менее выражена динамика исследуемых параметров ФВД, что подтверждается МВЛ — ППК 0,63, ОФВ1 — ППК 0,79 и ОФВ1/ФЖЕЛ — ППК 0,89. Выводы. Для улучшения вентиляционной функции легких у детей с ВП необходимо проводить процедуры бронходренажа, используя ВЧОГК в составе БТ, о чем свидетельствуют полученные положительные результаты спирометрии.

Institutional delivery dropout (IDD), defined as delivery outside a health facility despite attending antenatal care (ANC), remains a significant barrier to reducing maternal mortality in Nigeria. Traditional statistical models often fall short of capturing the complex, non-linear interactions among the socio-demographic factors that drive this critical health behavior. Using a comprehensive dataset of 16,100 women from the 2018 Nigeria Demographic and Health Survey (NDHS), we applied and compared seven diverse machine learning (ML) algorithms, including models such as Support Vector Machine (SVM), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost). The model performance was systematically evaluated using metrics such as accuracy, Area Under the Receiver Operating Characteristic curve (AUROC), F1-score, and detailed confusion matrices. Furthermore, SHapley Additive explanations (SHAP) were used to provide transparent interpretations of feature importance and predictive contributions. Gradient Boosting was the best-performing model, achieving the highest F1-score (0.755) and AUROC (0.82). SVM achieved the highest accuracy (0.740) and recall (0.780). SHAP identified education level, household wealth, and religion as strong predictors of IDD. The performance metrics reported with confidence intervals showed modest variability across the models. Machine learning approaches were effective in identifying women at an increased risk of institutional delivery dropout. SHAP analysis provides insights into the key sociodemographic predictors of IDD, highlighting the value of interpretable ML methods in maternal health research.

Although the goal of rectal cancer treatment is to restore gastrointestinal continuity, some patients with rectal cancer develop a permanent stoma (PS) after sphincter-saving operations. Although many studies have identified the risk factors and causes of PS, few have precisely predicted the probability of PS formation before surgery. To validate whether an artificial intelligence model can accurately predict PS formation in patients with rectal cancer after sphincter-saving operations. Patients with rectal cancer who underwent a sphincter-saving operation at Taipei Medical University Hospital between January 1, 2012, and December 31, 2021, were retrospectively included in this study. A machine learning technique was used to predict whether a PS would form after a sphincter-saving operation. We included 19 routinely available preoperative variables in the artificial intelligence analysis. To evaluate the efficiency of the model, 6 performance metrics were utilized: accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiving operating characteristic curve. In our classification pipeline, the data were randomly divided into a training set (80% of the data) and a validation set (20% of the data). The artificial intelligence models were trained using the training dataset, and their performance was evaluated using the validation dataset. Synthetic minority oversampling was used to solve the data imbalance. A total of 428 patients were included, and the PS rate was 13.6% (58/428) in the training set. The logistic regression (LR), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), random forest, decision tree and light gradient boosting machine (LightGBM) algorithms were employed. The accuracies of the logistic regression (LR), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), random forest (RF), decision tree (DT) and light gradient boosting machine (LightGBM) models were 70%, 76%, 89%, 93%, 95%, 79% and 93%, respectively. The area under the receiving operating characteristic curve values were 0.79 for the LR model, 0.84 for the GNB, 0.95 for the XGB, 0.95 for the GB, 0.99 for the RF model, 0.79 for the DT model and 0.98 for the LightGBM model. The key predictors that were identified were the distance of the lesion from the anal verge, clinical N stage, age, sex, American Society of Anesthesiologists score, and preoperative albumin and carcinoembryonic antigen levels. Integration of artificial intelligence with available preoperative data can potentially predict stoma outcomes after sphincter-saving operations. Our model exhibited excellent predictive ability and can improve the process of obtaining informed consent.

Objectives: Herpes virus entry mediator (HVEM) is a costimulatory molecule, and has been proved to play an important role in airway inflammatory and remodeling processes of asthma. We aimed to investigate the expression of HVEM gene in patients with asthma as a means of assessing disease severity.Methods: This study was carried out on 59 subjects, 16 patients with mild persistent asthma, 11 patients with moderate persistent asthma, 13 patients with severe persistent asthma, and 19 age and gender matched healthy controls. The HVEM mRNA expressions of all subjects were determined by real time PCR. Correlations between HVEM mRNA expression and fractional exhaled nitric oxide (FeNO), pulmonary function test values, total blood white cell count and differential, total immunoglobulin E (IgE) level, and Asthma Control Test (ACT) score were analyzed, respectively. The discrimination abilities of HVEM mRNA between different groups were tested using receiver operating characteristics (ROC) curve analyses.Results: This study showed the expressions of HVEM mRNA were significantly higher in the patients with severe and moderate persistent asthma than in patients with mild persistent asthma and healthy subjects (2.97 ± 1.23 vs. 1.17 ± 0.42 vs. 0.62 ± 0.38 vs. 0.46 ± 0.18/NAPDH, p < 0.001), but there was no significant difference between patients with mild persistent asthma and health controls (0.62 ± 0.38 vs. 0.46 ± 0.18/NAPDH, p = 0.557). HVEM mRNA expression at cut off point [1.01/NAPDH, area under the ROC curve (AUC) = 0.99] is sufficient to discriminate severe patients from mild-to-moderate patients, and at cut off point (0.93/NAPDH, AUC = 0.91) for discrimination of moderate-to-severe patients from mild ones, while at cut off point (0.76/NAPDH, AUC = 0.75) for discrimination of asthmatic patients from controls. Furthermore, HVEM mRNA expression was positively correlated with FeNO level (r = 0.524, p = 0.015), and total lymphocyte count (r = 0.426, p = 0.017) in patients with persistent asthma.Conclusions: HVEM gene expressions can be used as a potential biomarker for evaluating the severity of patients with persistent asthma.

Preoperative assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) is of high clinical relevance for treatment decision-making and prognosis. To investigate the associations of preoperative clinical and magnetic resonance imaging (MRI) characteristics with LVI and disease-free survival (DFS) by using machine learning methods in patients with IBC. Retrospective. Five hundred and seventy-five women (range: 24-79 years) with IBC who underwent preoperative MRI examinations at two hospitals, divided into the training (N=386) and validation datasets (N=189). Axial fat-suppressed T2-weighted turbo spin-echo sequence and dynamic contrast-enhanced with fat-suppressed T1-weighted three-dimensional gradient echo imaging. MRI characteristics (clinical T stage, breast edema score, MRI axillary lymph node status, multicentricity or multifocality, enhancement pattern, adjacent vessel sign, and increased ipsilateral vascularity) were reviewed independently by three radiologists. Logistic regression (LR), eXtreme Gradient Boosting (XGBoost), k-Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms were used to establish the models by combing preoperative clinical and MRI characteristics for assessing LVI status in the training dataset, and the methods were further applied in the validation dataset. The LVI score was calculated using the best-performing of the four models to analyze the association with DFS. Chi-squared tests, variance inflation factors, receiver operating characteristics (ROC), Kaplan-Meier curve, log-rank, Cox regression, and intraclass correlation coefficient were performed. The area under the ROC curve (AUC) and hazard ratios (HR) were calculated. A P-value <0.05 was considered statistically significant. The model established by the XGBoost algorithm had better performance than LR, SVM, and KNN models, achieving an AUC of 0.832 (95% confidence interval [CI]: 0.789, 0.876) in the training dataset and 0.838 (95% CI: 0.775, 0.901) in the validation dataset. The LVI score established by the XGBoost model was an independent indicator of DFS (adjusted HR: 2.66, 95% CI: 1.22-5.80). The XGBoost model based on preoperative clinical and MRI characteristics may help to investigate the LVI status and survival in patients with IBC. 3 TECHNICAL EFFICACY: Stage 2.

Soluble urokinase plasminogen activator receptor (suPAR) is a strong and nonspecific inflammatory biomarker that reflects various immunologic reactions, organ damage, and risk of mortality in the general population. Although prior research in acute medical patients showed that an elevation in suPAR is related to intensive care unit admission and risk of readmission and mortality, no studies have focused on the predictive value of suPAR for preventable emergency attendance (PEA). This study aims to evaluate the predictive value of suPAR, which consists of a combination of white blood cell count (WBC), C-reactive protein (CRP), and the National Early Warning Score (NEWS), for PEA in older patients (>65 years) without trauma who presented to the emergency department (ED). This single-center prospective pilot study was conducted in the ED of the Association of EISEIKAI Medical and Healthcare Corporation Minamitama Hospital, in Hachiouji City, Tokyo, Japan, from September 16, 2020, to June 21, 2022. The study included all patients without trauma aged 65 years or older who were living in their home or a facility and presented to the ED when medical professionals decided an emergency consultation was required. Discrimination was assessed by plotting the receiver-operating characteristic (ROC) curve and calculating the area under the ROC curve (AUC). During the study period, 49 eligible older patients were included, and thirteen (26.5%) PEA cases were detected. The median suPAR was significantly lower in the PEA group than in the non-PEA group (p<0.05). For suPAR, the AUC for the prediction of PEA was 0.678 (95% CI 0.499-0.842, p<0.05), and there was no significant difference from other variables as follows: 0.801 (95% CI 0.673-0.906, p<0.001) for WBC, 0.833 (95% CI 0.717-0.934, p<0.001) for CRP, and 0.693 (95% CI 0.495-0.862, p<0.05) for NEWS. Furthermore, the AUC for predicting PEA was 0.867 (95% CI 0.741-0.959, p<0.001) for suPAR + WBC + CRP + NEWS, which was significantly higher than that of the original suPAR (p<0.01). The cutoff values, sensitivity, specificity, and odds ratio of suPAR and suPAR + WBC + CRP + NEWS were 7.5 and 22.88, 80.6% and 83.3%, 53.8% and 76.9%, and 4.83 and 16.67, respectively. This study has several limitations. First, this was pilot study, and we included a small number of older patients. Second, the COVID-19 pandemic occurred during the study period, so that there may be selection bias in the study population. Third, our hospital is a secondary emergency medical institution, and as such, we did not treat very fatal cases, which could be another cause of selection bias. Our single-center study has demonstrated the moderate utility of the combined suPAR as a triage tool for predicting PEA in older patients without trauma receiving home medical care. Before introducing suPAR to the prehospital setting, evidence from multicenter studies is needed.

Objective To explore the value of quantitative T2 weighted magnetic resonance imaging (T2WI) and diffusion tensor imaging (DTI) parameters for predicting functional outcomes after surgery for cervical spondylotic myelopathy (CSM). Methods One hundred and forty CSM patients received T2WI and DTI before surgery at Tianjin Hospital between April 2014 and April 2016. They were then given systematic rehabilitation treatment after the surgery. The Japanese Orthopaedics Association (JOA) scoring system was applied to evaluate their neurological function before and after the surgery. According to the JOA recovery rate at 1-year follow-up, the patients were divided into a good recovery group (recovery rate≥50%) and a poor recovery group (recovery rate <50%). Maximum spinal cord compression (MSCC), signal change ratio (SCR), transverse area (TA), apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were compared between the two groups. Receiver operating characteristics (ROC) curves were used to measure the prognostic ability and determine the best cut-off value for each variable. The independent predictors of a poor recovery were estimated using univariate and multivariate analysis. Results ROC analysis showed that the area under the ROC curve (AUC) of MSCC was 0.593. For SCR it was 0.682. For the TA it was 0.706. For the ADC it was 0.719 and for FA it was 0.749. The respective cut-off values were 44% compression, 1.561, 0.46 cm2, 1.339×10-3 mm2/s and 0.386. FA had the largest AUC, followed by ADC, TA, SCR and MSCC. MSCC and SCR had low discrimination power (AUC<0.7) in predicting a poor recovery, whereas TA, ADC and FA had moderate discrimination power (AUC 0.7-0.9). Logistic multivariate regression showed that a low JOA score, TA≤0.46 cm2 or FA≤0.386 were independent risk factors for a poor recovery. A predicting model built according to the results of the logistic regression analysis gave an AUC of 0.87, significantly better than that of the JOA score. With a cut-off value of 0.36, the sensitivity and specificity were 80% and 77% respectively. Conclusions Combining T2WI and DTI parameters with the JOA score may better predict the recovery of patients with CSM. The values can also provide references for making up rehabilitation plans. Key words: T2 weighted magnetic resonance imaging; Diffusion tensor imaging; Cervical vertebrae; Spinal cord compression; Prognosis

Identifying optimal candidates for tumor resection among borderline and locally advanced pancreatic cancer: A population-based predictive model

Hypoxemia is a common complication associated with anesthesia in painless gastroscopy. With the aging of the social population, the number of cases of hypoxemia among middle-aged and elderly patients is increasing. However, tools for predicting hypoxemia in middle-aged and elderly patients are lacking. In this study, we investigated the risk factors for hypoxemia in middle-aged and elderly outpatients undergoing painless gastroscopy based on machine learning and constructed a risk prediction model. In this retrospective study, we included the data on 1,348 outpatients undergoing painless gastroscopy. In total, 26 characteristic variables, including demographic information, past medical history, and clinical data of the patients were included, and BorutaShap was used for feature selection. Five machine learning algorithm models, including logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM), were selected. The best models were selected based on the area under the receiver operating characteristic curve (AUROC). Model feature importance was explained and analyzed using Shapley Additive Explanations (SHAP). The endpoint event of this study was considered to be hypoxemia during the procedure, defined as at least one occurrence of pulse oxygen saturation below 90% without probe misalignment or interference from the beginning of anesthesia induction to the end of painless gastroscopy. In the final cohort of 984 patients, 11% of patients (108/984) experienced hypoxemia during the painless gastroscopy procedure. The AUROCs of the five models were as follows: Logistic Regression (AUROC = 0.893, 95CI: 0.881–0.899), SVM (AUROC = 0.855, 95CI: 0.812–0.884), Random Forest (AUROC = 0.914, 95CI: 0.889–0.924), XGB (AUROC = 0.902, 95CI: 0.865–0.919), and LightGBM (AUROC = 0.891, 95CI: 0.847–0.917). Regarding the explanation of the importance of SHAP features, preoperative variables (baseline SpO2, body mass index, and micrognathia) and intraoperative variables (operating time of gastroscopy, induction dose of etomidate and propofol mixture, append anesthetic, cough, and repeated pharyngeal irritation) significantly contributed to the model. We identified eight potential risk factors related to the occurrence of hypoxemia in middle-aged and elderly patients undergoing painless gastroscopy, based on machine learning feature engineering. Among the five machine learning algorithms, RF exhibited the best predictive performance in the internal test set and had a certain degree of generalization ability in the external validation set, which indicated that the RF model was more suitable for the data framework of this study. This model was more likely to enhance the accuracy of hypoxemia prediction in middle-aged and elderly patients undergoing painless gastroscopy, and thus, it is suitable for assisting anesthesiologists in clinical decision-making.

Restriction spectrum imaging (RSI), as an advanced quantitative diffusion-weighted magnetic resonance imaging technique, has the potential to distinguish primary benign and malignant lung lesions. To explore how well the tri-compartmental RSIperforms in distinguishing primary benign from malignant lung lesions compared with diffusion-weighted imaging (DWI), and to further explore whether positron emission tomography/magnetic resonance imaging (PET/MRI) can improve diagnostic efficacy. Prospective. 137 patients, including 108 malignant and 29 benign lesions (85 males, 52 females; average age = 60.0 ± 10.0 years). T2WI, T1WI, multi-b value DWI, MR-based attenuation correction, and PET imaging on a 3.0 T whole-body PET/MR system. The apparent diffusion coefficient (ADC), RSI-derived parameters (restricted diffusion , hindered diffusion , and free diffusion ) and the maximum standardized uptake value (SUVmax) were calculated and analyzed for diagnostic efficacy individually or in combination. Student's t-test, Mann-Whitney U test, receiver operating characteristic (ROC) curves, Delong test, Spearman's correlation analysis. P < 0.05 was considered statistically significant. The , SUVmax were significantly higher, and , ADC were significantly lower in the malignant group [0.717 ± 0.131, 9.125 (5.753, 13.058), 0.194 ± 0.099, 1.240 (0.972, 1.407)] compared to the benign group [0.504 ± 0.236, 3.390 (1.673, 6.030), 0.398 ± 0.195, 1.485 ± 0.382]. The area under the ROC curve (AUC) values ranked from highest to lowest as follows: AUC (SUVmax) > AUC ( ) > AUC ( ) > AUC (ADC) > AUC ( ) (AUC = 0.819, 0.811, 0.770, 0.745, 0549). The AUC (AUC = 0.900) of the combined model of RSI with PET was significantly higher than that of either single-modality imaging. RSI-derived parameters ( , ) might help to distinguish primary benign and malignant lung lesions and the discriminatory utility of was not observed. The RSI exhibits comparable or potentially enhanced performance compared with DWI, and the combined RSI and PET model might improve diagnostic efficacy. Stage 2.

BackgroundThermal imaging (TI) is a portable, low cost imaging tool with high feasibility for use. Clinical joint assessment.Is routinely performed in rheumatoid arthritis (RA) patient care.ObjectivesTo assess a combined TI...

BackgroundThe relationship between the triglyceride–glucose (TyG) index and no-reflow phenomenon after percutaneous coronary intervention (PCI) in patients with type 2 diabetes mellitus (T2DM) and acute ST-segment elevation myocardial infarction (STEMI) remains unclear. This study aimed to investigate the relationship between baseline TyG index and no-reflow phenomenon in STEMI patients with T2DM after PCI.MethodsThis study enrolled 695 patients with T2DM and STEMI from the General Hospital of Ningxia Medical University (2014–2019). Patients were divided into tertiles according to the TyG index levels. The incidence of no-reflow phenomenon was recorded. A multivariate regression model was developed to analyze the association between the baseline TyG index and no-reflow phenomenon. The linear association between the baseline TyG index and no-reflow phenomenon was explored using smooth curve fitting with parallel subgroup analyses. Receiver operating characteristic (ROC) curves were generated to determine the predictive power of the TyG index.ResultsA multivariate logistic regression model revealed that the TyG index was an independent risk factor of no-reflow phenomenon [OR = 3.23, 95%CI: 2.15–4.86, P < 0.001], and the occurrence of no-reflow phenomenon increased gradually with the increase of TyG index tertile interval (P < 0.001). Smooth curve fitting showed that the TyG index was linearly related to the risk of no-reflow. Subgroup analysis showed that they participated in this positive correlation. The area under the ROC curve (AUC) of the TyG index for evaluating the occurrence of no-reflow was 0.710 (95% CI: 0.640–0.780; P < 0.01).ConclusionsThe TyG index is independently associated with no-reflow phenomenon, suggesting that the simple index of the TyG index can be used for risk assessment of no-reflow phenomenon after PCI in STEMI patients with T2DM.