BACKGROUND

Down syndrome or trisomy 21, the most common chromosomal disorder, results from the presence of a third copy of chromosome 21 and manifests as mild to moderate intellectual disability, growth retardation, congenital heart defects, gastrointestinal abnormalities, and characteristic facial features. Several methods have been used to screen for Down syndrome in the prenatal period, such as ultrasound, biomarkers, cell-free DNA testing, and combinations of these tests. A positive result from one or more of these screening tests signals the need for confirmatory karyotyping to clinch the diagnosis. Ultrasound between 11 to 14 weeks of gestation can evaluate nuchal translucency (NT) to screen for Down syndrome. During the second trimester, a triple or quadruple test can also be performed alone or in addition to NT to quantify Down syndrome risk. In limited resource settings however, only the measurement of NT via ultrasound can be performed since biomarker tests are either unavailable or inaccessible. While the diagnostic performance of NT measurement alone has been investigated in several observational studies, there is no consensus on its performance as a sole test to screen for Down syndrome.

To determine the diagnostic performance of NT during prenatal first-trimester ultrasound as a screening test for Down syndrome.

We performed a systematic search on the PubMed, ProQuest, and Cochrane Library databases for recent systematic reviews and meta-analyses that addressed the objective. The existing reviews found were then independently appraised by the two reviewers with the AMSTAR-2 checklist. To update the existing reviews, a systematic search was done in the same databases to identify additional primary diagnostic studies, which were appraised using the QUADAS-2 tool. Random-effects univariate meta-analysis and summary receiving operator curve (HSROC) analysis for the outcomes were performed using Review Manager version 5.4 and R version 4.2.2, respectively. Subgroup analysis was performed by stratifying the baseline risk of mothers for fetal anomaly as low- or high-risk. Highrisk mothers were defined as women with risk factors such as advanced age, positive serum screen, presence of other ultrasound anomalies, and history of previous fetus with anomaly.

We found 22 cohort studies (n=225,846) of women at low-risk for fetal anomaly. The pooled sensitivity was 67.8% (95% CI: 61.4%-73.6%, I2=70.4%) and specificity was 96.3% (95% CI: 95.5%-96.9%, I2=96.7%). For low-risk women, the overall certainty of evidence was low, due to different modes of verification and heterogeneity not completely explained by variability in baseline risk or cut-points. Seven studies (n=9,197) were on high-risk women. The pooled sensitivity was 62.2% (95% CI: 54.1%-69.7%, I2=38.8%) and specificity was 96.5% (95% CI: 93.6%-98.1%, I2=95.5%). For women at high-risk, the evidence was rated as moderate due to differential verification.

Our analysis showed that NT measured through first-trimester ultrasound is specific for Down syndrome but has low sensitivity. Despite this, it is a useful screening test for Down syndrome in low-resource settings where other strategies may not be available or accessible. Furthermore, interpretation of NT results must take into consideration its limited sensitivity as this may lead to missed cases.

Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% ( P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.
Electroencephalography/methods* ; Brain-Computer Interfaces ; Humans ; Imagination/physiology* ; Signal Processing, Computer-Assisted ; Movement/physiology* ; Signal-To-Noise Ratio ; Deep Learning ; Algorithms

OBJECTIVE: To investigate the diagnostic efficacy of ^99mTc-MDP three-phase bone scintigraphy (TPBS) combined with C-reactive protein (CRP) for periprosthetic joint infection (PJI). METHODS: The clinical data of 198 patients who underwent revision surgery of artificial joint between January 2017 and January 2024 and received TPBS examination before surgery were retrospectively analyzed. There were 77 males and 121 females with an average age of 63.74 years ranging from 24 to 92 years. There were 90 cases of hip arthroplasty and 108 cases of knee arthroplasty. PJI was diagnosed according to the 2013 American Musculoskeletal Infection Society (MSIS) standard diagnostic criteria. The sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predict value (PPV) were calculated. The receiver operating characteristic (ROC) curve was used to compare the diagnostic performance of the three methods, and the area under curve (AUC) was used to evaluate the diagnostic performance. RESULTS: According to the 2013 MSIS criteria, 116 cases were diagnosed as PJI, and the remaining 82 cases were aseptic loosening. The cases of PJI diagnosed by TPBS, CRP, and TPBS-CRP were 125, 109, and 137 respectively, and the cases of aseptic loosening were 73, 89, and 61 respectively. The sensitivity, accuracy, NPV, and PPV of TPBS-CRP combination in the diagnosis of PJI were higher than those of TPBS and CRP, but the specificity was lower than that of TPBS and CRP. ROC curve analysis further showed that the AUC value of TPBS-CRP combination was better than that of TPBS and CRP. The severity of bone defect and the duration of symptoms in patients with false positive TPBS diagnosis were worse than those in patients with true negative TPBS diagnosis (P<0.05), but there was no significant difference in the survival time of prosthesis between the two groups (P>0.05). Among the patients diagnosed with PJI by TPBS, CRP, and TPBS-CRP, 49, 35, and 54 patients had received antibiotic treatment 2 weeks before diagnosis, respectively. There was no significant difference in the diagnostic accuracy of TPBS and TPBS-CRP before diagnosis between patients treated with and without antibiotics and those not treated (P>0.05). The diagnostic accuracy of antibiotic therapy before CRP diagnosis was significantly lower than that of untreated patients (P<0.05). CONCLUSION TPBS and CRP have limited specificity in differentiating PJI from aseptic loosening. The TPBS-CRP combination diagnostic method can synergize the local bone metabolic characteristics and systemic inflammatory response to achieve higher diagnostic accuracy, but caution should be exercised in patients with severe bone defects and longer symptom duration.
Humans ; Prosthesis-Related Infections/blood* ; Middle Aged ; Male ; Female ; Aged ; C-Reactive Protein/metabolism* ; Retrospective Studies ; Adult ; Radionuclide Imaging/methods* ; Arthroplasty, Replacement, Knee/adverse effects* ; Aged, 80 and over ; Technetium Tc 99m Medronate ; Arthroplasty, Replacement, Hip/adverse effects* ; Sensitivity and Specificity ; Knee Prosthesis/adverse effects* ; ROC Curve ; Reoperation ; Radiopharmaceuticals ; Young Adult

OBJECTIVE: To compare the diagnostic accuracy of supraspinatus muscle outlet X-ray film, oblique sagittal multislice helical CT (MSCT), and oblique sagittal MRI in the diagnosis of subacromial impingement syndrome (SIS). METHODS: A retrospective analysis was conducted on the imaging data of 106 patients diagnosed with SIS between January 2023 and December 2024. The cohort consisted of 32 males and 74 females, with ages ranging from 43 to 70 years (mean, 60.19 years). All patients underwent supraspinatus muscle outlet X-ray film, MSCT, and MRI scans, with MSCT further subjected to three-dimensional reconstruction. Two experienced radiologists independently evaluated the acromion morphology in each imaging modality using the Bigliani classification system. Inter-observer reliability was assessed via Kappa statistics. The CT three-dimensional reconstructions were used as the "gold standard". The overall consistency, Kappa values, sensitivity, and specificity of the three imaging modalities were calculated. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was computed. RESULTS: The inter-observer reliability for supraspinatus muscle outlet X-ray film, oblique sagittal MSCT, and oblique sagittal MRI was moderate, with Kappa values of 0.62, 0.63, and 0.55, respectively. When compared to the CT three-dimensional reconstructions as the "gold standard", the overall consistency was 88.7% (94/106), 62.3% (66/106), and 58.5% (62/106), respectively. The supraspinatus muscle outlet X-ray film showed excellent consistency (Kappa=0.77), whereas the consistency of MSCT and MRI was lower (Kappa=0.34 and 0.29, respectively). In terms of diagnostic sensitivity and specificity, the supraspinatus muscle outlet X-ray film outperformed oblique sagittal MSCT and oblique sagittal MRI in distinguishing various acromion types. ROC analysis demonstrated that the AUC for the supraspinatus muscle outlet X-ray film was consistently higher than for oblique sagittal MSCT and oblique sagittal MRI, with the highest diagnostic performance observed for type Ⅲ hooked acromion (AUC=0.939). CONCLUSION Supraspinatus muscle outlet X-ray film provides the highest diagnostic accuracy for acromion classification in SIS patients, particularly in identifying type Ⅲ hooked acromion, which is strongly associated with SIS. Given its superior sensitivity and consistency, it should be considered the primary screening tool. MSCT and MRI serve as valuable supplementary modalities for complex cases and preoperative evaluation.
Humans ; Middle Aged ; Male ; Female ; Shoulder Impingement Syndrome/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Retrospective Studies ; Aged ; Adult ; Imaging, Three-Dimensional ; Sensitivity and Specificity ; Tomography, Spiral Computed/methods* ; Multidetector Computed Tomography/methods* ; Reproducibility of Results

Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Methods 100 OA and 40 RA patients scheduled for total knee arthroplasty were enrolled respectively. Each patient's knee joint fluid was collected preoperatively. Nucleated cells were counted and classified. The expression levels of immune factors, including tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, IL-8, IL-15, matrix metalloproteinase 3 (MMP3), MMP9, MMP13, rheumatoid factor (RF), serum amyloid A (SAA), C-reactive protein (CRP), and others were measured. Smears and microscopic classification of all the immune factors were performed. Independent influencing factors for OA or RA were identified using univariate binary logistic regression, Lasso regression, and multivariate binary logistic regression. Based on the independent influencing factors, three machine learning models were constructed which are logistic regression, random forest, and support vector machine. Receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to evaluate and compare the models. Results A total of 5 indicators in the knee joint fluid were screened out to distinguish OA and RA, which were IL-1β(odds ratio(OR)=10.512, 95× confidence interval (95×CI) was 1.048-105.42, P=0.045), IL-6 (OR=1.007, 95×CI was 1.001-1.014, P=0.022), MMP9 (OR=3.202, 95×CI was 1.235-8.305, P=0.017), MMP13 (OR=1.002, 95× CI was 1-1.004, P=0.049), and RF (OR=1.091, 95×CI was 1.01-1.179, P=0.026). According to the results of ROC, calibration curve and DCA, the accuracy (0.979), sensitivity (0.98) and area under the curve (AUC, 0.996, 95×CI was 0.991-1) of the random forest model were the highest. It has good validity and feasibility, and its distinguishing ability is better than the other two models. Conclusion The machine learning model based on immune factors in the knee joint fluid holds significant value in distinguishing OA and RA. It provides an important reference for the clinical early differential diagnosis, prevention and treatment of OA and RA.
Humans ; Arthritis, Rheumatoid/metabolism* ; Machine Learning ; Male ; Female ; Middle Aged ; Aged ; Synovial Fluid/immunology* ; Osteoarthritis, Knee/metabolism* ; Knee Joint/metabolism* ; ROC Curve ; Diagnosis, Differential

Objective To mine and analyze the routine blood test data of children with allergic rhinitis (AR), identify routine blood parameters related to childhood allergic rhinitis, establish an effective diagnostic model, and evaluate the performance of the model. Methods This study was a retrospective study of clinical cases. The experimental group comprised a total of 1110 children diagnosed with AR at the First Affiliated Hospital of Air Force Medical University during the period from December 12, 2020 to December 12, 2021, while the control group included 1109 children without a history of allergic rhinitis or other allergic diseases who underwent routine physical examinations during the same period. Information such as age, sex and routine blood test results was collected for all subjects. The levels of routine blood test indicators were compared between AR children and healthy children using comprehensive intelligent baseline analysis, with indicators of P≥0.05 excluded; variables were screened by Lasso regression. Binary Logistic regression was used to further evaluate the influence of multiple routine blood indexes on the results. Five kinds of machine model algorithms were used, namely extreme value gradient lift (XGBoost), logistic regression (LR), gradient lift decision tree (LGBMC), Random forest (RF) and adaptive lift algorithm (AdaBoost), to establish the diagnostic models. The receiver operating characteristic (ROC) curve was used to screen the optimal model. The best LightGBM algorithm was used to build an online patient risk assessment tool for clinical application. Results Statistically significant differences were observed between the AR group and the control group in the following routine blood test indicators: mean cellular hemoglobin concentration (MCHC), hemoglobin (HGB), absolute value of basophils (BASO), absolute value of eosinophils (EOS), large platelet ratio (P-LCR), mean platelet volume (MPV), platelet distribution width (PDW), platelet count (PLT), absolute values of leukocyte neutrophil (W-LCC), leukocyte monocyte (W-MCC), leukocyte lymphocyte (W-SCC), and age. Lasso regression identified these variables as important predictors, and binary Logistic regression further analyzed the significant influence of these variables on the results. The optimal machine learning algorithm LightGBM was used to establish a multi-index joint detection model. The model showed robust prediction performance in the training set, with AUC values of 0.8512 and 0.8103 in the internal validation set. Conclusion The identified routine blood parameters can be used as potential biomarkers for early diagnosis and risk assessment of AR, which can improve the accuracy and efficiency of diagnosis. The established model provides scientific basis for more accurate diagnostic tools and personalized prevention strategies. Future studies should prospectively validate these findings and explore their applicability in other related diseases.
Humans ; Male ; Female ; Rhinitis, Allergic/blood* ; Child ; Biomarkers/blood* ; Retrospective Studies ; Early Diagnosis ; Child, Preschool ; ROC Curve ; Logistic Models ; Hematologic Tests ; Algorithms ; Adolescent ; Machine Learning

Objective To explore the expression of coagulation indexes in rheumatoid arthritis (RA) and dry syndrome (pSS) and their relationships with inflammation and immune function. Methods A total of 61 patients with RA who were hospitalized in the Department of Rheumatology of Anhui Provincial Hospital of Traditional Chinese Medicine from March 12 to September 9, 2024 were selected as the RA group. And 61 patients with pSS who were hospitalized in the Department of Rheumatology of the same hospital September 4, 2023, to August 17, 2024, were selected as the pSS group. 61 healthy individuals who underwent routine medical checkups at the Physical Examination Center of Anhui Provincial Hospital of Traditional Chinese Medicine during the same period were included as the control group. Baseline clinical indexes before treatment were collected from patients in each group, including prothrombin time(PT), international normalized ratio(INR), thrombia time(TT), fibrinogen(FBG), activated partial thromboplastin time(APTT), fibrin (ogen) degradation products(FDP) and D-Dimer(D-D). Results The expression levels of PT, FBG, TT, FDP, and D-D in the RA group, the pSS group, and the normal group were significantly different. The expression levels of PT, FBG, FDP, and D-D in the RA group were all higher than those in the pSS group and the control group, respectively. And the expression level of TT in the pSS group was lower than that in control group. ROC curve analysis showed that the AUC of PT was 0.638, the AUC of FBG was 0.899, the AUC of FDP was 0.866, and the AUC of D-D was 0.919 in the RA group compared with the normal group. And the AUC of coagulation indexes for joint diagnosis of RA was higher than that of the indexes detected individually. pSS group had an AUC of PT of 0.618 compared with that of the normal group. The AUC of TT was 0.645, and the AUC of coagulation indexes for the joint diagnosis of pSS was higher than the AUC of each index detected separately. Association rule analysis showed that elevated D-D in RA patients had a significant correlation with elevated hs-CRP, CCP and RF, and elevated FBG had a significant correlation with elevated hs-CRP, ESR, RF and CCP. Elevated D-D in pSS patients had a correlation with elevated hs-CRP and anti-SSA, and elevated INR has correlation with elevated hs-CRP, anti-SSA and anti-SSB. Correlation analysis showed that PT, INR, FBG, FDP, and D-D were positively correlated with CRP and ESR, and TT was negatively correlated with CRP and ESR in the RA group. FBG, FDP, and D-D were positively correlated with CRP and ESR in the pSS group. Moreover, coagulation indexes were positively correlated with immune indexes in RA group and pSS group which were all significant. The results of multiple linear regression analysis showed that FBG was a positive correlate of hs-CRP and ESR in RA patients. For PSS patients, FBG and FDP were positive correlates of hs-CRP. APTT and FBG were positive correlates of ESR. Conclusion Compared with pSS, coagulation indexes (especially PT, FBG, FDP and D-D) are more informative for the early diagnosis of RA and the judgment of the degree of the disease, and can be used as an important predictor for the confirmation of the diagnosis of RA.
Humans ; Female ; Male ; Arthritis, Rheumatoid/diagnosis* ; Middle Aged ; Fibrin Fibrinogen Degradation Products/analysis* ; Blood Coagulation ; Adult ; Fibrinogen/metabolism* ; Partial Thromboplastin Time ; Prothrombin Time ; Aged ; Inflammation/immunology* ; ROC Curve

PURPOSE: Ultrasonography has been used increasingly in orthopaedic practice credited to its low cost, easy accessibility, non-invasiveness, reproducibility, and safety from radiation. The purpose of this study was to test the validity and efficacy of ultrasonography as an adjunct in the assessment of fracture healing in long bones treated with intramedullary interlocking devices and its predictive value in determining the need for a secondary surgical procedure. METHODS: This was a descriptive longitudinal study of 40 skeletally mature patients from November 2016 to February 2019, who sustained long bone fractures of the tibia or femur treated using intramedullary interlocking nails. Patients with comminuted and segmental fracture patterns were excluded from the study. Each patient was evaluated at 6- and 12-week post-surgery using standard orthogonal radiographs and ultrasonography to assess fracture healing. Patients were then followed up until fracture union. Quantitative data was analyzed using frequency statistics and descriptive data with inferential statistics. RESULTS: Ultrasonography predicted 87.5% union and 12.5% delayed or non-union as early as 6 weeks after surgery, while radiographs predicted 22.5% union as late as 3 months of follow-up. The sensitivity and specificity of ultrasonography in assessing fracture healing were 100% and 97.2%, respectively, with a positive predictive value of 80.0%. Vascular resistance index was less than 0.5 in all patients who developed delayed or non-union. CONCLUSION Ultrasonography is able to predict fracture outcomes much earlier than standardized radiographs with comparable sensitivity and specificity. Vascular resistance index is an objective parameter in assessing callus quality and predicting fracture outcomes.
Humans ; Fracture Fixation, Intramedullary/methods* ; Male ; Female ; Ultrasonography/methods* ; Adult ; Fracture Healing ; Tibial Fractures/diagnostic imaging* ; Middle Aged ; Femoral Fractures/diagnostic imaging* ; Longitudinal Studies ; Bone Nails ; Young Adult ; Predictive Value of Tests ; Aged ; Treatment Outcome ; Adolescent

PURPOSE: To analyze the diagnostic efficacy of computed tomography angiography compared to digital cerebral angiography for the diagnosis of traumatic aneurysms (TAs) associated with combat-related penetrating head injuries and propose the most suitable angiography protocol in this clinical context. METHODS: A retrospective analysis was conducted on patients admitted to the neurosurgical clinic for penetrating traumatic brain injuries between February, 2022 and July, 2024, for whom both cerebral multidetector computed tomography angiography (MCTA) and digital cerebral angiography (DCA) were available. The inclusion were patients (1) with penetrating head injuries, (2) with missile trajectory traverses through the Sylvian or great longitudinal fissure, (3) basal cisterns with/or major subarachnoid hemorrhage. The sensitivity, specificity, positive predictive value, and negative predictive value of MCTA were calculated. DCA was considered as the gold standard of diagnosis. The sensitivity, specificity, positive predictive value, and negative predictive value of MCTA were calculated. Descriptive statistics and nonparametric statistics were used to analyze the study results and their differences, respectively. RESULTS: A total of 40 patients with 45 TAs were included in the study. Of these, 26 patients (65.0%) were found to have aneurysms on MCTA. The median diameter of the aneurysms diagnosed by MCTA was 4.9 (3.6, 4.8) mm (range of 2.5 - 10.4 mm). However, the mean diameter of TAs not detected by MCTA but diagnosed by DCA was (3.0 ± 1.3) mm (range of 1.3 - 4.9 mm). MCTA demonstrated sensitivity and specificity of 35.5% and 99.5%, respectively, with positive and negative predictive values of 92.3% and 90.7%. CONCLUSIONS A low sensitivity of MCTA for the diagnosis of TAs associated with combat-related penetrating head injuries was reported. When MCTA is inconclusive in the setting of radiologic predictors of cerebral artery injury, DSA may be required.
Humans ; Male ; Retrospective Studies ; Adult ; Multidetector Computed Tomography/methods* ; Intracranial Aneurysm/etiology* ; Computed Tomography Angiography/methods* ; Female ; Head Injuries, Penetrating/diagnostic imaging* ; Middle Aged ; Cerebral Angiography/methods* ; Predictive Value of Tests ; Sensitivity and Specificity ; Young Adult

PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness