Objective:To establish and validate the diagnostic model of acute pulmonary embolism (APE) based on clinical and laboratory variables.Methods:This retrospective analysis was conducted on patients with suspected APE who underwent CT pulmonary angiography at the First Affiliated Hospital of Henan University of Chinese Medicine between February 2015 and December 2023. The patients were randomly divided into a training set and a validation set at a ratio of 7∶3. Clinical and laboratory data of the enrolled patients were collected, and patients were divided into an APE group and a non-APE group according to CT pulmonary angiography results. In the training set, univariate and multivariate logistic regression as well as Lasso regression were used to identify risk factors for APE, and a diagnostic model was developed and validated. Receiver operating characteristic curves were plotted, and calibration and decision curves were used to assess the performance of the diagnostic model. The diagnostic efficacy of the model was compared with that of the Wells score and the revised Geneva score using the DeLong test.Results:A total of 752 patients were enrolled, aged (64±15) years, including 417 (55.5%) males. The training set included 526 patients and the validation set included 226 patients. The incidence of APE in this cohort was 48.7% (366/752), with 366 cases in the APE group and 386 in the non-APE group. Multivariable logistic regression analysis showed that cyanosis ( OR=8.88, 95% CI 2.04-49.11), elevated neutrophil count ( OR=1.82, 95% CI 1.06-3.15), elevated creatine kinase isoenzyme ( OR=3.45, 95% CI 1.76-6.91), decreased partial pressure of carbon dioxide ( OR=12.88, 95% CI 7.64-22.34), elevated age-adjusted D-dimer ( OR=2.53, 95% CI 1.10-6.20), prolonged thrombin time ( OR=4.08, 95% CI 2.06-8.33), and positive lower limb venous ultrasound for thrombus ( OR=4.39, 95% CI 2.59-7.58) were risk factors associated with APE. The area under the curve ( AUC) of the diagnostic model was 0.92 (95% CI 0.90-0.94) in the training set and 0.92 (95% CI 0.89-0.95) in the validation set. The diagnostic efficacy of this model was superior to that of the Wells score ( AUC: 0.92 vs. 0.63, P<0.01) and the revised Geneva score ( AUC: 0.92 vs. 0.59, P<0.01). Conclusion:The diagnostic model for acute pulmonary embolism constructed based on clinical and laboratory parameters demonstrates excellent diagnostic performance and may facilitate rapid and accurate screening in clinical practice.

Purpose To achieve intelligent detection of acute pulmonary embolism(APE)in CT pulmonary angiography based on the Res2Net attention mechanism network.Materials and Methods Retrospectively included patients with suspected of APE who underwent CT pulmonary angiography examination and were diagnosed as APE at the First Affiliated Hospital of Henan University of Chinese Medicine from February 2015 to May 2023.The dataset was randomly divided into training,validation and test set in a ratio of 7∶2∶1.The model was trained based on the Res2Net network,combined with atrous spatial pyramid pooling and attention mechanism modules,and was performed five-fold cross internal validation.Using the area under the receiver operator characteristic curve,sensitivity and specificity to assess the diagnostic performance of the model.Dice similarity coefficient,precision and intersection over union(IOU)were used to assess the segmentation performance of thrombus on the test set and plot the corresponding curves.The performance of the Res2Net attention mechanism network was compared with the classic U-Net and CE-Net model.Results A total of 303 patients with APE were included in this study.There were 212,61 and 30 cases in the training set,validation set and test set,respectively.The model's area under the curve was 0.95,sensitivity was 0.90,specificity was 1.00,Dice similarity coefficient was 0.86,precision was 0.90,Pos-IOU was 0.78 and Neg-IOU was 1.00,respectively.The parameter curves and radar chart showed that the Res2Net attention mechanism network performed better than the U-Net and CE-Net models.The visualization results of the segmentation comparison showed that the Res2Net attention mechanism network achieved higher precision in segmenting pulmonary artery thrombus.Conclusion The Res2Net attention mechanism network has good performance for detection of APE.

Purpose To achieve intelligent detection of acute pulmonary embolism(APE)in CT pulmonary angiography based on the Res2Net attention mechanism network.Materials and Methods Retrospectively included patients with suspected of APE who underwent CT pulmonary angiography examination and were diagnosed as APE at the First Affiliated Hospital of Henan University of Chinese Medicine from February 2015 to May 2023.The dataset was randomly divided into training,validation and test set in a ratio of 7∶2∶1.The model was trained based on the Res2Net network,combined with atrous spatial pyramid pooling and attention mechanism modules,and was performed five-fold cross internal validation.Using the area under the receiver operator characteristic curve,sensitivity and specificity to assess the diagnostic performance of the model.Dice similarity coefficient,precision and intersection over union(IOU)were used to assess the segmentation performance of thrombus on the test set and plot the corresponding curves.The performance of the Res2Net attention mechanism network was compared with the classic U-Net and CE-Net model.Results A total of 303 patients with APE were included in this study.There were 212,61 and 30 cases in the training set,validation set and test set,respectively.The model's area under the curve was 0.95,sensitivity was 0.90,specificity was 1.00,Dice similarity coefficient was 0.86,precision was 0.90,Pos-IOU was 0.78 and Neg-IOU was 1.00,respectively.The parameter curves and radar chart showed that the Res2Net attention mechanism network performed better than the U-Net and CE-Net models.The visualization results of the segmentation comparison showed that the Res2Net attention mechanism network achieved higher precision in segmenting pulmonary artery thrombus.Conclusion The Res2Net attention mechanism network has good performance for detection of APE.

Objective:To establish and validate the diagnostic model of acute pulmonary embolism (APE) based on clinical and laboratory variables.Methods:This retrospective analysis was conducted on patients with suspected APE who underwent CT pulmonary angiography at the First Affiliated Hospital of Henan University of Chinese Medicine between February 2015 and December 2023. The patients were randomly divided into a training set and a validation set at a ratio of 7∶3. Clinical and laboratory data of the enrolled patients were collected, and patients were divided into an APE group and a non-APE group according to CT pulmonary angiography results. In the training set, univariate and multivariate logistic regression as well as Lasso regression were used to identify risk factors for APE, and a diagnostic model was developed and validated. Receiver operating characteristic curves were plotted, and calibration and decision curves were used to assess the performance of the diagnostic model. The diagnostic efficacy of the model was compared with that of the Wells score and the revised Geneva score using the DeLong test.Results:A total of 752 patients were enrolled, aged (64±15) years, including 417 (55.5%) males. The training set included 526 patients and the validation set included 226 patients. The incidence of APE in this cohort was 48.7% (366/752), with 366 cases in the APE group and 386 in the non-APE group. Multivariable logistic regression analysis showed that cyanosis ( OR=8.88, 95% CI 2.04-49.11), elevated neutrophil count ( OR=1.82, 95% CI 1.06-3.15), elevated creatine kinase isoenzyme ( OR=3.45, 95% CI 1.76-6.91), decreased partial pressure of carbon dioxide ( OR=12.88, 95% CI 7.64-22.34), elevated age-adjusted D-dimer ( OR=2.53, 95% CI 1.10-6.20), prolonged thrombin time ( OR=4.08, 95% CI 2.06-8.33), and positive lower limb venous ultrasound for thrombus ( OR=4.39, 95% CI 2.59-7.58) were risk factors associated with APE. The area under the curve ( AUC) of the diagnostic model was 0.92 (95% CI 0.90-0.94) in the training set and 0.92 (95% CI 0.89-0.95) in the validation set. The diagnostic efficacy of this model was superior to that of the Wells score ( AUC: 0.92 vs. 0.63, P<0.01) and the revised Geneva score ( AUC: 0.92 vs. 0.59, P<0.01). Conclusion:The diagnostic model for acute pulmonary embolism constructed based on clinical and laboratory parameters demonstrates excellent diagnostic performance and may facilitate rapid and accurate screening in clinical practice.

OBJECTIVE: To retrospectively analysis the clinical data of facial nerve defects repair with greater auricular nerve graft. METHOD: The transmastoid approach was adopted to repair the facial nerve defects by means of nerve grafting. Preoperative and postoperative facial nerve functions were graded according to the House-Brackmann scale. RESULT: The patterns of temporal bone fracture in the 8 patients were longitudinal, most lesions occurred in the region of the second genu and its surrounding, preoperatively, all patients had Grade VI function. In 3 patients of facial nerve tumors, the tumors involved multiple nerve segments, and histologic results were all schwannomas, preoperatively, 1 case had Grade III function, 2 cases had Grade V function. In 2 patients of iatrogenic trauma of the facial nerve, the primary disease was chronic otitis media with cholesteatoma, the lesions were localized at the mastoid segment and the second genu respectively. In 1 patient of molten steel burn, the lesions was localized at the tympanic segment, preoperative facial nerve function was Grade VI. In addition to 3 cases lost to follow-up, the remaining patients, 4 recovered to a Grade III, 3 to a Grade VI, 2 to a Grade V and 2 remained at Grade VI. CONCLUSION In present study, the most common cause of facial nerve transection was temporal bone fracture. Facial nerve reconstruction by means of greater auricular nerve grafting was a practical and effective method, the best postoperative recovery of facial nerve function was Grade III.
Adult ; Cranial Nerves ; transplantation ; Ear ; innervation ; Facial Nerve Injuries ; surgery ; Facial Paralysis ; surgery ; Female ; Humans ; Male ; Mastoid ; surgery ; Middle Aged ; Nerve Regeneration ; Neurosurgical Procedures ; methods ; Retrospective Studies ; Temporal Bone ; injuries ; surgery ; Young Adult