BACKGROUND AND OBJECTIVES: The incidence of imminent new vertebral fracture (NVF) is notably high after vertebral augmentation (VA), but accurately assessing the imminent risk of NVF remains a great challenge. The aim of this study was to investigate whether the MRI-based vertebral bone quality (VBQ) score can predict the risk of imminent NVF after VA within a 2-year period. METHODS: A total of 135 patients age 50 years and older who suffered from painful osteoporotic vertebral compression fracture and treated with VA were enrolled in this retrospective study. Each patient's VBQ scores were calculated from T1-weighted, T2-weighted, and short tau inversion recovery sequences of preoperative lumbar MRI. The clinical factors and VBQ score were integrated to create a predictive model by using the logistic regression algorithm and visualize by nomogram. Receiver operating characteristic curve, calibration curve, and decision curve analyses were used to evaluate the predictive performance of the nomogram. RESULTS: The mean VBQ-T1WI and VBQ-T2WI scores of the NVF group were 4.61 ± 0.55 and 0.89 ± 0.14, respectively, which were significantly higher than those of the without NVF group (3.99 ± 0.54 and 0.79 ± 0.12, respectively, P < .001), as well as the VBQ-combined score (0.75 ± 1.30 vs −0.80 ± 1.26, P < .001), which is the combination of VBQ-T1WI and VBQ-T2WI scores. On multivariate analysis, the predictors of imminent NVF included age (odds ratio [OR] = 1.064, 95% CI = 1.009-1.122, P = .022), previous vertebral fracture (OR = 2.089, 95% CI = 0.888-4.915, P = .091), and VBQ-combined score (OR = 2.239, 95% CI = 1.529-3.279, P < .001). The nomogram achieved superior performance with an area under the receiver operating characteristic curve of 0.838 (95% CI: 0.773-0.904) in predicting the imminent NVF compared to the clinical factors or VBQ-combined score alone. CONCLUSION: The VBQ score obtained from lumbar MRI can be used to assess the VBQ and predict the imminent NVF after VA in patients with osteoporotic vertebral compression fracture.

Early identification for hematoma expansion can help improve patient outcomes. Presently, there are many methods to predict hematoma expansion. This study compared a variety of models to find a model suitable for clinical promotion. Non-contrast head CT images and clinical data were collected from 203 patients diagnosed with hypertensive intracerebral hemorrhage. Radiomics features were extracted from all CT images, and the dataset was randomly divided into training and validation sets (7:3 ratio) after applying the synthetic minority oversampling method. The radiomics score (Radscore) was calculated using least absolute shrinkage and selection operator (LASSO) regression, combined with selected clinical predictors, to develop a nomogram and four machine learning (ML) models: logistic regression, random forest, support vector machine, and extreme gradient boosting (XGBoost). Discrimination, calibration and clinical usefulness of the nomogram and ML models were assessed. The nomogram and ML models were integrated with Radscore and clinical predictors. The nomogram demonstrated favorable discriminatory ability in the training set with an AUC of 0.80, which was confirmed in the validation set (AUC=0.76). Among the ML models, the XGBoost model achieved the highest AUC (training set=0.89 and validation set=0.85), surpassing that of the nomogram. The XGBoost model exhibited good clinical usefulness. Both the nomogram and ML models constructed by non-contrast head CT image-based Radscore integrated with clinical predictors can predict early hematoma expansion of hypertensive intracerebral hemorrhage, and the XGBoost model had the highest prediction performance and best clinical usefulness.

Background: Accurately predicting the occurrence of imminent new vertebral fractures (NVFs) in patients with osteoporotic vertebral compression fractures (OVCFs) undergoing vertebral augmentation (VA) is challenging with yet no effective approach. This study aim to examine a machine learning model based on radiomics signature and clinical factors in predicting imminent new vertebral fractures after vertebral augmentation. Methods: A total of 235 eligible patients with OVCFs who underwent VA procedures were recruited from two independent institutions and categorized into three groups, including training set (n = 138), internal validation set (n = 59), and external validation set (n = 38). In the training set, radiomics features were computationally retrieved from L1 or adjacent vertebral body (T12 or L2) on T1-w MRI images, and a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm (LASSO). Predictive radiomics signature and clinical factors were fitted into two final prediction models using the random survival forest (RSF) algorithm or COX proportional hazard (CPH) analysis. Independent internal and external validation sets were used to validate the prediction models. Results: The two prediction models were integrated with radiomics signature and intravertebral cleft (IVC). The RSF model with C-indices of 0.763, 0.773, and 0.731 and time-dependent AUC (2 years) of 0.855, 0.907, and 0.839 (p < 0.001 for all) was found to be better predictive than the CPH model in training, internal and external validation sets. The RSF model provided better calibration, larger net benefits (determined by decision curve analysis), and lower prediction error (time-dependent brier score of 0.156, 0.151, and 0.146, respectively) than the CPH model. Conclusions: The integrated RSF model showed the potential to predict imminent NVFs following vertebral augmentation, which will aid in postoperative follow-up and treatment.

Purpose: This study aimed to evaluate the influence of the triglyceride-glucose index (TyG index) on clinical outcomes and to develop nomogram models for predicting early neurological deterioration (END) and long-term prognosis in acute ischemic stroke (AIS) patients following intravenous thrombolytic (IVT) therapy. Methods: We conducted a multi-center retrospective cohort study involving 333 AIS patients treated with IVT. The short-term and long-term outcomes were defined as the occurrence of END and 90-day prognosis. Multivariate logistic regression was used to develop nomogram models for forecasting these clinical outcomes. Results: Patients in the high-TyG group exhibited significantly higher risks of END ( P = 0.0010) and poor 90-day outcomes ( P = 0.0012). Independent risk factors for END included a lower baseline NIHSS score, delayed door-to-needle time (DNT), reduced ASPECTS score, elevated TyG index, higher potassium (K+) levels, and incomplete Willis artery. Additionally, a higher initial NIHSS, increased TyG levels, presence of END, and a history of hypertension were predictors of poor prognosis. Based on the identified risk factors, two nomogram models yielded AUC values of 0.746 and 0.849 for predicting END and poor prognosis, respectively. NIHSS scores, TyG index, and admission glucose levels (Glu) emerged as prognostic indicators across all patients, while higher mean platelet volume (MPV) and history of stroke were identified as novel risk factors for poor prognosis in NO-END group. Conclusion: A higher TyG index correlates with poor clinical outcomes in AIS patients post-IVT. The nomograms combining the TyG index with various factors enhanced risk prediction for END and poor prognosis.